huaxingao commented on code in PR #6622:
URL: https://github.com/apache/iceberg/pull/6622#discussion_r1096627129
##########
spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkScanBuilder.java:
##########
@@ -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) {
+ try (ManifestReader<DataFile> reader = ManifestFiles.read(manifest,
table.io())) {
+ for (DataFile dataFile : reader) {
+ aggregateEvaluator.update(dataFile.copy());
+ }
+ } catch (IOException e) {
+ LOG.info("Can't push down aggregates: " + e.getMessage());
+ return false;
+ }
+ }
+
+ Object[] res = aggregateEvaluator.result();
+ applyDataTypeConversionIfNecessary(res);
+
+ List<Object> valuesInSparkInternalRow = java.util.Arrays.asList(res);
+ this.pushedAggregateRows = new InternalRow[1];
+ pushedAggregateRows[0] =
+
InternalRow.fromSeq(JavaConverters.asScalaBuffer(valuesInSparkInternalRow).toSeq());
+ pushedAggregateSchema =
+ SparkSchemaUtil.convert(new
Schema(aggregateEvaluator.resultType().fields()));
+ return true;
+ }
+
+ private boolean pushDownAggregate(Aggregation aggregation) {
+ if (!(table instanceof BaseTable)) {
+ return false;
+ }
+
+ if (!readConf.aggregatePushDown()) {
+ return false;
+ }
+
+ Snapshot snapshot = getSnapshot();
+ if (snapshot == null) {
+ return false;
+ } else {
+ Map<String, String> map = snapshot.summary();
+ // if there are row-level deletes in current snapshot, the statistics
+ // maybe changed, so disable push down aggregate.
+ if (Integer.parseInt(map.getOrDefault("total-position-deletes", "0")) > 0
+ || Integer.parseInt(map.getOrDefault("total-equality-deletes", "0"))
> 0) {
+ LOG.info("Cannot push down aggregates when row level deletes exist.)");
Review Comment:
I have changed the code to check the deletes in the tasks and abort the push
down if deletes are present.
I also agree it may be better to introduce another setting to get an
approximate number if there are deletes. Probably we can do this in a follow up
PR.
##########
spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkScanBuilder.java:
##########
@@ -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) {
+ try (ManifestReader<DataFile> reader = ManifestFiles.read(manifest,
table.io())) {
+ for (DataFile dataFile : reader) {
+ aggregateEvaluator.update(dataFile.copy());
+ }
+ } catch (IOException e) {
+ LOG.info("Can't push down aggregates: " + e.getMessage());
+ return false;
+ }
+ }
+
+ Object[] res = aggregateEvaluator.result();
+ applyDataTypeConversionIfNecessary(res);
+
+ List<Object> valuesInSparkInternalRow = java.util.Arrays.asList(res);
+ this.pushedAggregateRows = new InternalRow[1];
+ pushedAggregateRows[0] =
+
InternalRow.fromSeq(JavaConverters.asScalaBuffer(valuesInSparkInternalRow).toSeq());
+ pushedAggregateSchema =
+ SparkSchemaUtil.convert(new
Schema(aggregateEvaluator.resultType().fields()));
+ return true;
+ }
+
+ private boolean pushDownAggregate(Aggregation aggregation) {
+ if (!(table instanceof BaseTable)) {
+ return false;
+ }
+
+ if (!readConf.aggregatePushDown()) {
+ return false;
+ }
+
+ Snapshot snapshot = getSnapshot();
+ if (snapshot == null) {
+ return false;
+ } else {
+ Map<String, String> map = snapshot.summary();
+ // if there are row-level deletes in current snapshot, the statistics
+ // maybe changed, so disable push down aggregate.
+ if (Integer.parseInt(map.getOrDefault("total-position-deletes", "0")) > 0
+ || Integer.parseInt(map.getOrDefault("total-equality-deletes", "0"))
> 0) {
+ LOG.info("Cannot push down aggregates when row level deletes exist.)");
+ return false;
+ }
+ }
+
+ // If group by expression is the same as the partition, the statistics
information can still
+ // be used to calculate min/max/count, will enable aggregate push down in
next phase.
+ // TODO: enable aggregate push down for partition col group by expression
+ if (aggregation.groupByExpressions().length > 0) {
+ LOG.info("Group by aggregation push down is not supported yet.");
Review Comment:
Fixed. thanks
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