Comet Metrics

Spark SQL Metrics

Set spark.comet.metrics.detailed=true to see all available Comet metrics.

CometScanExec

Metric	Description
scan time	Total time to scan a Parquet file. This is not comparable to the same metric in Spark because Comet's scan metric is more accurate. Although both Comet and Spark measure the time in nanoseconds, Spark rounds this time to the nearest millisecond per batch and Comet does not.

Exchange

Comet adds some additional metrics:

Metric	Description
native shuffle time	Total time in native code excluding any child operators.
repartition time	Time to repartition batches.
memory pool time	Time interacting with memory pool.
encoding and compression time	Time to encode batches in IPC format and compress using ZSTD.

Native Metrics

Setting spark.comet.explain.native.enabled=true will cause native plans to be logged in each executor. Metrics are logged for each native plan (and there is one plan per task, so this is very verbose).

Here is a guide to some of the native metrics.

ScanExec

Metric	Description
elapsed_compute	Total time spent in this operator, fetching batches from a JVM iterator.
jvm_fetch_time	Time spent in the JVM fetching input batches to be read by this ScanExec instance.
arrow_ffi_time	Time spent using Arrow FFI to create Arrow batches from the memory addresses returned from the JVM.

ShuffleWriterExec

Metric	Description
elapsed_compute	Total time excluding any child operators.
repart_time	Time to repartition batches.
ipc_time	Time to encode batches in IPC format and compress using ZSTD.
mempool_time	Time interacting with memory pool.
write_time	Time spent writing bytes to disk.