prometrix 0.2.5

A Python Prometheus client for all Prometheus instances.

Prometrix - Unified Prometheus Client

Overview

This Python package provides a unified Prometheus client that can be used to connect to and query various types of Prometheus instances. The package is based on the prometheus-api-client package , which serves as the foundation for our extended functionality.

The prometrix package enhances the prometheus-api-client by adding vendor-specific authentication methods and other features to handle authorization and signatures for all supported clients. This ensures a secure and seamless connection to the various types of Prometheus instances.

1. Coralogix
2. GKE (Google Kubernetes Engine)
3. Azure
4. EKS (Amazon Elastic Kubernetes Service)
5. Thanos
6. Victoria Metrics

The main function, get_custom_prometheus_connect, allows you to create a custom Prometheus client based on the provided configuration. The configurations for special Prometheus versions are defined through specific classes, each extending the base PrometheusConfig class.

Installation

You can install the package using pip:

pip install prometrix

⚠️ Note: For Python 3.8 support, you must use version 0.2.3 or below of prometrix.
From 0.2.4 onward, prometrix requires Python ≥ 3.9.

Usage

Importing the package

from prometrix import get_custom_prometheus_connect
from prometrix.config import (
    PrometheusConfig,
    AWSPrometheusConfig,
    CoralogixPrometheusConfig,
    VictoriaMetricsPrometheusConfig,
    AzurePrometheusConfig,
    PrometheusApis,
)

Creating a Custom Prometheus Client

To create a custom Prometheus client, you need to pass the appropriate configuration to the get_custom_prometheus_connect function. The function returns an instance of the CustomPrometheusConnect class that represents the client.

# Create a custom Prometheus client for Coralogix Prometheus
coralogix_config = CoralogixPrometheusConfig(
    url="https://coralogix-prometheus.example.com",
    prometheus_token="YOUR_CORALOGIX_PROMETHEUS_TOKEN",
    additional_labels={"job": "coralogix-prometheus"},
)
coralogix_client = get_custom_prometheus_connect(coralogix_config)

# Create a custom Prometheus client for GKE Prometheus
gke_config = PrometheusConfig(
    url="https://gke-prometheus.example.com",
    disable_ssl=False,
    headers={"Authorization": "Bearer YOUR_GKE_PROMETHEUS_TOKEN"},
    additional_labels={"job": "gke-prometheus"},
)
gke_client = get_custom_prometheus_connect(gke_config)

# Create a custom Prometheus client for Azure Prometheus
azure_config = AzurePrometheusConfig(
    url="https://azure-prometheus.example.com",
    disable_ssl=False,
    headers={"Authorization": "Bearer YOUR_AZURE_PROMETHEUS_TOKEN"},
    azure_resource="YOUR_AZURE_RESOURCE",
    azure_metadata_endpoint="https://azure-metadata.example.com",
    azure_token_endpoint="https://azure-token.example.com",
    azure_client_id="YOUR_AZURE_CLIENT_ID",
    azure_tenant_id="YOUR_AZURE_TENANT_ID",
    azure_client_secret="YOUR_AZURE_CLIENT_SECRET",
    additional_labels={"job": "azure-prometheus"},
)
azure_client = get_custom_prometheus_connect(azure_config)

Similar configuration and creation can be done for EKS, Thanos, and Victoria Metrics Prometheus.

NOTE: You need to replace the placeholder values (e.g., YOUR_CORALOGIX_PROMETHEUS_TOKEN) with your actual credentials and endpoints.

Supported APIs

The prometrix package extends the prometheus-api-client class PrometheusConnect with the following additional functionality:

get_prometheus_flags(self) -> Optional[Dict]

This function allows you to receive the configured flags from Prometheus. It returns a dictionary containing the flags and their respective values set in the Prometheus instance.

check_prometheus_connection(self, params: dict = None)

The check_prometheus_connection function enables you to check the connection status with the Prometheus instance. You can pass an optional dictionary of parameters to customize the connection check. This function returns true if it is able to connect.

safe_custom_query_range

The safe_custom_query_range function retrieves time-series data from Prometheus over a specified range. It returns the queried data in JSON format or raises an exception if the request fails.

Differences between custom_query_range and safe_custom_query_range:

• custom_query_range is a feature of the prometheus_api_client library, utilized internally by Prometrix.
• safe_custom_query_range returns the entire data dictionary from the Prometheus query response, whereas custom_query_range only returns the result section.

safe_custom_query

The safe_custom_query function executes a single-point Prometheus query and returns the result in JSON format. It throws an exception in the event of an error.

Differences between custom_query and safe_custom_query:

• custom_query is part of the prometheus_api_client library, used internally by Prometrix.
• safe_custom_query returns the complete data dictionary of the Prometheus query response, in contrast to custom_query, which only returns the result section.

Contributing

If you'd like to contribute to this package, please follow the guidelines specified in the CONTRIBUTING.md file in the repository.

Releasing

To release a new version, bump the version number in pyproject.toml and run:

poetry publish --build --username=<username> --password=<password>

We're planning to automate this with GitHub actions but it hasn't been fully setup or tested yet.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

This package was inspired by the need to have a flexible and generic Prometheus client that can be easily extended to connect with different Prometheus types. Special thanks to the Prometheus team and the open-source community for their contributions.