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title: "python.d.plugin"
custom_edit_url: https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/README.md
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# python.d.plugin

`python.d.plugin` is a Netdata external plugin. It is an **orchestrator** for data collection modules written in `python`.

1.  It runs as an independent process `ps fax` shows it
2.  It is started and stopped automatically by Netdata
3.  It communicates with Netdata via a unidirectional pipe (sending data to the `netdata` daemon)
4.  Supports any number of data collection **modules**
5.  Allows each **module** to have one or more data collection **jobs**
6.  Each **job** is collecting one or more metrics from a single data source

## Disclaimer

Every module should be compatible with python2 and python3.
All third party libraries should be installed system-wide or in `python_modules` directory.
Module configurations are written in YAML and **pyYAML is required**.

Every configuration file must have one of two formats:

-   Configuration for only one job:

```yaml
update_every : 2 # update frequency
priority     : 20000 # where it is shown on dashboard

other_var1   : bla  # variables passed to module
other_var2   : alb
```

-   Configuration for many jobs (ex. mysql):

```yaml
# module defaults:
update_every : 2
priority     : 20000

local:  # job name
  update_every : 5 # job update frequency
  other_var1   : some_val # module specific variable

other_job:
  priority     : 5 # job position on dashboard
  other_var2   : val # module specific variable
```

`update_every` and `priority` are always optional.

## How to debug a python module

```
# become user netdata
sudo su -s /bin/bash netdata
```

Depending on where Netdata was installed, execute one of the following commands to trace the execution of a python module:

```
# execute the plugin in debug mode, for a specific module
/opt/netdata/usr/libexec/netdata/plugins.d/python.d.plugin <module> debug trace
/usr/libexec/netdata/plugins.d/python.d.plugin <module> debug trace
```

Where `[module]` is the directory name under <https://github.com/netdata/netdata/tree/master/collectors/python.d.plugin> 

**Note**: If you would like execute a collector in debug mode while it is still running by Netdata, you can pass the `nolock` CLI option to the above commands.

## How to write a new module

Writing new python module is simple. You just need to remember to include 5 major things:

-   **ORDER** global list
-   **CHART** global dictionary
-   **Service** class
-   **\_get_data** method
-   all code needs to be compatible with Python 2 (**≥ 2.7**) *and* 3 (**≥ 3.1**)

If you plan to submit the module in a PR, make sure and go through the [PR checklist for new modules](#pull-request-checklist-for-python-plugins) beforehand to make sure you have updated all the files you need to. 

For a quick start, you can look at the [example
plugin](https://raw.githubusercontent.com/netdata/netdata/master/collectors/python.d.plugin/example/example.chart.py).

**Note**: If you are working 'locally' on a new collector and would like to run it in an already installed and running Netdata (as opposed to having to install Netdata from source again with your new changes) to can copy over the relevant file to where Netdata expects it and then either `sudo service netdata restart` to have it be picked up and used by Netdata or you can just run the updated collector in debug mode by following a process like below (this assumes you have [installed Netdata from a GitHub fork](https://learn.netdata.cloud/docs/agent/packaging/installer/methods/manual) you have made to do your development on).

```bash
# clone your fork (done once at the start but shown here for clarity)
#git clone --branch my-example-collector https://github.com/mygithubusername/netdata.git --depth=100
# go into your netdata source folder
cd netdata
# git pull your latest changes (assuming you built from a fork you are using to develop on)
git pull
# instead of running the installer we can just copy over the updated collector files
#sudo ./netdata-installer.sh --dont-wait
# copy over the file you have updated locally (pretending we are working on the 'example' collector)
sudo cp collectors/python.d.plugin/example/example.chart.py /usr/libexec/netdata/python.d/
# become user netdata
sudo su -s /bin/bash netdata
# run your updated collector in debug mode to see if it works without having to reinstall netdata
/usr/libexec/netdata/plugins.d/python.d.plugin example debug trace nolock
```

### Global variables `ORDER` and `CHART`

`ORDER` list should contain the order of chart ids. Example:

```py
ORDER = ['first_chart', 'second_chart', 'third_chart']
```

`CHART` dictionary is a little bit trickier. It should contain the chart definition in following format:

```py
CHART = {
    id: {
        'options': [name, title, units, family, context, charttype],
        'lines': [
            [unique_dimension_name, name, algorithm, multiplier, divisor]
        ]}
```

All names are better explained in the [External Plugins](../) section.
Parameters like `priority` and `update_every` are handled by `python.d.plugin`.

### `Service` class

Every module needs to implement its own `Service` class. This class should inherit from one of the framework classes:

-   `SimpleService`
-   `UrlService`
-   `SocketService`
-   `LogService`
-   `ExecutableService`

Also it needs to invoke the parent class constructor in a specific way as well as assign global variables to class variables. 

Simple example:

```py
from base import UrlService
class Service(UrlService):
    def __init__(self, configuration=None, name=None):
        UrlService.__init__(self, configuration=configuration, name=name)
        self.order = ORDER
        self.definitions = CHARTS
```

### `_get_data` collector/parser

This method should grab raw data from `_get_raw_data`, parse it, and return a dictionary where keys are unique dimension names or `None` if no data is collected.

Example:

```py
def _get_data(self):
    try:
        raw = self._get_raw_data().split(" ")
        return {'active': int(raw[2])}
    except (ValueError, AttributeError):
        return None
```

# More about framework classes

Every framework class has some user-configurable variables which are specific to this particular class. Those variables should have default values initialized in the child class constructor.

If module needs some additional user-configurable variable, it can be accessed from the `self.configuration` list and assigned in constructor or custom `check` method. Example:

```py
def __init__(self, configuration=None, name=None):
    UrlService.__init__(self, configuration=configuration, name=name)
    try:
        self.baseurl = str(self.configuration['baseurl'])
    except (KeyError, TypeError):
        self.baseurl = "http://localhost:5001"
```

Classes implement `_get_raw_data` which should be used to grab raw data. This method usually returns a list of strings.

### `SimpleService`

_This is last resort class, if a new module cannot be written by using other framework class this one can be used._

_Example: `ceph`, `sensors`_

It is the lowest-level class which implements most of module logic, like:

-   threading
-   handling run times
-   chart formatting
-   logging
-   chart creation and updating

### `LogService`

_Examples: `apache_cache`, `nginx_log`_

_Variable from config file_: `log_path`.

Object created from this class reads new lines from file specified in `log_path` variable. It will check if file exists and is readable. Also `_get_raw_data` returns list of strings where each string is one line from file specified in `log_path`.

### `ExecutableService`

_Examples: `exim`, `postfix`_

_Variable from config file_: `command`.

This allows to execute a shell command in a secure way. It will check for invalid characters in `command` variable and won't proceed if there is one of:

-   '&'
-   '|'
-   ';'
-   '>'
-   '\<'

For additional security it uses python `subprocess.Popen` (without `shell=True` option) to execute command. Command can be specified with absolute or relative name. When using relative name, it will try to find `command` in `PATH` environment variable as well as in `/sbin` and `/usr/sbin`.

`_get_raw_data` returns list of decoded lines returned by `command`.

### UrlService

_Examples: `apache`, `nginx`, `tomcat`_

_Multiple Endpoints (urls) Examples: [`rabbitmq`](/collectors/python.d.plugin/rabbitmq/README.md) (simpler) ,
[`elasticsearch`](/collectors/python.d.plugin/elasticsearch/README.md) (threaded)_


_Variables from config file_: `url`, `user`, `pass`.

If data is grabbed by accessing service via HTTP protocol, this class can be used. It can handle HTTP Basic Auth when specified with `user` and `pass` credentials.

Please note that the config file can use different variables according to the specification of each module.

`_get_raw_data` returns list of utf-8 decoded strings (lines).

### SocketService

_Examples: `dovecot`, `redis`_

_Variables from config file_: `unix_socket`, `host`, `port`, `request`.

Object will try execute `request` using either `unix_socket` or TCP/IP socket with combination of `host` and `port`. This can access unix sockets with SOCK_STREAM or SOCK_DGRAM protocols and TCP/IP sockets in version 4 and 6 with SOCK_STREAM setting.

Sockets are accessed in non-blocking mode with 15 second timeout.

After every execution of `_get_raw_data` socket is closed, to prevent this module needs to set `_keep_alive` variable to `True` and implement custom `_check_raw_data` method.

`_check_raw_data` should take raw data and return `True` if all data is received otherwise it should return `False`. Also it should do it in fast and efficient way.

## Pull Request Checklist for Python Plugins

This is a generic checklist for submitting a new Python plugin for Netdata.  It is by no means comprehensive.

At minimum, to be buildable and testable, the PR needs to include:

-   The module itself, following proper naming conventions: `collectors/python.d.plugin/<module_dir>/<module_name>.chart.py`
-   A README.md file for the plugin under `collectors/python.d.plugin/<module_dir>`. 
-   The configuration file for the module: `collectors/python.d.plugin/<module_dir>/<module_name>.conf`. Python config files are in YAML format, and should include comments describing what options are present. The instructions are also needed in the configuration section of the README.md 
-   A basic configuration for the plugin in the appropriate global config file: `collectors/python.d.plugin/python.d.conf`, which is also in YAML format.  Either add a line that reads `# <module_name>: yes` if the module is to be enabled by default, or one that reads `<module_name>: no` if it is to be disabled by default.
-   A makefile for the plugin at `collectors/python.d.plugin/<module_dir>/Makefile.inc`.  Check an existing plugin for what this should look like.
-   A line in `collectors/python.d.plugin/Makefile.am` including the above-mentioned makefile. Place it with the other plugin includes (please keep the includes sorted alphabetically).
-   Optionally, chart information in `web/gui/dashboard_info.js`.  This generally involves specifying a name and icon for the section, and may include descriptions for the section or individual charts.
-   Optionally, some default alarm configurations for your collector in `health/health.d/<module_name>.conf` and a line adding `<module_name>.conf` in `health/Makefile.am`.

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