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.