amogh-jahagirdar commented on code in PR #15006:
URL: https://github.com/apache/iceberg/pull/15006#discussion_r2680332803
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core/src/main/java/org/apache/iceberg/MergingSnapshotProducer.java:
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@@ -1060,19 +1086,119 @@ private List<ManifestFile> newDeleteFilesAsManifests()
{
}
if (cachedNewDeleteManifests.isEmpty()) {
+ // Found duplicates, merge them and update newDeleteFilesBySpec to
remove duplicates and add
+ // the new merged one
+ if (!duplicateDVsForDataFile.isEmpty()) {
+ Map<String, DeleteFile> mergedDVs = mergeDuplicateDVs();
+ for (Map.Entry<String, DeleteFile> mergedDV : mergedDVs.entrySet()) {
+ String referencedFile = mergedDV.getKey();
+ DeleteFile newDV = mergedDV.getValue();
+ DeleteFileSet duplicateDVs =
duplicateDVsForDataFile.get(referencedFile);
+ DeleteFileSet allDeleteFilesForSpec =
newDeleteFilesBySpec.get(newDV.specId());
+ allDeleteFilesForSpec.removeAll(duplicateDVs);
+ allDeleteFilesForSpec.add(newDV);
+ }
+ }
+
newDeleteFilesBySpec.forEach(
(specId, deleteFiles) -> {
PartitionSpec spec = ops().current().spec(specId);
+ // Update summaries for all added delete files including eq.
deletes for this partition
+ // spec
+ deleteFiles.forEach(file -> addedFilesSummary.addedFile(spec,
file));
List<ManifestFile> newDeleteManifests =
writeDeleteManifests(deleteFiles, spec);
cachedNewDeleteManifests.addAll(newDeleteManifests);
});
this.hasNewDeleteFiles = false;
+ this.duplicateDVsForDataFile.clear();
}
return cachedNewDeleteManifests;
}
+ // Find duplicate DVs for a given partition spec, and return a Pair of the
new DVs and the DVs
+ // that were merged
+ private Map<String, DeleteFile> mergeDuplicateDVs() {
+ Map<String, DeleteFile> mergedDVs = Maps.newConcurrentMap();
+ Tasks.foreach(duplicateDVsForDataFile.entrySet())
+ .executeWith(ThreadPools.getDeleteWorkerPool())
+ .stopOnFailure()
+ .throwFailureWhenFinished()
+ .run(
+ dvsToMergeForDataFile -> {
+ String referencedLocation = dvsToMergeForDataFile.getKey();
+ mergedDVs.put(
+ referencedLocation,
+ mergeAndWriteDV(referencedLocation,
dvsToMergeForDataFile.getValue()));
+ });
+
+ return mergedDVs;
+ }
+
+ private DeleteFile mergeAndWriteDV(String referencedDataFile, DeleteFileSet
dvs) {
+ Iterator<DeleteFile> dvIterator = dvs.iterator();
+ DeleteFile firstDV = dvIterator.next();
+ PositionDeleteIndex positionDeleteIndex =
+ Deletes.readDV(firstDV, ops().io(), ops().encryption());
+ PartitionSpec spec = spec(firstDV.specId());
+ while (dvIterator.hasNext()) {
+ DeleteFile dv = dvIterator.next();
+ Preconditions.checkArgument(
+ Objects.equals(dv.dataSequenceNumber(),
firstDV.dataSequenceNumber()),
+ "Cannot merge duplicate added DVs when data sequence numbers are
different,"
+ + "expected all to be added with sequence %s, but got %s",
+ firstDV.dataSequenceNumber(),
+ dv.dataSequenceNumber());
+ Preconditions.checkArgument(
+ dv.specId() == firstDV.specId(),
+ "Cannot merge duplicate added DVs when partition specs are
different,"
+ + "expected all to be added with spec %s, but got %s",
+ firstDV.specId(),
+ dv.specId());
+ Preconditions.checkArgument(
+ Objects.equals(dv.partition(), firstDV.partition()),
+ "Cannot merge duplicate added DVs when partition tuples are
different");
+ positionDeleteIndex.merge(Deletes.readDV(dv, ops().io(),
ops().encryption()));
+ }
+
+ return writeDV(
+ referencedDataFile,
+ positionDeleteIndex,
+ spec,
+ firstDV.partition(),
+ firstDV.dataSequenceNumber());
+ }
+
+ private DeleteFile writeDV(
Review Comment:
The worst case from a storage I/O perspective is where there's an
unclustered delete and many data files which are split, and DVs are produced
for some random set of positions in every data file. But in that case there's
almost certainly going to be a distributed engine at play, and in that case, I
think we could do an optimization in the spark integration before handing off
DVs to the commit path (basically a distributed fixup would be required in that
case).
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