aokolnychyi commented on code in PR #3457:
URL: https://github.com/apache/iceberg/pull/3457#discussion_r938126336
##########
spark/v3.2/spark/src/main/java/org/apache/iceberg/spark/actions/ExpireSnapshotsSparkAction.java:
##########
@@ -222,13 +228,34 @@ private ExpireSnapshots.Result doExecute() {
}
}
+ /**
+ * Builds a dataset of reachable files from given table metadata
+ *
+ * @param metadata table metadata
+ * @return a dataset of files: schema(file_path, file_type)
+ */
private Dataset<Row> buildValidFileDF(TableMetadata metadata) {
Table staticTable = newStaticTable(metadata, table.io());
return buildValidContentFileWithTypeDF(staticTable)
.union(withFileType(buildManifestFileDF(staticTable), MANIFEST))
.union(withFileType(buildManifestListDF(staticTable), MANIFEST_LIST));
}
+ /**
+ * Builds a dataset of reachable files from given table metadata, with a
snapshot filter
+ *
+ * @param metadata table metadata
+ * @param snapshotsToExclude files reachable by this snapshot will be
filtered out
+ * @return a dataset of files: schema(file_path, file_type)
+ */
+ private Dataset<Row> buildFilteredValidDataDF(
+ TableMetadata metadata, Set<Long> snapshotsToExclude) {
+ Table staticTable = newStaticTable(metadata, table.io());
+ return buildValidContentFileWithTypeDF(staticTable, snapshotsToExclude)
Review Comment:
I was thinking about something like this:
- Cache/persist a dataset of unique manifests in still valid snapshots
- Cache/persist a dataset of unique manifests in expired snapshots
- Find expired manifests that are no longer referenced by still valid
snapshots by anti-joining manifest datasets
- Read expired manifests to build a dataset of content files referenced by
expired manifests
- Read still valid manifests to build a dataset of still valid content files
- Find expired content files by anti-joining content file datasets
- Find expired manifest lists
- Union datasets for different expired results
If we do it this way, we read `all_manifests` table only once and also skip
still live manifests when looking for expired content files. The extra shuffle
is a valid concern but I think we can avoid it. Right now, we are doing a
round-robin partitioning when building unique manifests. Instead, we can assign
a deterministic hash partitioning to both datasets to avoid a shuffle during
anti-joins. Spark won't shuffle datasets if they are partitioned in the same
way. We are shuffling that dataset anyway as we deduplicate manifests before
opening them up.
@karuppayya is also working on some benchmarks for actions, which would be
really handy to evaluate the impact here.
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