How To Export Prometheus Data to CSV
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Introduction
Exporting data from Prometheus to CSV can streamline your data analysis processes. This guide will walk you through the steps required to export your data efficiently.
Understanding how to export Prometheus metrics to CSV is essential for deeper data insight. Using CSV files, you can manipulate and analyze your data with greater flexibility.
We will also explore how Sourcetable lets you analyze your exported data with AI in a simple to use spreadsheet.
Exporting Prometheus Data to CSV Format
- Prometheus allows querying of its data through a robust API. The API endpoint for querying is <code>http://prom_server:9090/api/v1/query</code>, where <code>query</code> is a parameter that takes the metric name as its value. Prometheus enables querying specific metrics or all metric names for comprehensive data analysis.
- By default, Prometheus outputs query results in JSON format, which may not always meet users' requirements. The JSON structure can be cumbersome for some data processing tasks, making it necessary to convert this data into a more user-friendly format like CSV.
- A custom Python script can convert Prometheus JSON output to CSV format. The script can query Prometheus, retrieve data for individual or multiple metrics, and then save the results in a CSV file. This method provides flexibility and can be adapted to suit various data export needs.
- To automate the data export process, the Python script can be set to run as a cron job. By configuring the cron job to run hourly, users can ensure timely updates and continuous data archiving. This automation is particularly useful for monitoring and maintaining consistent records of time-series data.
- The API allows for time-range queries using specific suffixes in the query parameter. Adding <code>[1h]</code> retrieves data from the last hour, and <code>[1m]</code> fetches data from the last minute. This feature enables precise data extraction based on the required timeframe.
- The Python script not only converts JSON to CSV but also saves the converted data into an archive file. This ensures the output is preserved for future reference, analysis, or auditing. The script can be easily modified to store data in a preferred file structure or naming convention.
- The Python script can be tailored to query all metric names or specified individual metrics, depending on the user's needs. This versatility ensures that users can obtain a complete dataset or focus on specific metrics for detailed analyses.
- Exporting Prometheus data to CSV format involves querying the Prometheus API, using a custom Python script to convert JSON outputs to CSV, and automating the process with cron jobs. This approach simplifies the data handling process, making it accessible and usable for various analytical tasks.
Querying Prometheus Data via API
Exporting Data in JSON Format
Using Python Script for Data Conversion
Scheduling with Cron Jobs
Retrieving Specific Time Ranges
Saving Results to CSV
Comprehensive Metric Queries
Conclusion
How to Export Your Data to CSV Format from Prometheus
Exporting data from Prometheus to CSV format can be achieved by querying the Prometheus API and converting the results from JSON to CSV using a Python script. This process allows for more manageable data storage and analysis.
Querying Prometheus Data via API
Prometheus offers an API for querying data. An example of querying CPU data is: http://prom_server:9090/api/v1/query?query=cpu. To get data for a specific time period, use the filters [1h] or [1m]. For instance, http://prom_server:9090/api/v1/query?query=cpu[1h] retrieves CPU data for the past hour.
Why JSON Format is Not Ideal
While the Prometheus API returns data in JSON format, this output is not always desirable for analysis and reporting. Converting JSON to CSV provides a more accessible and analyzable format.
Using a Python Script for CSV Conversion
A Python script can be used to query Prometheus metrics and convert the JSON output to CSV format. One can modify a script from Robust Perception to query all metric names and fetch individual metrics data.
Automating with Cron Jobs
To automate the data export process, run the Python script as a cron job. The cron job can be configured to run hourly, ensuring continuous and up-to-date data extraction. The script will save the data in an archive file.
Steps to Export Data to CSV
The steps to export Prometheus data to CSV include querying the Prometheus API for all metric names, querying each metric for the past hour or desired timeframe, and converting and saving the results to a CSV file. This can be efficiently achieved using a Python script and cron job.
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Use Cases for Prometheus
Monitoring and Instance Performance |
Prometheus is highly effective at gathering numeric metrics from constantly running services. It monitors critical system performance metrics such as memory utilization, CPU utilization, and the number of threads. This functionality ensures optimal resource management and system performance. |
Website Uptime and HTTP Requests |
Prometheus is widely used to monitor website uptime and track the number of HTTP requests for each page. This tracking is crucial for maintaining web performance and ensuring timely responses to downtime issues. |
Metrics and Alerting |
Prometheus excels in metrics collection, alerting, and incident response. With its support for powerful queries, precise alerting, and integration with incident management systems, it ensures quick identification and resolution of any issues. |
Cloud Infrastructure Monitoring |
Google Cloud’s Managed Service for Prometheus offers a fully managed, multi-cloud compatible monitoring solution. It collects metrics from Prometheus exporters and retains data for 24 months, facilitating extensive monitoring of Kubernetes, VMs, and serverless workloads on Cloud Run. |
Integration with Third-Party Services |
Prometheus integrates seamlessly with various third-party services such as Docker, HAProxy, StatsD, and JMX metrics. This integration extends the monitoring capabilities, providing a comprehensive solution for diverse environments. |
Data Visualization and Storage |
Prometheus supports multiple modes for data visualization including ad-hoc graphs and tables, as well as efficient storage solutions. It uses a custom format to store time-series data in memory and on local disk, ensuring effective and scalable data handling. |
Open-Source and Community-Driven |
Prometheus is an open-source project under the Cloud Native Computing Foundation. It is community-driven, and all components are available under the Apache 2 License on GitHub, promoting innovation and continuous improvement. |
Why Choose Sourcetable Over Prometheus
Looking for a more intuitive way to handle your data? Sourcetable offers a spreadsheet interface that collects data from multiple sources, enabling real-time queries and seamless data manipulation.
Unlike Prometheus, which specializes in metrics and monitoring, Sourcetable centralizes all your data in a user-friendly, spreadsheet-like format. This empowers users to easily access, query, and analyze their data without needing advanced technical skills.
Simplify your data management processes. Sourcetable integrates with various databases, providing real-time access to essential insights, all within a familiar spreadsheet interface.
Frequently Asked Questions
How can I query Prometheus data to export it to CSV format?
You can query Prometheus data via API and use a python script to convert the JSON output into CSV format.
What is the default output format when querying Prometheus, and how can it be converted to CSV?
The default output format for Prometheus queries is JSON. You can use a python script to convert this JSON data into CSV format.
Can the process of exporting Prometheus data to CSV be automated?
Yes, the python script used to query Prometheus and convert the data to CSV can be run as a cron job, which can be set to run at regular intervals, such as hourly.
What kind of queries can be executed using the python script for Prometheus data export?
The python script can query all metric names as well as individual metrics. It can also be modified to get data from specific time ranges, such as the last hour.
How can the exported CSV data be handled after the script execution?
The python script can save the queried data to a file and can also be modified to save the data in an archive file.
Conclusion
Exporting data from Prometheus to CSV is straightforward with the use of Prometheus HTTP API. By querying the data and writing the results to a CSV file, you can easily manipulate and analyze your metrics data.
Now that you have your data in CSV format, you can leverage more advanced tools for deeper analysis. Sign up for Sourcetable to analyze your exported CSV data with AI in a simple to use spreadsheet.
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Get insights into your CSV data
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