Re: [I] How to properly measure off-heap memory usage for Comet? [datafusion-comet]

["andygrove (via GitHub)" <gi...@apache.org>]

andygrove commented on issue #1894:
URL: https://github.com/apache/datafusion-comet/issues/1894#issuecomment-4178475112

   Here are a few approaches depending on what you're trying to measure:
   
   ### Measuring Comet's off-heap memory usage
   
   **Option 1: Tracing with jemalloc (recommended for overall native memory)**
   
   Build Comet with jemalloc enabled:
   ```
   make release COMET_FEATURES="jemalloc"
   ```
   
   Then enable tracing:
   ```
   spark.comet.tracing.enabled=true
   ```
   
   This generates a `comet-event-trace.json` file (Chrome Trace Event Format) that includes:
   - `jemalloc_allocated` — total native memory allocated by the executor process
   - `task_memory_comet_NNN` — off-heap memory allocated by Comet for query execution
   - `comet_shuffle_NNN` — off-heap memory allocated for columnar shuffle
   
   You can view this in `chrome://tracing` or [Perfetto](https://ui.perfetto.dev/).
   
   **Option 2: Debug memory logging**
   
   Set `spark.comet.debug.memory=true` to log all memory pool reservation grow/shrink operations. This gives fine-grained visibility into DataFusion memory pool usage.
   
   ### Profiling native threads
   
   For CPU profiling of native code, [cargo-flamegraph](https://github.com/flamegraph-rs/flamegraph) works well for benchmarks. For profiling within a Spark job, async-profiler (which you're already using) is the right tool — it can capture both JVM and native frames in the same flamegraph.
   
   For more details, see our contributor guides:
   - [Tracing](https://datafusion.apache.org/comet/contributor-guide/tracing.html)
   - [Profiling Native Code](https://datafusion.apache.org/comet/contributor-guide/profiling_native_code.html)
   - [Debugging](https://datafusion.apache.org/comet/contributor-guide/debugging.html)


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