mbutrovich commented on code in PR #4733:
URL: https://github.com/apache/datafusion-comet/pull/4733#discussion_r3477744687
##########
spark/src/main/scala/org/apache/spark/sql/comet/CometMetricNode.scala:
##########
@@ -259,12 +273,28 @@ object CometMetricNode {
SQLMetrics.createMetric(sc, "Rows filtered out by parquet page index"),
"page_index_rows_matched" ->
SQLMetrics.createMetric(sc, "Rows passed through the parquet page
index"),
+ "page_index_pages_pruned" ->
+ SQLMetrics.createMetric(sc, "Pages filtered out by parquet page
index"),
+ "page_index_pages_matched" ->
+ SQLMetrics.createMetric(sc, "Pages passed through the parquet page
index"),
"page_index_eval_time" ->
SQLMetrics.createNanoTimingMetric(sc, "Time spent evaluating parquet
page index filters"),
"metadata_load_time" ->
SQLMetrics.createNanoTimingMetric(
sc,
- "Time spent reading and parsing metadata from the footer"))
+ "Total time spent reading and parsing metadata from the footer"),
+ "predicate_cache_inner_records" ->
+ SQLMetrics.createMetric(
+ sc,
+ "Predicate cache: rows physically read and decoded from the Parquet
file (cache misses)"),
+ "predicate_cache_records" ->
+ SQLMetrics.createMetric(
+ sc,
+ "Predicate cache: records read from the cache (reused after
predicate evaluation)"),
+ "scan_efficiency_ratio_total" ->
+ SQLMetrics.createSizeMetric(
+ sc,
+ "Total file size, denominator of scan_efficiency_ratio =
bytes_scanned / total_file_size"))
Review Comment:
This is an awkward one anyway. It's a made up metric from DataFusion (it's a
ratio) so the best I could come up with was explaining how to compute the math
again.
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