danny0405 commented on code in PR #7826:
URL: https://github.com/apache/hudi/pull/7826#discussion_r1108271487
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hudi-client/hudi-client-common/src/main/java/org/apache/hudi/client/BaseHoodieTableServiceClient.java:
##########
@@ -707,20 +709,33 @@ protected List<String>
getInstantsToRollback(HoodieTableMetaClient metaClient, H
}
}).collect(Collectors.toList());
} else if (cleaningPolicy.isLazy()) {
- return inflightInstantsStream.filter(instant -> {
- try {
- return heartbeatClient.isHeartbeatExpired(instant.getTimestamp());
- } catch (IOException io) {
- throw new HoodieException("Failed to check heartbeat for instant " +
instant, io);
- }
- }).map(HoodieInstant::getTimestamp).collect(Collectors.toList());
+ return getInstantsToRollbackForLazyCleanPolicy(metaClient,
inflightInstantsStream);
} else if (cleaningPolicy.isNever()) {
return Collections.emptyList();
} else {
throw new IllegalArgumentException("Invalid Failed Writes Cleaning
Policy " + config.getFailedWritesCleanPolicy());
}
}
+ @VisibleForTesting
+ public List<String>
getInstantsToRollbackForLazyCleanPolicy(HoodieTableMetaClient metaClient,
+
Stream<HoodieInstant> inflightInstantsStream) {
+ // Get expired instants, must store them into list before double-checking
+ List<String> expiredInstants = inflightInstantsStream.filter(instant -> {
+ try {
+ // An instant transformed from inflight to completed have no heartbeat
file and will be detected as expired instant here
+ return heartbeatClient.isHeartbeatExpired(instant.getTimestamp());
+ } catch (IOException io) {
+ throw new HoodieException("Failed to check heartbeat for instant " +
instant, io);
+ }
+ }).map(HoodieInstant::getTimestamp).collect(Collectors.toList());
+
+ // Double check whether the heartbeat-expired instant is an inflight
instant
+ metaClient.reloadActiveTimeline();
Review Comment:
> Yes，we use a separate spark job to run table services and the instant in
completed by the flink writer
But from the code, the compaction and clustering instants are excluded, can
you elaborate a little more, what kind of table service the Spark offline job
takes over?
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