Pythond

PythonD 是定时触发用户自定义 Python 采集脚本的一整套方案。

配置¶

进入 DataKit 安装目录下的 conf.d/pythond 目录，复制 pythond.conf.sample 并命名为 pythond.conf。示例如下：

[[inputs.pythond]]
  # Python input name
  name = 'some-python-inputs'  # required

  # System environments to run Python
  #envs = ['LD_LIBRARY_PATH=/path/to/lib:$LD_LIBRARY_PATH',]

  # Python path(recomment abstract Python path)
  cmd = "python3" # required. python3 is recommended.

  # Python scripts relative path
  dirs = []

Python 环境¶

目前处于 alpha 阶段，只兼容 Python 3+。已测试的版本：

3.10.1

需要安装以下依赖库：

requests

安装方法如下：

# python3
python3 -m pip install requests

上述的安装需要安装 pip，如果你没有，可以参考以下方法（源自这里）：

# Linux/MacOS
python -m ensurepip --upgrade

# Windows
py -m ensurepip --upgrade

编写用户自定义脚本¶

在 datakit/python.d 目录下创建以 "Python 包名" 命名的目录，然后在该目录下创建 Python 脚本(.py)。

以包名 Demo 为例，其路径结构如下。其中 demo.py 为 Python 脚本，Python 脚本的文件名可以自定义：

datakit
   └── python.d
       ├── Demo
       │   ├── demo.py

Python 脚本需要用户继承 DataKitFramework 类，然后对 run 方法进行改写。

DataKitFramework 类的源代码文件路径是 datakit_framework.py 在 datakit/python.d/core/datakit_framework.py。

Python 脚本源码参考示例

#encoding: utf-8

from datakit_framework import DataKitFramework

class Demo(DataKitFramework):
    name = 'Demo'
    interval = 10 # triggered interval seconds.

    # if your datakit ip is 127.0.0.1 and port is 9529, you won't need use this,
    # just comment it.
    # def __init__(self, **kwargs):
    #     super().__init__(ip = '127.0.0.1', port = 9529)

    # General report example.
    def run(self):
        print("Demo")
        data = [
                {
                    "measurement": "abc",
                    "tags": {
                    "t1": "b",
                    "t2": "d"
                    },
                    "fields": {
                    "f1": 123,
                    "f2": 3.4,
                    "f3": "strval"
                    },
                    # "time": 1624550216 # you don't need this
                },

                {
                    "measurement": "def",
                    "tags": {
                    "t1": "b",
                    "t2": "d"
                    },
                    "fields": {
                    "f1": 123,
                    "f2": 3.4,
                    "f3": "strval"
                    },
                    # "time": 1624550216 # you don't need this
                }
            ]

        in_data = {
            'M':data, # 'M' for metrics, 'L' for logging, 'R' for rum, 'O' for object, 'CO' for custom object, 'E' for event.
            'input': "datakitpy"
        }

        return self.report(in_data) # you must call self.report here

    # # KeyEvent report example.
    # def run(self):
    #     print("Demo")

    #     tags = {"tag1": "val1", "tag2": "val2"}
    #     date_range = 10
    #     status = 'info'
    #     event_id = 'event_id'
    #     title = 'title'
    #     message = 'message'
    #     kwargs = {"custom_key1":"custom_value1", "custom_key2": "custom_value2", "custom_key3": "custom_value3"}

    #     # Feed df_source=user event.
    #     user_id="user_id"
    #     return self.feed_user_event(
    #         user_id,
    #         tags, date_range, status, event_id, title, message, **kwargs
    #         )

    #     # Feed df_source=monitor event.
    #     dimension_tags='{"host":"web01"}' # dimension_tags must be the String(JSON format).
    #     return self.feed_monitor_event(
    #         dimension_tags,
    #         tags, date_range, status, event_id, title, message, **kwargs
    #         )

    #     # Feed df_source=system event.
    #     return self.feed_system_event(
    #         tags, date_range, status, event_id, title, message, **kwargs
    #         )

    # # metrics, logging, object example.
    # def run(self):
    #     print("Demo")

    #     measurement = "mydata"
    #     tags = {"tag1": "val1", "tag2": "val2"}
    #     fields = {"custom_field1": "val1","custom_field2": 1000}
    #     kwargs = {"custom_key1":"custom_value1", "custom_key2": "custom_value2", "custom_key3": "custom_value3"}

    #     # Feed metrics example.
    #     return self.feed_metric(
    #         measurement=measurement,
    #         tags=tags,
    #         fields=fields,
    #         **kwargs
    #         )

    #     # Feed logging example.
    #     message = "This is the message for testing"
    #     return self.feed_logging(
    #         source=measurement,
    #         tags=tags,
    #         message=message,
    #         **kwargs
    #         )

    #     # Feed object example.
    #     name = "name"
    #     return self.feed_object(
    #         cls=measurement,
    #         name=name,
    #         tags=tags,
    #         fields=fields,
    #         **kwargs
    #         )

Python SDK API 定义(详情参见 datakit_framework.py)：

上报 metrics 数据：feed_metric(self, input=None, measurement=None, tags=None, fields=None, time=None, **kwargs);
上报 logging 数据：feed_logging(self, input=None, source=None, tags=None, message=None, time=None, **kwargs);
上报 object 数据：feed_object(self, input=None, cls=None, name=None, tags=None, fields=None, time=None, **kwargs); （cls 就是 class。因为 class 是 Python 的关键字，所以里把 class 缩写为 cls）

编写 Pythond 上报 event 事件¶

可以使用以下三个内置函数来上报 event 事件：

上报 df_source = user 的事件：feed_user_event(self, df_user_id=None, tags=None, df_date_range=10, df_status=None, df_event_id=None, df_title=None, df_message=None, **kwargs)
上报 df_source = monitor 的事件：feed_monitor_event(self, df_dimension_tags=None, tags=None, df_date_range=10, df_status=None, df_event_id=None, df_title=None, df_message=None, **kwargs)
上报 df_source = system 的事件：feed_system_event(self, tags=None, df_date_range=10, df_status=None, df_event_id=None, df_title=None, df_message=None, **kwargs)

通用 event 字段说明：

字段名	类型	是否必须	说明
df_date_range	Integer	必须	时间范围。单位 s
df_source	String	必须	数据来源。取值 `system` , `monitor` , `user`
df_status	Enum	必须	状态。取值 `ok` , `info` , `warning` , `error` , `critical` , `nodata`
df_event_id	String	必须	event ID
df_title	String	必须	标题
df_message	String		详细描述
{其他字段}	`kwargs`, 例如 `k1=5, k2=6`		其他额外字段

当 df_source = monitor 时：

表示由观测云检测功能产生的事件，额外存在以下字段：

额外字段名	类型	是否必须	说明
df_dimension_tags	String(JSON format)	必须	检测纬度标签，如 `{"host":"web01"}`

当 df_source = user 时：

表示由用户直接创建的事件，额外存在以下字段：

额外字段名	类型	是否必须	说明
df_user_id	String	必须	用户 ID

当 df_source = system 时：

表示为系统生成的事件，不存在额外字段。

使用示例：

#encoding: utf-8

from datakit_framework import DataKitFramework

class Demo(DataKitFramework):
    name = 'Demo'
    interval = 10 # triggered interval seconds.

    # if your datakit ip is 127.0.0.1 and port is 9529, you won't need use this,
    # just comment it.
    # def __init__(self, **kwargs):
    #     super().__init__(ip = '127.0.0.1', port = 9529)

    # KeyEvent report example.
    def run(self):
        print("Demo")

        tags = {"tag1": "val1", "tag2": "val2"}
        date_range = 10
        status = 'info'
        event_id = 'event_id'
        title = 'title'
        message = 'message'
        kwargs = {"custom_key1":"custom_value1", "custom_key2": "custom_value2", "custom_key3": "custom_value3"}

        # Feed df_source=user event.
        user_id="user_id"
        return self.feed_user_event(
            df_user_id=user_id,
            tags=tags, df_date_range=date_range, df_status=status, df_event_id=event_id, df_title=title, df_message=message, **kwargs
            )

        # Feed df_source=monitor event.
        dimension_tags='{"host":"web01"}' # dimension_tags must be the String(JSON format).
        return self.feed_monitor_event(
            df_dimension_tags=dimension_tags,
            tags=tags, df_date_range=date_range, df_status=status, df_event_id=event_id, df_title=title, df_message=message, **kwargs
            )

        # Feed df_source=system event.
        return self.feed_system_event(
            tags=tags, df_date_range=date_range, df_status=status, df_event_id=event_id, df_title=title, df_message=message, **kwargs
            )

Git 支持¶

支持使用 git repo，一旦开启 git repo 功能，则 conf 里面的 args 里面填写的路径是相对于 gitrepos 的路径。比如下面这种情况，args 就填写 mytest：

├── datakit
└── gitrepos
    └── myconf
        ├── conf.d
        │   └── pythond.conf
        └── python.d
            └── mytest
                └── mytest.py

完整示例¶

第一步：写一个类，继承 DataKitFramework：

from datakit_framework import DataKitFramework

class MyTest(DataKitFramework):
    name = 'MyTest'
    interval = 10 # triggered interval seconds.

    # if your datakit ip is 127.0.0.1 and port is 9529, you won't need use this,
    # just comment it.
    # def __init__(self, **kwargs):
    #     super().__init__(ip = '127.0.0.1', port = 9529)

    def run(self):
        print("MyTest")
        data = [
                {
                    "measurement": "abc",
                    "tags": {
                      "t1": "b",
                      "t2": "d"
                    },
                    "fields": {
                      "f1": 123,
                      "f2": 3.4,
                      "f3": "strval"
                    },
                    # "time": 1624550216 # you don't need this
                },

                {
                    "measurement": "def",
                    "tags": {
                      "t1": "b",
                      "t2": "d"
                    },
                    "fields": {
                      "f1": 123,
                      "f2": 3.4,
                      "f3": "strval"
                    },
                    # "time": 1624550216 # you don't need this
                }
            ]

        in_data = {
            'M':data,
            'input': "datakitpy"
        }

        return self.report(in_data) # you must call self.report here

第二步：我们这里不开启 git repo 功能。将 test.py 放到 python.d 的 mytest 文件夹下：

└── python.d
    ├── mytest
    │   ├── test.py

第三步：配置 pythond.conf:

[[inputs.pythond]]

  # Python 采集器名称
  name = 'some-python-inputs'  # required

  # 运行 Python 采集器所需的环境变量
  #envs = ['LD_LIBRARY_PATH=/path/to/lib:$LD_LIBRARY_PATH',]

  # Python 采集器可执行程序路径(尽可能写绝对路径)
  cmd = "python3" # required. python3 is recommended.

  # 用户脚本的相对路径(填写文件夹，填好后该文件夹下一级目录的模块和 py 文件都将得到应用)
  dirs = ["mytest"]

第四步：重启 DataKit:

sudo datakit service -R

FAQ¶

如何排查错误¶

如果结果不及预期，可以查看以下日志文件：

~/_datakit_pythond_cli.log
_datakit_pythond_framework_[pythond name]_.log

错误提示 "[module] not found"¶

首先检测模块名的大小写是否一致；
如果 1 没有问题，则检测 python 脚本中引入的模块是否全部都有安装；