Prometheus or InfluxDB ? which one is better for storing Performance testing results

Last week, I embarked on a small project to store JMeter performance testing results in a time-series database on my local setup. I had two options to choose from: InfluxDB and Prometheus. Having previously worked with Prometheus to implement Node Exporter for infrastructure monitoring, I initially thought of using it for JMeter as well. However, after configuring Prometheus and the Prometheus JMeter listener to store data, I found the setup a bit tricky. Queries at the Grafana level seemed cumbersome.

In contrast, when I switched to InfluxDB, I found the setup far more intuitive, especially for storing load test results. Below, I’ll highlight the key differences between InfluxDB and Prometheus for storing JMeter performance testing results and why InfluxDB proved to be a better fit for my use case.

Choosing InfluxDB over Prometheus for storing JMeter results depends on several factors. Both InfluxDB and Prometheus are time-series databases, but they have different strengths, especially when dealing with use cases like storing and querying JMeter results.

Here are some reasons you might choose InfluxDB over Prometheus for storing JMeter results:

1. Flexibility in Data Schema:

• InfluxDB is a more general-purpose time-series database with a flexible schema. It allows you to store any type of time-series data, including JMeter test results with a variety of dimensions (e.g., HTTP request timings, response codes, throughput, etc.).
• Prometheus, on the other hand, is primarily designed for monitoring and alerting, and its schema is more rigid. It's based on metric names, labels, and values, which can make it harder to store complex JMeter results or non-standard metrics.

2. Long-Term Data Storage:

• InfluxDB is optimized for long-term storage and retrieval of time-series data. It offers built-in features like data retention policies, continuous queries, and downsampling, which make it easier to manage large volumes of historical JMeter results over time.
• Prometheus is typically used for short-term monitoring data and doesn’t handle long-term storage as well. For long-term retention, you would typically need to set up a remote storage solution (e.g., Thanos or Cortex), which adds complexity.

3. Handling High Cardinality Data:

• JMeter results can have high cardinality, especially if you have many different test cases, URLs, or metrics being collected. InfluxDB handles high cardinality quite well, especially with its flexible schema and indexing.
• Prometheus can struggle with very high cardinality (e.g., storing individual results for every unique combination of labels), which could be problematic if you're collecting results for many different tests or users in JMeter.

4. Complex Querying and Visualization:

• InfluxDB provides a powerful query language called InfluxQL (or Flux for advanced queries) that allows for complex queries, aggregation, and transformation of your data. This is helpful when analyzing JMeter test results, as you may want to perform complex aggregations or filter based on various tags (e.g., HTTP status codes, request times).
• Prometheus, while it has its own query language (PromQL), is more focused on monitoring and alerting and may not be as well-suited for complex analysis of non-metric data like JMeter results.

5. Integration with Grafana:

• Both Prometheus and InfluxDB integrate well with Grafana for visualization, but InfluxDB has more established integration for detailed time-series analysis, especially when working with custom data sources like JMeter results. The flexibility of InfluxDB’s schema also makes it easier to structure your data in a way that works well for creating rich, customizable dashboards in Grafana.

6. Ease of Use for Test Results:

• JMeter results can include a variety of metrics, such as response times, throughput, success rates, and errors, which can be stored as time-series data in InfluxDB. InfluxDB's simple measurement/tag/field structure allows you to model JMeter's diverse test metrics efficiently.
• With Prometheus, representing JMeter results may require more effort in terms of defining and organizing metrics and labels, which may not be as natural a fit for complex, multi-dimensional JMeter test results.

7. Retention and Cleanup:

• InfluxDB supports automatic data retention policies and continuous queries for cleaning up old data, downsampling, and summarizing over time, which can be useful when storing large volumes of JMeter results.
• In Prometheus, managing data retention can be more challenging, and you might need to configure external solutions or scripts to handle cleanup and aggregation of older results.

8. Use Case Fit:

• Prometheus is designed with real-time monitoring and alerting in mind, so it's great for monitoring system and application metrics. It shines in scenarios where you're constantly scraping metrics at high frequency (e.g., every 15 or 30 seconds).
• InfluxDB is better suited for storing and analyzing historical data (such as JMeter test results) over long periods, especially when you need to work with batch-like data or want to run detailed analyses after your tests are complete.

When would you still choose Prometheus for JMeter?

• If your JMeter tests are integrated into a continuous monitoring or CI/CD pipeline, where real-time performance monitoring and alerting are needed, Prometheus might be a better fit.
• If your monitoring needs extend beyond JMeter and include system-level monitoring (e.g., using Node Exporter or application metrics), then Prometheus would be the natural choice.

Conclusion:

If you're primarily interested in long-term storage, flexibility, and advanced querying of JMeter results, InfluxDB is likely the better choice. It's easier to store complex, high-cardinality test results, and you can easily manage large datasets over time. However, if you're focused on real-time monitoring, alerting, and integration with Prometheus-based systems, Prometheus might still be a good choice—though it will require more customization for storing JMeter results.

If you're specifically looking to analyze JMeter performance data post-test, InfluxDB's combination of flexible data storage and query capabilities can be much more useful for that purpose.