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https://github.com/4n4nd/prometheus-api-client-python

A python wrapper for the prometheus http api
https://github.com/4n4nd/prometheus-api-client-python

artificial-intelligence collects-metrics hacktoberfest metrics-data metrics-fetched metrics-processing prometheus prometheus-api prometheus-metrics series-data

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A python wrapper for the prometheus http api

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README 

        
# prometheus-api-client



[![PyPI version](https://badge.fury.io/py/prometheus-api-client.svg)](https://badge.fury.io/py/prometheus-api-client) [![PyPI download month](https://img.shields.io/pypi/dm/prometheus-api-client.svg)](https://pypi.python.org/pypi/prometheus-api-client/)



A Python wrapper for the Prometheus http api and some tools for metrics processing.



## Installation



To install the latest release:



`pip install prometheus-api-client`



To install directly from this branch:



`pip install https://github.com/4n4nd/prometheus-api-client-python/zipball/master`



## Links



- [Slack](https://join.slack.com/share/zt-kw3v8t1e-hbcVH7X7bXORiQuQtsNZ4A)

- [Google Chat](https://chat.google.com/room/AAAAzFPwq5s)

- [Documentation](https://prometheus-api-client-python.readthedocs.io/en/master/source/prometheus_api_client.html)



## Getting Started



### Usage

[Prometheus](https://prometheus.io/), a Cloud Native Computing Foundation project, is a systems and service monitoring system. It collects metrics (time series data) from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. The raw time series data obtained from a Prometheus host can sometimes be hard to interpret. To help better understand these metrics we have created a Python wrapper for the Prometheus http api for easier metrics processing and analysis.



The `prometheus-api-client` library consists of multiple modules which assist in connecting to a Prometheus host, fetching the required metrics and performing various aggregation operations on the time series data.



#### Connecting and Collecting Metrics from a Prometheus host

The `PrometheusConnect` module of the library can be used to connect to a Prometheus host. This module is essentially a class created for the collection of metrics from a Prometheus host. It stores the following connection parameters:



-   **url** - (str) url for the prometheus host

-   **headers** – (dict) A dictionary of http headers to be used to communicate with the host. Example: {“Authorization”: “bearer my_oauth_token_to_the_host”}

-   **disable_ssl** – (bool) If set to True, will disable ssl certificate verification for the http requests made to the prometheus host



```python

from prometheus_api_client import PrometheusConnect

prom = PrometheusConnect(url ="", disable_ssl=True)



# Get the list of all the metrics that the Prometheus host scrapes

prom.all_metrics()

```



You can also fetch the time series data for a specific metric using custom queries as follows:



```python

prom = PrometheusConnect()

my_label_config = {'cluster': 'my_cluster_id', 'label_2': 'label_2_value'}

prom.get_current_metric_value(metric_name='up', label_config=my_label_config)



# Here, we are fetching the values of a particular metric name

prom.custom_query(query="prometheus_http_requests_total")



# Now, lets try to fetch the `sum` of the metrics

prom.custom_query(query="sum(prometheus_http_requests_total)")

```



We can also use custom queries for fetching the metric data in a specific time interval. For example, let's try to fetch the past 2 days of data for a particular metric in chunks of 1 day:



```python

# Import the required datetime functions

from prometheus_api_client.utils import parse_datetime

from datetime import timedelta



start_time = parse_datetime("2d")

end_time = parse_datetime("now")

chunk_size = timedelta(days=1)



metric_data = prom.get_metric_range_data(

    "up{cluster='my_cluster_id'}",  # this is the metric name and label config

    start_time=start_time,

    end_time=end_time,

    chunk_size=chunk_size,

)

```



For more functions included in the `PrometheusConnect` module, refer to this [documentation.](https://prometheus-api-client-python.readthedocs.io/en/master/source/prometheus_api_client.html#module-prometheus_api_client.prometheus_connect)



#### Understanding the Metrics Data Fetched

The `MetricsList` module initializes a list of Metric objects for the metrics fetched from a Prometheus host as a result of a promql query.



```python

# Import the MetricsList and Metric modules

from prometheus_api_client import PrometheusConnect, MetricsList, Metric



prom = PrometheusConnect()

my_label_config = {'cluster': 'my_cluster_id', 'label_2': 'label_2_value'}

metric_data = prom.get_metric_range_data(metric_name='up', label_config=my_label_config)



metric_object_list = MetricsList(metric_data) # metric_object_list will be initialized as

                                              # a list of Metric objects for all the

                                              # metrics downloaded using get_metric query



# We can see what each of the metric objects look like

for item in metric_object_list:

    print(item.metric_name, item.label_config, "\n")

```



Each of the items in the `metric_object_list` are initialized as a `Metric` class object. Let's look at one of the metrics from the `metric_object_list` to learn more about the `Metric` class:



```python

my_metric_object = metric_object_list[1] # one of the metrics from the list

print(my_metric_object)

```



For more functions included in the `MetricsList` and `Metrics` module, refer to this [documentation.](https://prometheus-api-client-python.readthedocs.io/en/master/source/prometheus_api_client.html#module-prometheus_api_client.metric)



#### Additional Metric Functions

The `Metric` class also supports multiple functions such as adding, equating and plotting various metric objects.



##### Adding Metrics

You can add add two metric objects for the same time-series as follows:



```python

metric_1 = Metric(metric_data_1)

metric_2 = Metric(metric_data_2)

metric_12 = metric_1 + metric_2 # will add the data in ``metric_2`` to ``metric_1``

                                # so if any other parameters are set in ``metric_1``

                                # will also be set in ``metric_12``

                                # (like ``oldest_data_datetime``)

```



##### Equating Metrics

Overloading operator =, to check whether two metrics are the same (are the same time-series regardless of their data)

```python

metric_1 = Metric(metric_data_1)

metric_2 = Metric(metric_data_2)

print(metric_1 == metric_2) # will print True if they belong to the same time-series

```



##### Plotting Metric Objects

Plot a very simple line graph for the metric time series:



```python

from prometheus_api_client import PrometheusConnect, MetricsList, Metric



prom = PrometheusConnect()

my_label_config = {'cluster': 'my_cluster_id', 'label_2': 'label_2_value'}

metric_data = prom.get_metric_range_data(metric_name='up', label_config=my_label_config)



metric_object_list = MetricsList(metric_data)

my_metric_object = metric_object_list[1] # one of the metrics from the list

my_metric_object.plot()

```



#### Getting Metrics Data as pandas DataFrames

To perform data analysis and manipulation, it is often helpful to have the data represented using a [pandas DataFrame](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe). There are two modules in this library that can be used to process the raw metrics fetched into a DataFrame.



The `MetricSnapshotDataFrame` module converts "current metric value" data to a DataFrame representation, and the `MetricRangeDataFrame` converts "metric range values" data to a DataFrame representation. Example usage of these classes can be seen below:



```python

import datetime as dt

from prometheus_api_client import PrometheusConnect,  MetricSnapshotDataFrame, MetricRangeDataFrame



prom = PrometheusConnect()

my_label_config = {'cluster': 'my_cluster_id', 'label_2': 'label_2_value'}



# metric current values

metric_data = prom.get_current_metric_value(

    metric_name='up',

    label_config=my_label_config,

)

metric_df = MetricSnapshotDataFrame(metric_data)

metric_df.head()

""" Output:

+-------------------------+-----------------+------------+-------+

| __name__ | cluster      | label_2         | timestamp  | value |

+==========+==============+=================+============+=======+

| up       | cluster_id_0 | label_2_value_2 | 1577836800 | 0     |

+-------------------------+-----------------+------------+-------+

| up       | cluster_id_1 | label_2_value_3 | 1577836800 | 1     |

+-------------------------+-----------------+------------+-------+

"""



# metric values for a range of timestamps

metric_data = prom.get_metric_range_data(

    metric_name='up',

    label_config=my_label_config,

    start_time=(dt.datetime.now() - dt.timedelta(minutes=30)),

    end_time=dt.datetime.now(),

)

metric_df = MetricRangeDataFrame(metric_data)

metric_df.head()

""" Output:

+------------+------------+-----------------+--------------------+-------+

|            |  __name__  | cluster         | label_2            | value |

+-------------------------+-----------------+--------------------+-------+

| timestamp  |            |                 |                    |       |

+============+============+=================+====================+=======+

| 1577836800 | up         | cluster_id_0    | label_2_value_2    | 0     |

+-------------------------+-----------------+--------------------+-------+

| 1577836801 | up         | cluster_id_1    | label_2_value_3    | 1     |

+-------------------------+-----------------+------------=-------+-------+

"""

```



For more functions included in the `prometheus-api-client` library, please refer to this [documentation.](https://prometheus-api-client-python.readthedocs.io/en/master/source/prometheus_api_client.html)



## Running tests



`PROM_URL="http://demo.robustperception.io:9090/" pytest`



## Code Styling and Linting



Prometheus Api client uses [pre-commit](https://pre-commit.com) framework to maintain the code linting and python code styling.


The AICoE-CI would run the pre-commit check on each pull request.


We encourage our contributors to follow the same pattern, while contributing to the code.


we would like to keep the same standard and maintain the code for better quality and readability.



The pre-commit configuration file is present in the repository `.pre-commit-config.yaml`


It contains the different code styling and linting guide which we use for the application.



we just need to run [pre-commit](https://pre-commit.com/#install) before raising a Pull Request.


Following command can be used to run the pre-commit:


`pre-commit run --all-files`



If pre-commit is not installed in your system, it can be install with : `pip install pre-commit`