HeartSaVioR commented on code in PR #50559:
URL: https://github.com/apache/spark/pull/50559#discussion_r2043090343
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sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/state/RocksDBCheckpointFailureInjectionSuite.scala:
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@@ -414,6 +414,84 @@ class RocksDBCheckpointFailureInjectionSuite extends
StreamTest
}
}
+ case class FailureConf2(logType: String, checkpointFormatVersion: String) {
+ override def toString: String = {
+ s"logType = $logType, checkpointFormatVersion = $checkpointFormatVersion"
+ }
+ }
+
+ // tests to validate the behavior after failures when writing to the commit
and offset logs
+ Seq(
+ FailureConf2("commits", checkpointFormatVersion = "1"),
+ FailureConf2("commits", checkpointFormatVersion = "2"),
+ FailureConf2("offsets", checkpointFormatVersion = "1"),
+ FailureConf2("offsets", checkpointFormatVersion = "2")).foreach {
failureConf =>
+ test(s"Progress log fails to write $failureConf") {
+ val hadoopConf = new Configuration()
+ hadoopConf.set(STREAMING_CHECKPOINT_FILE_MANAGER_CLASS.parent.key,
fileManagerClassName)
+ val rocksdbChangelogCheckpointingConfKey =
+ RocksDBConf.ROCKSDB_SQL_CONF_NAME_PREFIX +
".changelogCheckpointing.enabled"
+
+ withTempDirAllowFailureInjection { (checkpointDir, injectionState) =>
+ withSQLConf(
+ rocksdbChangelogCheckpointingConfKey -> "true",
+ SQLConf.STATE_STORE_MIN_DELTAS_FOR_SNAPSHOT.key -> "2") {
+ val inputData = MemoryStream[Int]
+ val aggregated =
+ inputData.toDF()
+ .groupBy($"value")
+ .agg(count("*"))
+ .as[(Int, Long)]
+
+ // This should cause the second batch to fail
+ injectionState.createAtomicDelayCloseRegex =
Seq(s".*/${failureConf.logType}/1")
+
+ val additionalConfs = Map(
+ rocksdbChangelogCheckpointingConfKey -> "true",
+ SQLConf.STATE_STORE_CHECKPOINT_FORMAT_VERSION.key ->
+ failureConf.checkpointFormatVersion,
+ STREAMING_CHECKPOINT_FILE_MANAGER_CLASS.parent.key ->
fileManagerClassName)
+
+ testStream(aggregated, Update)(
+ StartStream(
+ checkpointLocation = checkpointDir.getAbsolutePath,
+ additionalConfs = additionalConfs),
+ AddData(inputData, 3),
+ CheckLastBatch((3, 1)),
+ AddData(inputData, 3, 2),
+ // We should categorize this error.
+ // TODO after the error is categorized, we should check error class
+ ExpectFailure[IOException] { _ => () }
+ )
+
+ injectionState.createAtomicDelayCloseRegex = Seq.empty
+
+ inputData.addData(3, 1)
+
+ // The query will restart successfully and start at the checkpoint
after Batch 1
+ testStream(aggregated, Update)(
+ StartStream(
+ checkpointLocation = checkpointDir.getAbsolutePath,
+ additionalConfs = additionalConfs),
+ AddData(inputData, 4),
+ if (failureConf.logType == "commits") {
+ // If the failure is in the commit log, data is already
committed. The batch will
Review Comment:
If that's hard to achieve, I'm OK with more direct code comment about "sink"
status - e.g. (3, 2) and (2, 1) were already emitted to sink as batch 1 even
with failure on commit log. The write against sink for retried batch 1 should
have been ignored (memory sink), but we restart the query and the state in the
sink has reset, hence the write against sink for batch 1 in new query has
effect.
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