ericm-db commented on code in PR #50195:
URL: https://github.com/apache/spark/pull/50195#discussion_r1989829264
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/state/RocksDB.scala:
##########
@@ -1465,6 +1470,7 @@ class RocksDB(
log"with uniqueId: ${MDC(LogKeys.UUID, snapshot.uniqueId)} " +
log"time taken: ${MDC(LogKeys.TIME_UNITS, uploadTime)} ms. " +
log"Current lineage: ${MDC(LogKeys.LINEAGE, lineageManager)}")
+ lastUploadedSnapshotVersion.set(snapshot.version)
Review Comment:
Hm, do we need to do something like
`lastUploadedSnapshotVersion.set(max(lastUploadedSnapshot, snapshot.version))`?
Is there a case where this might be necessary?
cc @HeartSaVioR
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala:
##########
@@ -203,20 +203,40 @@ trait StateStoreWriter
def operatorStateMetadataVersion: Int = 1
- override lazy val metrics = statefulOperatorCustomMetrics ++ Map(
- "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output
rows"),
- "numRowsDroppedByWatermark" -> SQLMetrics.createMetric(sparkContext,
- "number of rows which are dropped by watermark"),
- "numTotalStateRows" -> SQLMetrics.createMetric(sparkContext, "number of
total state rows"),
- "numUpdatedStateRows" -> SQLMetrics.createMetric(sparkContext, "number of
updated state rows"),
- "allUpdatesTimeMs" -> SQLMetrics.createTimingMetric(sparkContext, "time to
update"),
- "numRemovedStateRows" -> SQLMetrics.createMetric(sparkContext, "number of
removed state rows"),
- "allRemovalsTimeMs" -> SQLMetrics.createTimingMetric(sparkContext, "time
to remove"),
- "commitTimeMs" -> SQLMetrics.createTimingMetric(sparkContext, "time to
commit changes"),
- "stateMemory" -> SQLMetrics.createSizeMetric(sparkContext, "memory used by
state"),
- "numStateStoreInstances" -> SQLMetrics.createMetric(sparkContext,
- "number of state store instances")
- ) ++ stateStoreCustomMetrics ++ pythonMetrics
+ override lazy val metrics = {
+ // Lazy initialize instance metrics, but do not include these with regular
metrics
+ instanceMetrics
+ statefulOperatorCustomMetrics ++ Map(
+ "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of
output rows"),
+ "numRowsDroppedByWatermark" -> SQLMetrics
+ .createMetric(sparkContext, "number of rows which are dropped by
watermark"),
+ "numTotalStateRows" -> SQLMetrics.createMetric(sparkContext, "number of
total state rows"),
+ "numUpdatedStateRows" -> SQLMetrics
+ .createMetric(sparkContext, "number of updated state rows"),
+ "allUpdatesTimeMs" -> SQLMetrics.createTimingMetric(sparkContext, "time
to update"),
+ "numRemovedStateRows" -> SQLMetrics
+ .createMetric(sparkContext, "number of removed state rows"),
+ "allRemovalsTimeMs" -> SQLMetrics.createTimingMetric(sparkContext, "time
to remove"),
+ "commitTimeMs" -> SQLMetrics.createTimingMetric(sparkContext, "time to
commit changes"),
+ "stateMemory" -> SQLMetrics.createSizeMetric(sparkContext, "memory used
by state"),
+ "numStateStoreInstances" -> SQLMetrics
+ .createMetric(sparkContext, "number of state store instances")
+ ) ++ stateStoreCustomMetrics ++ pythonMetrics
+ }
+
+ lazy val instanceMetrics = stateStoreInstanceMetrics
Review Comment:
Can you leave a comment about what this is
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