Question 1

How do I use pg_flame with Docker?

Accepted Answer

Pull the Docker image with 'docker pull mgartner/pg_flame', then pipe your JSON query plan to it, e.g., 'cat plan.json | docker run -i pg_flame > flamegraph.html'. The README provides a specific Docker usage example for isolated execution.

Question 2

Can pg_flame work with MySQL or other databases?

Accepted Answer

No, pg_flame is specifically designed for PostgreSQL EXPLAIN ANALYZE output in JSON format. It won't work with other databases unless their output is manually converted to a compatible JSON structure, which isn't supported natively.

Question 3

pg_flame vs pgBadger: which is better for query analysis?

Accepted Answer

pg_flame excels at visualizing individual query plans with interactive flamegraphs for time distribution, while pgBadger is a log analyzer for aggregated performance trends over time. Use pg_flame for deep-dive query debugging and pgBadger for overall database health monitoring.

Question 4

How to interpret the flamegraph from pg_flame?

Accepted Answer

The flamegraph shows execution time hierarchy where wider bars indicate more time spent. Hover over nodes to see details like node type and time. The demo in the README offers an interactive example to practice reading and identifying bottlenecks.

Question 5

Does pg_flame support real-time monitoring of queries?

Accepted Answer

No, pg_flame is a post-analysis tool that requires pre-captured EXPLAIN ANALYZE JSON output. For real-time monitoring, you'd need to integrate it with continuous logging and scripting, but it lacks built-in live features.

Question 6

How can I save the flamegraph as an image for reports?

Accepted Answer

pg_flame outputs HTML, so you can use browser developer tools or headless browsers like Puppeteer to capture screenshots. The tool doesn't provide direct image export, requiring additional steps for static visualizations.

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