rdblue commented on code in PR #6622:
URL: https://github.com/apache/iceberg/pull/6622#discussion_r1094803125
##########
spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkScanBuilder.java:
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@@ -158,6 +182,141 @@ public Filter[] pushedFilters() {
return pushedFilters;
}
+ @Override
+ public boolean pushAggregation(Aggregation aggregation) {
+ if (!pushDownAggregate(aggregation)) {
+ return false;
+ }
+
+ AggregateEvaluator aggregateEvaluator;
+ try {
+ List<Expression> aggregates =
+ Arrays.stream(aggregation.aggregateExpressions())
+ .map(agg -> SparkAggregates.convert(agg))
+ .collect(Collectors.toList());
+ aggregateEvaluator = AggregateEvaluator.create(schema, aggregates);
+ } catch (Exception e) {
+ LOG.info("Can't push down aggregates: " + e.getMessage());
+ return false;
+ }
+
+ if
(!metricsModeSupportsAggregatePushDown(aggregateEvaluator.aggregates())) {
+ LOG.info("The MetricsMode doesn't support aggregate push down.");
+ return false;
+ }
+
+ List<ManifestFile> manifests = getSnapshot().allManifests(table.io());
+
+ for (ManifestFile manifest : manifests) {
Review Comment:
This is going to read all table metadata, which could be really large.
Instead, I think this should use scan planning to get the data files. That will
allow this to apply filters and skip a lot of data, and it would also
parallelize manifest scanning using a `ParallelIterator`.
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