What are the best practices for using Prometheus with Python metrics?

What are the best practices for using Prometheus with Python metrics?

When using Prometheus with Python, it's essential to follow best practices like using the right metric types, labeling effectively, and ensuring efficient data collection.

Can you explain the different metric types in Prometheus?

Sure! Prometheus supports several metric types: Counter, Gauge, Histogram, and Summary. Each serves a different purpose in monitoring.

How should I structure my labels for metrics?

Labels should be used to differentiate dimensions of your metrics, but avoid high cardinality. Stick to meaningful and necessary labels.

What are some common pitfalls to avoid?

Common pitfalls include overusing labels, not aggregating metrics properly, and neglecting to monitor the performance of your metrics collection.

Can you recommend any libraries for integrating Prometheus with Python?

Yes! The prometheus_client library is widely used for integrating Prometheus with Python applications.