From 450d078729df6a76a073a1d6fa6f15bd065f3257 Mon Sep 17 00:00:00 2001 From: Marvin Zhang Date: Sun, 16 Jun 2019 10:52:18 +0800 Subject: [PATCH 1/3] fixed scheduler issue --- crawlab/app.py | 2 +- crawlab/routes/base.py | 11 ++++++++--- crawlab/tasks/scheduler.py | 7 +++++++ docker-compose.yml | 8 ++++++-- frontend/Dockerfile | 36 ------------------------------------ 5 files changed, 22 insertions(+), 42 deletions(-) delete mode 100644 frontend/Dockerfile diff --git a/crawlab/app.py b/crawlab/app.py index eabb2bc3..e3741dd1 100644 --- a/crawlab/app.py +++ b/crawlab/app.py @@ -103,4 +103,4 @@ if not os.path.exists(PROJECT_LOGS_FOLDER): if __name__ == '__main__': # run app instance - app.run(host=FLASK_HOST, port=FLASK_PORT, threaded=True) + app.run(host=FLASK_HOST, port=FLASK_PORT, threaded=False, processes=4) diff --git a/crawlab/routes/base.py b/crawlab/routes/base.py index e068e4a2..3bb2c1b0 100644 --- a/crawlab/routes/base.py +++ b/crawlab/routes/base.py @@ -107,11 +107,12 @@ class BaseApi(Resource): for k in args.keys(): if k not in DEFAULT_ARGS: item[k] = args.get(k) - item = db_manager.save(col_name=self.col_name, item=item) + id = db_manager.save(col_name=self.col_name, item=item) - self.after_update() + # execute after_update hook + self.after_update(id) - return jsonify(item) + return jsonify(id) def update(self, id: str = None) -> (dict, tuple): """ @@ -169,6 +170,10 @@ class BaseApi(Resource): """ # perform delete action db_manager.remove_one(col_name=self.col_name, id=id) + + # execute after_update hook + self.after_update(id) + return { 'status': 'ok', 'message': 'deleted successfully', diff --git a/crawlab/tasks/scheduler.py b/crawlab/tasks/scheduler.py index c600029c..b4aad5df 100644 --- a/crawlab/tasks/scheduler.py +++ b/crawlab/tasks/scheduler.py @@ -23,6 +23,9 @@ class Scheduler(object): scheduler = BackgroundScheduler(jobstores=jobstores) def execute_spider(self, id: str, params: str = None): + print(f'executing spider {id}') + print(f'params: {params}') + self.scheduler.print_jobs(jobstore='mongo') query = {} if params is not None: query['params'] = params @@ -33,6 +36,7 @@ class Scheduler(object): ), query) def update(self): + print('updating...') # remove all existing periodic jobs self.scheduler.remove_all_jobs() self.mongo[MONGO_DB][self.task_col].remove() @@ -57,6 +61,9 @@ class Scheduler(object): hour=hour, minute=minute, second=second) + self.scheduler.print_jobs(jobstore='mongo') + print(f'state: {self.scheduler.state}') + print(f'running: {self.scheduler.running}') def run(self): self.update() diff --git a/docker-compose.yml b/docker-compose.yml index dcf85bdb..8e4c5eaa 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,8 +1,8 @@ version: '3.3' services: master: - image: crawlab:latest - restart: always + image: tikazyq/crawlab:latest + container_name: crawlab volumns: - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件 - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件 @@ -12,6 +12,10 @@ services: depends_on: - mongo - redis + entrypoint: + - /bin/sh + - /opt/crawlab/docker_init.sh + - master mongo: image: mongo:latest restart: always diff --git a/frontend/Dockerfile b/frontend/Dockerfile deleted file mode 100644 index bfcc0d49..00000000 --- a/frontend/Dockerfile +++ /dev/null @@ -1,36 +0,0 @@ -# images -FROM node:8.12 - -# source files -ADD . /opt/crawlab/frontend - -#更新apt-get源 使用163的源 -#RUN mv /etc/apt/sources.list /etc/apt/sources.list.bak -#COPY sources.list /etc/apt/sources.list - -# environment variables -#ENV NVM_DIR /usr/local/nvm -#ENV NODE_VERSION 8.12.0 -#ENV WORK_DIR /opt/crawlab/frontend - -# install git curl -RUN apt-get update && apt-get install -y nginx -#RUN apt-get install -y git curl - -# install nvm -#RUN curl https://raw.githubusercontent.com/creationix/nvm/v0.24.0/install.sh | bash \ -# && . $NVM_DIR/nvm.sh \ -# && nvm install v$NODE_VERSION \ -# && nvm use v$NODE_VERSION \ -# && nvm alias default v$NODE_VERSION -#ENV NODE_PATH $NVM_DIR/versions/node/v$NODE_VERSION/lib/node_modules -#ENV PATH $NVM_DIR/versions/node/v$NODE_VERSION/bin:$PATH - -# install frontend -RUN npm install -g yarn pm2 --registry=https://registry.npm.taobao.org -RUN cd /opt/crawlab/frontend && yarn install --registry=https://registry.npm.taobao.org - -# nginx config & start frontend -RUN cp $WORK_DIR/conf/crawlab.conf /etc/nginx/conf.d && service nginx reload - -CMD ["npm", "run", "build:prod"] From 01a1b39ad11d3f616509db905f615c5e9ad21db6 Mon Sep 17 00:00:00 2001 From: Marvin Zhang Date: Sun, 16 Jun 2019 12:54:36 +0800 Subject: [PATCH 2/3] updated docs --- CHANGELOG.md | 4 + gitbook/Installation/Direct.md | 88 ++ gitbook/Installation/Docker.md | 158 ++++ gitbook/Installation/Preview.md | 9 + gitbook/Installation/README.md | 4 + gitbook/QuickStart/Installation.md | 22 - gitbook/QuickStart/README.md | 4 - gitbook/QuickStart/Run.md | 53 -- gitbook/README.md | 165 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gitbook/_book/img/crawlab-architecture.png create mode 100644 gitbook/_book/img/payment.jpg create mode 100644 gitbook/_book/img/用户使用流程图.png create mode 100644 gitbook/_book/index.html create mode 100644 gitbook/_book/search_index.json diff --git a/CHANGELOG.md b/CHANGELOG.md index cc910ea9..8d4cfe09 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,7 @@ +# 0.2.4 (unreleased) +- **Documentation**: Better and much more detailed documentation. + + # 0.2.3 (2019-06-12) ### Features / Enhancement - **Docker**: User can run docker image to speed up deployment. diff --git a/gitbook/Installation/Direct.md b/gitbook/Installation/Direct.md new file mode 100644 index 00000000..c0cc3fc4 --- /dev/null +++ b/gitbook/Installation/Direct.md @@ -0,0 +1,88 @@ +## 直接部署 + +直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。 + +### 拉取代码 + +首先是将github上的代码拉取到本地。 + +```bash +git clone https://github.com/tikazyq/crawlab +``` + +### 安装 + +安装前端所需库。 + +```bash +npm install -g yarn pm2 +cd frontend +yarn install +``` + +安装后端所需库。 + +```bash +cd ../crawlab +pip install -r requirements +``` + +### 配置 + +分别配置前端配置文件`./frontend/.env.production`和后端配置文件`./crawlab/config/config.py`。分别需要对部署后API地址以及数据库地址进行配置。 + +### 构建 + +这里的构建是指前端构建，需要执行以下命令。 + +```bash +cd ../frontend +npm run build:prod +``` + +构建完成后，会在`./frontend`目录下创建一个`dist`文件夹，里面是打包好后的静态文件。 + +### Nginx + +安装`nginx`，在`ubuntu 16.04`是以下命令。 + +```bash +sudo apt-get install nginx +``` + +添加`/etc/nginx/conf.d/crawlab.conf`文件，输入以下内容。 + +``` +server { + listen 8080; + server_name dev.crawlab.com; + root /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist; + index index.html; +} +``` + +其中，`root`是静态文件的根目录，这里是`npm`打包好后的静态文件。 + +现在，只需要启动`nginx`服务就完成了启动前端服务。 + +```bash +nginx reload +``` + +### 启动服务 + +这里是指启动后端服务。我们用`pm2`来管理进程。执行以下命令。 + +```bash +pm2 start app.py # API服务 +pm2 start worker.py # Worker +pm2 start flower.py # Flower +``` + +这样，`pm2`会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。 + +```bash +pm2 logs [app] +``` + +然后在浏览器中输入`http://localhost:8080`就可以看到界面了。 \ No newline at end of file diff --git a/gitbook/Installation/Docker.md b/gitbook/Installation/Docker.md new file mode 100644 index 00000000..fc4c017c --- /dev/null +++ b/gitbook/Installation/Docker.md @@ -0,0 +1,158 @@ +## Docker安装部署 + +这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新[v0.2.3](https://github.com/tikazyq/crawlab/releases/tag/v0.2.3)中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。 + +对Docker不了解的开发者，可以参考一下这篇文章（[9102 年了，学点 Docker 知识](https://juejin.im/post/5c2c69cee51d450d9707236e)）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需`docker run -d --name mongo -p 27017:27017 mongo`一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。 + +### 下载镜像 + +我们已经在[DockerHub](https://hub.docker.com/r/tikazyq/crawlab)上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建`/etc/docker/daemon.json`文件，在其中输入如下内容。 + +```json +{ + "registry-mirrors": ["https://registry.docker-cn.com"] +} +``` + +这样的话，pull镜像的速度会比不改变镜像源的速度快很多。 + +执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。 + +```bash +docker pull tikazyq/crawlab:latest +``` + +### 更改配置文件 + +拷贝一份后端配置文件`./crawlab/config/config.py`以及前端配置文件`./frontend/.env.production`到某一个地方。例如我的例子，分别为`/home/yeqing/config.py`和`/home/yeqing/.env.production`。 + +更改后端配置文件`config.py`，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是`localhost`，而是`172.17.0.1`（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件`.env.production`，将API地址`VUE_APP_BASE_URL`更改为宿主机所在的IP地址，例如`http://192.168.0.8:8000`，这将是前端调用API会用到的URL。 + +### 运行Docker容器 + +更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。 + +```bash +docker run -d --rm --name crawlab \ + -p 8080:8080 \ + -p 8000:8000 \ + -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \ + -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \ + tikazyq/crawlab master +``` + +其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件`/home/yeqing/.env.production`和后端配置文件`/home/yeqing/config.py`映射到了容器相应的目录下。传入参数`master`是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是`worker`模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为`http://localhost:8080`。 + +![](https://user-gold-cdn.xitu.io/2019/6/12/16b4c3ed5dcd6cfc?w=2532&h=1300&f=png&s=146531) + +### Docker-Compose + +当然，也可以用`docker-compose`的方式来部署。`docker-compose`是一个集群管理方式，可以利用名为`docker-compose.yml`的`yaml`文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的`docker-compose.yml`定义如下。 + +```yaml +version: '3.3' +services: + master: + image: tikazyq/crawlab:latest + container_name: crawlab + volumns: + - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件 + - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件 + ports: + - "8080:8080" # nginx + - "8000:8000" # app + depends_on: + - mongo + - redis + entrypoint: + - /bin/sh + - /opt/crawlab/docker_init.sh + - master + mongo: + image: mongo:latest + restart: always + ports: + - "27017:27017" + redis: + image: redis:latest + restart: always + ports: + - "6379:6379" +``` + +这里先定义了`master`节点，也就是Crawlab的主节点。`master`依赖于`mongo`和`redis`容器，因此在启动之前会同时启动`mongo`和`redis`容器。这样就不需要单独配置`mongo`和`redis`服务了，大大节省了环境配置的时间。 + +安装`docker-compose`也很简单，大家去网上百度一下就可以了。 + +安装完`docker-compose`和定义好`docker-compose.yml`后，只需要运行以下命令就可以启动Crawlab。 + +```bash +docker-compose up +``` + +同样，在浏览器中输入`http://localhost:8080`就可以看到界面。 + +### 多节点模式 + +`docker-compose`的方式很适合多节点部署，在原有的`master`基础上增加几个`worker`节点，达到多节点部署的目的。将`docker-compose.yml`更改为如下内容。 + +```yaml +version: '3.3' +services: + master: + image: tikazyq/crawlab:latest + container_name: crawlab + volumns: + - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件 + - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件 + ports: + - "8080:8080" # nginx + - "8000:8000" # app + depends_on: + - mongo + - redis + entrypoint: + - /bin/sh + - /opt/crawlab/docker_init.sh + - master + worker1: + image: tikazyq/crawlab:latest + volumns: + - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件 + - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件 + ports: + - "8001:8000" # app + depends_on: + - mongo + - redis + entrypoint: + - /bin/sh + - /opt/crawlab/docker_init.sh + - worker + worker2: + image: tikazyq/crawlab:latest + volumns: + - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件 + - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件 + ports: + - "8002:8000" # app + depends_on: + - mongo + - redis + entrypoint: + - /bin/sh + - /opt/crawlab/docker_init.sh + - worker + mongo: + image: mongo:latest + restart: always + ports: + - "27017:27017" + redis: + image: redis:latest + restart: always + ports: + - "6379:6379" +``` + +这里启动了多增加了两个`worker`节点，以`worker`模式启动。这样，多节点部署，也就是分布式部署就完成了。 \ No newline at end of file diff --git a/gitbook/Installation/Preview.md b/gitbook/Installation/Preview.md new file mode 100644 index 00000000..b37cebd4 --- /dev/null +++ b/gitbook/Installation/Preview.md @@ -0,0 +1,9 @@ +## 预览模式 + +**预览模式**是一种让用户比较快的上手的一种部署模式。跟**直接部署**类似，但不用经过`构建`、`nginx`和`启动服务`的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。 + +```bash +python manage.py serve +``` + +该模式同样会启动3个后端服务和1个前端服务。前端服务是通过`npm run serve`来进行的，因此是开发者模式。**注意：强烈不建议在生产环境中用预览模式**。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。 \ No newline at end of file diff --git a/gitbook/Installation/README.md b/gitbook/Installation/README.md new file mode 100644 index 00000000..1f3bfabe --- /dev/null +++ b/gitbook/Installation/README.md @@ -0,0 +1,4 @@ +本小节将介绍三种安装Docker的方式: +1. [Docker](/Installation/Docker.md) +2. [直接部署](/Installation/Direct.md) +3. [预览模式](/Installation/Preview.md) \ No newline at end of file diff --git a/gitbook/QuickStart/Installation.md b/gitbook/QuickStart/Installation.md deleted file mode 100644 index 3fce3e1c..00000000 --- a/gitbook/QuickStart/Installation.md +++ /dev/null @@ -1,22 +0,0 @@ -# 安装 - -最快安装Crawlab的方式是克隆一份代码到本地 - -```bash -git clone https://github.com/tikazyq/crawlab -``` - -安装类库 - -```bash -# 安装后台类库 -pip install -r requirements.txt -``` - -```bash -# 安装前台类库 -cd frontend -npm install -``` - - diff --git a/gitbook/QuickStart/README.md b/gitbook/QuickStart/README.md deleted file mode 100644 index 6a6ea76f..00000000 --- a/gitbook/QuickStart/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# 快速开始 - -- [安装](Installation.md) -- [运行](Run.md) diff --git a/gitbook/QuickStart/Run.md b/gitbook/QuickStart/Run.md deleted file mode 100644 index bd6b9ba9..00000000 --- a/gitbook/QuickStart/Run.md +++ /dev/null @@ -1,53 +0,0 @@ -# 运行 - -在运行之前需要对Crawlab进行一些配置，配置文件为`config.py`。 - -```python -# project variables -PROJECT_SOURCE_FILE_FOLDER = '/Users/yeqing/projects/crawlab/spiders' # 爬虫源码根目录 -PROJECT_DEPLOY_FILE_FOLDER = '/var/crawlab' # 爬虫部署根目录 -PROJECT_LOGS_FOLDER = '/var/logs/crawlab' # 日志目录 -PROJECT_TMP_FOLDER = '/tmp' # 临时文件目录 - -# celery variables -BROKER_URL = 'redis://192.168.99.100:6379/0' # 中间者URL，连接redis -CELERY_RESULT_BACKEND = 'mongodb://192.168.99.100:27017/' # CELERY后台URL -CELERY_MONGODB_BACKEND_SETTINGS = { - 'database': 'crawlab_test', - 'taskmeta_collection': 'tasks_celery', -} -CELERY_TIMEZONE = 'Asia/Shanghai' -CELERY_ENABLE_UTC = True - -# flower variables -FLOWER_API_ENDPOINT = 'http://localhost:5555/api' # Flower服务地址 - -# database variables -MONGO_HOST = '192.168.99.100' -MONGO_PORT = 27017 -MONGO_DB = 'crawlab_test' - -# flask variables -DEBUG = True -FLASK_HOST = '127.0.0.1' -FLASK_PORT = 8000 -``` - -启动后端API，也就是一个Flask App，可以直接启动，或者用gunicorn代替。 - -```bash -python manage.py app -``` - -启动本地Worker。在其他节点中如果想只是想执行任务的话，只需要启动这一个服务就可以了。 - -```bash -python manage.py worker -``` - -启动前端服务器。 - -```bash -cd frontend -npm run serve -``` diff --git a/gitbook/README.md b/gitbook/README.md index 613decad..6112b7cc 100644 --- a/gitbook/README.md +++ b/gitbook/README.md @@ -1,167 +1,14 @@ # Crawlab 基于Celery的爬虫分布式爬虫管理平台，支持多种编程语言以及多种爬虫框架. -[查看演示 Demo](http://139.129.230.98:8080) +[查看演示 Demo](http://114.67.75.98:8080) -[English Documentation](https://github.com/tikazyq/crawlab/blob/master/README.md) +Crawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。 -## 要求 -- Python3 -- MongoDB -- Redis +项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。 -## 安装 +Crawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。 -```bash -# 安装后台类库 -pip install -r requirements.txt -``` +本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。 -```bash -# 安装前台类库 -cd frontend -npm install -``` - -## 配置 - -请更改配置文件`config.py`，配置API和数据库连接. - -## 快速开始 -```bash -# 启动后端API -python app.py - -# 启动Flower服务 -python ./bin/run_flower.py - -# 启动worker -python ./bin/run_worker.py -``` - -```bash -# 运行前端 -cd frontend -npm run serve -``` - -## 截图 - -#### 首页 -![home](./img/screenshot-home.png) - -#### 爬虫列表 - -![spider-list](./img/screenshot-spiders.png) - -#### 爬虫详情 - 概览 - -![spider-list](./img/screenshot-spider-detail-overview.png) - -#### 任务详情 - 抓取结果 - -![spider-list](./img/screenshot-task-detail-results.png) - -## 架构 - -Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。 - -![crawlab-architecture](./img/crawlab-architecture.png) - -### 节点 - -节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。 - -### 爬虫 - -##### 自动发现 - -在`config.py`文件中，修改变量`PROJECT_SOURCE_FILE_FOLDER`作为爬虫项目所在的目录。Crawlab后台程序会自动发现这些爬虫项目并储存到数据库中。是不是很方便？ - -##### 部署爬虫 - -所有爬虫需要在抓取前被部署当相应当节点中。在"爬虫详情"页面点击"Deploy"按钮，爬虫将被部署到所有有效到节点中。 - -##### 运行爬虫 - -部署爬虫之后，你可以在"爬虫详情"页面点击"Run"按钮来启动爬虫。一个爬虫任务将被触发，你可以在任务列表页面中看到这个任务。 - -### 任务 - -任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。 - -### 后台应用 - -这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行`python manage.py app`或`python ./bin/run_app.py`来启动应用。 - -### 中间者 - -中间者跟Celery中定义的一样，作为运行异步任务的队列。 - -### 前端 - -前端其实就是一个基于[Vue-Element-Admin](https://github.com/PanJiaChen/vue-element-admin)的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。 - -## 与其他框架的集成 - -任务是利用python的`subprocess`模块中的`Popen`来实现的。任务ID将以环境变量`CRAWLAB_TASK_ID`的形式存在于爬虫任务运行的进程中，并以此来关联抓取数据。 - -在你的爬虫程序中，你需要将`CRAWLAB_TASK_ID`的值以`task_id`作为可以存入数据库中。这样Crawlab就直到如何将爬虫任务与抓取数据关联起来了。当前，Crawlab只支持MongoDB。 - -### Scrapy - -以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。 - -```python -import os -from pymongo import MongoClient - -MONGO_HOST = '192.168.99.100' -MONGO_PORT = 27017 -MONGO_DB = 'crawlab_test' - -# scrapy example in the pipeline -class JuejinPipeline(object): - mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT) - db = mongo[MONGO_DB] - col_name = os.environ.get('CRAWLAB_COLLECTION') - if not col_name: - col_name = 'test' - col = db[col_name] - - def process_item(self, item, spider): - item['task_id'] = os.environ.get('CRAWLAB_TASK_ID') - self.col.save(item) - return item -``` - -## 与其他框架比较 - -限制以及有一些爬虫管理框架了，因此为啥还要用Crawlab？ - -因为很多现有当平台都依赖于Scrapyd，限制了爬虫的编程语言以及框架，爬虫工程师只能用scrapy和python。当然，scrapy是非常优秀的爬虫框架，但是它不能做一切事情。 - -Crawlab使用起来很方便，也很通用，可以适用于几乎任何主流语言和框架。它还有一个精美的前端界面，让用户可以方便的管理和运行爬虫。 - -|框架 | 类型 | 分布式 | 前端 | 依赖于Scrapyd | -|:---:|:---:|:---:|:---:|:---:| -| [Crawlab](https://github.com/tikazyq/crawlab) | 管理平台 | Y | Y | N -| [Gerapy](https://github.com/Gerapy/Gerapy) | 管理平台 | Y | Y | Y -| [SpiderKeeper](https://github.com/DormyMo/SpiderKeeper) | 管理平台 | Y | Y | Y -| [ScrapydWeb](https://github.com/my8100/scrapydweb) | 管理平台 | Y | Y | Y -| [Scrapyd](https://github.com/scrapy/scrapyd) | 网络服务 | Y | N | N/A - -## TODOs -##### 后端 -- [ ] 文件管理 -- [ ] MySQL数据库支持 -- [ ] 重跑任务 -- [ ] 节点监控 -- [ ] 更多爬虫例子 - -##### 前端 -- [ ] 任务数据统计 -- [ ] 表格过滤 -- [x] 多语言支持 (中文) -- [ ] 登录和用户管理 -- [ ] 全局搜索 +首先，我们来看如何安装Crawlab吧，请查看[安装](/Installation/README.md)。 \ No newline at end of file diff --git a/gitbook/SUMMARY.md b/gitbook/SUMMARY.md index 9cbc8dec..ffecadff 100644 --- a/gitbook/SUMMARY.md +++ b/gitbook/SUMMARY.md @@ -1,18 +1,31 @@ # Summary -* [简介](README.md) -* [快速开始](QuickStart/README.md) - * [安装](QuickStart/Installation.md) - * [运行](QuickStart/Run.md) -* [概念](Concept/README.md) - * [节点](Concept/Node.md) - * [爬虫](Concept/Spider.md) - * [任务](Concept/Task.md) - * [部署](Concept/Deploy.md) +* [Crawlab简介](README.md) +* [安装Crawlab](Installation/README.md) + * [Docker](Installation/Docker.md) + * [直接部署](Installation/Direct.md) + * [预览模式](Installation/Preview.md) +* [使用Crawlab](Usage/README.md) + * [节点](Usage/Node/README.md) + * [查看节点列表](Usage/Node/View.md) + * [修改节点信息](Usage/Node/Edit.md) + * [爬虫](Usage/Spider/README.md) + * [创建爬虫](Usage/Spider/Create.md) + * [自定义爬虫](Usage/Spider/CustomizedSpider.md) + * [可配置爬虫](Usage/Spider/ConfigurableSpider.md) + * [部署爬虫](Usage/Spider/Deploy.md) + * [运行爬虫](Usage/Spider/Run.md) + * [统计数据](Usage/Spider/Analytics.md) + * [任务](Usage/Task/README.md) + * [查看任务](Usage/Task/View.md) + * [删除任务](Usage/Task/Delete.md) + * [下载结果](Usage/Task/DownloadResults.md) + * [定时任务](Usage/Schedule/README.md) + * [网站](Usage/Site/README.md) * [架构](Architecture/README.md) * [Celery](Architecture/Celery.md) * [App](Architecture/App.md) -* [Examples](Examples/README.md) +* [样例](Examples/README.md) * [与Scrapy集成](Examples/README.md) * [与Puppeteer集成](Examples/README.md) diff --git a/gitbook/Usage/Node/Edit.md b/gitbook/Usage/Node/Edit.md new file mode 100644 index 00000000..37e2b533 --- /dev/null +++ b/gitbook/Usage/Node/Edit.md @@ -0,0 +1,9 @@ +## 修改节点信息 + +后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在`节点列表`中点击`操作`列里的蓝色查看按钮进入到节点详情。节点详情样子如下。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/node-detail.png) + +在右侧分别输入该节点对应的`节点IP`和`节点端口`，然后点击`保存`按钮，保存该节点信息。 + +这样，我们就完成了节点的配置工作。 \ No newline at end of file diff --git a/gitbook/Usage/Node/README.md b/gitbook/Usage/Node/README.md new file mode 100644 index 00000000..f132dcdf --- /dev/null +++ b/gitbook/Usage/Node/README.md @@ -0,0 +1,6 @@ +## 节点 + +节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为`localhost:8000`）。 + +1. [查看节点](/Usage/Node/View.md) +2. [修改节点信息](/Usage/Node/Edit.md) diff --git a/gitbook/Usage/Node/View.md b/gitbook/Usage/Node/View.md new file mode 100644 index 00000000..86c150d8 --- /dev/null +++ b/gitbook/Usage/Node/View.md @@ -0,0 +1,5 @@ +## 查看节点列表 + +点击`侧边栏`的`节点`导航至`节点列表`，可以看到已上线的节点。这里的节点其实就是已经运行起来的`celery worker`，他们通过连接到配置好的`broker`（通常是`redis`）来进行与主机的通信。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/node-list.png) diff --git a/gitbook/Usage/README.md b/gitbook/Usage/README.md new file mode 100644 index 00000000..dbfb648f --- /dev/null +++ b/gitbook/Usage/README.md @@ -0,0 +1,6 @@ +本小节将介绍如何使用Crawlab，包括如下内容： + +1. [节点](/Usage/Node/README.md) +2. [爬虫](/Usage/Spider/README.md) +3. [任务](/Usage/Task/README.md) +4. [定时任务](/Usage/Schedule/README.md) \ No newline at end of file diff --git a/gitbook/Usage/Schedule/README.md b/gitbook/Usage/Schedule/README.md new file mode 100644 index 00000000..e69de29b diff --git a/gitbook/Usage/Site/README.md b/gitbook/Usage/Site/README.md new file mode 100644 index 00000000..e69de29b diff --git a/gitbook/Usage/Spider/Analytics.md b/gitbook/Usage/Spider/Analytics.md new file mode 100644 index 00000000..066a3258 --- /dev/null +++ b/gitbook/Usage/Spider/Analytics.md @@ -0,0 +1,7 @@ +## 统计数据 + +在运行了一段时间之后，爬虫会积累一些统计数据，例如`运行成功率`、`任务数`、`运行时长`等指标。Crawlab将这些指标汇总并呈现给开发者。 + +要查看统计数据的话，只需要在`爬虫详情`中，点击`分析`标签，就可以看到爬虫的统计数据了。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/spider-detail-analytics.png) \ No newline at end of file diff --git a/gitbook/Usage/Spider/ConfigurableSpider.md b/gitbook/Usage/Spider/ConfigurableSpider.md new file mode 100644 index 00000000..4e7c5ff2 --- /dev/null +++ b/gitbook/Usage/Spider/ConfigurableSpider.md @@ -0,0 +1,64 @@ +## 可配置爬虫 + +可配置爬虫是版本[v0.2.1](https://github.com/tikazyq/crawlab/releases/tag/v0.2.1)开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。 + +Crawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持[自定义爬虫](/Usage/Spider/CustomizedSpider)的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。 + +### 添加爬虫 + +在`侧边栏`点击`爬虫`导航至`爬虫列表`，点击**添加爬虫**按钮。 + +![爬虫列表](https://user-gold-cdn.xitu.io/2019/5/27/16af74ec408111a7?w=1662&h=702&f=png&s=98898) + +点击**可配置爬虫**。 + +![爬虫列表-添加爬虫](https://user-gold-cdn.xitu.io/2019/5/27/16af74f4c75346da?w=1667&h=703&f=png&s=92067) + +输入完基本信息，点击**添加**。 + +![爬虫列表-爬虫信息](https://user-gold-cdn.xitu.io/2019/5/27/16af751c5d8d984d?w=1666&h=688&f=png&s=90926) + +### 配置爬虫 + +添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击**查看**进入到**爬虫详情**。 + +![](https://user-gold-cdn.xitu.io/2019/5/27/16af754c6f000698?w=1645&h=739&f=png&s=103908) + +点击**配置**标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。 + +![](https://user-gold-cdn.xitu.io/2019/5/27/16af756d003eae66?w=1659&h=726&f=png&s=92224) + +这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。 + +#### 抓取类别 + +这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有**仅列表**、**仅详情页**、**列表+详情页**。 +- 仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。 +- 仅详情页。爬虫只抓取详情页。 +- 列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。 + +这里我们选择**列表+详情页**。 + +#### 列表项选择器 & 分页选择器 + +列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。 + +#### 开始URL + +爬虫最开始遍历的网址。 + +#### 遵守Robots协议 + +这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。 + +#### 列表页字段 & 详情页字段 + +这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。 + +在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。 + +![](https://user-gold-cdn.xitu.io/2019/5/27/16af7685423c7d57?w=1653&h=873&f=png&s=117230) + +点击保存、预览，查看预览内容。 + +![](https://user-gold-cdn.xitu.io/2019/5/27/16af769811d7bd0c?w=1720&h=663&f=png&s=123762) diff --git a/gitbook/Usage/Spider/Create.md b/gitbook/Usage/Spider/Create.md new file mode 100644 index 00000000..1b934523 --- /dev/null +++ b/gitbook/Usage/Spider/Create.md @@ -0,0 +1,7 @@ +## 创建爬虫 + +Crawlab允许用户创建两种爬虫： +1. [自定义爬虫](/Usage/Spider/CustomizedSpider.md) +2. [可配置爬虫](/Usage/Spider/ConfigurableSpider.md) + +前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。 diff --git a/gitbook/Usage/Spider/CustomizedSpider.md b/gitbook/Usage/Spider/CustomizedSpider.md new file mode 100644 index 00000000..0115ab80 --- /dev/null +++ b/gitbook/Usage/Spider/CustomizedSpider.md @@ -0,0 +1,31 @@ +## 自定义爬虫 + +自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。 + +自定义爬虫的添加有两种方式： +1. 通过Web界面上传爬虫 +2. 通过创建项目目录 + +### 通过Web界面上传 + +在通过Web界面上传之前，需要将爬虫项目文件打包成`zip`格式。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/spider-list.png) + +然后，在`侧边栏`点击`爬虫`导航至`爬虫列表`，点击`添加爬虫`按钮，选择`自定义爬虫`，点击`上传`按钮，选择刚刚打包好的`zip`文件。上传成功后，`爬虫列表`中会出现新添加的自定义爬虫。这样就算添加好了。 + +这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。 + +### 通过添加项目目录 + +Crawlab会自动发现`PROJECT_SOURCE_FILE_FOLDER`目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到`PROJECT_SOURCE_FILE_FOLDER`目录下，就可以添加一个爬虫了。 + +这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。 + +### 配置爬虫 + +在定义爬虫中，我们需要配置一下`执行命令`（运行爬虫时后台执行的`shell`命令）和`结果集`（通过`CRAWLAB_COLLECTION`传递给爬虫程序，爬虫程序存储结果的地方），然后点击`保存`按钮保存爬虫信息。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/spider-detail-overview.png) + +接下来，我们就可以部署、运行自定义爬虫了。 diff --git a/gitbook/Usage/Spider/Deploy.md b/gitbook/Usage/Spider/Deploy.md new file mode 100644 index 00000000..a7f46130 --- /dev/null +++ b/gitbook/Usage/Spider/Deploy.md @@ -0,0 +1,10 @@ +## 部署爬虫 + +这里的爬虫部署是指[自定义爬虫](/Usage/Spider/CustomizedSpider)的部署，因为[可配置爬虫](/Usage/Spider/ConfigurableSpider)已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过`HTTP`的方式打包传输至`worker`节点上，因此节点就可以运行传输过来的爬虫了。 + +部署爬虫很简单，有三种方式： +1. 在`爬虫列表`中点击`部署所有爬虫`，将所有爬虫部署到所有在线节点中； +2. 在`爬虫列表`中点击`操作`列的`部署`按钮，将指定爬虫部署到所有在线节点中； +3. 在`爬虫详情`的`概览`标签中，点击`部署`按钮，将指定爬虫部署到所有在线节点中。 + +部署好之后，我们就可以运行爬虫了。 diff --git a/gitbook/Usage/Spider/README.md b/gitbook/Usage/Spider/README.md new file mode 100644 index 00000000..c346fdd0 --- /dev/null +++ b/gitbook/Usage/Spider/README.md @@ -0,0 +1,9 @@ +## 爬虫 + +爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容： + +1. [创建爬虫](/Usage/Spider/Create.md) +2. [部署爬虫](/Usage/Spider/Deploy.md) +3. [运行爬虫](/Usage/Spider/Run.md) +4. [可配置爬虫](/Usage/Spider/ConfigurableSpider.md) +5. [统计数据](/Usage/Spider/Analytics.md) \ No newline at end of file diff --git a/gitbook/Usage/Spider/Run.md b/gitbook/Usage/Spider/Run.md new file mode 100644 index 00000000..83ede39f --- /dev/null +++ b/gitbook/Usage/Spider/Run.md @@ -0,0 +1,17 @@ +## 运行爬虫 + +我们有两种运行爬虫的方式： +1. 手动触发 +2. 定时任务触发 + +### 手动触发 + +1. 在`爬虫列表`中`操作`列点击`运行`按钮，或者 +2. 在`爬虫详情`中`概览`标签下点击`运行`按钮，或者 +3. 对于`自定义爬虫`，可以在`配置`标签下点击`运行`按钮 + +然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在`爬虫详情`左侧看到新创建的任务。点击`创建时间`可以导航至`任务详情`。 + +### 定时任务触发 + +`定时任务触发`是比较常用的功能，对于`增量抓取`或对实时性有要求的任务很重要。这在[定时任务](/Usage/Schedule/README.md)中会详细介绍。 \ No newline at end of file diff --git a/gitbook/Usage/Task/README.md b/gitbook/Usage/Task/README.md new file mode 100644 index 00000000..e69de29b diff --git a/gitbook/_book/Architecture/App.html b/gitbook/_book/Architecture/App.html new file mode 100644 index 00000000..1a86914f --- /dev/null +++ b/gitbook/_book/Architecture/App.html @@ -0,0 +1,629 @@ + + + + + + + App · GitBook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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          + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gitbook/_book/Functions/FunctionList.md b/gitbook/_book/Functions/FunctionList.md new file mode 100644 index 00000000..636126fa --- /dev/null +++ b/gitbook/_book/Functions/FunctionList.md @@ -0,0 +1,61 @@ +# 功能列表 + +类别 | 功能名称 | 已统计 | 备注 +--- | --- | --- | --- +全局 | 打开页面 | Y | _trackPageview +全局 | 切换中英文 | Y +全局 | 允许/禁止统计 | Y +节点 | 刷新 | Y +节点 | 查看 | Y +节点 | 删除 | Y +节点详情 | 保存 | Y +节点详情 | 切换节点 | Y +爬虫 | 部署所有爬虫 | Y +爬虫 | 导入爬虫 | Y +爬虫 | 添加爬虫-可配置爬虫 | Y +爬虫 | 添加爬虫-自定义爬虫 | Y +爬虫 | 刷新 | Y +爬虫 | 查看 | Y +爬虫 | 删除 | Y +爬虫 | 部署 | Y +爬虫 | 运行 | Y +爬虫 | 搜索网站 | Y +爬虫详情 | 切换爬虫 | Y +爬虫详情 | 切换标签 | Y +爬虫详情-概览 | 保存 | Y +爬虫详情-概览 | 部署 | Y +爬虫详情-概览 | 运行 | Y +爬虫详情-环境 | 添加 | Y +爬虫详情-环境 | 删除 | Y +爬虫详情-环境 | 保存 | Y +爬虫详情-配置 | 保存 | Y +爬虫详情-配置 | 预览 | Y +爬虫详情-配置 | 提取字段 | Y +爬虫详情-配置 | 运行 | Y +爬虫详情-配置 | 添加字段 | Y +爬虫详情-配置 | 更改字段 | Y +爬虫详情-配置 | 删除字段 | Y +爬虫详情-配置 | 设置详情页URL | Y +任务 | 选择节点 | Y +任务 | 选择爬虫 | Y +任务 | 点击爬虫详情 | Y +任务 | 点击节点详情 | Y +任务 | 搜索 | Y +任务 | 查看 | Y +任务 | 删除 | Y +任务详情 | 切换标签 | Y +任务详情-概览 | 点击爬虫详情 | Y +任务详情-概览 | 点击节点详情 | Y +任务详情-结果 | 下载CSV | Y +定时任务 | 添加 | Y +定时任务 | 修改 | Y +定时任务 | 删除 | Y +定时任务 | 提交 | Y +部署 | 刷新 | Y +网站 | 搜索 | Y +网站 | 选择主类别 | Y +网站 | 选择类别 | Y +网站 | 点击域名 | Y +网站 | 点击爬虫数 | Y +网站 | 点击Robots协议 | N + diff --git a/gitbook/_book/Installation/Direct.html b/gitbook/_book/Installation/Direct.html new file mode 100644 index 00000000..8e0a38cb --- /dev/null +++ b/gitbook/_book/Installation/Direct.html @@ -0,0 +1,677 @@ + + + + + + + 直接部署 · GitBook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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          + +
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          + +
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          直接部署

          +

          直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。

          +

          拉取代码

          +

          首先是将github上的代码拉取到本地。

          +
          git clone https://github.com/tikazyq/crawlab
+
          +

          安装

          +

          安装前端所需库。

          +
          npm install -g yarn pm2
+cd frontend
+yarn install
+
          +

          安装后端所需库。

          +
          cd ../crawlab
+pip install -r requirements
+
          +

          配置

          +

          分别配置前端配置文件./frontend/.env.production和后端配置文件./crawlab/config/config.py。分别需要对部署后API地址以及数据库地址进行配置。

          +

          构建

          +

          这里的构建是指前端构建，需要执行以下命令。

          +
          cd ../frontend
+npm run build:prod
+
          +

          构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。

          +

          Nginx

          +

          安装nginx，在ubuntu 16.04是以下命令。

          +
          sudo apt-get install nginx
+
          +

          添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。

          +
          server {
+    listen    8080;
+    server_name    dev.crawlab.com;
+    root    /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist;
+    index    index.html;
+}
+

          其中，root是静态文件的根目录，这里是npm打包好后的静态文件。

          +

          现在，只需要启动nginx服务就完成了启动前端服务。

          +
          nginx reload
+
          +

          启动服务

          +

          这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。

          +
          pm2 start app.py # API服务
+pm2 start worker.py # Worker
+pm2 start flower.py # Flower
+
          +

          这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。

          +
          pm2 logs [app]
+
          +

          然后在浏览器中输入http://localhost:8080就可以看到界面了。

          + + +
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            Docker安装部署

            +

            这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。

            +

            对Docker不了解的开发者，可以参考一下这篇文章（9102 年了，学点 Docker 知识）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需docker run -d --name mongo -p 27017:27017 mongo一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。

            +

            下载镜像

            +

            我们已经在DockerHub上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。

            +
            {
+  "registry-mirrors": ["https://registry.docker-cn.com"]
+}
+
            +

            这样的话，pull镜像的速度会比不改变镜像源的速度快很多。

            +

            执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。

            +
            docker pull tikazyq/crawlab:latest
+
            +

            更改配置文件

            +

            拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。

            +

            更改后端配置文件config.py，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将API地址VUE_APP_BASE_URL更改为宿主机所在的IP地址，例如http://192.168.0.8:8000，这将是前端调用API会用到的URL。

            +

            运行Docker容器

            +

            更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。

            +
            docker run -d --rm --name crawlab \
+    -p 8080:8080 \
+    -p 8000:8000 \
+    -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \
+    -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \
+    tikazyq/crawlab master
+
            +

            其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件/home/yeqing/.env.production和后端配置文件/home/yeqing/config.py映射到了容器相应的目录下。传入参数master是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是worker模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为http://localhost:8080。

            +

            +

            Docker-Compose

            +

            当然，也可以用docker-compose的方式来部署。docker-compose是一个集群管理方式，可以利用名为docker-compose.yml的yaml文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的docker-compose.yml定义如下。

            +
            version: '3.3'
+services:
+  master: 
+    image: tikazyq/crawlab:latest
+    container_name: crawlab
+    volumns:
+      - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:    
+      - "8080:8080" # nginx
+      - "8000:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - master
+  mongo:
+    image: mongo:latest
+    restart: always
+    ports:
+      - "27017:27017"
+  redis:
+    image: redis:latest
+    restart: always
+    ports:
+      - "6379:6379"
+
            +

            这里先定义了master节点，也就是Crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。

            +

            安装docker-compose也很简单，大家去网上百度一下就可以了。

            +

            安装完docker-compose和定义好docker-compose.yml后，只需要运行以下命令就可以启动Crawlab。

            +
            docker-compose up
+
            +

            同样，在浏览器中输入http://localhost:8080就可以看到界面。

            +

            多节点模式

            +

            docker-compose的方式很适合多节点部署，在原有的master基础上增加几个worker节点，达到多节点部署的目的。将docker-compose.yml更改为如下内容。

            +
            version: '3.3'
+services:
+  master: 
+    image: tikazyq/crawlab:latest
+    container_name: crawlab
+    volumns:
+      - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:    
+      - "8080:8080" # nginx
+      - "8000:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - master
+  worker1: 
+    image: tikazyq/crawlab:latest
+    volumns:
+      - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:
+      - "8001:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - worker
+  worker2: 
+    image: tikazyq/crawlab:latest
+    volumns:
+      - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:
+      - "8002:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - worker
+  mongo:
+    image: mongo:latest
+    restart: always
+    ports:
+      - "27017:27017"
+  redis:
+    image: redis:latest
+    restart: always
+    ports:
+      - "6379:6379"
+
            +

            这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。

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              预览模式

              +

              预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。

              +
              python manage.py serve
+
              +

              该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm run serve来进行的，因此是开发者模式。注意：强烈不建议在生产环境中用预览模式。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。

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                本小节将介绍三种安装Docker的方式:

                +
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                  修改节点信息

                  +

                  后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在节点列表中点击操作列里的蓝色查看按钮进入到节点详情。节点详情样子如下。

                  +

                  +

                  在右侧分别输入该节点对应的节点IP和节点端口，然后点击保存按钮，保存该节点信息。

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                  这样，我们就完成了节点的配置工作。

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                    查看节点列表

                    +

                    点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celery worker，他们通过连接到配置好的broker（通常是redis）来进行与主机的通信。

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                      节点

                      +

                      节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为localhost:8000）。

                      +
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                            统计数据

                            +

                            在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。Crawlab将这些指标汇总并呈现给开发者。

                            +

                            要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。

                            +

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                              可配置爬虫

                              +

                              可配置爬虫是版本v0.2.1开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。

                              +

                              Crawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持自定义爬虫的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。

                              +

                              添加爬虫

                              +

                              在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮。

                              +

                              爬虫列表

                              +

                              点击可配置爬虫。

                              +

                              爬虫列表-添加爬虫

                              +

                              输入完基本信息，点击添加。

                              +

                              爬虫列表-爬虫信息

                              +

                              配置爬虫

                              +

                              添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。

                              +

                              +

                              点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。

                              +

                              +

                              这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。

                              +

                              抓取类别

                              +

                              这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。

                              +
                                +
                              • 仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。
                                • +
                              • 仅详情页。爬虫只抓取详情页。
                                • +
                              • 列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。
                                • +
                              +

                              这里我们选择列表+详情页。

                              +

                              列表项选择器 & 分页选择器

                              +

                              列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。

                              +

                              开始URL

                              +

                              爬虫最开始遍历的网址。

                              +

                              遵守Robots协议

                              +

                              这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。

                              +

                              列表页字段 & 详情页字段

                              +

                              这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。

                              +

                              在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。

                              +

                              +

                              点击保存、预览，查看预览内容。

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                                创建爬虫

                                +

                                Crawlab允许用户创建两种爬虫：

                                +
                                  +
                                1. 自定义爬虫
                                  2. +
                                2. 可配置爬虫
                                  3. +
                                +

                                前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。

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                                  自定义爬虫

                                  +

                                  自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。

                                  +

                                  自定义爬虫的添加有两种方式：

                                  +
                                    +
                                  1. 通过Web界面上传爬虫
                                    2. +
                                  2. 通过创建项目目录
                                    3. +
                                  +

                                  通过Web界面上传

                                  +

                                  在通过Web界面上传之前，需要将爬虫项目文件打包成zip格式。

                                  +

                                  +

                                  然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。

                                  +

                                  这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。

                                  +

                                  通过添加项目目录

                                  +

                                  Crawlab会自动发现PROJECT_SOURCE_FILE_FOLDER目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到PROJECT_SOURCE_FILE_FOLDER目录下，就可以添加一个爬虫了。

                                  +

                                  这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。

                                  +

                                  配置爬虫

                                  +

                                  在定义爬虫中，我们需要配置一下执行命令（运行爬虫时后台执行的shell命令）和结果集（通过CRAWLAB_COLLECTION传递给爬虫程序，爬虫程序存储结果的地方），然后点击保存按钮保存爬虫信息。

                                  +

                                  +

                                  接下来，我们就可以部署、运行自定义爬虫了。

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                                    部署爬虫

                                    +

                                    这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过HTTP的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。

                                    +

                                    部署爬虫很简单，有三种方式：

                                    +
                                      +
                                    1. 在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；
                                      2. +
                                    2. 在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；
                                      3. +
                                    3. 在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。
                                      4. +
                                    +

                                    部署好之后，我们就可以运行爬虫了。

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                                      运行爬虫

                                      +

                                      我们有两种运行爬虫的方式：

                                      +
                                        +
                                      1. 手动触发
                                        2. +
                                      2. 定时任务触发
                                        3. +
                                      +

                                      手动触发

                                      +
                                        +
                                      1. 在爬虫列表中操作列点击运行按钮，或者
                                        2. +
                                      2. 在爬虫详情中概览标签下点击运行按钮，或者
                                        3. +
                                      3. 对于自定义爬虫，可以在配置标签下点击运行按钮
                                        4. +
                                      +

                                      然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。

                                      +

                                      定时任务触发

                                      +

                                      定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。

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                                        爬虫

                                        +

                                        爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：

                                        +
                                          +
                                        1. 创建爬虫
                                          2. +
                                        2. 部署爬虫
                                          3. +
                                        3. 运行爬虫
                                          4. +
                                        4. 可配置爬虫
                                          5. +
                                        5. 统计数据
                                          6. +
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                                            本小节将介绍如何使用Crawlab，包括如下内容：

                                            +
                                              +
                                            1. 节点
                                              2. +
                                            2. 爬虫
                                              3. +
                                            3. 任务
                                              4. +
                                            4. 定时任务
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Default themes + var THEMES = [ + { + config: 'white', + text: 'White', + id: 0 + }, + { + config: 'sepia', + text: 'Sepia', + id: 1 + }, + { + config: 'night', + text: 'Night', + id: 2 + } + ]; + + // Default font families + var FAMILIES = [ + { + config: 'serif', + text: 'Serif', + id: 0 + }, + { + config: 'sans', + text: 'Sans', + id: 1 + } + ]; + + // Return configured themes + function getThemes() { + return THEMES; + } + + // Modify configured themes + function setThemes(themes) { + THEMES = themes; + updateButtons(); + } + + // Return configured font families + function getFamilies() { + return FAMILIES; + } + + // Modify configured font families + function setFamilies(families) { + FAMILIES = families; + updateButtons(); + } + + // Save current font settings + function saveFontSettings() { + gitbook.storage.set('fontState', fontState); + update(); + } + + // Increase font size + function enlargeFontSize(e) { + e.preventDefault(); + if (fontState.size >= MAX_SIZE) return; + + fontState.size++; + saveFontSettings(); + } + + // Decrease font size + function reduceFontSize(e) { + e.preventDefault(); + if (fontState.size <= MIN_SIZE) return; + + fontState.size--; + saveFontSettings(); + } + + // Change font family + function changeFontFamily(configName, e) { + if (e && e instanceof Event) { + e.preventDefault(); + } + + var familyId = getFontFamilyId(configName); + fontState.family = familyId; + saveFontSettings(); + } + + // Change type of color theme + function changeColorTheme(configName, e) { + if (e && e instanceof Event) { + e.preventDefault(); + } + + var $book = gitbook.state.$book; + + // Remove currently applied color theme + if (fontState.theme !== 0) + $book.removeClass('color-theme-'+fontState.theme); + + // Set new color theme + var themeId = getThemeId(configName); + fontState.theme = themeId; + if (fontState.theme !== 0) + $book.addClass('color-theme-'+fontState.theme); + + saveFontSettings(); + } + + // Return the correct id for a font-family config key + // Default to first font-family + function getFontFamilyId(configName) { + // Search for plugin configured font family + var configFamily = $.grep(FAMILIES, function(family) { + return family.config == configName; + })[0]; + // Fallback to default font family + return (!!configFamily)? configFamily.id : 0; + } + + // Return the correct id for a theme config key + // Default to first theme + function getThemeId(configName) { + // Search for plugin configured theme + var configTheme = $.grep(THEMES, function(theme) { + return theme.config == configName; + })[0]; + // Fallback to default theme + return (!!configTheme)? configTheme.id : 0; + } + + function update() { + var $book = gitbook.state.$book; + + $('.font-settings .font-family-list li').removeClass('active'); + $('.font-settings .font-family-list li:nth-child('+(fontState.family+1)+')').addClass('active'); + + $book[0].className = $book[0].className.replace(/\bfont-\S+/g, ''); + $book.addClass('font-size-'+fontState.size); + $book.addClass('font-family-'+fontState.family); + + if(fontState.theme !== 0) { + $book[0].className = $book[0].className.replace(/\bcolor-theme-\S+/g, ''); + $book.addClass('color-theme-'+fontState.theme); + } + } + + function init(config) { + // Search for plugin configured font family + var configFamily = getFontFamilyId(config.family), + configTheme = getThemeId(config.theme); + + // Instantiate font state object + fontState = gitbook.storage.get('fontState', { + size: config.size || 2, + family: configFamily, + theme: configTheme + }); + + update(); + } + + function updateButtons() { + // Remove existing fontsettings buttons + if (!!BUTTON_ID) { + gitbook.toolbar.removeButton(BUTTON_ID); + } + + // Create buttons in toolbar + BUTTON_ID = gitbook.toolbar.createButton({ + icon: 'fa fa-font', + label: 'Font Settings', + className: 'font-settings', + dropdown: [ + [ + { + text: 'A', + className: 'font-reduce', + onClick: reduceFontSize + }, + { + text: 'A', + className: 'font-enlarge', + onClick: enlargeFontSize + } + ], + $.map(FAMILIES, function(family) { + family.onClick = function(e) { + return changeFontFamily(family.config, e); + }; + + return family; + }), + $.map(THEMES, function(theme) { + theme.onClick = function(e) { + return changeColorTheme(theme.config, e); + }; + + return theme; + }) + ] + }); + } + + // Init configuration at start + gitbook.events.bind('start', function(e, config) { + var opts = config.fontsettings; + + // Generate buttons at start + updateButtons(); + + // Init current settings + init(opts); + }); + + // Expose API + gitbook.fontsettings = { + enlargeFontSize: enlargeFontSize, + reduceFontSize: reduceFontSize, + setTheme: changeColorTheme, + setFamily: changeFontFamily, + getThemes: getThemes, + setThemes: setThemes, + getFamilies: getFamilies, + setFamilies: setFamilies + }; +}); + + diff --git a/gitbook/_book/gitbook/gitbook-plugin-fontsettings/website.css b/gitbook/_book/gitbook/gitbook-plugin-fontsettings/website.css new file mode 100644 index 00000000..26591fe8 --- /dev/null +++ b/gitbook/_book/gitbook/gitbook-plugin-fontsettings/website.css @@ -0,0 +1,291 @@ +/* + * Theme 1 + */ +.color-theme-1 .dropdown-menu { + background-color: #111111; + border-color: #7e888b; +} +.color-theme-1 .dropdown-menu .dropdown-caret .caret-inner { + border-bottom: 9px solid #111111; +} +.color-theme-1 .dropdown-menu .buttons { + border-color: #7e888b; +} +.color-theme-1 .dropdown-menu .button { + color: #afa790; +} +.color-theme-1 .dropdown-menu .button:hover { + color: #73553c; +} +/* + * Theme 2 + */ +.color-theme-2 .dropdown-menu { + background-color: #2d3143; + border-color: #272a3a; +} +.color-theme-2 .dropdown-menu .dropdown-caret .caret-inner { + border-bottom: 9px solid #2d3143; +} +.color-theme-2 .dropdown-menu .buttons { + border-color: #272a3a; +} +.color-theme-2 .dropdown-menu .button { + color: #62677f; +} +.color-theme-2 .dropdown-menu .button:hover { + color: #f4f4f5; +} +.book .book-header .font-settings .font-enlarge { + line-height: 30px; + font-size: 1.4em; +} +.book .book-header .font-settings .font-reduce { + line-height: 30px; + font-size: 1em; +} +.book.color-theme-1 .book-body { + color: #704214; + background: #f3eacb; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section { + background: #f3eacb; +} +.book.color-theme-2 .book-body { + color: #bdcadb; + background: #1c1f2b; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section { + background: #1c1f2b; +} +.book.font-size-0 .book-body .page-inner section { + font-size: 1.2rem; +} +.book.font-size-1 .book-body .page-inner section { + font-size: 1.4rem; +} +.book.font-size-2 .book-body .page-inner section { + font-size: 1.6rem; +} +.book.font-size-3 .book-body .page-inner section { + font-size: 2.2rem; +} +.book.font-size-4 .book-body .page-inner section { + font-size: 4rem; +} +.book.font-family-0 { + font-family: Georgia, serif; +} +.book.font-family-1 { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal { + color: #704214; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal a { + color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h3, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h4, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h5, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { + color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h2 { + border-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal h6 { + color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal hr { + background-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal blockquote { + border-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code { + background: #fdf6e3; + color: #657b83; + border-color: #f8df9c; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal .highlight { + background-color: inherit; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table th, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table td { + border-color: #f5d06c; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table tr { + color: inherit; + background-color: #fdf6e3; + border-color: #444444; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { + background-color: #fbeecb; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal { + color: #bdcadb; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal a { + color: #3eb1d0; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h2, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h3, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h4, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h5, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h6 { + color: #fffffa; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h1, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h2 { + border-color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal h6 { + color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal hr { + background-color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal blockquote { + border-color: #373b4e; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal pre, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal code { + color: #9dbed8; + background: #2d3143; + border-color: #2d3143; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal .highlight { + background-color: #282a39; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table th, +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table td { + border-color: #3b3f54; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr { + color: #b6c2d2; + background-color: #2d3143; + border-color: #3b3f54; +} +.book.color-theme-2 .book-body .page-wrapper .page-inner section.normal table tr:nth-child(2n) { + background-color: #35394b; +} +.book.color-theme-1 .book-header { + color: #afa790; + background: transparent; +} +.book.color-theme-1 .book-header .btn { + color: #afa790; +} +.book.color-theme-1 .book-header .btn:hover { + color: #73553c; + background: none; +} +.book.color-theme-1 .book-header h1 { + color: #704214; +} +.book.color-theme-2 .book-header { + color: #7e888b; + background: transparent; +} +.book.color-theme-2 .book-header .btn { + color: #3b3f54; +} +.book.color-theme-2 .book-header .btn:hover { + color: #fffff5; + background: none; +} +.book.color-theme-2 .book-header h1 { + color: #bdcadb; +} +.book.color-theme-1 .book-body .navigation { + color: #afa790; +} +.book.color-theme-1 .book-body .navigation:hover { + color: #73553c; +} +.book.color-theme-2 .book-body .navigation { + color: #383f52; +} +.book.color-theme-2 .book-body .navigation:hover { + color: #fffff5; +} +/* + * Theme 1 + */ +.book.color-theme-1 .book-summary { + color: #afa790; + background: #111111; + border-right: 1px solid rgba(0, 0, 0, 0.07); +} +.book.color-theme-1 .book-summary .book-search { + background: transparent; +} +.book.color-theme-1 .book-summary .book-search input, +.book.color-theme-1 .book-summary .book-search input:focus { + border: 1px solid transparent; +} +.book.color-theme-1 .book-summary ul.summary li.divider { + background: #7e888b; + box-shadow: none; +} +.book.color-theme-1 .book-summary ul.summary li i.fa-check { + color: #33cc33; +} +.book.color-theme-1 .book-summary ul.summary li.done > a { + color: #877f6a; +} +.book.color-theme-1 .book-summary ul.summary li a, +.book.color-theme-1 .book-summary ul.summary li span { + color: #877f6a; + background: transparent; + font-weight: normal; +} +.book.color-theme-1 .book-summary ul.summary li.active > a, +.book.color-theme-1 .book-summary ul.summary li a:hover { + color: #704214; + background: transparent; + font-weight: normal; +} +/* + * Theme 2 + */ +.book.color-theme-2 .book-summary { + color: #bcc1d2; + background: #2d3143; + border-right: none; +} +.book.color-theme-2 .book-summary .book-search { + background: transparent; +} +.book.color-theme-2 .book-summary .book-search input, +.book.color-theme-2 .book-summary .book-search input:focus { + border: 1px solid transparent; +} +.book.color-theme-2 .book-summary ul.summary li.divider { + background: #272a3a; + box-shadow: none; +} +.book.color-theme-2 .book-summary ul.summary li i.fa-check { + color: #33cc33; +} +.book.color-theme-2 .book-summary ul.summary li.done > a { + color: #62687f; +} +.book.color-theme-2 .book-summary ul.summary li a, +.book.color-theme-2 .book-summary ul.summary li span { + color: #c1c6d7; + background: transparent; + font-weight: 600; +} +.book.color-theme-2 .book-summary ul.summary li.active > a, +.book.color-theme-2 .book-summary ul.summary li a:hover { + color: #f4f4f5; + background: #252737; + font-weight: 600; +} diff --git a/gitbook/_book/gitbook/gitbook-plugin-highlight/ebook.css b/gitbook/_book/gitbook/gitbook-plugin-highlight/ebook.css new file mode 100644 index 00000000..cecaaab5 --- /dev/null +++ b/gitbook/_book/gitbook/gitbook-plugin-highlight/ebook.css @@ -0,0 +1,135 @@ +pre, +code { + /* http://jmblog.github.io/color-themes-for-highlightjs */ + /* Tomorrow Comment */ + /* Tomorrow Red */ + /* Tomorrow Orange */ + /* Tomorrow Yellow */ + /* Tomorrow Green */ + /* Tomorrow Aqua */ + /* Tomorrow Blue */ + /* Tomorrow Purple */ +} +pre .hljs-comment, +code .hljs-comment, +pre .hljs-title, +code .hljs-title { + color: #8e908c; +} +pre .hljs-variable, +code .hljs-variable, +pre .hljs-attribute, +code .hljs-attribute, +pre .hljs-tag, +code .hljs-tag, +pre .hljs-regexp, +code .hljs-regexp, +pre .hljs-deletion, +code .hljs-deletion, +pre .ruby .hljs-constant, +code .ruby .hljs-constant, +pre .xml .hljs-tag .hljs-title, +code .xml .hljs-tag .hljs-title, +pre .xml .hljs-pi, +code .xml .hljs-pi, +pre .xml .hljs-doctype, +code .xml .hljs-doctype, +pre .html .hljs-doctype, +code .html .hljs-doctype, +pre .css .hljs-id, +code .css .hljs-id, +pre .css .hljs-class, +code .css .hljs-class, +pre .css .hljs-pseudo, +code .css .hljs-pseudo { + color: #c82829; +} +pre .hljs-number, +code .hljs-number, +pre .hljs-preprocessor, +code .hljs-preprocessor, +pre .hljs-pragma, +code .hljs-pragma, +pre .hljs-built_in, +code .hljs-built_in, +pre .hljs-literal, +code .hljs-literal, +pre .hljs-params, +code .hljs-params, +pre .hljs-constant, +code .hljs-constant { + color: #f5871f; +} +pre .ruby .hljs-class .hljs-title, +code .ruby .hljs-class .hljs-title, +pre .css .hljs-rules .hljs-attribute, +code .css .hljs-rules .hljs-attribute { + color: #eab700; +} +pre .hljs-string, +code .hljs-string, +pre .hljs-value, +code .hljs-value, +pre .hljs-inheritance, +code .hljs-inheritance, +pre .hljs-header, +code .hljs-header, +pre .hljs-addition, +code .hljs-addition, +pre .ruby .hljs-symbol, +code .ruby .hljs-symbol, +pre .xml .hljs-cdata, +code .xml .hljs-cdata { + color: #718c00; +} +pre .css .hljs-hexcolor, +code .css .hljs-hexcolor { + color: #3e999f; +} +pre .hljs-function, +code .hljs-function, +pre .python .hljs-decorator, +code .python .hljs-decorator, +pre .python .hljs-title, +code .python .hljs-title, +pre .ruby .hljs-function .hljs-title, +code .ruby .hljs-function .hljs-title, +pre .ruby .hljs-title .hljs-keyword, +code .ruby .hljs-title .hljs-keyword, +pre .perl .hljs-sub, +code .perl .hljs-sub, +pre .javascript .hljs-title, +code .javascript .hljs-title, +pre .coffeescript .hljs-title, +code .coffeescript .hljs-title { + color: #4271ae; +} +pre .hljs-keyword, +code .hljs-keyword, +pre .javascript .hljs-function, +code .javascript .hljs-function { + color: #8959a8; +} +pre .hljs, +code .hljs { + display: block; + background: white; + color: #4d4d4c; + padding: 0.5em; +} +pre .coffeescript .javascript, +code .coffeescript .javascript, +pre .javascript .xml, +code .javascript .xml, +pre .tex .hljs-formula, +code .tex .hljs-formula, +pre .xml .javascript, +code .xml .javascript, +pre .xml .vbscript, +code .xml .vbscript, +pre .xml .css, +code .xml .css, +pre .xml .hljs-cdata, +code .xml .hljs-cdata { + opacity: 0.5; +} diff --git a/gitbook/_book/gitbook/gitbook-plugin-highlight/website.css b/gitbook/_book/gitbook/gitbook-plugin-highlight/website.css new file mode 100644 index 00000000..6674448f --- /dev/null +++ b/gitbook/_book/gitbook/gitbook-plugin-highlight/website.css @@ -0,0 +1,434 @@ +.book .book-body .page-wrapper .page-inner section.normal pre, +.book .book-body .page-wrapper .page-inner section.normal code { + /* http://jmblog.github.com/color-themes-for-google-code-highlightjs */ + /* Tomorrow Comment */ + /* Tomorrow Red */ + /* Tomorrow Orange */ + /* Tomorrow Yellow */ + /* Tomorrow Green */ + /* Tomorrow Aqua */ + /* Tomorrow Blue */ + /* Tomorrow Purple */ +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-comment, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-comment, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-title { + color: #8e908c; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-variable, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-variable, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-attribute, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-attribute, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-tag, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-tag, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-regexp, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-regexp, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-deletion, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-deletion, +.book .book-body .page-wrapper .page-inner section.normal pre .ruby .hljs-constant, +.book .book-body .page-wrapper .page-inner section.normal code .ruby .hljs-constant, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .hljs-tag .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .xml .hljs-tag .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .hljs-pi, +.book .book-body .page-wrapper .page-inner section.normal code .xml .hljs-pi, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .hljs-doctype, +.book .book-body .page-wrapper .page-inner section.normal code .xml .hljs-doctype, +.book .book-body .page-wrapper .page-inner section.normal pre .html .hljs-doctype, +.book .book-body .page-wrapper .page-inner section.normal code .html .hljs-doctype, +.book .book-body .page-wrapper .page-inner section.normal pre .css .hljs-id, +.book .book-body .page-wrapper .page-inner section.normal code .css .hljs-id, +.book .book-body .page-wrapper .page-inner section.normal pre .css .hljs-class, +.book .book-body .page-wrapper .page-inner section.normal code .css .hljs-class, +.book .book-body .page-wrapper .page-inner section.normal pre .css .hljs-pseudo, +.book .book-body .page-wrapper .page-inner section.normal code .css .hljs-pseudo { + color: #c82829; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-number, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-number, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-preprocessor, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-preprocessor, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-pragma, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-pragma, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-built_in, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-built_in, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-literal, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-literal, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-params, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-params, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-constant, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-constant { + color: #f5871f; +} +.book .book-body .page-wrapper .page-inner section.normal pre .ruby .hljs-class .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .ruby .hljs-class .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal pre .css .hljs-rules .hljs-attribute, +.book .book-body .page-wrapper .page-inner section.normal code .css .hljs-rules .hljs-attribute { + color: #eab700; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-string, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-string, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-value, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-value, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-inheritance, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-inheritance, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-header, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-header, +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-addition, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-addition, +.book .book-body .page-wrapper .page-inner section.normal pre .ruby .hljs-symbol, +.book .book-body .page-wrapper .page-inner section.normal code .ruby .hljs-symbol, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .hljs-cdata, +.book .book-body .page-wrapper .page-inner section.normal code .xml .hljs-cdata { + color: #718c00; +} +.book .book-body .page-wrapper .page-inner section.normal pre .css .hljs-hexcolor, +.book .book-body .page-wrapper .page-inner section.normal code .css .hljs-hexcolor { + color: #3e999f; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-function, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-function, +.book .book-body .page-wrapper .page-inner section.normal pre .python .hljs-decorator, +.book .book-body .page-wrapper .page-inner section.normal code .python .hljs-decorator, +.book .book-body .page-wrapper .page-inner section.normal pre .python .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .python .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal pre .ruby .hljs-function .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .ruby .hljs-function .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal pre .ruby .hljs-title .hljs-keyword, +.book .book-body .page-wrapper .page-inner section.normal code .ruby .hljs-title .hljs-keyword, +.book .book-body .page-wrapper .page-inner section.normal pre .perl .hljs-sub, +.book .book-body .page-wrapper .page-inner section.normal code .perl .hljs-sub, +.book .book-body .page-wrapper .page-inner section.normal pre .javascript .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .javascript .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal pre .coffeescript .hljs-title, +.book .book-body .page-wrapper .page-inner section.normal code .coffeescript .hljs-title { + color: #4271ae; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs-keyword, +.book .book-body .page-wrapper .page-inner section.normal code .hljs-keyword, +.book .book-body .page-wrapper .page-inner section.normal pre .javascript .hljs-function, +.book .book-body .page-wrapper .page-inner section.normal code .javascript .hljs-function { + color: #8959a8; +} +.book .book-body .page-wrapper .page-inner section.normal pre .hljs, +.book .book-body .page-wrapper .page-inner section.normal code .hljs { + display: block; + background: white; + color: #4d4d4c; + padding: 0.5em; +} +.book .book-body .page-wrapper .page-inner section.normal pre .coffeescript .javascript, +.book .book-body .page-wrapper .page-inner section.normal code .coffeescript .javascript, +.book .book-body .page-wrapper .page-inner section.normal pre .javascript .xml, +.book .book-body .page-wrapper .page-inner section.normal code .javascript .xml, +.book .book-body .page-wrapper .page-inner section.normal pre .tex .hljs-formula, +.book .book-body .page-wrapper .page-inner section.normal code .tex .hljs-formula, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .javascript, +.book .book-body .page-wrapper .page-inner section.normal code .xml .javascript, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .vbscript, +.book .book-body .page-wrapper .page-inner section.normal code .xml .vbscript, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .css, +.book .book-body .page-wrapper .page-inner section.normal code .xml .css, +.book .book-body .page-wrapper .page-inner section.normal pre .xml .hljs-cdata, +.book .book-body .page-wrapper .page-inner section.normal code .xml .hljs-cdata { + opacity: 0.5; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code { + /* + +Orginal Style from ethanschoonover.com/solarized (c) Jeremy Hull + +*/ + /* Solarized Green */ + /* Solarized Cyan */ + /* Solarized Blue */ + /* Solarized Yellow */ + /* Solarized Orange */ + /* Solarized Red */ + /* Solarized Violet */ +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre .hljs, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code .hljs { + display: block; + padding: 0.5em; + background: #fdf6e3; + color: #657b83; +} +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre .hljs-comment, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code .hljs-comment, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre .hljs-template_comment, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code .hljs-template_comment, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre .diff .hljs-header, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code .diff .hljs-header, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal pre .hljs-doctype, +.book.color-theme-1 .book-body .page-wrapper .page-inner section.normal code .hljs-doctype, +.book.color-theme-1 .book-body .page-wrapper .page-inner 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t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.5.12",t.utils={},t.utils.warn=function(t){return function(e){t.console&&console.warn&&console.warn(e)}}(this),t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var t=Array.prototype.slice.call(arguments),e=t.pop(),n=t;if("function"!=typeof e)throw new TypeError("last argument must be a function");n.forEach(function(t){this.hasHandler(t)||(this.events[t]=[]),this.events[t].push(e)},this)},t.EventEmitter.prototype.removeListener=function(t,e){if(this.hasHandler(t)){var n=this.events[t].indexOf(e);this.events[t].splice(n,1),this.events[t].length||delete this.events[t]}},t.EventEmitter.prototype.emit=function(t){if(this.hasHandler(t)){var e=Array.prototype.slice.call(arguments,1);this.events[t].forEach(function(t){t.apply(void 0,e)})}},t.EventEmitter.prototype.hasHandler=function(t){return t in this.events},t.tokenizer=function(t){return 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a=o.reduce(function(t,e){return t.intersect(e)});return a.map(function(t){return{ref:t,score:i.similarity(this.documentVector(t))}},this).sort(function(t,e){return e.score-t.score})},t.Index.prototype.documentVector=function(e){for(var n=this.documentStore.get(e),i=n.length,o=new t.Vector,r=0;i>r;r++){var s=n.elements[r],a=this.tokenStore.get(s)[e].tf,h=this.idf(s);o.insert(this.corpusTokens.indexOf(s),a*h)}return o},t.Index.prototype.toJSON=function(){return{version:t.version,fields:this._fields,ref:this._ref,documentStore:this.documentStore.toJSON(),tokenStore:this.tokenStore.toJSON(),corpusTokens:this.corpusTokens.toJSON(),pipeline:this.pipeline.toJSON()}},t.Index.prototype.use=function(t){var e=Array.prototype.slice.call(arguments,1);e.unshift(this),t.apply(this,e)},t.Store=function(){this.store={},this.length=0},t.Store.load=function(e){var n=new this;return n.length=e.length,n.store=Object.keys(e.store).reduce(function(n,i){return n[i]=t.SortedSet.load(e.store[i]),n},{}),n},t.Store.prototype.set=function(t,e){this.has(t)||this.length++,this.store[t]=e},t.Store.prototype.get=function(t){return this.store[t]},t.Store.prototype.has=function(t){return t in this.store},t.Store.prototype.remove=function(t){this.has(t)&&(delete this.store[t],this.length--)},t.Store.prototype.toJSON=function(){return{store:this.store,length:this.length}},t.stemmer=function(){var t={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},e={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",o=n+"[^aeiouy]*",r=i+"[aeiou]*",s="^("+o+")?"+r+o,a="^("+o+")?"+r+o+"("+r+")?$",h="^("+o+")?"+r+o+r+o,l="^("+o+")?"+i,u=new RegExp(s),c=new RegExp(h),f=new RegExp(a),d=new RegExp(l),p=/^(.+?)(ss|i)es$/,m=/^(.+?)([^s])s$/,v=/^(.+?)eed$/,y=/^(.+?)(ed|ing)$/,g=/.$/,S=/(at|bl|iz)$/,w=new RegExp("([^aeiouylsz])\\1$"),x=new RegExp("^"+o+i+"[^aeiouwxy]$"),k=/^(.+?[^aeiou])y$/,b=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,E=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,_=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,F=/^(.+?)(s|t)(ion)$/,O=/^(.+?)e$/,P=/ll$/,N=new RegExp("^"+o+i+"[^aeiouwxy]$"),T=function(n){var i,o,r,s,a,h,l;if(n.length<3)return n;if(r=n.substr(0,1),"y"==r&&(n=r.toUpperCase()+n.substr(1)),s=p,a=m,s.test(n)?n=n.replace(s,"$1$2"):a.test(n)&&(n=n.replace(a,"$1$2")),s=v,a=y,s.test(n)){var T=s.exec(n);s=u,s.test(T[1])&&(s=g,n=n.replace(s,""))}else if(a.test(n)){var T=a.exec(n);i=T[1],a=d,a.test(i)&&(n=i,a=S,h=w,l=x,a.test(n)?n+="e":h.test(n)?(s=g,n=n.replace(s,"")):l.test(n)&&(n+="e"))}if(s=k,s.test(n)){var T=s.exec(n);i=T[1],n=i+"i"}if(s=b,s.test(n)){var T=s.exec(n);i=T[1],o=T[2],s=u,s.test(i)&&(n=i+t[o])}if(s=E,s.test(n)){var T=s.exec(n);i=T[1],o=T[2],s=u,s.test(i)&&(n=i+e[o])}if(s=_,a=F,s.test(n)){var T=s.exec(n);i=T[1],s=c,s.test(i)&&(n=i)}else if(a.test(n)){var T=a.exec(n);i=T[1]+T[2],a=c,a.test(i)&&(n=i)}if(s=O,s.test(n)){var T=s.exec(n);i=T[1],s=c,a=f,h=N,(s.test(i)||a.test(i)&&!h.test(i))&&(n=i)}return s=P,a=c,s.test(n)&&a.test(n)&&(s=g,n=n.replace(s,"")),"y"==r&&(n=r.toLowerCase()+n.substr(1)),n};return T}(),t.Pipeline.registerFunction(t.stemmer,"stemmer"),t.stopWordFilter=function(e){return e&&t.stopWordFilter.stopWords[e]!==e?e:void 0},t.stopWordFilter.stopWords={a:"a",able:"able",about:"about",across:"across",after:"after",all:"all",almost:"almost",also:"also",am:"am",among:"among",an:"an",and:"and",any:"any",are:"are",as:"as",at:"at",be:"be",because:"because",been:"been",but:"but",by:"by",can:"can",cannot:"cannot",could:"could",dear:"dear",did:"did","do":"do",does:"does",either:"either","else":"else",ever:"ever",every:"every","for":"for",from:"from",get:"get",got:"got",had:"had",has:"has",have:"have",he:"he",her:"her",hers:"hers",him:"him",his:"his",how:"how",however:"however",i:"i","if":"if","in":"in",into:"into",is:"is",it:"it",its:"its",just:"just",least:"least",let:"let",like:"like",likely:"likely",may:"may",me:"me",might:"might",most:"most",must:"must",my:"my",neither:"neither",no:"no",nor:"nor",not:"not",of:"of",off:"off",often:"often",on:"on",only:"only",or:"or",other:"other",our:"our",own:"own",rather:"rather",said:"said",say:"say",says:"says",she:"she",should:"should",since:"since",so:"so",some:"some",than:"than",that:"that",the:"the",their:"their",them:"them",then:"then",there:"there",these:"these",they:"they","this":"this",tis:"tis",to:"to",too:"too",twas:"twas",us:"us",wants:"wants",was:"was",we:"we",were:"were",what:"what",when:"when",where:"where",which:"which","while":"while",who:"who",whom:"whom",why:"why",will:"will","with":"with",would:"would",yet:"yet",you:"you",your:"your"},t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter"),t.trimmer=function(t){var e=t.replace(/^\W+/,"").replace(/\W+$/,"");return""===e?void 0:e},t.Pipeline.registerFunction(t.trimmer,"trimmer"),t.TokenStore=function(){this.root={docs:{}},this.length=0},t.TokenStore.load=function(t){var e=new this;return e.root=t.root,e.length=t.length,e},t.TokenStore.prototype.add=function(t,e,n){var n=n||this.root,i=t[0],o=t.slice(1);return i in n||(n[i]={docs:{}}),0===o.length?(n[i].docs[e.ref]=e,void(this.length+=1)):this.add(o,e,n[i])},t.TokenStore.prototype.has=function(t){if(!t)return!1;for(var e=this.root,n=0;no;o++){for(var r=t[o],s=0;i>s&&(r=this._stack[s](r,o,t),void 0!==r);s++);void 0!==r&&e.push(r)}return e},t.Pipeline.prototype.reset=function(){this._stack=[]},t.Pipeline.prototype.toJSON=function(){return this._stack.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Vector=function(){this._magnitude=null,this.list=void 0,this.length=0},t.Vector.Node=function(t,e,n){this.idx=t,this.val=e,this.next=n},t.Vector.prototype.insert=function(e,n){this._magnitude=void 0;var i=this.list;if(!i)return this.list=new t.Vector.Node(e,n,i),this.length++;if(en.idx?n=n.next:(i+=e.val*n.val,e=e.next,n=n.next);return i},t.Vector.prototype.similarity=function(t){return this.dot(t)/(this.magnitude()*t.magnitude())},t.SortedSet=function(){this.length=0,this.elements=[]},t.SortedSet.load=function(t){var e=new this;return e.elements=t,e.length=t.length,e},t.SortedSet.prototype.add=function(){var t,e;for(t=0;t1;){if(r===t)return o;t>r&&(e=o),r>t&&(n=o),i=n-e,o=e+Math.floor(i/2),r=this.elements[o]}return r===t?o:-1},t.SortedSet.prototype.locationFor=function(t){for(var e=0,n=this.elements.length,i=n-e,o=e+Math.floor(i/2),r=this.elements[o];i>1;)t>r&&(e=o),r>t&&(n=o),i=n-e,o=e+Math.floor(i/2),r=this.elements[o];return r>t?o:t>r?o+1:void 0},t.SortedSet.prototype.intersect=function(e){for(var n=new t.SortedSet,i=0,o=0,r=this.length,s=e.length,a=this.elements,h=e.elements;;){if(i>r-1||o>s-1)break;a[i]!==h[o]?a[i]h[o]&&o++:(n.add(a[i]),i++,o++)}return n},t.SortedSet.prototype.clone=function(){var e=new t.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},t.SortedSet.prototype.union=function(t){var e,n,i;return this.length>=t.length?(e=this,n=t):(e=t,n=this),i=e.clone(),i.add.apply(i,n.toArray()),i},t.SortedSet.prototype.toJSON=function(){return this.toArray()},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.Store,this.tokenStore=new t.TokenStore,this.corpusTokens=new t.SortedSet,this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var t=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,t)},t.Index.prototype.off=function(t,e){return this.eventEmitter.removeListener(t,e)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;return n._fields=e.fields,n._ref=e.ref,n.documentStore=t.Store.load(e.documentStore),n.tokenStore=t.TokenStore.load(e.tokenStore),n.corpusTokens=t.SortedSet.load(e.corpusTokens),n.pipeline=t.Pipeline.load(e.pipeline),n},t.Index.prototype.field=function(t,e){var e=e||{},n={name:t,boost:e.boost||1};return this._fields.push(n),this},t.Index.prototype.ref=function(t){return this._ref=t,this},t.Index.prototype.add=function(e,n){var i={},o=new t.SortedSet,r=e[this._ref],n=void 0===n?!0:n;this._fields.forEach(function(n){var r=this.pipeline.run(t.tokenizer(e[n.name]));i[n.name]=r,t.SortedSet.prototype.add.apply(o,r)},this),this.documentStore.set(r,o),t.SortedSet.prototype.add.apply(this.corpusTokens,o.toArray());for(var s=0;s0&&(i=1+Math.log(this.documentStore.length/n)),this._idfCache[e]=i},t.Index.prototype.search=function(e){var n=this.pipeline.run(t.tokenizer(e)),i=new t.Vector,o=[],r=this._fields.reduce(function(t,e){return t+e.boost},0),s=n.some(function(t){return this.tokenStore.has(t)},this);if(!s)return[];n.forEach(function(e,n,s){var a=1/s.length*this._fields.length*r,h=this,l=this.tokenStore.expand(e).reduce(function(n,o){var r=h.corpusTokens.indexOf(o),s=h.idf(o),l=1,u=new t.SortedSet;if(o!==e){var c=Math.max(3,o.length-e.length);l=1/Math.log(c)}return r>-1&&i.insert(r,a*s*l),Object.keys(h.tokenStore.get(o)).forEach(function(t){u.add(t)}),n.union(u)},new t.SortedSet);o.push(l)},this);var a=o.reduce(function(t,e){return t.intersect(e)});return a.map(function(t){return{ref:t,score:i.similarity(this.documentVector(t))}},this).sort(function(t,e){return e.score-t.score})},t.Index.prototype.documentVector=function(e){for(var n=this.documentStore.get(e),i=n.length,o=new t.Vector,r=0;i>r;r++){var s=n.elements[r],a=this.tokenStore.get(s)[e].tf,h=this.idf(s);o.insert(this.corpusTokens.indexOf(s),a*h)}return o},t.Index.prototype.toJSON=function(){return{version:t.version,fields:this._fields,ref:this._ref,documentStore:this.documentStore.toJSON(),tokenStore:this.tokenStore.toJSON(),corpusTokens:this.corpusTokens.toJSON(),pipeline:this.pipeline.toJSON()}},t.Index.prototype.use=function(t){var e=Array.prototype.slice.call(arguments,1);e.unshift(this),t.apply(this,e)},t.Store=function(){this.store={},this.length=0},t.Store.load=function(e){var n=new this;return n.length=e.length,n.store=Object.keys(e.store).reduce(function(n,i){return n[i]=t.SortedSet.load(e.store[i]),n},{}),n},t.Store.prototype.set=function(t,e){this.has(t)||this.length++,this.store[t]=e},t.Store.prototype.get=function(t){return this.store[t]},t.Store.prototype.has=function(t){return t in this.store},t.Store.prototype.remove=function(t){this.has(t)&&(delete this.store[t],this.length--)},t.Store.prototype.toJSON=function(){return{store:this.store,length:this.length}},t.stemmer=function(){var t={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},e={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",o=n+"[^aeiouy]*",r=i+"[aeiou]*",s="^("+o+")?"+r+o,a="^("+o+")?"+r+o+"("+r+")?$",h="^("+o+")?"+r+o+r+o,l="^("+o+")?"+i,u=new RegExp(s),c=new RegExp(h),f=new RegExp(a),d=new RegExp(l),p=/^(.+?)(ss|i)es$/,m=/^(.+?)([^s])s$/,v=/^(.+?)eed$/,y=/^(.+?)(ed|ing)$/,g=/.$/,S=/(at|bl|iz)$/,w=new RegExp("([^aeiouylsz])\\1$"),x=new RegExp("^"+o+i+"[^aeiouwxy]$"),k=/^(.+?[^aeiou])y$/,b=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,E=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,_=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,F=/^(.+?)(s|t)(ion)$/,O=/^(.+?)e$/,P=/ll$/,N=new RegExp("^"+o+i+"[^aeiouwxy]$"),T=function(n){var i,o,r,s,a,h,l;if(n.length<3)return n;if(r=n.substr(0,1),"y"==r&&(n=r.toUpperCase()+n.substr(1)),s=p,a=m,s.test(n)?n=n.replace(s,"$1$2"):a.test(n)&&(n=n.replace(a,"$1$2")),s=v,a=y,s.test(n)){var T=s.exec(n);s=u,s.test(T[1])&&(s=g,n=n.replace(s,""))}else if(a.test(n)){var T=a.exec(n);i=T[1],a=d,a.test(i)&&(n=i,a=S,h=w,l=x,a.test(n)?n+="e":h.test(n)?(s=g,n=n.replace(s,"")):l.test(n)&&(n+="e"))}if(s=k,s.test(n)){var T=s.exec(n);i=T[1],n=i+"i"}if(s=b,s.test(n)){var T=s.exec(n);i=T[1],o=T[2],s=u,s.test(i)&&(n=i+t[o])}if(s=E,s.test(n)){var T=s.exec(n);i=T[1],o=T[2],s=u,s.test(i)&&(n=i+e[o])}if(s=_,a=F,s.test(n)){var T=s.exec(n);i=T[1],s=c,s.test(i)&&(n=i)}else if(a.test(n)){var T=a.exec(n);i=T[1]+T[2],a=c,a.test(i)&&(n=i)}if(s=O,s.test(n)){var T=s.exec(n);i=T[1],s=c,a=f,h=N,(s.test(i)||a.test(i)&&!h.test(i))&&(n=i)}return s=P,a=c,s.test(n)&&a.test(n)&&(s=g,n=n.replace(s,"")),"y"==r&&(n=r.toLowerCase()+n.substr(1)),n};return T}(),t.Pipeline.registerFunction(t.stemmer,"stemmer"),t.stopWordFilter=function(e){return e&&t.stopWordFilter.stopWords[e]!==e?e:void 0},t.stopWordFilter.stopWords={a:"a",able:"able",about:"about",across:"across",after:"after",all:"all",almost:"almost",also:"also",am:"am",among:"among",an:"an",and:"and",any:"any",are:"are",as:"as",at:"at",be:"be",because:"because",been:"been",but:"but",by:"by",can:"can",cannot:"cannot",could:"could",dear:"dear",did:"did","do":"do",does:"does",either:"either","else":"else",ever:"ever",every:"every","for":"for",from:"from",get:"get",got:"got",had:"had",has:"has",have:"have",he:"he",her:"her",hers:"hers",him:"him",his:"his",how:"how",however:"however",i:"i","if":"if","in":"in",into:"into",is:"is",it:"it",its:"its",just:"just",least:"least",let:"let",like:"like",likely:"likely",may:"may",me:"me",might:"might",most:"most",must:"must",my:"my",neither:"neither",no:"no",nor:"nor",not:"not",of:"of",off:"off",often:"often",on:"on",only:"only",or:"or",other:"other",our:"our",own:"own",rather:"rather",said:"said",say:"say",says:"says",she:"she",should:"should",since:"since",so:"so",some:"some",than:"than",that:"that",the:"the",their:"their",them:"them",then:"then",there:"there",these:"these",they:"they","this":"this",tis:"tis",to:"to",too:"too",twas:"twas",us:"us",wants:"wants",was:"was",we:"we",were:"were",what:"what",when:"when",where:"where",which:"which","while":"while",who:"who",whom:"whom",why:"why",will:"will","with":"with",would:"would",yet:"yet",you:"you",your:"your"},t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter"),t.trimmer=function(t){var e=t.replace(/^\W+/,"").replace(/\W+$/,"");return""===e?void 0:e},t.Pipeline.registerFunction(t.trimmer,"trimmer"),t.TokenStore=function(){this.root={docs:{}},this.length=0},t.TokenStore.load=function(t){var e=new this;return e.root=t.root,e.length=t.length,e},t.TokenStore.prototype.add=function(t,e,n){var n=n||this.root,i=t[0],o=t.slice(1);return i in n||(n[i]={docs:{}}),0===o.length?(n[i].docs[e.ref]=e,void(this.length+=1)):this.add(o,e,n[i])},t.TokenStore.prototype.has=function(t){if(!t)return!1;for(var e=this.root,n=0;n element for each result + res.results.forEach(function(res) { + var $li = $('

                                            • ', { + 'class': 'search-results-item' + }); + + var $title = $('

                                              '); + + var $link = $('', { + 'href': gitbook.state.basePath + '/' + res.url, + 'text': res.title + }); + + var content = res.body.trim(); + if (content.length > MAX_DESCRIPTION_SIZE) { + content = content.slice(0, MAX_DESCRIPTION_SIZE).trim()+'...'; + } + var $content = $('

                                              ').html(content); + + $link.appendTo($title); + $title.appendTo($li); + $content.appendTo($li); + $li.appendTo($searchList); + }); + } + + function launchSearch(q) { + // Add class for loading + $body.addClass('with-search'); + $body.addClass('search-loading'); + + // Launch search query + throttle(gitbook.search.query(q, 0, MAX_RESULTS) + .then(function(results) { + displayResults(results); + }) + .always(function() { + $body.removeClass('search-loading'); + }), 1000); + } + + function closeSearch() { + $body.removeClass('with-search'); + $bookSearchResults.removeClass('open'); + } + + function launchSearchFromQueryString() { + var q = getParameterByName('q'); + if (q && q.length > 0) { + // Update search input + $searchInput.val(q); + + // Launch search + launchSearch(q); + } + } + + function bindSearch() { + // Bind DOM + $searchInput = $('#book-search-input input'); + $bookSearchResults = $('#book-search-results'); + $searchList = $bookSearchResults.find('.search-results-list'); + $searchTitle = $bookSearchResults.find('.search-results-title'); + $searchResultsCount = $searchTitle.find('.search-results-count'); + $searchQuery = $searchTitle.find('.search-query'); + + // Launch query based on input content + function handleUpdate() { + var q = $searchInput.val(); + + if (q.length == 0) { + closeSearch(); + } + else { + launchSearch(q); + } + } + + // Detect true content change in search input + // Workaround for IE < 9 + var propertyChangeUnbound = false; + $searchInput.on('propertychange', function(e) { + if (e.originalEvent.propertyName == 'value') { + handleUpdate(); + } + }); + + // HTML5 (IE9 & others) + $searchInput.on('input', function(e) { + // Unbind propertychange event for IE9+ + if (!propertyChangeUnbound) { + $(this).unbind('propertychange'); + propertyChangeUnbound = true; + } + + handleUpdate(); + }); + + // Push to history on blur + $searchInput.on('blur', function(e) { + // Update history state + if (usePushState) { + var uri = updateQueryString('q', $(this).val()); + history.pushState({ path: uri }, null, uri); + } + }); + } + + gitbook.events.on('page.change', function() { + bindSearch(); + closeSearch(); + + // Launch search based on query parameter + if (gitbook.search.isInitialized()) { + launchSearchFromQueryString(); + } + }); + + gitbook.events.on('search.ready', function() { + bindSearch(); + + // Launch search from query param at start + launchSearchFromQueryString(); + }); + + function getParameterByName(name) { + var url = window.location.href; + name = name.replace(/[\[\]]/g, '\\$&'); + var regex = new RegExp('[?&]' + name + '(=([^&#]*)|&|#|$)', 'i'), + results = regex.exec(url); + if (!results) return null; + if (!results[2]) return ''; + return decodeURIComponent(results[2].replace(/\+/g, ' ')); + } + + function updateQueryString(key, value) { + value = encodeURIComponent(value); + + var url = window.location.href; + var re = new RegExp('([?&])' + key + '=.*?(&|#|$)(.*)', 'gi'), + hash; + + if (re.test(url)) { + if (typeof value !== 'undefined' && value !== null) + return url.replace(re, '$1' + key + '=' + value + '$2$3'); + else { + hash = url.split('#'); + url = hash[0].replace(re, '$1$3').replace(/(&|\?)$/, ''); + if (typeof hash[1] !== 'undefined' && hash[1] !== null) + url += '#' + hash[1]; + return url; + } + } + else { + if (typeof value !== 'undefined' && value !== null) { + var separator = url.indexOf('?') !== -1 ? '&' : '?'; + hash = url.split('#'); + url = hash[0] + separator + key + '=' + value; + if (typeof hash[1] !== 'undefined' && hash[1] !== null) + url += '#' + hash[1]; + return url; + } + else + return url; + } + } +}); diff --git a/gitbook/_book/gitbook/gitbook-plugin-sharing/buttons.js b/gitbook/_book/gitbook/gitbook-plugin-sharing/buttons.js new file mode 100644 index 00000000..709a4e4c --- /dev/null +++ b/gitbook/_book/gitbook/gitbook-plugin-sharing/buttons.js @@ -0,0 +1,90 @@ +require(['gitbook', 'jquery'], function(gitbook, $) { + var SITES = { + 'facebook': { + 'label': 'Facebook', + 'icon': 'fa fa-facebook', + 'onClick': function(e) { + e.preventDefault(); + window.open('http://www.facebook.com/sharer/sharer.php?s=100&p[url]='+encodeURIComponent(location.href)); + } + }, + 'twitter': { + 'label': 'Twitter', + 'icon': 'fa fa-twitter', + 'onClick': function(e) { + e.preventDefault(); + window.open('http://twitter.com/home?status='+encodeURIComponent(document.title+' '+location.href)); + } + }, + 'google': { + 'label': 'Google+', + 'icon': 'fa fa-google-plus', + 'onClick': function(e) { + e.preventDefault(); + window.open('https://plus.google.com/share?url='+encodeURIComponent(location.href)); + } + }, + 'weibo': { + 'label': 'Weibo', + 'icon': 'fa fa-weibo', + 'onClick': function(e) { + e.preventDefault(); + window.open('http://service.weibo.com/share/share.php?content=utf-8&url='+encodeURIComponent(location.href)+'&title='+encodeURIComponent(document.title)); + } + }, + 'instapaper': { + 'label': 'Instapaper', + 'icon': 'fa fa-instapaper', + 'onClick': function(e) { + e.preventDefault(); + window.open('http://www.instapaper.com/text?u='+encodeURIComponent(location.href)); + } + }, + 'vk': { + 'label': 'VK', + 'icon': 'fa fa-vk', + 'onClick': function(e) { + e.preventDefault(); + window.open('http://vkontakte.ru/share.php?url='+encodeURIComponent(location.href)); + } + } + }; + + + + gitbook.events.bind('start', function(e, config) { + var opts = config.sharing; + + // Create dropdown menu + var menu = $.map(opts.all, function(id) { + var site = SITES[id]; + + return { + text: site.label, + onClick: site.onClick + }; + }); + + // Create main button with dropdown + if (menu.length > 0) { + gitbook.toolbar.createButton({ + icon: 'fa fa-share-alt', + label: 'Share', + position: 'right', + dropdown: [menu] + }); + } + + // Direct actions to share + $.each(SITES, function(sideId, site) { + if (!opts[sideId]) return; + + gitbook.toolbar.createButton({ + icon: site.icon, + label: site.text, + position: 'right', + onClick: site.onClick + }); + }); + }); +}); diff --git a/gitbook/_book/gitbook/gitbook.js b/gitbook/_book/gitbook/gitbook.js new file mode 100644 index 00000000..13077b45 --- /dev/null +++ b/gitbook/_book/gitbook/gitbook.js @@ -0,0 +1,4 @@ +!function e(t,n,r){function o(s,a){if(!n[s]){if(!t[s]){var u="function"==typeof require&&require;if(!a&&u)return u(s,!0);if(i)return i(s,!0);var c=new Error("Cannot find module 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Demo\nCrawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。\n项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。\nCrawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。\n本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。\n首先，我们来看如何安装Crawlab吧，请查看安装。\n"},"Installation/":{"url":"Installation/","title":"安装Crawlab","keywords":"","body":"本小节将介绍三种安装Docker的方式:\n\nDocker\n直接部署\n预览模式\n\n"},"Installation/Docker.html":{"url":"Installation/Docker.html","title":"Docker","keywords":"","body":"Docker安装部署\n这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。\n对Docker不了解的开发者，可以参考一下这篇文章（9102 年了，学点 Docker 知识）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需docker run -d --name mongo -p 27017:27017 mongo一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。\n下载镜像\n我们已经在DockerHub上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。\n{\n \"registry-mirrors\": [\"https://registry.docker-cn.com\"]\n}\n\n这样的话，pull镜像的速度会比不改变镜像源的速度快很多。\n执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。\ndocker pull tikazyq/crawlab:latest\n\n更改配置文件\n拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。\n更改后端配置文件config.py，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将API地址VUE_APP_BASE_URL更改为宿主机所在的IP地址，例如http://192.168.0.8:8000，这将是前端调用API会用到的URL。\n运行Docker容器\n更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。\ndocker run -d --rm --name crawlab \\\n -p 8080:8080 \\\n -p 8000:8000 \\\n -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \\\n -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \\\n tikazyq/crawlab master\n\n其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件/home/yeqing/.env.production和后端配置文件/home/yeqing/config.py映射到了容器相应的目录下。传入参数master是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是worker模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为http://localhost:8080。\n\nDocker-Compose\n当然，也可以用docker-compose的方式来部署。docker-compose是一个集群管理方式，可以利用名为docker-compose.yml的yaml文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的docker-compose.yml定义如下。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里先定义了master节点，也就是Crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。\n安装docker-compose也很简单，大家去网上百度一下就可以了。\n安装完docker-compose和定义好docker-compose.yml后，只需要运行以下命令就可以启动Crawlab。\ndocker-compose up\n\n同样，在浏览器中输入http://localhost:8080就可以看到界面。\n多节点模式\ndocker-compose的方式很适合多节点部署，在原有的master基础上增加几个worker节点，达到多节点部署的目的。将docker-compose.yml更改为如下内容。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n worker1: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8001:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n worker2: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8002:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。\n"},"Installation/Direct.html":{"url":"Installation/Direct.html","title":"直接部署","keywords":"","body":"直接部署\n直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。\n拉取代码\n首先是将github上的代码拉取到本地。\ngit clone https://github.com/tikazyq/crawlab\n\n安装\n安装前端所需库。\nnpm install -g yarn pm2\ncd frontend\nyarn install\n\n安装后端所需库。\ncd ../crawlab\npip install -r requirements\n\n配置\n分别配置前端配置文件./frontend/.env.production和后端配置文件./crawlab/config/config.py。分别需要对部署后API地址以及数据库地址进行配置。\n构建\n这里的构建是指前端构建，需要执行以下命令。\ncd ../frontend\nnpm run build:prod\n\n构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。\nNginx\n安装nginx，在ubuntu 16.04是以下命令。\nsudo apt-get install nginx\n\n添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。\nserver {\n listen 8080;\n server_name dev.crawlab.com;\n root /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist;\n index index.html;\n}\n其中，root是静态文件的根目录，这里是npm打包好后的静态文件。\n现在，只需要启动nginx服务就完成了启动前端服务。\nnginx reload\n\n启动服务\n这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。\npm2 start app.py # API服务\npm2 start worker.py # Worker\npm2 start flower.py # Flower\n\n这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。\npm2 logs [app]\n\n然后在浏览器中输入http://localhost:8080就可以看到界面了。\n"},"Installation/Preview.html":{"url":"Installation/Preview.html","title":"预览模式","keywords":"","body":"预览模式\n预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。\npython manage.py serve\n\n该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm run serve来进行的，因此是开发者模式。注意：强烈不建议在生产环境中用预览模式。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。\n"},"Usage/":{"url":"Usage/","title":"使用Crawlab","keywords":"","body":"本小节将介绍如何使用Crawlab，包括如下内容：\n\n节点\n爬虫\n任务\n定时任务\n\n"},"Usage/Node/":{"url":"Usage/Node/","title":"节点","keywords":"","body":"节点\n节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为localhost:8000）。\n\n查看节点\n修改节点信息\n\n"},"Usage/Node/View.html":{"url":"Usage/Node/View.html","title":"查看节点列表","keywords":"","body":"查看节点列表\n点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celery worker，他们通过连接到配置好的broker（通常是redis）来进行与主机的通信。\n\n"},"Usage/Node/Edit.html":{"url":"Usage/Node/Edit.html","title":"修改节点信息","keywords":"","body":"修改节点信息\n后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在节点列表中点击操作列里的蓝色查看按钮进入到节点详情。节点详情样子如下。\n\n在右侧分别输入该节点对应的节点IP和节点端口，然后点击保存按钮，保存该节点信息。\n这样，我们就完成了节点的配置工作。\n"},"Usage/Spider/":{"url":"Usage/Spider/","title":"爬虫","keywords":"","body":"爬虫\n爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：\n\n创建爬虫\n部署爬虫\n运行爬虫\n可配置爬虫\n统计数据\n\n"},"Usage/Spider/Create.html":{"url":"Usage/Spider/Create.html","title":"创建爬虫","keywords":"","body":"创建爬虫\nCrawlab允许用户创建两种爬虫：\n\n自定义爬虫\n可配置爬虫\n\n前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。\n"},"Usage/Spider/CustomizedSpider.html":{"url":"Usage/Spider/CustomizedSpider.html","title":"自定义爬虫","keywords":"","body":"自定义爬虫\n自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。\n自定义爬虫的添加有两种方式：\n\n通过Web界面上传爬虫\n通过创建项目目录\n\n通过Web界面上传\n在通过Web界面上传之前，需要将爬虫项目文件打包成zip格式。\n\n然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。\n这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。\n通过添加项目目录\nCrawlab会自动发现PROJECT_SOURCE_FILE_FOLDER目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到PROJECT_SOURCE_FILE_FOLDER目录下，就可以添加一个爬虫了。\n这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。\n配置爬虫\n在定义爬虫中，我们需要配置一下执行命令（运行爬虫时后台执行的shell命令）和结果集（通过CRAWLAB_COLLECTION传递给爬虫程序，爬虫程序存储结果的地方），然后点击保存按钮保存爬虫信息。\n\n接下来，我们就可以部署、运行自定义爬虫了。\n"},"Usage/Spider/ConfigurableSpider.html":{"url":"Usage/Spider/ConfigurableSpider.html","title":"可配置爬虫","keywords":"","body":"可配置爬虫\n可配置爬虫是版本v0.2.1开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。\nCrawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持自定义爬虫的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。\n添加爬虫\n在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮。\n\n点击可配置爬虫。\n\n输入完基本信息，点击添加。\n\n配置爬虫\n添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。\n\n点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。\n\n这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。\n抓取类别\n这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。\n\n仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。\n仅详情页。爬虫只抓取详情页。\n列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。\n\n这里我们选择列表+详情页。\n列表项选择器 & 分页选择器\n列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。\n开始URL\n爬虫最开始遍历的网址。\n遵守Robots协议\n这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。\n列表页字段 & 详情页字段\n这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。\n在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。\n\n点击保存、预览，查看预览内容。\n\n"},"Usage/Spider/Deploy.html":{"url":"Usage/Spider/Deploy.html","title":"部署爬虫","keywords":"","body":"部署爬虫\n这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过HTTP的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。\n部署爬虫很简单，有三种方式：\n\n在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；\n在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；\n在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。\n\n部署好之后，我们就可以运行爬虫了。\n"},"Usage/Spider/Run.html":{"url":"Usage/Spider/Run.html","title":"运行爬虫","keywords":"","body":"运行爬虫\n我们有两种运行爬虫的方式：\n\n手动触发\n定时任务触发\n\n手动触发\n\n在爬虫列表中操作列点击运行按钮，或者\n在爬虫详情中概览标签下点击运行按钮，或者\n对于自定义爬虫，可以在配置标签下点击运行按钮\n\n然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。\n定时任务触发\n定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。\n"},"Usage/Spider/Analytics.html":{"url":"Usage/Spider/Analytics.html","title":"统计数据","keywords":"","body":"统计数据\n在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。Crawlab将这些指标汇总并呈现给开发者。\n要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。\n\n"},"Usage/Task/":{"url":"Usage/Task/","title":"任务","keywords":"","body":""},"Usage/Schedule/":{"url":"Usage/Schedule/","title":"定时任务","keywords":"","body":""},"Usage/Site/":{"url":"Usage/Site/","title":"网站","keywords":"","body":""},"Architecture/":{"url":"Architecture/","title":"架构","keywords":"","body":"架构\n"},"Architecture/Celery.html":{"url":"Architecture/Celery.html","title":"Celery","keywords":"","body":"Celery\n"},"Architecture/App.html":{"url":"Architecture/App.html","title":"App","keywords":"","body":"App\n"},"Examples/":{"url":"Examples/","title":"样例","keywords":"","body":"Examples\n"}}} 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gitbook/_book/Usage/Task/Action.html create mode 100644 gitbook/_book/Usage/Task/DownloadResults.html create mode 100644 gitbook/_book/Usage/Task/View.html delete mode 100644 gitbook/img/crawlab-architecture.png delete mode 100644 gitbook/img/payment.jpg delete mode 100644 gitbook/img/用户使用流程图.png diff --git a/README-zh.md b/README-zh.md index 2f407f00..8fdcb0c9 100644 --- a/README-zh.md +++ b/README-zh.md @@ -20,25 +20,10 @@ ## 安装 -```bash -# 安装后台类库 -pip install -r requirements.txt -``` - -```bash -# 安装前台类库 -cd frontend -npm install -``` - -## 配置 - -请更改配置文件`config.py`，配置API和数据库连接. - -## 快速开始 -```bash -python manage.py serve -``` +三种方式: +1. [Docker](https://tikazyq.github.io/crawlab/Installation/Docker.md)（推荐） +2. [直接部署](https://tikazyq.github.io/crawlab/Installation/Direct.md) +3. [预览模式](https://tikazyq.github.io/crawlab/Installation/Direct.md)（快速体验） ## 截图 @@ -48,7 +33,7 @@ python manage.py serve #### 爬虫列表 -![](https://user-gold-cdn.xitu.io/2019/3/6/169524daf9c8ccef?imageView2/0/w/1280/h/960/format/webp/ignore-error/1) +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/spider-list.png) #### 爬虫详情 - 概览 @@ -56,52 +41,42 @@ python manage.py serve #### 任务详情 - 抓取结果 -![](https://user-gold-cdn.xitu.io/2019/3/6/169524e4064c7f0a?imageView2/0/w/1280/h/960/format/webp/ignore-error/1) - -## 使用流程 - -![user-process](./docs/img/用户使用流程图.png) +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/task-detail-results.png) ## 架构 -Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。 +Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。架构图如下。 -![crawlab-architecture](./docs/img/crawlab-architecture.png) +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/architecture.png) -### 节点 +### 节点 Node -节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。 +节点其实就是Celery中的`worker`。一个节点运行时会连接到一个任务队列（例如`Redis`）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。 -### 爬虫 +### 后台应用 Backend App -##### 自动发现 +这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行`python app.py`来启动应用。 -在`config.py`文件中，修改变量`PROJECT_SOURCE_FILE_FOLDER`作为爬虫项目所在的目录。Crawlab后台程序会自动发现这些爬虫项目并储存到数据库中。是不是很方便？ +### 爬虫 Spider -##### 部署爬虫 +爬虫源代码或配置规则储存在`App`上，需要被部署到各个`worker`节点中。 -所有爬虫需要在抓取前被部署当相应当节点中。在"爬虫详情"页面点击"Deploy"按钮，爬虫将被部署到所有有效到节点中。 - -##### 运行爬虫 - -部署爬虫之后，你可以在"爬虫详情"页面点击"Run"按钮来启动爬虫。一个爬虫任务将被触发，你可以在任务列表页面中看到这个任务。 - -### 任务 +### 任务 Task 任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。 -### 后台应用 - -这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行`python manage.py app`或`python ./bin/run_app.py`来启动应用。 - -### 中间者 +### 中间者 Broker 中间者跟Celery中定义的一样，作为运行异步任务的队列。 -### 前端 +### 前端 Frontend 前端其实就是一个基于[Vue-Element-Admin](https://github.com/PanJiaChen/vue-element-admin)的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。 +### Flower + +一个Celery的插件，用于监控Celery节点。 + ## 与其他框架的集成 任务是利用python的`subprocess`模块中的`Popen`来实现的。任务ID将以环境变量`CRAWLAB_TASK_ID`的形式存在于爬虫任务运行的进程中，并以此来关联抓取数据。 @@ -151,26 +126,11 @@ Crawlab使用起来很方便，也很通用，可以适用于几乎任何主流 | [ScrapydWeb](https://github.com/my8100/scrapydweb) | 管理平台 | Y | Y | Y | [Scrapyd](https://github.com/scrapy/scrapyd) | 网络服务 | Y | N | N/A -## TODOs -##### 后端 -- [ ] 文件管理 -- [ ] MySQL数据库支持 -- [ ] 重跑任务 -- [ ] 节点监控 -- [ ] 更多爬虫例子 - -##### 前端 -- [x] 任务数据统计 -- [x] 表格过滤 -- [x] 多语言支持 (中文) -- [ ] 登录和用户管理 -- [ ] 全局搜索 - ## 社区 & 赞助 如果您觉得Crawlab对您的日常开发或公司有帮助，请加作者微信 tikazyq1 并注明"Crawlab"，作者会将你拉入群。或者，您可以扫下方支付宝二维码给作者打赏去升级团队协作软件或买一杯咖啡。

                                              - - + +

                                              diff --git a/docs/Architecture/App.html b/docs/Architecture/App.html deleted file mode 100644 index 0cb7c156..00000000 --- a/docs/Architecture/App.html +++ /dev/null @@ -1,436 +0,0 @@ - - - - - - - App · GitBook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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                                                  • -
                                                • +
                                                • - + - 快速开始 + 安装Crawlab @@ -116,12 +116,12 @@
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                                                  • - + - 安装 + Docker @@ -129,12 +129,25 @@
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                                                        -

                                                        架构

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                                                        架构

                                                        +

                                                        Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。架构图如下。

                                                        +

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                                                        节点 Node

                                                        +

                                                        节点其实就是Celery中的worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。

                                                        +

                                                        后台应用 Backend App

                                                        +

                                                        这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行python app.py来启动应用。

                                                        +

                                                        爬虫 Spider

                                                        +

                                                        爬虫源代码或配置规则储存在App上，需要被部署到各个worker节点中。

                                                        +

                                                        任务 Task

                                                        +

                                                        任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。

                                                        +

                                                        中间者 Broker

                                                        +

                                                        中间者跟Celery中定义的一样，作为运行异步任务的队列。

                                                        +

                                                        前端 Frontend

                                                        +

                                                        前端其实就是一个基于Vue-Element-Admin的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。

                                                        +

                                                        Flower

                                                        +

                                                        一个Celery的插件，用于监控Celery节点。

                                                        @@ -381,12 +545,12 @@ - + - + @@ -397,7 +561,7 @@ diff --git a/docs/Concept/Deploy.html b/docs/Concept/Deploy.html deleted file mode 100644 index e4e24233..00000000 --- a/docs/Concept/Deploy.html +++ /dev/null @@ -1,438 +0,0 @@ - - - - - - - 部署 · GitBook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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                                                        所有爬虫在运行前需要被部署当相应当节点中。

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                                                        部署时，爬虫会被打包到相应的目录中，方便环境隔离，开发环境的爬虫和生产环境的爬虫需要打包部署来实现隔离。

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                                                            所有爬虫需要在抓取前被部署当相应当节点中。在"爬虫详情"页面点击"Deploy"按钮，爬虫将被部署到所有有效到节点中。

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                                                            运行爬虫

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                                                            部署爬虫之后，你可以在"爬虫详情"页面点击"Run"按钮来启动爬虫。一个爬虫任务将被触发，你可以在任务列表页面中看到这个任务。

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                                                                    - App + 与Scrapy集成

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                                                                    -

                                                                    App

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                                                                    与Scrapy集成

                                                                    +

                                                                    以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。

                                                                    +
                                                                    import os
+from pymongo import MongoClient
+
+MONGO_HOST = '192.168.99.100'
+MONGO_PORT = 27017
+MONGO_DB = 'crawlab_test'
+
+# scrapy example in the pipeline
+class JuejinPipeline(object):
+    mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)
+    db = mongo[MONGO_DB]
+    col_name = os.environ.get('CRAWLAB_COLLECTION')
+    if not col_name:
+        col_name = 'test'
+    col = db[col_name]
+
+    def process_item(self, item, spider):
+        item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')
+        self.col.save(item)
+        return item
+
                                                                    @@ -574,15 +549,11 @@ - + - - - - @@ -590,7 +561,7 @@ diff --git a/docs/Examples/index.html b/docs/Examples/index.html index 9e7c85b0..4f84748b 100644 --- a/docs/Examples/index.html +++ b/docs/Examples/index.html @@ -4,7 +4,7 @@ - Examples · GitBook + 样例 · GitBook @@ -57,10 +57,10 @@ - + - + @@ -94,7 +94,7 @@ - 简介 + Crawlab简介 @@ -102,12 +102,12 @@ -
                                                                  • +
                                                                  • - + - 快速开始 + 安装Crawlab @@ -116,12 +116,12 @@
                                                                      -
                                                                    • +
                                                                    • - + - 安装 + Docker @@ -129,12 +129,25 @@
                                                                      • -
                                                                    • +
                                                                    • - + - 运行 + 直接部署 + + + + + +
                                                                      • + +
                                                                    • + + + + + 预览模式 @@ -147,12 +160,12 @@
                                                                      • -
                                                                    • +
                                                                    • - + - 概念 + 使用Crawlab @@ -161,9 +174,9 @@
                                                                        -
                                                                      • +
                                                                      • - + 节点 @@ -172,11 +185,43 @@ + + +
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                                                                      • + + 爬虫 @@ -185,11 +230,101 @@ + + +
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                                                                      • + + 任务 @@ -198,14 +333,72 @@ -
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                                                                      • + + + + + 网站 @@ -229,38 +422,6 @@ - -
                                                                      • @@ -268,7 +429,7 @@ - Examples + 样例 @@ -277,9 +438,9 @@
                                                                          -
                                                                        • +
                                                                        • - + 与Scrapy集成 @@ -288,19 +449,6 @@ -
                                                                          • - -
                                                                        • - - - - - 与Puppeteer集成 - - - - -
                                                                          • @@ -338,7 +486,7 @@

                                                                          - Examples + 样例

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                                                                          -

                                                                          Examples

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                                                                          样例

                                                                          +
                                                                            +
                                                                          1. 与Scrapy集成
                                                                            2. +
                                                                          @@ -381,12 +532,12 @@ - + - + @@ -397,7 +548,7 @@ diff --git a/docs/Functions/FunctionList.md b/docs/Functions/FunctionList.md deleted file mode 100644 index 636126fa..00000000 --- a/docs/Functions/FunctionList.md +++ /dev/null @@ -1,61 +0,0 @@ -# 功能列表 - -类别 | 功能名称 | 已统计 | 备注 ---- | --- | --- | --- -全局 | 打开页面 | Y | _trackPageview -全局 | 切换中英文 | Y -全局 | 允许/禁止统计 | Y -节点 | 刷新 | Y -节点 | 查看 | Y -节点 | 删除 | Y -节点详情 | 保存 | Y -节点详情 | 切换节点 | Y -爬虫 | 部署所有爬虫 | Y -爬虫 | 导入爬虫 | Y -爬虫 | 添加爬虫-可配置爬虫 | Y -爬虫 | 添加爬虫-自定义爬虫 | Y -爬虫 | 刷新 | Y -爬虫 | 查看 | Y -爬虫 | 删除 | Y -爬虫 | 部署 | Y -爬虫 | 运行 | Y -爬虫 | 搜索网站 | Y -爬虫详情 | 切换爬虫 | Y -爬虫详情 | 切换标签 | Y -爬虫详情-概览 | 保存 | Y -爬虫详情-概览 | 部署 | Y -爬虫详情-概览 | 运行 | Y -爬虫详情-环境 | 添加 | Y -爬虫详情-环境 | 删除 | Y -爬虫详情-环境 | 保存 | Y -爬虫详情-配置 | 保存 | Y -爬虫详情-配置 | 预览 | Y -爬虫详情-配置 | 提取字段 | Y -爬虫详情-配置 | 运行 | Y -爬虫详情-配置 | 添加字段 | Y -爬虫详情-配置 | 更改字段 | Y -爬虫详情-配置 | 删除字段 | Y -爬虫详情-配置 | 设置详情页URL | Y -任务 | 选择节点 | Y -任务 | 选择爬虫 | Y -任务 | 点击爬虫详情 | Y -任务 | 点击节点详情 | Y -任务 | 搜索 | Y -任务 | 查看 | Y -任务 | 删除 | Y -任务详情 | 切换标签 | Y -任务详情-概览 | 点击爬虫详情 | Y -任务详情-概览 | 点击节点详情 | Y -任务详情-结果 | 下载CSV | Y -定时任务 | 添加 | Y -定时任务 | 修改 | Y -定时任务 | 删除 | Y -定时任务 | 提交 | Y -部署 | 刷新 | Y -网站 | 搜索 | Y -网站 | 选择主类别 | Y -网站 | 选择类别 | Y -网站 | 点击域名 | Y -网站 | 点击爬虫数 | Y -网站 | 点击Robots协议 | N - diff --git a/docs/Installation/Direct.html b/docs/Installation/Direct.html new file mode 100644 index 00000000..5a3e8135 --- /dev/null +++ b/docs/Installation/Direct.html @@ -0,0 +1,632 @@ + + + + + + + 直接部署 · GitBook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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                                                                          + + + + + + + + +
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                                                                          +
                                                                          + +
                                                                          + +

                                                                          直接部署

                                                                          +

                                                                          直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。

                                                                          +

                                                                          拉取代码

                                                                          +

                                                                          首先是将github上的代码拉取到本地。

                                                                          +
                                                                          git clone https://github.com/tikazyq/crawlab
+
                                                                          +

                                                                          安装

                                                                          +

                                                                          安装前端所需库。

                                                                          +
                                                                          npm install -g yarn pm2
+cd frontend
+yarn install
+
                                                                          +

                                                                          安装后端所需库。

                                                                          +
                                                                          cd ../crawlab
+pip install -r requirements
+
                                                                          +

                                                                          配置

                                                                          +

                                                                          分别配置前端配置文件./frontend/.env.production和后端配置文件./crawlab/config/config.py。分别需要对部署后API地址以及数据库地址进行配置。

                                                                          +

                                                                          构建

                                                                          +

                                                                          这里的构建是指前端构建，需要执行以下命令。

                                                                          +
                                                                          cd ../frontend
+npm run build:prod
+
                                                                          +

                                                                          构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。

                                                                          +

                                                                          Nginx

                                                                          +

                                                                          安装nginx，在ubuntu 16.04是以下命令。

                                                                          +
                                                                          sudo apt-get install nginx
+
                                                                          +

                                                                          添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。

                                                                          +
                                                                          server {
+    listen    8080;
+    server_name    dev.crawlab.com;
+    root    /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist;
+    index    index.html;
+}
+

                                                                          其中，root是静态文件的根目录，这里是npm打包好后的静态文件。

                                                                          +

                                                                          现在，只需要启动nginx服务就完成了启动前端服务。

                                                                          +
                                                                          nginx reload
+
                                                                          +

                                                                          启动服务

                                                                          +

                                                                          这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。

                                                                          +
                                                                          pm2 start app.py # API服务
+pm2 start worker.py # Worker
+pm2 start flower.py # Flower
+
                                                                          +

                                                                          这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。

                                                                          +
                                                                          pm2 logs [app]
+
                                                                          +

                                                                          然后在浏览器中输入http://localhost:8080就可以看到界面了。

                                                                          + + +
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                                                                            Docker安装部署

                                                                            +

                                                                            这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。

                                                                            +

                                                                            对Docker不了解的开发者，可以参考一下这篇文章（9102 年了，学点 Docker 知识）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需docker run -d --name mongo -p 27017:27017 mongo一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。

                                                                            +

                                                                            下载镜像

                                                                            +

                                                                            我们已经在DockerHub上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。

                                                                            +
                                                                            {
+  "registry-mirrors": ["https://registry.docker-cn.com"]
+}
+
                                                                            +

                                                                            这样的话，pull镜像的速度会比不改变镜像源的速度快很多。

                                                                            +

                                                                            执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。

                                                                            +
                                                                            docker pull tikazyq/crawlab:latest
+
                                                                            +

                                                                            更改配置文件

                                                                            +

                                                                            拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。

                                                                            +

                                                                            更改后端配置文件config.py，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将API地址VUE_APP_BASE_URL更改为宿主机所在的IP地址，例如http://192.168.0.8:8000，这将是前端调用API会用到的URL。

                                                                            +

                                                                            运行Docker容器

                                                                            +

                                                                            更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。

                                                                            +
                                                                            docker run -d --rm --name crawlab \
+    -p 8080:8080 \
+    -p 8000:8000 \
+    -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \
+    -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \
+    tikazyq/crawlab master
+
                                                                            +

                                                                            其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件/home/yeqing/.env.production和后端配置文件/home/yeqing/config.py映射到了容器相应的目录下。传入参数master是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是worker模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为http://localhost:8080。

                                                                            +

                                                                            +

                                                                            Docker-Compose

                                                                            +

                                                                            当然，也可以用docker-compose的方式来部署。docker-compose是一个集群管理方式，可以利用名为docker-compose.yml的yaml文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的docker-compose.yml定义如下。

                                                                            +
                                                                            version: '3.3'
+services:
+  master: 
+    image: tikazyq/crawlab:latest
+    container_name: crawlab
+    volumns:
+      - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:    
+      - "8080:8080" # nginx
+      - "8000:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - master
+  mongo:
+    image: mongo:latest
+    restart: always
+    ports:
+      - "27017:27017"
+  redis:
+    image: redis:latest
+    restart: always
+    ports:
+      - "6379:6379"
+
                                                                            +

                                                                            这里先定义了master节点，也就是Crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。

                                                                            +

                                                                            安装docker-compose也很简单，大家去网上百度一下就可以了。

                                                                            +

                                                                            安装完docker-compose和定义好docker-compose.yml后，只需要运行以下命令就可以启动Crawlab。

                                                                            +
                                                                            docker-compose up
+
                                                                            +

                                                                            同样，在浏览器中输入http://localhost:8080就可以看到界面。

                                                                            +

                                                                            多节点模式

                                                                            +

                                                                            docker-compose的方式很适合多节点部署，在原有的master基础上增加几个worker节点，达到多节点部署的目的。将docker-compose.yml更改为如下内容。

                                                                            +
                                                                            version: '3.3'
+services:
+  master: 
+    image: tikazyq/crawlab:latest
+    container_name: crawlab
+    volumns:
+      - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:    
+      - "8080:8080" # nginx
+      - "8000:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - master
+  worker1: 
+    image: tikazyq/crawlab:latest
+    volumns:
+      - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:
+      - "8001:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - worker
+  worker2: 
+    image: tikazyq/crawlab:latest
+    volumns:
+      - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件
+      - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件
+    ports:
+      - "8002:8000" # app
+    depends_on:
+      - mongo
+      - redis
+    entrypoint:
+      - /bin/sh
+      - /opt/crawlab/docker_init.sh
+      - worker
+  mongo:
+    image: mongo:latest
+    restart: always
+    ports:
+      - "27017:27017"
+  redis:
+    image: redis:latest
+    restart: always
+    ports:
+      - "6379:6379"
+
                                                                            +

                                                                            这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。

                                                                            + + +
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                                                                              预览模式

                                                                              +

                                                                              预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。

                                                                              +
                                                                              python manage.py serve
+
                                                                              +

                                                                              该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm run serve来进行的，因此是开发者模式。注意：强烈不建议在生产环境中用预览模式。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。

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                                                                              • +
                                                                              • - + 安装Crawlab @@ -116,9 +116,9 @@
                                                                                  -
                                                                                • +
                                                                                • - + Docker @@ -129,9 +129,9 @@
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                                                                                  • @@ -470,9 +438,9 @@
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                                                                                    • - + 与Scrapy集成 @@ -481,19 +449,6 @@ -
                                                                                      • - -
                                                                                    • - - - - - 与Puppeteer集成 - - - - -
                                                                                      • @@ -531,7 +486,7 @@

                                                                                      - Celery + 安装Crawlab

                                                                                      @@ -546,7 +501,12 @@
                                                                                      -

                                                                                      Celery

                                                                                      +

                                                                                      本小节将介绍三种安装Docker的方式:

                                                                                      +
                                                                                        +
                                                                                      1. Docker
                                                                                        2. +
                                                                                      2. 直接部署
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                                                                                      3. 预览模式
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                                                                                      @@ -574,12 +534,12 @@ - + - + @@ -590,7 +550,7 @@ diff --git a/docs/QuickStart/Installation.html b/docs/QuickStart/Installation.html deleted file mode 100644 index 9fddabdf..00000000 --- a/docs/QuickStart/Installation.html +++ /dev/null @@ -1,447 +0,0 @@ - - - - - - - 安装 · GitBook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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                                                                                      安装

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                                                                                      最快安装Crawlab的方式是克隆一份代码到本地

                                                                                      -
                                                                                      git clone https://github.com/tikazyq/crawlab
-
                                                                                      -

                                                                                      安装类库

                                                                                      -
                                                                                      # 安装后台类库
-pip install -r requirements.txt
-
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                                                                                      # 安装前台类库
-cd frontend
-npm install
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                                                                                        运行

                                                                                        -

                                                                                        在运行之前需要对Crawlab进行一些配置，配置文件为config.py。

                                                                                        -
                                                                                        # project variables
-PROJECT_SOURCE_FILE_FOLDER = '/Users/yeqing/projects/crawlab/spiders' # 爬虫源码根目录
-PROJECT_DEPLOY_FILE_FOLDER = '/var/crawlab'  # 爬虫部署根目录
-PROJECT_LOGS_FOLDER = '/var/logs/crawlab'  # 日志目录
-PROJECT_TMP_FOLDER = '/tmp'  # 临时文件目录
-
-# celery variables
-BROKER_URL = 'redis://192.168.99.100:6379/0'  # 中间者URL，连接redis
-CELERY_RESULT_BACKEND = 'mongodb://192.168.99.100:27017/'  # CELERY后台URL
-CELERY_MONGODB_BACKEND_SETTINGS = {
-    'database': 'crawlab_test',
-    'taskmeta_collection': 'tasks_celery',
-}
-CELERY_TIMEZONE = 'Asia/Shanghai'
-CELERY_ENABLE_UTC = True
-
-# flower variables
-FLOWER_API_ENDPOINT = 'http://localhost:5555/api'  # Flower服务地址
-
-# database variables
-MONGO_HOST = '192.168.99.100'
-MONGO_PORT = 27017
-MONGO_DB = 'crawlab_test'
-
-# flask variables
-DEBUG = True
-FLASK_HOST = '127.0.0.1'
-FLASK_PORT = 8000
-
                                                                                        -

                                                                                        启动后端API，也就是一个Flask App，可以直接启动，或者用gunicorn代替。

                                                                                        -
                                                                                        python manage.py app
-
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                                                                                        启动本地Worker。在其他节点中如果想只是想执行任务的话，只需要启动这一个服务就可以了。

                                                                                        -
                                                                                        python manage.py worker
-
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                                                                                        启动前端服务器。

                                                                                        -
                                                                                        cd frontend
-npm run serve
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                                                                                            修改节点信息

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                                                                                            后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在节点列表中点击操作列里的蓝色查看按钮进入到节点详情。节点详情样子如下。

                                                                                            +

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                                                                                            在右侧分别输入该节点对应的节点IP和节点端口，然后点击保存按钮，保存该节点信息。

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                                                                                            这样，我们就完成了节点的配置工作。

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                                                                                              查看节点列表

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                                                                                              点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celery worker，他们通过连接到配置好的broker（通常是redis）来进行与主机的通信。

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                                                                                                节点

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                                                                                                节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为localhost:8000）。

                                                                                                +
                                                                                                  +
                                                                                                1. 查看节点
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                                                                                                2. 修改节点信息
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                                                                                                  定时任务

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                                                                                                  定时任务是指定某个时刻，重复性地执行的任务，英文叫做Periodical Tasks，在Linux中也被称为Crontab。定时任务可以让任务可以被执行多次，而用户则不用手动的操作来执行任务。在生产环境中，这非常常见。定时任务对于对增量抓取或对数据实时性有要求的用户来说非常有用。

                                                                                                  +

                                                                                                  在Crawlab中，定时任务是通过apscheduler来实现的。创建一个定时任务之后，会在名为mongo的jobstore中创建一个periodical job，apscheduler调度引擎将会不断的去数据库中匹配任务的执行时间，如果执行时间满足要求，则会在后台触发一次任务运行。

                                                                                                  +

                                                                                                  定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。

                                                                                                  +

                                                                                                  创建定时任务

                                                                                                  +

                                                                                                  导航至定时任务页面，可以看到定时任务的列表。

                                                                                                  +

                                                                                                  点击添加定时任务，弹出创建定时任务的弹框。填写相应的内容，点击提交按钮创建定时任务。

                                                                                                  +

                                                                                                  +

                                                                                                  这里的Cron跟Linux中的crontab是一致的。如果对crontab不了解，可以参考这篇文章。

                                                                                                  +

                                                                                                  修改定时任务

                                                                                                  +

                                                                                                  导航至定时任务页面，点击操作列的修改按钮，弹出修改定时任务的弹框。填写相应的内容，点击提交按钮修改定时任务。

                                                                                                  +

                                                                                                  删除定时任务

                                                                                                  +

                                                                                                  导航至定时任务页面，点击操作列的删除按钮，确认删除该任务。

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                                                                                                    网站

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                                                                                                    网站信息是帮助用户查看站长之家收录网站的信息的，包含Robots协议、首页响应等信息。

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                                                                                                      统计数据

                                                                                                      +

                                                                                                      在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。Crawlab将这些指标汇总并呈现给开发者。

                                                                                                      +

                                                                                                      要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。

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                                                                                                        可配置爬虫

                                                                                                        +

                                                                                                        可配置爬虫是版本v0.2.1开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。

                                                                                                        +

                                                                                                        Crawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持自定义爬虫的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。

                                                                                                        +

                                                                                                        添加爬虫

                                                                                                        +

                                                                                                        在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮。

                                                                                                        +

                                                                                                        爬虫列表

                                                                                                        +

                                                                                                        点击可配置爬虫。

                                                                                                        +

                                                                                                        爬虫列表-添加爬虫

                                                                                                        +

                                                                                                        输入完基本信息，点击添加。

                                                                                                        +

                                                                                                        爬虫列表-爬虫信息

                                                                                                        +

                                                                                                        配置爬虫

                                                                                                        +

                                                                                                        添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。

                                                                                                        +

                                                                                                        +

                                                                                                        点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。

                                                                                                        +

                                                                                                        +

                                                                                                        这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。

                                                                                                        +

                                                                                                        抓取类别

                                                                                                        +

                                                                                                        这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。

                                                                                                        +
                                                                                                          +
                                                                                                        • 仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。
                                                                                                          • +
                                                                                                        • 仅详情页。爬虫只抓取详情页。
                                                                                                          • +
                                                                                                        • 列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。
                                                                                                          • +
                                                                                                        +

                                                                                                        这里我们选择列表+详情页。

                                                                                                        +

                                                                                                        列表项选择器 & 分页选择器

                                                                                                        +

                                                                                                        列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。

                                                                                                        +

                                                                                                        开始URL

                                                                                                        +

                                                                                                        爬虫最开始遍历的网址。

                                                                                                        +

                                                                                                        遵守Robots协议

                                                                                                        +

                                                                                                        这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。

                                                                                                        +

                                                                                                        列表页字段 & 详情页字段

                                                                                                        +

                                                                                                        这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。

                                                                                                        +

                                                                                                        在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。

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                                                                                                        +

                                                                                                        点击保存、预览，查看预览内容。

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                                                                                                          创建爬虫

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                                                                                                          Crawlab允许用户创建两种爬虫：

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                                                                                                            +
                                                                                                          1. 自定义爬虫
                                                                                                            2. +
                                                                                                          2. 可配置爬虫
                                                                                                            3. +
                                                                                                          +

                                                                                                          前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。

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                                                                                                            自定义爬虫

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                                                                                                            自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。

                                                                                                            +

                                                                                                            自定义爬虫的添加有两种方式：

                                                                                                            +
                                                                                                              +
                                                                                                            1. 通过Web界面上传爬虫
                                                                                                              2. +
                                                                                                            2. 通过创建项目目录
                                                                                                              3. +
                                                                                                            +

                                                                                                            通过Web界面上传

                                                                                                            +

                                                                                                            在通过Web界面上传之前，需要将爬虫项目文件打包成zip格式。

                                                                                                            +

                                                                                                            +

                                                                                                            然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。

                                                                                                            +

                                                                                                            这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。

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                                                                                                            通过添加项目目录

                                                                                                            +

                                                                                                            Crawlab会自动发现PROJECT_SOURCE_FILE_FOLDER目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到PROJECT_SOURCE_FILE_FOLDER目录下，就可以添加一个爬虫了。

                                                                                                            +

                                                                                                            这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。

                                                                                                            +

                                                                                                            配置爬虫

                                                                                                            +

                                                                                                            在定义爬虫中，我们需要配置一下执行命令（运行爬虫时后台执行的shell命令）和结果集（通过CRAWLAB_COLLECTION传递给爬虫程序，爬虫程序存储结果的地方），然后点击保存按钮保存爬虫信息。

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                                                                                                            +

                                                                                                            接下来，我们就可以部署、运行自定义爬虫了。

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                                                                                                              部署爬虫

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                                                                                                              这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过HTTP的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。

                                                                                                              +

                                                                                                              部署爬虫很简单，有三种方式：

                                                                                                              +
                                                                                                                +
                                                                                                              1. 在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；
                                                                                                                2. +
                                                                                                              2. 在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；
                                                                                                                3. +
                                                                                                              3. 在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。
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                                                                                                              部署好之后，我们就可以运行爬虫了。

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                                                                                                                运行爬虫

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                                                                                                                我们有两种运行爬虫的方式：

                                                                                                                +
                                                                                                                  +
                                                                                                                1. 手动触发
                                                                                                                  2. +
                                                                                                                2. 定时任务触发
                                                                                                                  3. +
                                                                                                                +

                                                                                                                手动触发

                                                                                                                +
                                                                                                                  +
                                                                                                                1. 在爬虫列表中操作列点击运行按钮，或者
                                                                                                                  2. +
                                                                                                                2. 在爬虫详情中概览标签下点击运行按钮，或者
                                                                                                                  3. +
                                                                                                                3. 对于自定义爬虫，可以在配置标签下点击运行按钮
                                                                                                                  4. +
                                                                                                                +

                                                                                                                然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。

                                                                                                                +

                                                                                                                定时任务触发

                                                                                                                +

                                                                                                                定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。

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                                                                                                                  爬虫

                                                                                                                  +

                                                                                                                  爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：

                                                                                                                  +
                                                                                                                    +
                                                                                                                  1. 创建爬虫
                                                                                                                    2. +
                                                                                                                  2. 部署爬虫
                                                                                                                    3. +
                                                                                                                  3. 运行爬虫
                                                                                                                    4. +
                                                                                                                  4. 可配置爬虫
                                                                                                                    5. +
                                                                                                                  5. 统计数据
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                                                                                                                    操作任务

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                                                                                                                    停止任务

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                                                                                                                    当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在Crawlab中停止该任务。

                                                                                                                    +

                                                                                                                    导航至需要停止的任务的任务详情，点击停止按钮来终止任务。

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                                                                                                                    删除任务

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                                                                                                                    在任务列表中，点击操作列中的删除按钮，确认删除该任务。

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                                                                                                                      下载结果

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                                                                                                                      结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。

                                                                                                                      +

                                                                                                                      导航至任务详情，点击结果标签，点击下载CSV按钮，等待一会儿，结果就会以CSV的形式下载到本地。

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                                                                                                                        查看任务

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                                                                                                                        任务列表

                                                                                                                        +

                                                                                                                        点击侧边栏的任务导航至任务列表。可以看到最近的10个生成的任务。可以根据节点、爬虫来过滤任务。

                                                                                                                        +

                                                                                                                        +

                                                                                                                        点击操作列的查看按钮，进入到该任务的任务详情。

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                                                                                                                        任务日志

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                                                                                                                        点击日志标签，可以查看任务日志。

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                                                                                                                        任务结果

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                                                                                                                        点击结果标签，可以查看任务结果。

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                                                                                                                          任务

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                                                                                                                          任务其实就是指某一次抓取任务或采集任务。任务与爬虫关联，其执行的也是爬虫指定的执行命令或采集规则。抓取或采集的结果与任务关联，因此可以查看到每一次任务的结果集。Crawlab的任务是整个采集流程的核心，抓取的过程都是跟任务关联起来的，因此任务对于Crawlab来说非常重要。任务被app触发，worker通过任务队列接收任务，然后在其所在节点上执行任务。

                                                                                                                          +

                                                                                                                          本小节将介绍以下内容：

                                                                                                                          +
                                                                                                                            +
                                                                                                                          1. 查看任务
                                                                                                                            2. +
                                                                                                                          2. 操作任务
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                                                                                                                          3. 下载结果
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                                                                                                                          • +
                                                                                                                          • - + - 快速开始 + 安装Crawlab @@ -116,12 +116,12 @@
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                                                                                                                            • +
                                                                                                                            • - + - 安装 + Docker @@ -129,12 +129,25 @@
                                                                                                                              • -
                                                                                                                            • +
                                                                                                                            • - + - 运行 + 直接部署 + + + + + +
                                                                                                                              • + +
                                                                                                                            • + + + + + 预览模式 @@ -152,7 +165,7 @@ - 概念 + 使用Crawlab @@ -161,9 +174,9 @@
                                                                                                                                -
                                                                                                                              • +
                                                                                                                              • - + 节点 @@ -172,11 +185,43 @@ + + +
                                                                                                                                • + +
                                                                                                                              • + + 爬虫 @@ -185,11 +230,101 @@ + + +
                                                                                                                                • + +
                                                                                                                              • + + 任务 @@ -198,14 +333,72 @@ -
                                                                                                                                • + + + + +
                                                                                                                              • + + + + + 定时任务 + + + + + +
                                                                                                                                • + +
                                                                                                                              • + + + + + 网站 @@ -229,38 +422,6 @@ - -
                                                                                                                              • @@ -268,7 +429,7 @@ - Examples + 样例 @@ -277,9 +438,9 @@
                                                                                                                                  -
                                                                                                                                • +
                                                                                                                                • - + 与Scrapy集成 @@ -288,19 +449,6 @@ -
                                                                                                                                  • - -
                                                                                                                                • - - - - - 与Puppeteer集成 - - - - -
                                                                                                                                  • @@ -338,7 +486,7 @@

                                                                                                                                  - 概念 + 使用Crawlab

                                                                                                                                  @@ -353,7 +501,13 @@
                                                                                                                                  -

                                                                                                                                  概念

                                                                                                                                  +

                                                                                                                                  本小节将介绍如何使用Crawlab，包括如下内容：

                                                                                                                                  +
                                                                                                                                    +
                                                                                                                                  1. 节点
                                                                                                                                    2. +
                                                                                                                                  2. 爬虫
                                                                                                                                    3. +
                                                                                                                                  3. 任务
                                                                                                                                    4. +
                                                                                                                                  4. 定时任务
                                                                                                                                    5. +
                                                                                                                                  @@ -381,12 +535,12 @@ - + - + @@ -397,7 +551,7 @@ diff --git a/docs/img/crawlab-architecture.png b/docs/img/crawlab-architecture.png deleted file mode 100644 index fcac460f20acbb6e9918ba0cefee6c92b3a74779..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 47842 zcmd?RbyQXD_b<8t1w^_f1(cTVmQ+L#q(mA-Lb{Ze4grx61*AlfMy0zIL_PHaO4yh zV$^j8MRO8Lu%6NcydJ_x6nu?AM=8%ONR1zbpw3jirQA%I=**QuT17?+6RYR%)T986Gr5`Mn>BA)^jG394)+41%hDJu^gawA*zJG7~ z7SG1-f98`a<68rFPaDD@);w}vDYyeSRw!}&enAh*!9gcuShORV9}7X`!-s_lBU05I zWQhm}ls;SaB+uA34GvQDycKQ@Ct*Pnru}}P`_Mmt{Ct1gsA!J;V7m^d=gW(rRP;-w zW(w$+e0+Uf8I!vIbLlH8BxYs|!}CDJg>gIZDyNMxA{J5dNz*m7Fz)gAOgRu8+) zLjphI^y+%-j|u6-0$#o(MgoX2bqaLb?`uehWMo`*TOBEDi~a2SN4=`1CTYYkB0SGW z!fTgZ=47+gr~B_1pA6XhDZ5NmH@wdWu_daS}FKTtBQFH13m?r7@J5Gh+rmZcf z-}$MhqO!7iXAFI^kPWGu1mPwbNt|zFM8wTRPMxlK{Xf0wow@0Lkzc>wVofIuA5{}) 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z9aE^R6+kvcvq<*}v&a(Afo*7Q4MzwJLmPk$+i3DrP2z#s!dMS; z6E2(rnj~eOkWv_G4$5Q#{psFj?+KGsyJ?s71BQC&-|L3cwQ!yJyBwwI%Y$61x%g9} z!gX`nsL(9Y5jn1VQzYcMr01w^xaHe&!1Y{~=}V0y8zV)jmz!Hgr*V5M&#vCkH|0#} zr7KtL9p-Z#eR=+6D7OBT0mh!4nx2w8Gv#XX?pGp5(UyuD;b?ZgakB1>4S}Y-tp+#%UYR>D`4xtC4h2h9`8->hM zI7i>vIjs8eNIHUd6#eydl9&89N8$a?>iUnX>wnR!#+T1sxktd#ymlYGgt1q3Pt2}E GXa5Z!0gVd) diff --git a/docs/index.html b/docs/index.html index db12fef7..e53fa9a3 100644 --- a/docs/index.html +++ b/docs/index.html @@ -4,7 +4,7 @@ - 简介 · GitBook + Crawlab简介 · GitBook @@ -57,7 +57,7 @@ - + @@ -92,7 +92,7 @@ - 简介 + Crawlab简介 @@ -100,12 +100,12 @@ -
                                                                                                                                • +
                                                                                                                                • - + - 快速开始 + 安装Crawlab @@ -114,12 +114,12 @@
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                                                                                                                                  • +
                                                                                                                                  • - + - 安装 + Docker @@ -127,12 +127,25 @@
                                                                                                                                    • -
                                                                                                                                  • +
                                                                                                                                  • - + - 运行 + 直接部署 + + + + + +
                                                                                                                                    • + +
                                                                                                                                  • + + + + + 预览模式 @@ -145,12 +158,12 @@
                                                                                                                                    • -
                                                                                                                                  • +
                                                                                                                                  • - + - 概念 + 使用Crawlab @@ -159,9 +172,9 @@
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                                                                                                                                    • +
                                                                                                                                    • - + 节点 @@ -170,11 +183,43 @@ + + +
                                                                                                                                      • + +
                                                                                                                                    • + + 爬虫 @@ -183,11 +228,101 @@ + + +
                                                                                                                                      • + +
                                                                                                                                    • + + 任务 @@ -196,14 +331,72 @@ -
                                                                                                                                      • + + + + +
                                                                                                                                    • + + + + + 定时任务 + + + + + +
                                                                                                                                      • + +
                                                                                                                                    • + + + + + 网站 @@ -227,38 +420,6 @@ - -
                                                                                                                                    • @@ -266,7 +427,7 @@ - Examples + 样例 @@ -275,9 +436,9 @@
                                                                                                                                        -
                                                                                                                                      • +
                                                                                                                                      • - + 与Scrapy集成 @@ -286,19 +447,6 @@ -
                                                                                                                                        • - -
                                                                                                                                      • - - - - - 与Puppeteer集成 - - - - -
                                                                                                                                        • @@ -336,7 +484,7 @@

                                                                                                                                        - 简介 + Crawlab简介

                                                                                                                                        @@ -353,162 +501,12 @@

                                                                                                                                        Crawlab

                                                                                                                                        基于Celery的爬虫分布式爬虫管理平台，支持多种编程语言以及多种爬虫框架.

                                                                                                                                        -

                                                                                                                                        查看演示 Demo

                                                                                                                                        -

                                                                                                                                        English Documentation

                                                                                                                                        -

                                                                                                                                        要求

                                                                                                                                        -
                                                                                                                                          -
                                                                                                                                        • Python3
                                                                                                                                          • -
                                                                                                                                        • MongoDB
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                                                                                                                                        • Redis
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                                                                                                                                        -

                                                                                                                                        安装

                                                                                                                                        -
                                                                                                                                        # 安装后台类库
-pip install -r requirements.txt
-
                                                                                                                                        -
                                                                                                                                        # 安装前台类库
-cd frontend
-npm install
-
                                                                                                                                        -

                                                                                                                                        配置

                                                                                                                                        -

                                                                                                                                        请更改配置文件config.py，配置API和数据库连接.

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                                                                                                                                        快速开始

                                                                                                                                        -
                                                                                                                                        # 启动后端API
-python app.py
-
-# 启动Flower服务
-python ./bin/run_flower.py
-
-# 启动worker
-python ./bin/run_worker.py
-
                                                                                                                                        -
                                                                                                                                        # 运行前端
-cd frontend
-npm run serve
-
                                                                                                                                        -

                                                                                                                                        截图

                                                                                                                                        -

                                                                                                                                        首页

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                                                                                                                                        home

                                                                                                                                        -

                                                                                                                                        爬虫列表

                                                                                                                                        -

                                                                                                                                        spider-list

                                                                                                                                        -

                                                                                                                                        爬虫详情 - 概览

                                                                                                                                        -

                                                                                                                                        spider-list

                                                                                                                                        -

                                                                                                                                        任务详情 - 抓取结果

                                                                                                                                        -

                                                                                                                                        spider-list

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                                                                                                                                        架构

                                                                                                                                        -

                                                                                                                                        Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。

                                                                                                                                        -

                                                                                                                                        crawlab-architecture

                                                                                                                                        -

                                                                                                                                        节点

                                                                                                                                        -

                                                                                                                                        节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。

                                                                                                                                        -

                                                                                                                                        爬虫

                                                                                                                                        -
                                                                                                                                        自动发现
                                                                                                                                        -

                                                                                                                                        在config.py文件中，修改变量PROJECT_SOURCE_FILE_FOLDER作为爬虫项目所在的目录。Crawlab后台程序会自动发现这些爬虫项目并储存到数据库中。是不是很方便？

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                                                                                                                                        部署爬虫
                                                                                                                                        -

                                                                                                                                        所有爬虫需要在抓取前被部署当相应当节点中。在"爬虫详情"页面点击"Deploy"按钮，爬虫将被部署到所有有效到节点中。

                                                                                                                                        -
                                                                                                                                        运行爬虫
                                                                                                                                        -

                                                                                                                                        部署爬虫之后，你可以在"爬虫详情"页面点击"Run"按钮来启动爬虫。一个爬虫任务将被触发，你可以在任务列表页面中看到这个任务。

                                                                                                                                        -

                                                                                                                                        任务

                                                                                                                                        -

                                                                                                                                        任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。

                                                                                                                                        -

                                                                                                                                        后台应用

                                                                                                                                        -

                                                                                                                                        这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行python manage.py app或python ./bin/run_app.py来启动应用。

                                                                                                                                        -

                                                                                                                                        中间者

                                                                                                                                        -

                                                                                                                                        中间者跟Celery中定义的一样，作为运行异步任务的队列。

                                                                                                                                        -

                                                                                                                                        前端

                                                                                                                                        -

                                                                                                                                        前端其实就是一个基于Vue-Element-Admin的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。

                                                                                                                                        -

                                                                                                                                        与其他框架的集成

                                                                                                                                        -

                                                                                                                                        任务是利用python的subprocess模块中的Popen来实现的。任务ID将以环境变量CRAWLAB_TASK_ID的形式存在于爬虫任务运行的进程中，并以此来关联抓取数据。

                                                                                                                                        -

                                                                                                                                        在你的爬虫程序中，你需要将CRAWLAB_TASK_ID的值以task_id作为可以存入数据库中。这样Crawlab就直到如何将爬虫任务与抓取数据关联起来了。当前，Crawlab只支持MongoDB。

                                                                                                                                        -

                                                                                                                                        Scrapy

                                                                                                                                        -

                                                                                                                                        以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。

                                                                                                                                        -
                                                                                                                                        import os
-from pymongo import MongoClient
-
-MONGO_HOST = '192.168.99.100'
-MONGO_PORT = 27017
-MONGO_DB = 'crawlab_test'
-
-# scrapy example in the pipeline
-class JuejinPipeline(object):
-    mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)
-    db = mongo[MONGO_DB]
-    col_name = os.environ.get('CRAWLAB_COLLECTION')
-    if not col_name:
-        col_name = 'test'
-    col = db[col_name]
-
-    def process_item(self, item, spider):
-        item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')
-        self.col.save(item)
-        return item
-
                                                                                                                                        -

                                                                                                                                        与其他框架比较

                                                                                                                                        -

                                                                                                                                        限制以及有一些爬虫管理框架了，因此为啥还要用Crawlab？

                                                                                                                                        -

                                                                                                                                        因为很多现有当平台都依赖于Scrapyd，限制了爬虫的编程语言以及框架，爬虫工程师只能用scrapy和python。当然，scrapy是非常优秀的爬虫框架，但是它不能做一切事情。

                                                                                                                                        -

                                                                                                                                        Crawlab使用起来很方便，也很通用，可以适用于几乎任何主流语言和框架。它还有一个精美的前端界面，让用户可以方便的管理和运行爬虫。

                                                                                                                                        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                                                                                                                                        框架类型分布式前端依赖于Scrapyd
                                                                                                                                        Crawlab管理平台YYN
                                                                                                                                        Gerapy管理平台YYY
                                                                                                                                        SpiderKeeper管理平台YYY
                                                                                                                                        ScrapydWeb管理平台YYY
                                                                                                                                        Scrapyd网络服务YNN/A
                                                                                                                                        -

                                                                                                                                        TODOs

                                                                                                                                        -
                                                                                                                                        后端
                                                                                                                                        -
                                                                                                                                          -
                                                                                                                                        • [ ] 文件管理
                                                                                                                                          • -
                                                                                                                                        • [ ] MySQL数据库支持
                                                                                                                                          • -
                                                                                                                                        • [ ] 重跑任务
                                                                                                                                          • -
                                                                                                                                        • [ ] 节点监控
                                                                                                                                          • -
                                                                                                                                        • [ ] 更多爬虫例子
                                                                                                                                          • -
                                                                                                                                        -
                                                                                                                                        前端
                                                                                                                                        -
                                                                                                                                          -
                                                                                                                                        • [ ] 任务数据统计
                                                                                                                                          • -
                                                                                                                                        • [ ] 表格过滤
                                                                                                                                          • -
                                                                                                                                        • [x] 多语言支持 (中文)
                                                                                                                                          • -
                                                                                                                                        • [ ] 登录和用户管理
                                                                                                                                          • -
                                                                                                                                        • [ ] 全局搜索
                                                                                                                                          • -
                                                                                                                                        +

                                                                                                                                        查看演示 Demo

                                                                                                                                        +

                                                                                                                                        Crawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。

                                                                                                                                        +

                                                                                                                                        项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。

                                                                                                                                        +

                                                                                                                                        Crawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。

                                                                                                                                        +

                                                                                                                                        本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。

                                                                                                                                        +

                                                                                                                                        首先，我们来看如何安装Crawlab吧，请查看安装。

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./bin/run_app.py来启动应用。\n中间者\n中间者跟Celery中定义的一样，作为运行异步任务的队列。\n前端\n前端其实就是一个基于Vue-Element-Admin的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。\n与其他框架的集成\n任务是利用python的subprocess模块中的Popen来实现的。任务ID将以环境变量CRAWLAB_TASK_ID的形式存在于爬虫任务运行的进程中，并以此来关联抓取数据。\n在你的爬虫程序中，你需要将CRAWLAB_TASK_ID的值以task_id作为可以存入数据库中。这样Crawlab就直到如何将爬虫任务与抓取数据关联起来了。当前，Crawlab只支持MongoDB。\nScrapy\n以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。\nimport os\nfrom pymongo import MongoClient\n\nMONGO_HOST = '192.168.99.100'\nMONGO_PORT = 27017\nMONGO_DB = 'crawlab_test'\n\n# scrapy example in the pipeline\nclass JuejinPipeline(object):\n mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)\n db = mongo[MONGO_DB]\n col_name = os.environ.get('CRAWLAB_COLLECTION')\n if not col_name:\n col_name = 'test'\n col = db[col_name]\n\n def process_item(self, item, spider):\n item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')\n self.col.save(item)\n return item\n\n与其他框架比较\n限制以及有一些爬虫管理框架了，因此为啥还要用Crawlab？\n因为很多现有当平台都依赖于Scrapyd，限制了爬虫的编程语言以及框架，爬虫工程师只能用scrapy和python。当然，scrapy是非常优秀的爬虫框架，但是它不能做一切事情。\nCrawlab使用起来很方便，也很通用，可以适用于几乎任何主流语言和框架。它还有一个精美的前端界面，让用户可以方便的管理和运行爬虫。\n\n\n\n框架\n类型\n分布式\n前端\n依赖于Scrapyd\n\n\n\n\nCrawlab\n管理平台\nY\nY\nN\n\n\nGerapy\n管理平台\nY\nY\nY\n\n\nSpiderKeeper\n管理平台\nY\nY\nY\n\n\nScrapydWeb\n管理平台\nY\nY\nY\n\n\nScrapyd\n网络服务\nY\nN\nN/A\n\n\n\nTODOs\n后端\n\n[ ] 文件管理\n[ ] MySQL数据库支持\n[ ] 重跑任务\n[ ] 节点监控\n[ ] 更多爬虫例子\n\n前端\n\n[ ] 任务数据统计\n[ ] 表格过滤\n[x] 多语言支持 (中文)\n[ ] 登录和用户管理\n[ ] 全局搜索\n\n"},"QuickStart/":{"url":"QuickStart/","title":"快速开始","keywords":"","body":"快速开始\n\n安装\n运行\n\n"},"QuickStart/Installation.html":{"url":"QuickStart/Installation.html","title":"安装","keywords":"","body":"安装\n最快安装Crawlab的方式是克隆一份代码到本地\ngit clone https://github.com/tikazyq/crawlab\n\n安装类库\n# 安装后台类库\npip install -r requirements.txt\n\n# 安装前台类库\ncd frontend\nnpm install\n\n"},"QuickStart/Run.html":{"url":"QuickStart/Run.html","title":"运行","keywords":"","body":"运行\n在运行之前需要对Crawlab进行一些配置，配置文件为config.py。\n# project variables\nPROJECT_SOURCE_FILE_FOLDER = '/Users/yeqing/projects/crawlab/spiders' # 爬虫源码根目录\nPROJECT_DEPLOY_FILE_FOLDER = '/var/crawlab' # 爬虫部署根目录\nPROJECT_LOGS_FOLDER = '/var/logs/crawlab' # 日志目录\nPROJECT_TMP_FOLDER = '/tmp' # 临时文件目录\n\n# celery variables\nBROKER_URL = 'redis://192.168.99.100:6379/0' # 中间者URL，连接redis\nCELERY_RESULT_BACKEND = 'mongodb://192.168.99.100:27017/' # CELERY后台URL\nCELERY_MONGODB_BACKEND_SETTINGS = {\n 'database': 'crawlab_test',\n 'taskmeta_collection': 'tasks_celery',\n}\nCELERY_TIMEZONE = 'Asia/Shanghai'\nCELERY_ENABLE_UTC = True\n\n# flower variables\nFLOWER_API_ENDPOINT = 'http://localhost:5555/api' # Flower服务地址\n\n# database variables\nMONGO_HOST = '192.168.99.100'\nMONGO_PORT = 27017\nMONGO_DB = 'crawlab_test'\n\n# flask variables\nDEBUG = True\nFLASK_HOST = '127.0.0.1'\nFLASK_PORT = 8000\n\n启动后端API，也就是一个Flask App，可以直接启动，或者用gunicorn代替。\npython manage.py app\n\n启动本地Worker。在其他节点中如果想只是想执行任务的话，只需要启动这一个服务就可以了。\npython manage.py worker\n\n启动前端服务器。\ncd frontend\nnpm run 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爬虫信息。","在检查完目标网页的元素css选择器之后，我们输入列表项选择器、开始url、列表页/详情页等信息。注意勾选url为详情页url。","在爬虫列表中操作列点击运行按钮，或者","在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；","在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；","在爬虫详情中概览标签下点击运行按钮，或者","在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。","在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。crawlab将这些指标汇总并呈现给开发者。","在通过web界面上传之前，需要将爬虫项目文件打包成zip格式。","基于celery的爬虫分布式爬虫管理平台，支持多种编程语言以及多种爬虫框架.","多节点模式","安装","安装crawlab","安装docker","安装nginx，在ubuntu","安装前端所需库。","安装后端所需库。","安装完docker","定时任务","定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。","定时任务是指定某个时刻，重复性地执行的任务，英文叫做period","定时任务触发","定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。","对docker不了解的开发者，可以参考一下这篇文章（9102","对于自定义爬虫，可以在配置标签下点击运行按钮","导航至任务详情，点击结果标签，点击下载csv按钮，等待一会儿，结果就会以csv的形式下载到本地。","导航至定时任务页面，可以看到定时任务的列表。","导航至定时任务页面，点击操作列的修改按钮，弹出修改定时任务的弹框。填写相应的内容，点击提交按钮修改定时任务。","导航至定时任务页面，点击操作列的删除按钮，确认删除该任务。","导航至需要停止的任务的任务详情，点击停止按钮来终止任务。","年了，学点","开始url","当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在crawlab中停止该任务。","当然，也可以用docker","我们已经在dockerhub上构建了crawlab的镜像，开发者只需要将其pull下来使用。在pul","我们有两种运行爬虫的方式：","手动触发","执行以下命令将crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。","抓取类别","拉取代码","拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。","接下来，我们就可以部署、运行自定义爬虫了。","操作任务","更改后端配置文件config.py，将mongodb、redis的指向ip更改为自己数据的值。注意，容器中对应的宿主机的ip地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将api地址vue_app_base_url更改为宿主机所在的ip地址，例如http://192.168.0.8:8000，这将是前端调用api会用到的url。","更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。","更改配置文件","本使用手册会帮助您解决在安装使用crawlab遇到的任何问题。","本小节将介绍三种安装docker的方式:","本小节将介绍以下内容：","本小节将介绍如何使用crawlab，包括如下内容：","构建","构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。","架构","查看任务","查看演示","查看节点","查看节点列表","样例","添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。","添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。","添加爬虫","点击侧边栏的任务导航至任务列表。可以看到最近的10个生成的任务。可以根据节点、爬虫来过滤任务。","点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celeri","点击保存、预览，查看预览内容。","点击可配置爬虫。","点击操作列的查看按钮，进入到该任务的任务详情。","点击日志标签，可以查看任务日志。","点击添加定时任务，弹出创建定时任务的弹框。填写相应的内容，点击提交按钮创建定时任务。","点击结果标签，可以查看任务结果。","点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。","然后在浏览器中输入http://localhost:8080就可以看到界面了。","然后，crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。","然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。","爬虫","爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：","爬虫最开始遍历的网址。","爬虫源代码或配置规则储存在app上，需要被部署到各个worker节点中。","现在，只需要启动nginx服务就完成了启动前端服务。","直接部署","直接部署是之前没有docker时的部署方式，相对于docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解docker是如何构建crawlab镜像的。这里简单介绍一下。","知识）做进一步了解。简单来说，docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如nginx、mongodb、redis等等。用docker运行一个mongodb服务仅需dock","结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。","统计数据","网站","网站信息是帮助用户查看站长之家收录网站的信息的，包含robots协议、首页响应等信息。","自定义爬虫","自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，crawlab就可以将其集成到爬虫管理的系统中来。","自定义爬虫的添加有两种方式：","节点","节点其实就是celery中的worker。一个节点运行时会连接到一个任务队列（例如redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的ip地址和端口。","节点其实就是celery中的worker。一个节点运行时会连接到一个任务队列（例如redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的ip地址和端口（默认为localhost:8000）。","要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。","该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm","详情页字段","输入完基本信息，点击添加。","运行docker容器","运行爬虫","这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。","这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视robots协议的行为都有法律风险。","这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。","这些都是再列表页或详情页中需要提取的字段。字段由css选择器或者xpath来匹配提取。可以选择文本或者属性。","这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了docker功能，让大家可以利用docker来轻松部署crawlab。下面将一步一步介绍如何使用docker来部署crawlab。","这是一个flask应用，提供了必要的api来支持常规操作，例如crud、爬虫部署以及任务运行。每一个节点需要启动flask应用来支持爬虫部署。运行python","这样的话，pull镜像的速度会比不改变镜像源的速度快很多。","这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。","这样，我们就完成了节点的配置工作。","这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。","这里先定义了master节点，也就是crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。","这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。","这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。","这里我们选择列表+详情页。","这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。","这里的cron跟linux中的crontab是一致的。如果对crontab不了解，可以参考这篇文章。","这里的构建是指前端构建，需要执行以下命令。","这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过http的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。","通过web界面上传","通过web界面上传爬虫","通过创建项目目录","通过添加项目目录","遵守robots协议","部署好之后，我们就可以运行爬虫了。","部署爬虫","部署爬虫很简单，有三种方式：","配置","配置爬虫","镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用dockerhub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。","项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。","预览模式","预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。","首先是将github上的代码拉取到本地。","首先，我们来看如何安装crawlab吧，请查看安装。"],"pipeline":["stopWordFilter","stemmer"]},"store":{"./":{"url":"./","title":"Crawlab简介","keywords":"","body":"Crawlab\n基于Celery的爬虫分布式爬虫管理平台，支持多种编程语言以及多种爬虫框架.\n查看演示 Demo\nCrawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。\n项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。\nCrawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。\n本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。\n首先，我们来看如何安装Crawlab吧，请查看安装。\n"},"Installation/":{"url":"Installation/","title":"安装Crawlab","keywords":"","body":"本小节将介绍三种安装Docker的方式:\n\nDocker\n直接部署\n预览模式\n\n"},"Installation/Docker.html":{"url":"Installation/Docker.html","title":"Docker","keywords":"","body":"Docker安装部署\n这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。\n对Docker不了解的开发者，可以参考一下这篇文章（9102 年了，学点 Docker 知识）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需docker run -d --name mongo -p 27017:27017 mongo一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。\n下载镜像\n我们已经在DockerHub上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。\n{\n \"registry-mirrors\": [\"https://registry.docker-cn.com\"]\n}\n\n这样的话，pull镜像的速度会比不改变镜像源的速度快很多。\n执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。\ndocker pull tikazyq/crawlab:latest\n\n更改配置文件\n拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。\n更改后端配置文件config.py，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将API地址VUE_APP_BASE_URL更改为宿主机所在的IP地址，例如http://192.168.0.8:8000，这将是前端调用API会用到的URL。\n运行Docker容器\n更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。\ndocker run -d --rm --name crawlab \\\n -p 8080:8080 \\\n -p 8000:8000 \\\n -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \\\n -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \\\n tikazyq/crawlab master\n\n其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件/home/yeqing/.env.production和后端配置文件/home/yeqing/config.py映射到了容器相应的目录下。传入参数master是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是worker模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为http://localhost:8080。\n\nDocker-Compose\n当然，也可以用docker-compose的方式来部署。docker-compose是一个集群管理方式，可以利用名为docker-compose.yml的yaml文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的docker-compose.yml定义如下。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里先定义了master节点，也就是Crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。\n安装docker-compose也很简单，大家去网上百度一下就可以了。\n安装完docker-compose和定义好docker-compose.yml后，只需要运行以下命令就可以启动Crawlab。\ndocker-compose up\n\n同样，在浏览器中输入http://localhost:8080就可以看到界面。\n多节点模式\ndocker-compose的方式很适合多节点部署，在原有的master基础上增加几个worker节点，达到多节点部署的目的。将docker-compose.yml更改为如下内容。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n worker1: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8001:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n worker2: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8002:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。\n"},"Installation/Direct.html":{"url":"Installation/Direct.html","title":"直接部署","keywords":"","body":"直接部署\n直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。\n拉取代码\n首先是将github上的代码拉取到本地。\ngit clone https://github.com/tikazyq/crawlab\n\n安装\n安装前端所需库。\nnpm install -g yarn pm2\ncd frontend\nyarn install\n\n安装后端所需库。\ncd ../crawlab\npip install -r requirements\n\n配置\n分别配置前端配置文件./frontend/.env.production和后端配置文件./crawlab/config/config.py。分别需要对部署后API地址以及数据库地址进行配置。\n构建\n这里的构建是指前端构建，需要执行以下命令。\ncd ../frontend\nnpm run build:prod\n\n构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。\nNginx\n安装nginx，在ubuntu 16.04是以下命令。\nsudo apt-get install nginx\n\n添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。\nserver {\n listen 8080;\n server_name dev.crawlab.com;\n root /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist;\n index index.html;\n}\n其中，root是静态文件的根目录，这里是npm打包好后的静态文件。\n现在，只需要启动nginx服务就完成了启动前端服务。\nnginx reload\n\n启动服务\n这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。\npm2 start app.py # API服务\npm2 start worker.py # Worker\npm2 start flower.py # Flower\n\n这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。\npm2 logs [app]\n\n然后在浏览器中输入http://localhost:8080就可以看到界面了。\n"},"Installation/Preview.html":{"url":"Installation/Preview.html","title":"预览模式","keywords":"","body":"预览模式\n预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。\npython manage.py serve\n\n该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm run serve来进行的，因此是开发者模式。注意：强烈不建议在生产环境中用预览模式。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。\n"},"Usage/":{"url":"Usage/","title":"使用Crawlab","keywords":"","body":"本小节将介绍如何使用Crawlab，包括如下内容：\n\n节点\n爬虫\n任务\n定时任务\n\n"},"Usage/Node/":{"url":"Usage/Node/","title":"节点","keywords":"","body":"节点\n节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为localhost:8000）。\n\n查看节点\n修改节点信息\n\n"},"Usage/Node/View.html":{"url":"Usage/Node/View.html","title":"查看节点列表","keywords":"","body":"查看节点列表\n点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celery worker，他们通过连接到配置好的broker（通常是redis）来进行与主机的通信。\n\n"},"Usage/Node/Edit.html":{"url":"Usage/Node/Edit.html","title":"修改节点信息","keywords":"","body":"修改节点信息\n后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在节点列表中点击操作列里的蓝色查看按钮进入到节点详情。节点详情样子如下。\n\n在右侧分别输入该节点对应的节点IP和节点端口，然后点击保存按钮，保存该节点信息。\n这样，我们就完成了节点的配置工作。\n"},"Usage/Spider/":{"url":"Usage/Spider/","title":"爬虫","keywords":"","body":"爬虫\n爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：\n\n创建爬虫\n部署爬虫\n运行爬虫\n可配置爬虫\n统计数据\n\n"},"Usage/Spider/Create.html":{"url":"Usage/Spider/Create.html","title":"创建爬虫","keywords":"","body":"创建爬虫\nCrawlab允许用户创建两种爬虫：\n\n自定义爬虫\n可配置爬虫\n\n前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。\n"},"Usage/Spider/CustomizedSpider.html":{"url":"Usage/Spider/CustomizedSpider.html","title":"自定义爬虫","keywords":"","body":"自定义爬虫\n自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。\n自定义爬虫的添加有两种方式：\n\n通过Web界面上传爬虫\n通过创建项目目录\n\n通过Web界面上传\n在通过Web界面上传之前，需要将爬虫项目文件打包成zip格式。\n\n然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。\n这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。\n通过添加项目目录\nCrawlab会自动发现PROJECT_SOURCE_FILE_FOLDER目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到PROJECT_SOURCE_FILE_FOLDER目录下，就可以添加一个爬虫了。\n这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。\n配置爬虫\n在定义爬虫中，我们需要配置一下执行命令（运行爬虫时后台执行的shell命令）和结果集（通过CRAWLAB_COLLECTION传递给爬虫程序，爬虫程序存储结果的地方），然后点击保存按钮保存爬虫信息。\n\n接下来，我们就可以部署、运行自定义爬虫了。\n"},"Usage/Spider/ConfigurableSpider.html":{"url":"Usage/Spider/ConfigurableSpider.html","title":"可配置爬虫","keywords":"","body":"可配置爬虫\n可配置爬虫是版本v0.2.1开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。\nCrawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持自定义爬虫的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。\n添加爬虫\n在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮。\n\n点击可配置爬虫。\n\n输入完基本信息，点击添加。\n\n配置爬虫\n添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。\n\n点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。\n\n这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。\n抓取类别\n这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。\n\n仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。\n仅详情页。爬虫只抓取详情页。\n列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。\n\n这里我们选择列表+详情页。\n列表项选择器 & 分页选择器\n列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。\n开始URL\n爬虫最开始遍历的网址。\n遵守Robots协议\n这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。\n列表页字段 & 详情页字段\n这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。\n在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。\n\n点击保存、预览，查看预览内容。\n\n"},"Usage/Spider/Deploy.html":{"url":"Usage/Spider/Deploy.html","title":"部署爬虫","keywords":"","body":"部署爬虫\n这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过HTTP的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。\n部署爬虫很简单，有三种方式：\n\n在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；\n在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；\n在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。\n\n部署好之后，我们就可以运行爬虫了。\n"},"Usage/Spider/Run.html":{"url":"Usage/Spider/Run.html","title":"运行爬虫","keywords":"","body":"运行爬虫\n我们有两种运行爬虫的方式：\n\n手动触发\n定时任务触发\n\n手动触发\n\n在爬虫列表中操作列点击运行按钮，或者\n在爬虫详情中概览标签下点击运行按钮，或者\n对于自定义爬虫，可以在配置标签下点击运行按钮\n\n然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。\n定时任务触发\n定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。\n"},"Usage/Spider/Analytics.html":{"url":"Usage/Spider/Analytics.html","title":"统计数据","keywords":"","body":"统计数据\n在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。Crawlab将这些指标汇总并呈现给开发者。\n要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。\n\n"},"Usage/Task/":{"url":"Usage/Task/","title":"任务","keywords":"","body":"任务\n任务其实就是指某一次抓取任务或采集任务。任务与爬虫关联，其执行的也是爬虫指定的执行命令或采集规则。抓取或采集的结果与任务关联，因此可以查看到每一次任务的结果集。Crawlab的任务是整个采集流程的核心，抓取的过程都是跟任务关联起来的，因此任务对于Crawlab来说非常重要。任务被app触发，worker通过任务队列接收任务，然后在其所在节点上执行任务。\n本小节将介绍以下内容：\n\n查看任务\n操作任务\n下载结果\n\n"},"Usage/Task/View.html":{"url":"Usage/Task/View.html","title":"查看任务","keywords":"","body":"查看任务\n任务列表\n点击侧边栏的任务导航至任务列表。可以看到最近的10个生成的任务。可以根据节点、爬虫来过滤任务。\n\n点击操作列的查看按钮，进入到该任务的任务详情。\n任务日志\n点击日志标签，可以查看任务日志。\n\n任务结果\n点击结果标签，可以查看任务结果。\n\n"},"Usage/Task/Action.html":{"url":"Usage/Task/Action.html","title":"操作任务","keywords":"","body":"操作任务\n停止任务\n当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在Crawlab中停止该任务。\n导航至需要停止的任务的任务详情，点击停止按钮来终止任务。\n删除任务\n在任务列表中，点击操作列中的删除按钮，确认删除该任务。\n"},"Usage/Task/DownloadResults.html":{"url":"Usage/Task/DownloadResults.html","title":"下载结果","keywords":"","body":"下载结果\n结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。\n导航至任务详情，点击结果标签，点击下载CSV按钮，等待一会儿，结果就会以CSV的形式下载到本地。\n"},"Usage/Schedule/":{"url":"Usage/Schedule/","title":"定时任务","keywords":"","body":"定时任务\n定时任务是指定某个时刻，重复性地执行的任务，英文叫做Periodical Tasks，在Linux中也被称为Crontab。定时任务可以让任务可以被执行多次，而用户则不用手动的操作来执行任务。在生产环境中，这非常常见。定时任务对于对增量抓取或对数据实时性有要求的用户来说非常有用。\n在Crawlab中，定时任务是通过apscheduler来实现的。创建一个定时任务之后，会在名为mongo的jobstore中创建一个periodical job，apscheduler调度引擎将会不断的去数据库中匹配任务的执行时间，如果执行时间满足要求，则会在后台触发一次任务运行。\n定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。\n创建定时任务\n导航至定时任务页面，可以看到定时任务的列表。\n点击添加定时任务，弹出创建定时任务的弹框。填写相应的内容，点击提交按钮创建定时任务。\n\n这里的Cron跟Linux中的crontab是一致的。如果对crontab不了解，可以参考这篇文章。\n修改定时任务\n导航至定时任务页面，点击操作列的修改按钮，弹出修改定时任务的弹框。填写相应的内容，点击提交按钮修改定时任务。\n删除定时任务\n导航至定时任务页面，点击操作列的删除按钮，确认删除该任务。\n"},"Usage/Site/":{"url":"Usage/Site/","title":"网站","keywords":"","body":"网站\n网站信息是帮助用户查看站长之家收录网站的信息的，包含Robots协议、首页响应等信息。\n\n"},"Architecture/":{"url":"Architecture/","title":"架构","keywords":"","body":"架构\nCrawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。架构图如下。\n\n节点 Node\n节点其实就是Celery中的worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。\n后台应用 Backend App\n这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行python app.py来启动应用。\n爬虫 Spider\n爬虫源代码或配置规则储存在App上，需要被部署到各个worker节点中。\n任务 Task\n任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。\n中间者 Broker\n中间者跟Celery中定义的一样，作为运行异步任务的队列。\n前端 Frontend\n前端其实就是一个基于Vue-Element-Admin的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。\nFlower\n一个Celery的插件，用于监控Celery节点。\n"},"Examples/":{"url":"Examples/","title":"样例","keywords":"","body":"样例\n\n与Scrapy集成\n\n"},"Examples/ScrapyIntegration.html":{"url":"Examples/ScrapyIntegration.html","title":"与Scrapy集成","keywords":"","body":"与Scrapy集成\n以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。\nimport os\nfrom pymongo import MongoClient\n\nMONGO_HOST = '192.168.99.100'\nMONGO_PORT = 27017\nMONGO_DB = 'crawlab_test'\n\n# scrapy example in the pipeline\nclass JuejinPipeline(object):\n mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)\n db = mongo[MONGO_DB]\n col_name = os.environ.get('CRAWLAB_COLLECTION')\n if not col_name:\n col_name = 'test'\n col = db[col_name]\n\n def process_item(self, item, spider):\n item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')\n self.col.save(item)\n return item\n\n"}}} \ No newline at end of file diff --git a/gitbook/Architecture/App.md b/gitbook/Architecture/App.md deleted file mode 100644 index 5d5681fd..00000000 --- a/gitbook/Architecture/App.md +++ /dev/null @@ -1,2 +0,0 @@ -# App - diff --git a/gitbook/Architecture/Celery.md b/gitbook/Architecture/Celery.md deleted file mode 100644 index 96dcec50..00000000 --- a/gitbook/Architecture/Celery.md +++ /dev/null @@ -1,2 +0,0 @@ -# Celery - diff --git a/gitbook/Architecture/README.md b/gitbook/Architecture/README.md index 21444c29..b5c058f2 100644 --- a/gitbook/Architecture/README.md +++ b/gitbook/Architecture/README.md @@ -1,2 +1,33 @@ -# 架构 +## 架构 +Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。架构图如下。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/architecture.png) + +### 节点 Node + +节点其实就是Celery中的`worker`。一个节点运行时会连接到一个任务队列（例如`Redis`）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。 + +### 后台应用 Backend App + +这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行`python app.py`来启动应用。 + +### 爬虫 Spider + +爬虫源代码或配置规则储存在`App`上，需要被部署到各个`worker`节点中。 + +### 任务 Task + +任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。 + +### 中间者 Broker + +中间者跟Celery中定义的一样，作为运行异步任务的队列。 + +### 前端 Frontend + +前端其实就是一个基于[Vue-Element-Admin](https://github.com/PanJiaChen/vue-element-admin)的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。 + +### Flower + +一个Celery的插件，用于监控Celery节点。 diff --git a/gitbook/Concept/Deploy.md b/gitbook/Concept/Deploy.md deleted file mode 100644 index 12f55ebf..00000000 --- a/gitbook/Concept/Deploy.md +++ /dev/null @@ -1,6 +0,0 @@ -# 部署 - -所有爬虫在运行前需要被部署当相应当节点中。 - -部署时，爬虫会被打包到相应的目录中，方便环境隔离，开发环境的爬虫和生产环境的爬虫需要打包部署来实现隔离。 - diff --git a/gitbook/Concept/Node.md b/gitbook/Concept/Node.md deleted file mode 100644 index 3132f93f..00000000 --- a/gitbook/Concept/Node.md +++ /dev/null @@ -1,3 +0,0 @@ -# 节点 - -节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。 diff --git a/gitbook/Concept/README.md b/gitbook/Concept/README.md deleted file mode 100644 index a36e857f..00000000 --- a/gitbook/Concept/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# 概念 - diff --git a/gitbook/Concept/Spider.md b/gitbook/Concept/Spider.md deleted file mode 100644 index dd7bebc1..00000000 --- a/gitbook/Concept/Spider.md +++ /dev/null @@ -1,15 +0,0 @@ -# 爬虫 - -## 自动发现 - -在`config.py`文件中，修改变量`PROJECT_SOURCE_FILE_FOLDER`作为爬虫项目所在的目录。Crawlab后台程序会自动发现这些爬虫项目并储存到数据库中。是不是很方便？ - -## 部署爬虫 - -所有爬虫需要在抓取前被部署当相应当节点中。在"爬虫详情"页面点击"Deploy"按钮，爬虫将被部署到所有有效到节点中。 - -## 运行爬虫 - -部署爬虫之后，你可以在"爬虫详情"页面点击"Run"按钮来启动爬虫。一个爬虫任务将被触发，你可以在任务列表页面中看到这个任务。 - - diff --git a/gitbook/Concept/Task.md b/gitbook/Concept/Task.md deleted file mode 100644 index bd75b96f..00000000 --- a/gitbook/Concept/Task.md +++ /dev/null @@ -1,3 +0,0 @@ -# 任务 - -任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。 diff --git a/gitbook/Examples/PuppeteerIntegration.md b/gitbook/Examples/PuppeteerIntegration.md new file mode 100644 index 00000000..e69de29b diff --git a/gitbook/Examples/README.md b/gitbook/Examples/README.md index 65afe604..32cd4c75 100644 --- a/gitbook/Examples/README.md +++ b/gitbook/Examples/README.md @@ -1,2 +1,4 @@ -# Examples +## 样例 + +1. [与Scrapy集成](/Examples/ScrapyIntegration.md) diff --git a/gitbook/Examples/ScrapyIntegration.md b/gitbook/Examples/ScrapyIntegration.md new file mode 100644 index 00000000..d2deefe6 --- /dev/null +++ b/gitbook/Examples/ScrapyIntegration.md @@ -0,0 +1,26 @@ +### 与Scrapy集成 + +以下是Crawlab跟`Scrapy`集成的例子，利用了Crawlab传过来的`task_id`和`collection_name`。 + +```python +import os +from pymongo import MongoClient + +MONGO_HOST = '192.168.99.100' +MONGO_PORT = 27017 +MONGO_DB = 'crawlab_test' + +# scrapy example in the pipeline +class JuejinPipeline(object): + mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT) + db = mongo[MONGO_DB] + col_name = os.environ.get('CRAWLAB_COLLECTION') + if not col_name: + col_name = 'test' + col = db[col_name] + + def process_item(self, item, spider): + item['task_id'] = os.environ.get('CRAWLAB_TASK_ID') + self.col.save(item) + return item +``` diff --git a/gitbook/Functions/FunctionList.md b/gitbook/Functions/FunctionList.md deleted file mode 100644 index 636126fa..00000000 --- a/gitbook/Functions/FunctionList.md +++ /dev/null @@ -1,61 +0,0 @@ -# 功能列表 - -类别 | 功能名称 | 已统计 | 备注 ---- | --- | --- | --- -全局 | 打开页面 | Y | _trackPageview -全局 | 切换中英文 | Y -全局 | 允许/禁止统计 | Y -节点 | 刷新 | Y -节点 | 查看 | Y -节点 | 删除 | Y -节点详情 | 保存 | Y -节点详情 | 切换节点 | Y -爬虫 | 部署所有爬虫 | Y -爬虫 | 导入爬虫 | Y -爬虫 | 添加爬虫-可配置爬虫 | Y -爬虫 | 添加爬虫-自定义爬虫 | Y -爬虫 | 刷新 | Y -爬虫 | 查看 | Y -爬虫 | 删除 | Y -爬虫 | 部署 | Y -爬虫 | 运行 | Y -爬虫 | 搜索网站 | Y -爬虫详情 | 切换爬虫 | Y -爬虫详情 | 切换标签 | Y -爬虫详情-概览 | 保存 | Y -爬虫详情-概览 | 部署 | Y -爬虫详情-概览 | 运行 | Y -爬虫详情-环境 | 添加 | Y -爬虫详情-环境 | 删除 | Y -爬虫详情-环境 | 保存 | Y -爬虫详情-配置 | 保存 | Y -爬虫详情-配置 | 预览 | Y -爬虫详情-配置 | 提取字段 | Y -爬虫详情-配置 | 运行 | Y -爬虫详情-配置 | 添加字段 | Y -爬虫详情-配置 | 更改字段 | Y -爬虫详情-配置 | 删除字段 | Y -爬虫详情-配置 | 设置详情页URL | Y -任务 | 选择节点 | Y -任务 | 选择爬虫 | Y -任务 | 点击爬虫详情 | Y -任务 | 点击节点详情 | Y -任务 | 搜索 | Y -任务 | 查看 | Y -任务 | 删除 | Y -任务详情 | 切换标签 | Y -任务详情-概览 | 点击爬虫详情 | Y -任务详情-概览 | 点击节点详情 | Y -任务详情-结果 | 下载CSV | Y -定时任务 | 添加 | Y -定时任务 | 修改 | Y -定时任务 | 删除 | Y -定时任务 | 提交 | Y -部署 | 刷新 | Y -网站 | 搜索 | Y -网站 | 选择主类别 | Y -网站 | 选择类别 | Y -网站 | 点击域名 | Y -网站 | 点击爬虫数 | Y -网站 | 点击Robots协议 | N - diff --git a/gitbook/README.md b/gitbook/README.md index 6112b7cc..652ea003 100644 --- a/gitbook/README.md +++ b/gitbook/README.md @@ -7,7 +7,7 @@ Crawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言 项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。 -Crawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。 +Crawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂`scrapy`和`selenium`的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。 本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。 diff --git a/gitbook/SUMMARY.md b/gitbook/SUMMARY.md index ffecadff..e629f80f 100644 --- a/gitbook/SUMMARY.md +++ b/gitbook/SUMMARY.md @@ -18,14 +18,12 @@ * [统计数据](Usage/Spider/Analytics.md) * [任务](Usage/Task/README.md) * [查看任务](Usage/Task/View.md) - * [删除任务](Usage/Task/Delete.md) + * [操作任务](Usage/Task/Action.md) * [下载结果](Usage/Task/DownloadResults.md) * [定时任务](Usage/Schedule/README.md) * [网站](Usage/Site/README.md) * [架构](Architecture/README.md) - * [Celery](Architecture/Celery.md) - * [App](Architecture/App.md) * [样例](Examples/README.md) - * [与Scrapy集成](Examples/README.md) - * [与Puppeteer集成](Examples/README.md) + * [与Scrapy集成](Examples/ScrapyIntegration.md) + #* [与Puppeteer集成](Examples/PuppeteerIntegration.md) diff --git a/gitbook/Usage/Schedule/README.md b/gitbook/Usage/Schedule/README.md index e69de29b..b2d89a0c 100644 --- a/gitbook/Usage/Schedule/README.md +++ b/gitbook/Usage/Schedule/README.md @@ -0,0 +1,25 @@ +## 定时任务 + +定时任务是指定某个时刻，重复性地执行的任务，英文叫做`Periodical Tasks`，在`Linux`中也被称为`Crontab`。定时任务可以让任务可以被执行多次，而用户则不用手动的操作来执行任务。在生产环境中，这非常常见。定时任务对于对增量抓取或对数据实时性有要求的用户来说非常有用。 + +在Crawlab中，定时任务是通过`apscheduler`来实现的。创建一个定时任务之后，会在名为`mongo`的`jobstore`中创建一个`periodical job`，`apscheduler`调度引擎将会不断的去数据库中匹配任务的执行时间，如果执行时间满足要求，则会在后台触发一次任务运行。 + +定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。 + +### 创建定时任务 + +导航至`定时任务`页面，可以看到定时任务的列表。 + +点击`添加定时任务`，弹出创建定时任务的弹框。填写相应的内容，点击`提交`按钮创建定时任务。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/schedule-list-add.png) + +这里的`Cron`跟`Linux`中的`crontab`是一致的。如果对`crontab`不了解，可以参考[这篇文章](https://www.cnblogs.com/longjshz/p/5779215.html)。 + +### 修改定时任务 + +导航至`定时任务`页面，点击`操作`列的`修改`按钮，弹出修改定时任务的弹框。填写相应的内容，点击`提交`按钮修改定时任务。 + +### 删除定时任务 + +导航至`定时任务`页面，点击`操作`列的`删除`按钮，确认删除该任务。 diff --git a/gitbook/Usage/Site/README.md b/gitbook/Usage/Site/README.md index e69de29b..bbf3029b 100644 --- a/gitbook/Usage/Site/README.md +++ b/gitbook/Usage/Site/README.md @@ -0,0 +1,5 @@ +## 网站 + +网站信息是帮助用户查看[站长之家](http://top.chinaz.com/hangye/)收录网站的信息的，包含`Robots协议`、`首页响应`等信息。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/site-list.png) \ No newline at end of file diff --git a/gitbook/Usage/Task/Action.md b/gitbook/Usage/Task/Action.md new file mode 100644 index 00000000..f8742266 --- /dev/null +++ b/gitbook/Usage/Task/Action.md @@ -0,0 +1,11 @@ +## 操作任务 + +### 停止任务 + +当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在Crawlab中停止该任务。 + +导航至需要停止的任务的`任务详情`，点击`停止`按钮来终止任务。 + +### 删除任务 + +在`任务列表`中，点击`操作`列中的`删除`按钮，确认删除该任务。 \ No newline at end of file diff --git a/gitbook/Usage/Task/DownloadResults.md b/gitbook/Usage/Task/DownloadResults.md new file mode 100644 index 00000000..c9ed33f2 --- /dev/null +++ b/gitbook/Usage/Task/DownloadResults.md @@ -0,0 +1,5 @@ +## 下载结果 + +结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。 + +导航至`任务详情`，点击`结果`标签，点击`下载CSV`按钮，等待一会儿，结果就会以`CSV`的形式下载到本地。 diff --git a/gitbook/Usage/Task/README.md b/gitbook/Usage/Task/README.md index e69de29b..4017f61b 100644 --- a/gitbook/Usage/Task/README.md +++ b/gitbook/Usage/Task/README.md @@ -0,0 +1,8 @@ +## 任务 + +任务其实就是指某一次抓取任务或采集任务。任务与爬虫关联，其执行的也是爬虫指定的执行命令或采集规则。抓取或采集的结果与任务关联，因此可以查看到每一次任务的结果集。Crawlab的任务是整个采集流程的核心，抓取的过程都是跟任务关联起来的，因此任务对于Crawlab来说非常重要。任务被`app`触发，`worker`通过任务队列接收任务，然后在其所在节点上执行任务。 + +本小节将介绍以下内容： +1. [查看任务](/Usage/Task/View.md) +2. [操作任务](/Usage/Task/Delete.md) +3. [下载结果](/Usage/Task/DownloadResults.md) diff --git a/gitbook/Usage/Task/View.md b/gitbook/Usage/Task/View.md new file mode 100644 index 00000000..5ee0707b --- /dev/null +++ b/gitbook/Usage/Task/View.md @@ -0,0 +1,21 @@ +## 查看任务 + +### 任务列表 + +点击`侧边栏`的`任务`导航至`任务列表`。可以看到最近的10个生成的任务。可以根据`节点`、`爬虫`来过滤任务。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/task-list.png) + +点击`操作`列的`查看`按钮，进入到该任务的`任务详情`。 + +### 任务日志 + +点击`日志`标签，可以查看任务日志。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/task-detail-log.png) + +### 任务结果 + +点击`结果`标签，可以查看任务结果。 + +![](https://crawlab.oss-cn-hangzhou.aliyuncs.com/gitbook/task-detail-results.png) diff --git a/gitbook/_book/Architecture/index.html b/gitbook/_book/Architecture/index.html index 10220620..21e43e32 100644 --- a/gitbook/_book/Architecture/index.html +++ b/gitbook/_book/Architecture/index.html @@ -57,7 +57,7 @@ - + @@ -336,9 +336,9 @@
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                                                                                                                                            架构

                                                                                                                                            +

                                                                                                                                            架构

                                                                                                                                            +

                                                                                                                                            Crawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。架构图如下。

                                                                                                                                            +

                                                                                                                                            +

                                                                                                                                            节点 Node

                                                                                                                                            +

                                                                                                                                            节点其实就是Celery中的worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。

                                                                                                                                            +

                                                                                                                                            后台应用 Backend App

                                                                                                                                            +

                                                                                                                                            这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行python app.py来启动应用。

                                                                                                                                            +

                                                                                                                                            爬虫 Spider

                                                                                                                                            +

                                                                                                                                            爬虫源代码或配置规则储存在App上，需要被部署到各个worker节点中。

                                                                                                                                            +

                                                                                                                                            任务 Task

                                                                                                                                            +

                                                                                                                                            任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。

                                                                                                                                            +

                                                                                                                                            中间者 Broker

                                                                                                                                            +

                                                                                                                                            中间者跟Celery中定义的一样，作为运行异步任务的队列。

                                                                                                                                            +

                                                                                                                                            前端 Frontend

                                                                                                                                            +

                                                                                                                                            前端其实就是一个基于Vue-Element-Admin的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。

                                                                                                                                            +

                                                                                                                                            Flower

                                                                                                                                            +

                                                                                                                                            一个Celery的插件，用于监控Celery节点。

                                                                                                                                            @@ -579,7 +550,7 @@ - + @@ -590,7 +561,7 @@ diff --git a/gitbook/_book/Examples/PuppeteerIntegration.md b/gitbook/_book/Examples/PuppeteerIntegration.md new file mode 100644 index 00000000..e69de29b diff --git a/gitbook/_book/Examples/ScrapyIntegration.html b/gitbook/_book/Examples/ScrapyIntegration.html new file mode 100644 index 00000000..58f76420 --- /dev/null +++ b/gitbook/_book/Examples/ScrapyIntegration.html @@ -0,0 +1,600 @@ + + + + + + + 与Scrapy集成 · GitBook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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                                                                                                                                            与Scrapy集成

                                                                                                                                            +

                                                                                                                                            以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。

                                                                                                                                            +
                                                                                                                                            import os
+from pymongo import MongoClient
+
+MONGO_HOST = '192.168.99.100'
+MONGO_PORT = 27017
+MONGO_DB = 'crawlab_test'
+
+# scrapy example in the pipeline
+class JuejinPipeline(object):
+    mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)
+    db = mongo[MONGO_DB]
+    col_name = os.environ.get('CRAWLAB_COLLECTION')
+    if not col_name:
+        col_name = 'test'
+    col = db[col_name]
+
+    def process_item(self, item, spider):
+        item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')
+        self.col.save(item)
+        return item
+
                                                                                                                                            + + +
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                                                                                                                                            results matching ""

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                                                                                                                                                  Examples

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                                                                                                                                                  样例

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                                                                                                                                                    +
                                                                                                                                                  1. 与Scrapy集成
                                                                                                                                                    2. +
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                                                                                                                                                                                  定时任务

                                                                                                                                                                                  +

                                                                                                                                                                                  定时任务是指定某个时刻，重复性地执行的任务，英文叫做Periodical Tasks，在Linux中也被称为Crontab。定时任务可以让任务可以被执行多次，而用户则不用手动的操作来执行任务。在生产环境中，这非常常见。定时任务对于对增量抓取或对数据实时性有要求的用户来说非常有用。

                                                                                                                                                                                  +

                                                                                                                                                                                  在Crawlab中，定时任务是通过apscheduler来实现的。创建一个定时任务之后，会在名为mongo的jobstore中创建一个periodical job，apscheduler调度引擎将会不断的去数据库中匹配任务的执行时间，如果执行时间满足要求，则会在后台触发一次任务运行。

                                                                                                                                                                                  +

                                                                                                                                                                                  定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。

                                                                                                                                                                                  +

                                                                                                                                                                                  创建定时任务

                                                                                                                                                                                  +

                                                                                                                                                                                  导航至定时任务页面，可以看到定时任务的列表。

                                                                                                                                                                                  +

                                                                                                                                                                                  点击添加定时任务，弹出创建定时任务的弹框。填写相应的内容，点击提交按钮创建定时任务。

                                                                                                                                                                                  +

                                                                                                                                                                                  +

                                                                                                                                                                                  这里的Cron跟Linux中的crontab是一致的。如果对crontab不了解，可以参考这篇文章。

                                                                                                                                                                                  +

                                                                                                                                                                                  修改定时任务

                                                                                                                                                                                  +

                                                                                                                                                                                  导航至定时任务页面，点击操作列的修改按钮，弹出修改定时任务的弹框。填写相应的内容，点击提交按钮修改定时任务。

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                                                                                                                                                                                  删除定时任务

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                                                                                                                                                                                  导航至定时任务页面，点击操作列的删除按钮，确认删除该任务。

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                                                                                                                                                                                      网站

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                                                                                                                                                                                      网站信息是帮助用户查看站长之家收录网站的信息的，包含Robots协议、首页响应等信息。

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                                                                                                                                                                                                              • @@ -470,9 +438,9 @@
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                                                                                                                                                                                                                  • @@ -598,7 +553,7 @@ diff --git a/gitbook/_book/Usage/Task/Action.html b/gitbook/_book/Usage/Task/Action.html new file mode 100644 index 00000000..5cc72e98 --- /dev/null +++ b/gitbook/_book/Usage/Task/Action.html @@ -0,0 +1,589 @@ + + + + + + + 操作任务 · GitBook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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                                                                                                                                                                                                                  操作任务

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                                                                                                                                                                                                                  停止任务

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                                                                                                                                                                                                                  当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在Crawlab中停止该任务。

                                                                                                                                                                                                                  +

                                                                                                                                                                                                                  导航至需要停止的任务的任务详情，点击停止按钮来终止任务。

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                                                                                                                                                                                                                  删除任务

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                                                                                                                                                                                                                  在任务列表中，点击操作列中的删除按钮，确认删除该任务。

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                                                                                                                                                                                                                    下载结果

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                                                                                                                                                                                                                    结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。

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                                                                                                                                                                                                                    导航至任务详情，点击结果标签，点击下载CSV按钮，等待一会儿，结果就会以CSV的形式下载到本地。

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                                                                                                                                                                                                                      查看任务

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                                                                                                                                                                                                                      任务列表

                                                                                                                                                                                                                      +

                                                                                                                                                                                                                      点击侧边栏的任务导航至任务列表。可以看到最近的10个生成的任务。可以根据节点、爬虫来过滤任务。

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                                                                                                                                                                                                                      点击操作列的查看按钮，进入到该任务的任务详情。

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                                                                                                                                                                                                                      任务日志

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                                                                                                                                                                                                                      点击日志标签，可以查看任务日志。

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                                                                                                                                                                                                                      任务结果

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                                                                                                                                                                                                                      点击结果标签，可以查看任务结果。

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                                                                                                                                                                                                                            - +

                                                                                                                                                                                                                            任务

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                                                                                                                                                                                                                            任务其实就是指某一次抓取任务或采集任务。任务与爬虫关联，其执行的也是爬虫指定的执行命令或采集规则。抓取或采集的结果与任务关联，因此可以查看到每一次任务的结果集。Crawlab的任务是整个采集流程的核心，抓取的过程都是跟任务关联起来的，因此任务对于Crawlab来说非常重要。任务被app触发，worker通过任务队列接收任务，然后在其所在节点上执行任务。

                                                                                                                                                                                                                            +

                                                                                                                                                                                                                            本小节将介绍以下内容：

                                                                                                                                                                                                                            +
                                                                                                                                                                                                                              +
                                                                                                                                                                                                                            1. 查看任务
                                                                                                                                                                                                                              2. +
                                                                                                                                                                                                                            2. 操作任务
                                                                                                                                                                                                                              3. +
                                                                                                                                                                                                                            3. 下载结果
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                                                                                                                                                                                                                            @@ -578,7 +541,7 @@ - + @@ -589,7 +552,7 @@ diff --git a/gitbook/_book/Usage/index.html b/gitbook/_book/Usage/index.html index e49becec..c8262140 100644 --- a/gitbook/_book/Usage/index.html +++ b/gitbook/_book/Usage/index.html @@ -336,9 +336,9 @@
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                                                                                                                                                                                                                                    查看演示 Demo

                                                                                                                                                                                                                                    Crawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。

                                                                                                                                                                                                                                    项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。

                                                                                                                                                                                                                                    -

                                                                                                                                                                                                                                    Crawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。

                                                                                                                                                                                                                                    +

                                                                                                                                                                                                                                    Crawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。

                                                                                                                                                                                                                                    本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。

                                                                                                                                                                                                                                    首先，我们来看如何安装Crawlab吧，请查看安装。

                                                                                                                                                                                                                                    @@ -591,7 +546,7 @@ diff --git a/gitbook/_book/search_index.json b/gitbook/_book/search_index.json index 14b6b582..d98d5152 100644 --- a/gitbook/_book/search_index.json +++ b/gitbook/_book/search_index.json @@ -1 +1 @@ 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Demo\nCrawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。\n项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。\nCrawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。\n本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。\n首先，我们来看如何安装Crawlab吧，请查看安装。\n"},"Installation/":{"url":"Installation/","title":"安装Crawlab","keywords":"","body":"本小节将介绍三种安装Docker的方式:\n\nDocker\n直接部署\n预览模式\n\n"},"Installation/Docker.html":{"url":"Installation/Docker.html","title":"Docker","keywords":"","body":"Docker安装部署\n这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。\n对Docker不了解的开发者，可以参考一下这篇文章（9102 年了，学点 Docker 知识）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需docker run -d --name mongo -p 27017:27017 mongo一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。\n下载镜像\n我们已经在DockerHub上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。\n{\n \"registry-mirrors\": [\"https://registry.docker-cn.com\"]\n}\n\n这样的话，pull镜像的速度会比不改变镜像源的速度快很多。\n执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。\ndocker pull tikazyq/crawlab:latest\n\n更改配置文件\n拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。\n更改后端配置文件config.py，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将API地址VUE_APP_BASE_URL更改为宿主机所在的IP地址，例如http://192.168.0.8:8000，这将是前端调用API会用到的URL。\n运行Docker容器\n更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。\ndocker run -d --rm --name crawlab \\\n -p 8080:8080 \\\n -p 8000:8000 \\\n -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \\\n -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \\\n tikazyq/crawlab master\n\n其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件/home/yeqing/.env.production和后端配置文件/home/yeqing/config.py映射到了容器相应的目录下。传入参数master是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是worker模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为http://localhost:8080。\n\nDocker-Compose\n当然，也可以用docker-compose的方式来部署。docker-compose是一个集群管理方式，可以利用名为docker-compose.yml的yaml文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的docker-compose.yml定义如下。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里先定义了master节点，也就是Crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。\n安装docker-compose也很简单，大家去网上百度一下就可以了。\n安装完docker-compose和定义好docker-compose.yml后，只需要运行以下命令就可以启动Crawlab。\ndocker-compose up\n\n同样，在浏览器中输入http://localhost:8080就可以看到界面。\n多节点模式\ndocker-compose的方式很适合多节点部署，在原有的master基础上增加几个worker节点，达到多节点部署的目的。将docker-compose.yml更改为如下内容。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n worker1: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8001:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n worker2: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8002:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。\n"},"Installation/Direct.html":{"url":"Installation/Direct.html","title":"直接部署","keywords":"","body":"直接部署\n直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。\n拉取代码\n首先是将github上的代码拉取到本地。\ngit clone https://github.com/tikazyq/crawlab\n\n安装\n安装前端所需库。\nnpm install -g yarn pm2\ncd frontend\nyarn install\n\n安装后端所需库。\ncd ../crawlab\npip install -r requirements\n\n配置\n分别配置前端配置文件./frontend/.env.production和后端配置文件./crawlab/config/config.py。分别需要对部署后API地址以及数据库地址进行配置。\n构建\n这里的构建是指前端构建，需要执行以下命令。\ncd ../frontend\nnpm run build:prod\n\n构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。\nNginx\n安装nginx，在ubuntu 16.04是以下命令。\nsudo apt-get install nginx\n\n添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。\nserver {\n listen 8080;\n server_name dev.crawlab.com;\n root /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist;\n index index.html;\n}\n其中，root是静态文件的根目录，这里是npm打包好后的静态文件。\n现在，只需要启动nginx服务就完成了启动前端服务。\nnginx reload\n\n启动服务\n这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。\npm2 start app.py # API服务\npm2 start worker.py # Worker\npm2 start flower.py # Flower\n\n这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。\npm2 logs [app]\n\n然后在浏览器中输入http://localhost:8080就可以看到界面了。\n"},"Installation/Preview.html":{"url":"Installation/Preview.html","title":"预览模式","keywords":"","body":"预览模式\n预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。\npython manage.py serve\n\n该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm run serve来进行的，因此是开发者模式。注意：强烈不建议在生产环境中用预览模式。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。\n"},"Usage/":{"url":"Usage/","title":"使用Crawlab","keywords":"","body":"本小节将介绍如何使用Crawlab，包括如下内容：\n\n节点\n爬虫\n任务\n定时任务\n\n"},"Usage/Node/":{"url":"Usage/Node/","title":"节点","keywords":"","body":"节点\n节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为localhost:8000）。\n\n查看节点\n修改节点信息\n\n"},"Usage/Node/View.html":{"url":"Usage/Node/View.html","title":"查看节点列表","keywords":"","body":"查看节点列表\n点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celery worker，他们通过连接到配置好的broker（通常是redis）来进行与主机的通信。\n\n"},"Usage/Node/Edit.html":{"url":"Usage/Node/Edit.html","title":"修改节点信息","keywords":"","body":"修改节点信息\n后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在节点列表中点击操作列里的蓝色查看按钮进入到节点详情。节点详情样子如下。\n\n在右侧分别输入该节点对应的节点IP和节点端口，然后点击保存按钮，保存该节点信息。\n这样，我们就完成了节点的配置工作。\n"},"Usage/Spider/":{"url":"Usage/Spider/","title":"爬虫","keywords":"","body":"爬虫\n爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：\n\n创建爬虫\n部署爬虫\n运行爬虫\n可配置爬虫\n统计数据\n\n"},"Usage/Spider/Create.html":{"url":"Usage/Spider/Create.html","title":"创建爬虫","keywords":"","body":"创建爬虫\nCrawlab允许用户创建两种爬虫：\n\n自定义爬虫\n可配置爬虫\n\n前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。\n"},"Usage/Spider/CustomizedSpider.html":{"url":"Usage/Spider/CustomizedSpider.html","title":"自定义爬虫","keywords":"","body":"自定义爬虫\n自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。\n自定义爬虫的添加有两种方式：\n\n通过Web界面上传爬虫\n通过创建项目目录\n\n通过Web界面上传\n在通过Web界面上传之前，需要将爬虫项目文件打包成zip格式。\n\n然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。\n这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。\n通过添加项目目录\nCrawlab会自动发现PROJECT_SOURCE_FILE_FOLDER目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到PROJECT_SOURCE_FILE_FOLDER目录下，就可以添加一个爬虫了。\n这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。\n配置爬虫\n在定义爬虫中，我们需要配置一下执行命令（运行爬虫时后台执行的shell命令）和结果集（通过CRAWLAB_COLLECTION传递给爬虫程序，爬虫程序存储结果的地方），然后点击保存按钮保存爬虫信息。\n\n接下来，我们就可以部署、运行自定义爬虫了。\n"},"Usage/Spider/ConfigurableSpider.html":{"url":"Usage/Spider/ConfigurableSpider.html","title":"可配置爬虫","keywords":"","body":"可配置爬虫\n可配置爬虫是版本v0.2.1开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。\nCrawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持自定义爬虫的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。\n添加爬虫\n在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮。\n\n点击可配置爬虫。\n\n输入完基本信息，点击添加。\n\n配置爬虫\n添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。\n\n点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。\n\n这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。\n抓取类别\n这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。\n\n仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。\n仅详情页。爬虫只抓取详情页。\n列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。\n\n这里我们选择列表+详情页。\n列表项选择器 & 分页选择器\n列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。\n开始URL\n爬虫最开始遍历的网址。\n遵守Robots协议\n这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。\n列表页字段 & 详情页字段\n这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。\n在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。\n\n点击保存、预览，查看预览内容。\n\n"},"Usage/Spider/Deploy.html":{"url":"Usage/Spider/Deploy.html","title":"部署爬虫","keywords":"","body":"部署爬虫\n这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过HTTP的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。\n部署爬虫很简单，有三种方式：\n\n在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；\n在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；\n在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。\n\n部署好之后，我们就可以运行爬虫了。\n"},"Usage/Spider/Run.html":{"url":"Usage/Spider/Run.html","title":"运行爬虫","keywords":"","body":"运行爬虫\n我们有两种运行爬虫的方式：\n\n手动触发\n定时任务触发\n\n手动触发\n\n在爬虫列表中操作列点击运行按钮，或者\n在爬虫详情中概览标签下点击运行按钮，或者\n对于自定义爬虫，可以在配置标签下点击运行按钮\n\n然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。\n定时任务触发\n定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。\n"},"Usage/Spider/Analytics.html":{"url":"Usage/Spider/Analytics.html","title":"统计数据","keywords":"","body":"统计数据\n在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。Crawlab将这些指标汇总并呈现给开发者。\n要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。\n\n"},"Usage/Task/":{"url":"Usage/Task/","title":"任务","keywords":"","body":""},"Usage/Schedule/":{"url":"Usage/Schedule/","title":"定时任务","keywords":"","body":""},"Usage/Site/":{"url":"Usage/Site/","title":"网站","keywords":"","body":""},"Architecture/":{"url":"Architecture/","title":"架构","keywords":"","body":"架构\n"},"Architecture/Celery.html":{"url":"Architecture/Celery.html","title":"Celery","keywords":"","body":"Celery\n"},"Architecture/App.html":{"url":"Architecture/App.html","title":"App","keywords":"","body":"App\n"},"Examples/":{"url":"Examples/","title":"样例","keywords":"","body":"Examples\n"}}} \ No newline at end of file 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爬虫信息。","在检查完目标网页的元素css选择器之后，我们输入列表项选择器、开始url、列表页/详情页等信息。注意勾选url为详情页url。","在爬虫列表中操作列点击运行按钮，或者","在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；","在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；","在爬虫详情中概览标签下点击运行按钮，或者","在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。","在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。crawlab将这些指标汇总并呈现给开发者。","在通过web界面上传之前，需要将爬虫项目文件打包成zip格式。","基于celery的爬虫分布式爬虫管理平台，支持多种编程语言以及多种爬虫框架.","多节点模式","安装","安装crawlab","安装docker","安装nginx，在ubuntu","安装前端所需库。","安装后端所需库。","安装完docker","定时任务","定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。","定时任务是指定某个时刻，重复性地执行的任务，英文叫做period","定时任务触发","定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。","对docker不了解的开发者，可以参考一下这篇文章（9102","对于自定义爬虫，可以在配置标签下点击运行按钮","导航至任务详情，点击结果标签，点击下载csv按钮，等待一会儿，结果就会以csv的形式下载到本地。","导航至定时任务页面，可以看到定时任务的列表。","导航至定时任务页面，点击操作列的修改按钮，弹出修改定时任务的弹框。填写相应的内容，点击提交按钮修改定时任务。","导航至定时任务页面，点击操作列的删除按钮，确认删除该任务。","导航至需要停止的任务的任务详情，点击停止按钮来终止任务。","年了，学点","开始url","当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在crawlab中停止该任务。","当然，也可以用docker","我们已经在dockerhub上构建了crawlab的镜像，开发者只需要将其pull下来使用。在pul","我们有两种运行爬虫的方式：","手动触发","执行以下命令将crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。","抓取类别","拉取代码","拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。","接下来，我们就可以部署、运行自定义爬虫了。","操作任务","更改后端配置文件config.py，将mongodb、redis的指向ip更改为自己数据的值。注意，容器中对应的宿主机的ip地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将api地址vue_app_base_url更改为宿主机所在的ip地址，例如http://192.168.0.8:8000，这将是前端调用api会用到的url。","更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。","更改配置文件","本使用手册会帮助您解决在安装使用crawlab遇到的任何问题。","本小节将介绍三种安装docker的方式:","本小节将介绍以下内容：","本小节将介绍如何使用crawlab，包括如下内容：","构建","构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。","架构","查看任务","查看演示","查看节点","查看节点列表","样例","添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。","添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。","添加爬虫","点击侧边栏的任务导航至任务列表。可以看到最近的10个生成的任务。可以根据节点、爬虫来过滤任务。","点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celeri","点击保存、预览，查看预览内容。","点击可配置爬虫。","点击操作列的查看按钮，进入到该任务的任务详情。","点击日志标签，可以查看任务日志。","点击添加定时任务，弹出创建定时任务的弹框。填写相应的内容，点击提交按钮创建定时任务。","点击结果标签，可以查看任务结果。","点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。","然后在浏览器中输入http://localhost:8080就可以看到界面了。","然后，crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。","然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。","爬虫","爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：","爬虫最开始遍历的网址。","爬虫源代码或配置规则储存在app上，需要被部署到各个worker节点中。","现在，只需要启动nginx服务就完成了启动前端服务。","直接部署","直接部署是之前没有docker时的部署方式，相对于docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解docker是如何构建crawlab镜像的。这里简单介绍一下。","知识）做进一步了解。简单来说，docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如nginx、mongodb、redis等等。用docker运行一个mongodb服务仅需dock","结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。","统计数据","网站","网站信息是帮助用户查看站长之家收录网站的信息的，包含robots协议、首页响应等信息。","自定义爬虫","自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，crawlab就可以将其集成到爬虫管理的系统中来。","自定义爬虫的添加有两种方式：","节点","节点其实就是celery中的worker。一个节点运行时会连接到一个任务队列（例如redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的ip地址和端口。","节点其实就是celery中的worker。一个节点运行时会连接到一个任务队列（例如redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的ip地址和端口（默认为localhost:8000）。","要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。","该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm","详情页字段","输入完基本信息，点击添加。","运行docker容器","运行爬虫","这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。","这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视robots协议的行为都有法律风险。","这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。","这些都是再列表页或详情页中需要提取的字段。字段由css选择器或者xpath来匹配提取。可以选择文本或者属性。","这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了docker功能，让大家可以利用docker来轻松部署crawlab。下面将一步一步介绍如何使用docker来部署crawlab。","这是一个flask应用，提供了必要的api来支持常规操作，例如crud、爬虫部署以及任务运行。每一个节点需要启动flask应用来支持爬虫部署。运行python","这样的话，pull镜像的速度会比不改变镜像源的速度快很多。","这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。","这样，我们就完成了节点的配置工作。","这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。","这里先定义了master节点，也就是crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。","这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。","这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。","这里我们选择列表+详情页。","这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。","这里的cron跟linux中的crontab是一致的。如果对crontab不了解，可以参考这篇文章。","这里的构建是指前端构建，需要执行以下命令。","这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过http的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。","通过web界面上传","通过web界面上传爬虫","通过创建项目目录","通过添加项目目录","遵守robots协议","部署好之后，我们就可以运行爬虫了。","部署爬虫","部署爬虫很简单，有三种方式：","配置","配置爬虫","镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用dockerhub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。","项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。","预览模式","预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。","首先是将github上的代码拉取到本地。","首先，我们来看如何安装crawlab吧，请查看安装。"],"pipeline":["stopWordFilter","stemmer"]},"store":{"./":{"url":"./","title":"Crawlab简介","keywords":"","body":"Crawlab\n基于Celery的爬虫分布式爬虫管理平台，支持多种编程语言以及多种爬虫框架.\n查看演示 Demo\nCrawlab是基于Celery的分布式爬虫管理平台，可以集成任何语言和任何框架。\n项目自今年三月份上线以来受到爬虫爱好者们和开发者们的好评，不少使用者还表示会用Crawlab搭建公司的爬虫平台。经过近3个月的迭代，我们陆续上线了定时任务、数据分析、网站信息、可配置爬虫、自动提取字段、下载结果、上传爬虫等功能，将Crawlab打造得更加实用，更加全面，能够真正帮助用户解决爬虫管理困难的问题。\nCrawlab主要解决的是大量爬虫管理困难的问题，例如需要监控上百个网站的参杂scrapy和selenium的项目不容易做到同时管理，而且命令行管理的成本非常高，还容易出错。Crawlab支持任何语言和任何框架，配合任务调度、任务监控，很容易做到对成规模的爬虫项目进行有效监控管理。\n本使用手册会帮助您解决在安装使用Crawlab遇到的任何问题。\n首先，我们来看如何安装Crawlab吧，请查看安装。\n"},"Installation/":{"url":"Installation/","title":"安装Crawlab","keywords":"","body":"本小节将介绍三种安装Docker的方式:\n\nDocker\n直接部署\n预览模式\n\n"},"Installation/Docker.html":{"url":"Installation/Docker.html","title":"Docker","keywords":"","body":"Docker安装部署\n这应该是部署应用的最方便也是最节省时间的方式了。在最近的一次版本更新v0.2.3中，我们发布了Docker功能，让大家可以利用Docker来轻松部署Crawlab。下面将一步一步介绍如何使用Docker来部署Crawlab。\n对Docker不了解的开发者，可以参考一下这篇文章（9102 年了，学点 Docker 知识）做进一步了解。简单来说，Docker可以利用已存在的镜像帮助构建一些常用的服务和应用，例如Nginx、MongoDB、Redis等等。用Docker运行一个MongoDB服务仅需docker run -d --name mongo -p 27017:27017 mongo一行命令。如何安装Docker跟操作系统有关，这里就不展开讲了，需要的同学自行百度一下相关教程。\n下载镜像\n我们已经在DockerHub上构建了Crawlab的镜像，开发者只需要将其pull下来使用。在pull 镜像之前，我们需要配置一下镜像源。因为我们在墙内，使用原有的镜像源速度非常感人，因此将使用DockerHub在国内的加速器。创建/etc/docker/daemon.json文件，在其中输入如下内容。\n{\n \"registry-mirrors\": [\"https://registry.docker-cn.com\"]\n}\n\n这样的话，pull镜像的速度会比不改变镜像源的速度快很多。\n执行以下命令将Crawlab的镜像下载下来。镜像大小大概在几百兆，因此下载需要几分钟时间。\ndocker pull tikazyq/crawlab:latest\n\n更改配置文件\n拷贝一份后端配置文件./crawlab/config/config.py以及前端配置文件./frontend/.env.production到某一个地方。例如我的例子，分别为/home/yeqing/config.py和/home/yeqing/.env.production。\n更改后端配置文件config.py，将MongoDB、Redis的指向IP更改为自己数据的值。注意，容器中对应的宿主机的IP地址不是localhost，而是172.17.0.1（当然也可以用network来做，只是稍微麻烦一些）。更改前端配置文件.env.production，将API地址VUE_APP_BASE_URL更改为宿主机所在的IP地址，例如http://192.168.0.8:8000，这将是前端调用API会用到的URL。\n运行Docker容器\n更改好配置文件之后，接下来就是运行容器了。执行以下命令来启动容器。\ndocker run -d --rm --name crawlab \\\n -p 8080:8080 \\\n -p 8000:8000 \\\n -v /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production \\\n -v /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py \\\n tikazyq/crawlab master\n\n其中，我们映射了8080端口（Nginx前端静态文件）以及8000端口（后端API）到宿主机。另外还将前端配置文件/home/yeqing/.env.production和后端配置文件/home/yeqing/config.py映射到了容器相应的目录下。传入参数master是代表该启动方式为主机启动模式，也就是所有服务（前端、Api、Flower、Worker）都会启动。另外一个模式是worker模式，只会启动必要的Api和Worker服务，这个对于分布式部署比较有用。等待大约20-30秒的时间来build前端静态文件，之后就可以打开Crawlab界面地址地址看到界面了。界面地址默认为http://localhost:8080。\n\nDocker-Compose\n当然，也可以用docker-compose的方式来部署。docker-compose是一个集群管理方式，可以利用名为docker-compose.yml的yaml文件来定义需要启动的容器，可以是单个，也可以（通常）是多个的。Crawlab的docker-compose.yml定义如下。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里先定义了master节点，也就是Crawlab的主节点。master依赖于mongo和redis容器，因此在启动之前会同时启动mongo和redis容器。这样就不需要单独配置mongo和redis服务了，大大节省了环境配置的时间。\n安装docker-compose也很简单，大家去网上百度一下就可以了。\n安装完docker-compose和定义好docker-compose.yml后，只需要运行以下命令就可以启动Crawlab。\ndocker-compose up\n\n同样，在浏览器中输入http://localhost:8080就可以看到界面。\n多节点模式\ndocker-compose的方式很适合多节点部署，在原有的master基础上增加几个worker节点，达到多节点部署的目的。将docker-compose.yml更改为如下内容。\nversion: '3.3'\nservices:\n master: \n image: tikazyq/crawlab:latest\n container_name: crawlab\n volumns:\n - /home/yeqing/config.master.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.master:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports: \n - \"8080:8080\" # nginx\n - \"8000:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - master\n worker1: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8001:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n worker2: \n image: tikazyq/crawlab:latest\n volumns:\n - /home/yeqing/config.worker.py:/opt/crawlab/crawlab/config/config.py # 后端配置文件\n - /home/yeqing/.env.production.worker:/opt/crawlab/frontend/.env.production # 前端配置文件\n ports:\n - \"8002:8000\" # app\n depends_on:\n - mongo\n - redis\n entrypoint:\n - /bin/sh\n - /opt/crawlab/docker_init.sh\n - worker\n mongo:\n image: mongo:latest\n restart: always\n ports:\n - \"27017:27017\"\n redis:\n image: redis:latest\n restart: always\n ports:\n - \"6379:6379\"\n\n这里启动了多增加了两个worker节点，以worker模式启动。这样，多节点部署，也就是分布式部署就完成了。\n"},"Installation/Direct.html":{"url":"Installation/Direct.html","title":"直接部署","keywords":"","body":"直接部署\n直接部署是之前没有Docker时的部署方式，相对于Docker部署来说有些繁琐。但了解如何直接部署可以帮助更深入地理解Docker是如何构建Crawlab镜像的。这里简单介绍一下。\n拉取代码\n首先是将github上的代码拉取到本地。\ngit clone https://github.com/tikazyq/crawlab\n\n安装\n安装前端所需库。\nnpm install -g yarn pm2\ncd frontend\nyarn install\n\n安装后端所需库。\ncd ../crawlab\npip install -r requirements\n\n配置\n分别配置前端配置文件./frontend/.env.production和后端配置文件./crawlab/config/config.py。分别需要对部署后API地址以及数据库地址进行配置。\n构建\n这里的构建是指前端构建，需要执行以下命令。\ncd ../frontend\nnpm run build:prod\n\n构建完成后，会在./frontend目录下创建一个dist文件夹，里面是打包好后的静态文件。\nNginx\n安装nginx，在ubuntu 16.04是以下命令。\nsudo apt-get install nginx\n\n添加/etc/nginx/conf.d/crawlab.conf文件，输入以下内容。\nserver {\n listen 8080;\n server_name dev.crawlab.com;\n root /home/yeqing/jenkins_home/workspace/crawlab_develop/frontend/dist;\n index index.html;\n}\n其中，root是静态文件的根目录，这里是npm打包好后的静态文件。\n现在，只需要启动nginx服务就完成了启动前端服务。\nnginx reload\n\n启动服务\n这里是指启动后端服务。我们用pm2来管理进程。执行以下命令。\npm2 start app.py # API服务\npm2 start worker.py # Worker\npm2 start flower.py # Flower\n\n这样，pm2会启动3个守护进程来管理这3个服务。我们如果想看后端服务的日志的话，可以执行以下命令。\npm2 logs [app]\n\n然后在浏览器中输入http://localhost:8080就可以看到界面了。\n"},"Installation/Preview.html":{"url":"Installation/Preview.html","title":"预览模式","keywords":"","body":"预览模式\n预览模式是一种让用户比较快的上手的一种部署模式。跟直接部署类似，但不用经过构建、nginx和启动服务的步骤。在启动时只需要执行以下命令就可以了。相较于直接部署来说方便一些。\npython manage.py serve\n\n该模式同样会启动3个后端服务和1个前端服务。前端服务是通过npm run serve来进行的，因此是开发者模式。注意：强烈不建议在生产环境中用预览模式。预览模式只是让开发者快速体验Crawlab以及调试代码问题的一种方式，而不是用作生产环境部署的。\n"},"Usage/":{"url":"Usage/","title":"使用Crawlab","keywords":"","body":"本小节将介绍如何使用Crawlab，包括如下内容：\n\n节点\n爬虫\n任务\n定时任务\n\n"},"Usage/Node/":{"url":"Usage/Node/","title":"节点","keywords":"","body":"节点\n节点其实就是Celery中的Worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口（默认为localhost:8000）。\n\n查看节点\n修改节点信息\n\n"},"Usage/Node/View.html":{"url":"Usage/Node/View.html","title":"查看节点列表","keywords":"","body":"查看节点列表\n点击侧边栏的节点导航至节点列表，可以看到已上线的节点。这里的节点其实就是已经运行起来的celery worker，他们通过连接到配置好的broker（通常是redis）来进行与主机的通信。\n\n"},"Usage/Node/Edit.html":{"url":"Usage/Node/Edit.html","title":"修改节点信息","keywords":"","body":"修改节点信息\n后面我们需要让爬虫运行在各个节点上，需要让主机与节点进行通信，因此需要知道节点的IP地址和端口。我们需要手动配置一下节点的IP和端口。在节点列表中点击操作列里的蓝色查看按钮进入到节点详情。节点详情样子如下。\n\n在右侧分别输入该节点对应的节点IP和节点端口，然后点击保存按钮，保存该节点信息。\n这样，我们就完成了节点的配置工作。\n"},"Usage/Spider/":{"url":"Usage/Spider/","title":"爬虫","keywords":"","body":"爬虫\n爬虫就是我们通常说的网络爬虫了，本小节将介绍如下内容：\n\n创建爬虫\n部署爬虫\n运行爬虫\n可配置爬虫\n统计数据\n\n"},"Usage/Spider/Create.html":{"url":"Usage/Spider/Create.html","title":"创建爬虫","keywords":"","body":"创建爬虫\nCrawlab允许用户创建两种爬虫：\n\n自定义爬虫\n可配置爬虫\n\n前者可以通过Web界面和创建项目目录的方式来添加，后者由于没有源代码，只能通过Web界面来添加。\n"},"Usage/Spider/CustomizedSpider.html":{"url":"Usage/Spider/CustomizedSpider.html","title":"自定义爬虫","keywords":"","body":"自定义爬虫\n自定义爬虫是指用户可以添加的任何语言任何框架的爬虫，高度自定义化。当用户添加好自定义爬虫之后，Crawlab就可以将其集成到爬虫管理的系统中来。\n自定义爬虫的添加有两种方式：\n\n通过Web界面上传爬虫\n通过创建项目目录\n\n通过Web界面上传\n在通过Web界面上传之前，需要将爬虫项目文件打包成zip格式。\n\n然后，在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮，选择自定义爬虫，点击上传按钮，选择刚刚打包好的zip文件。上传成功后，爬虫列表中会出现新添加的自定义爬虫。这样就算添加好了。\n这个方式稍微有些繁琐，但是对于无法轻松获取服务器的读写权限时是非常有用的，适合在生产环境上使用。\n通过添加项目目录\nCrawlab会自动发现PROJECT_SOURCE_FILE_FOLDER目录下的所有爬虫目录，并将这些目录生成自定义爬虫并集成到Crawlab中。因此，将爬虫项目目录拷贝到PROJECT_SOURCE_FILE_FOLDER目录下，就可以添加一个爬虫了。\n这种方式非常方便，但是需要获得主机服务器的读写权限，因而比较适合在开发环境上采用。\n配置爬虫\n在定义爬虫中，我们需要配置一下执行命令（运行爬虫时后台执行的shell命令）和结果集（通过CRAWLAB_COLLECTION传递给爬虫程序，爬虫程序存储结果的地方），然后点击保存按钮保存爬虫信息。\n\n接下来，我们就可以部署、运行自定义爬虫了。\n"},"Usage/Spider/ConfigurableSpider.html":{"url":"Usage/Spider/ConfigurableSpider.html","title":"可配置爬虫","keywords":"","body":"可配置爬虫\n可配置爬虫是版本v0.2.1开发的功能。目的是将具有相似网站结构的爬虫项目可配置化，将开发爬虫的过程流程化，大大提高爬虫开发效率。\nCrawlab的可配置爬虫是基于Scrapy的，因此天生支持并发。而且，可配置爬虫完全支持自定义爬虫的一般功能，因此也支持任务调度、任务监控、日志监控、数据分析。\n添加爬虫\n在侧边栏点击爬虫导航至爬虫列表，点击添加爬虫按钮。\n\n点击可配置爬虫。\n\n输入完基本信息，点击添加。\n\n配置爬虫\n添加完成后，可以看到刚刚添加的可配置爬虫出现了在最下方，点击查看进入到爬虫详情。\n\n点击配置标签进入到配置页面。接下来，我们需要对爬虫规则进行配置。\n\n这里已经有一些配置好的初始输入项。我们简单介绍一下各自的含义。\n抓取类别\n这也是爬虫抓取采用的策略，也就是爬虫遍历网页是如何进行的。作为第一个版本，我们有仅列表、仅详情页、列表+详情页。\n\n仅列表页。这也是最简单的形式，爬虫遍历列表上的列表项，将数据抓取下来。\n仅详情页。爬虫只抓取详情页。\n列表+详情页。爬虫先遍历列表页，将列表项中的详情页地址提取出来并跟进抓取详情页。\n\n这里我们选择列表+详情页。\n列表项选择器 & 分页选择器\n列表项的匹和分页按钮的匹配查询，由CSS或XPath来进行匹配。\n开始URL\n爬虫最开始遍历的网址。\n遵守Robots协议\n这个默认是开启的。如果开启，爬虫将先抓取网站的robots.txt并判断页面是否可抓；否则，不会对此进行验证。用户可以选择将其关闭。请注意，任何无视Robots协议的行为都有法律风险。\n列表页字段 & 详情页字段\n这些都是再列表页或详情页中需要提取的字段。字段由CSS选择器或者XPath来匹配提取。可以选择文本或者属性。\n在检查完目标网页的元素CSS选择器之后，我们输入列表项选择器、开始URL、列表页/详情页等信息。注意勾选url为详情页URL。\n\n点击保存、预览，查看预览内容。\n\n"},"Usage/Spider/Deploy.html":{"url":"Usage/Spider/Deploy.html","title":"部署爬虫","keywords":"","body":"部署爬虫\n这里的爬虫部署是指自定义爬虫的部署，因为可配置爬虫已经内嵌到Crawlab中了，所有节点都可以使用，不需要额外部署。简单来说，就是将主机上的爬虫源代码通过HTTP的方式打包传输至worker节点上，因此节点就可以运行传输过来的爬虫了。\n部署爬虫很简单，有三种方式：\n\n在爬虫列表中点击部署所有爬虫，将所有爬虫部署到所有在线节点中；\n在爬虫列表中点击操作列的部署按钮，将指定爬虫部署到所有在线节点中；\n在爬虫详情的概览标签中，点击部署按钮，将指定爬虫部署到所有在线节点中。\n\n部署好之后，我们就可以运行爬虫了。\n"},"Usage/Spider/Run.html":{"url":"Usage/Spider/Run.html","title":"运行爬虫","keywords":"","body":"运行爬虫\n我们有两种运行爬虫的方式：\n\n手动触发\n定时任务触发\n\n手动触发\n\n在爬虫列表中操作列点击运行按钮，或者\n在爬虫详情中概览标签下点击运行按钮，或者\n对于自定义爬虫，可以在配置标签下点击运行按钮\n\n然后，Crawlab会提示任务已经派发到队列中去了，然后你可以在爬虫详情左侧看到新创建的任务。点击创建时间可以导航至任务详情。\n定时任务触发\n定时任务触发是比较常用的功能，对于增量抓取或对实时性有要求的任务很重要。这在定时任务中会详细介绍。\n"},"Usage/Spider/Analytics.html":{"url":"Usage/Spider/Analytics.html","title":"统计数据","keywords":"","body":"统计数据\n在运行了一段时间之后，爬虫会积累一些统计数据，例如运行成功率、任务数、运行时长等指标。Crawlab将这些指标汇总并呈现给开发者。\n要查看统计数据的话，只需要在爬虫详情中，点击分析标签，就可以看到爬虫的统计数据了。\n\n"},"Usage/Task/":{"url":"Usage/Task/","title":"任务","keywords":"","body":"任务\n任务其实就是指某一次抓取任务或采集任务。任务与爬虫关联，其执行的也是爬虫指定的执行命令或采集规则。抓取或采集的结果与任务关联，因此可以查看到每一次任务的结果集。Crawlab的任务是整个采集流程的核心，抓取的过程都是跟任务关联起来的，因此任务对于Crawlab来说非常重要。任务被app触发，worker通过任务队列接收任务，然后在其所在节点上执行任务。\n本小节将介绍以下内容：\n\n查看任务\n操作任务\n下载结果\n\n"},"Usage/Task/View.html":{"url":"Usage/Task/View.html","title":"查看任务","keywords":"","body":"查看任务\n任务列表\n点击侧边栏的任务导航至任务列表。可以看到最近的10个生成的任务。可以根据节点、爬虫来过滤任务。\n\n点击操作列的查看按钮，进入到该任务的任务详情。\n任务日志\n点击日志标签，可以查看任务日志。\n\n任务结果\n点击结果标签，可以查看任务结果。\n\n"},"Usage/Task/Action.html":{"url":"Usage/Task/Action.html","title":"操作任务","keywords":"","body":"操作任务\n停止任务\n当任务运行起来之后，我们因为某个原因可能需要终止任务，这时我们需要在Crawlab中停止该任务。\n导航至需要停止的任务的任务详情，点击停止按钮来终止任务。\n删除任务\n在任务列表中，点击操作列中的删除按钮，确认删除该任务。\n"},"Usage/Task/DownloadResults.html":{"url":"Usage/Task/DownloadResults.html","title":"下载结果","keywords":"","body":"下载结果\n结果储存在数据库中之后，我们有时候需要将其导出，这时可以在界面中进行导出操作。\n导航至任务详情，点击结果标签，点击下载CSV按钮，等待一会儿，结果就会以CSV的形式下载到本地。\n"},"Usage/Schedule/":{"url":"Usage/Schedule/","title":"定时任务","keywords":"","body":"定时任务\n定时任务是指定某个时刻，重复性地执行的任务，英文叫做Periodical Tasks，在Linux中也被称为Crontab。定时任务可以让任务可以被执行多次，而用户则不用手动的操作来执行任务。在生产环境中，这非常常见。定时任务对于对增量抓取或对数据实时性有要求的用户来说非常有用。\n在Crawlab中，定时任务是通过apscheduler来实现的。创建一个定时任务之后，会在名为mongo的jobstore中创建一个periodical job，apscheduler调度引擎将会不断的去数据库中匹配任务的执行时间，如果执行时间满足要求，则会在后台触发一次任务运行。\n定时任务列表会进行更新。每一次爬虫更新、删除、创建，以及定时任务的更新、删除、创建，都会触发定时任务列表的更新。\n创建定时任务\n导航至定时任务页面，可以看到定时任务的列表。\n点击添加定时任务，弹出创建定时任务的弹框。填写相应的内容，点击提交按钮创建定时任务。\n\n这里的Cron跟Linux中的crontab是一致的。如果对crontab不了解，可以参考这篇文章。\n修改定时任务\n导航至定时任务页面，点击操作列的修改按钮，弹出修改定时任务的弹框。填写相应的内容，点击提交按钮修改定时任务。\n删除定时任务\n导航至定时任务页面，点击操作列的删除按钮，确认删除该任务。\n"},"Usage/Site/":{"url":"Usage/Site/","title":"网站","keywords":"","body":"网站\n网站信息是帮助用户查看站长之家收录网站的信息的，包含Robots协议、首页响应等信息。\n\n"},"Architecture/":{"url":"Architecture/","title":"架构","keywords":"","body":"架构\nCrawlab的架构跟Celery非常相似，但是加入了包括前端、爬虫、Flower在内的额外模块，以支持爬虫管理的功能。架构图如下。\n\n节点 Node\n节点其实就是Celery中的worker。一个节点运行时会连接到一个任务队列（例如Redis）来接收和运行任务。所有爬虫需要在运行时被部署到节点上，用户在部署前需要定义节点的IP地址和端口。\n后台应用 Backend App\n这是一个Flask应用，提供了必要的API来支持常规操作，例如CRUD、爬虫部署以及任务运行。每一个节点需要启动Flask应用来支持爬虫部署。运行python app.py来启动应用。\n爬虫 Spider\n爬虫源代码或配置规则储存在App上，需要被部署到各个worker节点中。\n任务 Task\n任务被触发并被节点执行。用户可以在任务详情页面中看到任务到状态、日志和抓取结果。\n中间者 Broker\n中间者跟Celery中定义的一样，作为运行异步任务的队列。\n前端 Frontend\n前端其实就是一个基于Vue-Element-Admin的单页应用。其中重用了很多Element-UI的控件来支持相应的展示。\nFlower\n一个Celery的插件，用于监控Celery节点。\n"},"Examples/":{"url":"Examples/","title":"样例","keywords":"","body":"样例\n\n与Scrapy集成\n\n"},"Examples/ScrapyIntegration.html":{"url":"Examples/ScrapyIntegration.html","title":"与Scrapy集成","keywords":"","body":"与Scrapy集成\n以下是Crawlab跟Scrapy集成的例子，利用了Crawlab传过来的task_id和collection_name。\nimport os\nfrom pymongo import MongoClient\n\nMONGO_HOST = '192.168.99.100'\nMONGO_PORT = 27017\nMONGO_DB = 'crawlab_test'\n\n# scrapy example in the pipeline\nclass JuejinPipeline(object):\n mongo = MongoClient(host=MONGO_HOST, port=MONGO_PORT)\n db = mongo[MONGO_DB]\n col_name = os.environ.get('CRAWLAB_COLLECTION')\n if not col_name:\n col_name = 'test'\n col = db[col_name]\n\n def process_item(self, item, spider):\n item['task_id'] = os.environ.get('CRAWLAB_TASK_ID')\n self.col.save(item)\n return item\n\n"}}} \ No newline at end of file diff --git a/gitbook/img/crawlab-architecture.png b/gitbook/img/crawlab-architecture.png deleted file mode 100644 index fcac460f20acbb6e9918ba0cefee6c92b3a74779..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 47842 zcmd?RbyQXD_b<8t1w^_f1(cTVmQ+L#q(mA-Lb{Ze4grx61*AlfMy0zIL_PHaO4yh zV$^j8MRO8Lu%6NcydJ_x6nu?AM=8%ONR1zbpw3jirQA%I=**QuT17?+6RYR%)T986Gr5`Mn>BA)^jG394)+41%hDJu^gawA*zJG7~ z7SG1-f98`a<68rFPaDD@);w}vDYyeSRw!}&enAh*!9gcuShORV9}7X`!-s_lBU05I zWQhm}ls;SaB+uA34GvQDycKQ@Ct*Pnru}}P`_Mmt{Ct1gsA!J;V7m^d=gW(rRP;-w zW(w$+e0+Uf8I!vIbLlH8BxYs|!}CDJg>gIZDyNMxA{J5dNz*m7Fz)gAOgRu8+) zLjphI^y+%-j|u6-0$#o(MgoX2bqaLb?`uehWMo`*TOBEDi~a2SN4=`1CTYYkB0SGW z!fTgZ=47+gr~B_1pA6XhDZ5NmH@wdWu_daS}FKTtBQFH13m?r7@J5Gh+rmZcf 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