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Spark: Support aggregate pushdown for identity partition column GROUP BY #16176

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fde0869
Spark: Support aggregate pushdown for identity partition column GROUP BY
hemanthboyina Apr 30, 2026
751c3b1
fix complexity and checkstyle
hemanthboyina Apr 30, 2026
8e38932
fix spec evolution changes
hemanthboyina May 3, 2026
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227 changes: 222 additions & 5 deletions spark/v4.1/spark/src/main/java/org/apache/iceberg/spark/source/SparkScanBuilder.java
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@

import java.io.IOException;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import org.apache.iceberg.BaseMetadataTable;
import org.apache.iceberg.BaseTable;
Expand All @@ -28,6 +29,8 @@
import org.apache.iceberg.IncrementalAppendScan;
import org.apache.iceberg.MetricsConfig;
import org.apache.iceberg.MetricsModes;
import org.apache.iceberg.PartitionField;
import org.apache.iceberg.PartitionSpec;
import org.apache.iceberg.ScanTask;
import org.apache.iceberg.Schema;
import org.apache.iceberg.Snapshot;
Expand All @@ -41,12 +44,15 @@
import org.apache.iceberg.io.CloseableIterable;
import org.apache.iceberg.relocated.com.google.common.base.Preconditions;
import org.apache.iceberg.relocated.com.google.common.collect.Lists;
import org.apache.iceberg.relocated.com.google.common.collect.Maps;
import org.apache.iceberg.spark.Spark3Util;
import org.apache.iceberg.spark.SparkAggregates;
import org.apache.iceberg.spark.SparkSchemaUtil;
import org.apache.iceberg.spark.SparkTableUtil;
import org.apache.iceberg.spark.SparkUtil;
import org.apache.iceberg.spark.TimeTravel;
import org.apache.iceberg.types.Type;
import org.apache.iceberg.types.Types;
import org.apache.iceberg.util.Pair;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.catalyst.InternalRow;
Expand Down Expand Up @@ -157,6 +163,10 @@ public boolean pushAggregation(Aggregation aggregation) {
return false;
}

if (aggregation.groupByExpressions().length > 0) {
return pushGroupByAggregation(aggregation, expressions);
}

try (CloseableIterable<FileScanTask> fileScanTasks = planFilesWithStats()) {
for (FileScanTask task : fileScanTasks) {
if (!task.deletes().isEmpty()) {
Expand Down Expand Up @@ -186,6 +196,213 @@ public boolean pushAggregation(Aggregation aggregation) {
return true;
}

/**
* Push down aggregation with GROUP BY on identity partition columns. When all GROUP BY columns
* are identity partition fields, aggregates can be computed from file metadata grouped by
* partition values, avoiding reading any data files.
*/
private boolean pushGroupByAggregation(
Aggregation aggregation, List<BoundAggregate<?, ?>> boundAggregates) {
PartitionSpec spec = table().spec();
Schema tableSchema = table().schema();

List<Integer> groupByPositions = Lists.newArrayList();
List<Types.NestedField> groupByFields = Lists.newArrayList();
if (!resolveGroupByPartitions(
aggregation, spec, tableSchema, groupByPositions, groupByFields)) {
return false;
}

Map<List<Object>, AggregateEvaluator> evaluatorsByPartition =
groupFilesByPartition(spec, groupByPositions, boundAggregates);
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@singhpk234 singhpk234 May 2, 2026

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i am not confident this is correct, plus we are just checking the recent partitioning, a table could comprise of lot of different partition spec files which evolved across snapshots

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@hemanthboyina hemanthboyina May 2, 2026

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Thanks for the review @singhpk234 You raised a valid point. the current implementation only considers the current partition spec and bails out for files from different specs. Will look into handling spec evolution properly and update the PR.

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@hemanthboyina hemanthboyina May 3, 2026

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handled partition spec evolution changes, can you please review

if (evaluatorsByPartition == null) {
return false;
}

localScan = buildGroupedLocalScan(groupByFields, evaluatorsByPartition);
return localScan != null;
}

private boolean resolveGroupByPartitions(
Aggregation aggregation,
PartitionSpec spec,
Schema tableSchema,
List<Integer> groupByPositions,
List<Types.NestedField> groupByFields) {
for (org.apache.spark.sql.connector.expressions.Expression groupByExpr :
aggregation.groupByExpressions()) {
String colName =
SparkUtil.toColumnName(
(org.apache.spark.sql.connector.expressions.NamedReference) groupByExpr);
Types.NestedField sourceField = tableSchema.findField(colName);
if (sourceField == null) {
LOG.info("Skipping grouped aggregate pushdown: cannot find field {}", colName);
return false;
}

int pos = findIdentityPartitionPosition(spec, sourceField.fieldId());
if (pos < 0) {
LOG.info(
"Skipping grouped aggregate pushdown: {} is not an identity partition field", colName);
return false;
}

groupByPositions.add(pos);
groupByFields.add(sourceField);
}

return true;
}

private Map<List<Object>, AggregateEvaluator> groupFilesByPartition(
PartitionSpec spec,
List<Integer> groupByPositions,
List<BoundAggregate<?, ?>> boundAggregates) {
Map<List<Object>, AggregateEvaluator> evaluatorsByPartition = Maps.newLinkedHashMap();

try (CloseableIterable<FileScanTask> fileScanTasks = planFilesWithStats()) {
for (FileScanTask task : fileScanTasks) {
if (!task.deletes().isEmpty()) {
LOG.info("Skipping grouped aggregate pushdown: detected row level deletes");
return null;
}

if (task.file().specId() != spec.specId()) {
LOG.info(
"Skipping grouped aggregate pushdown: file from different spec {}",
task.file().specId());
return null;
}

StructLike partition = task.file().partition();
List<Object> key = Lists.newArrayListWithCapacity(groupByPositions.size());
for (int pos : groupByPositions) {
key.add(partition.get(pos, Object.class));
}

evaluatorsByPartition
.computeIfAbsent(key, k -> AggregateEvaluator.create(boundAggregates))
.update(task.file());
}
} catch (IOException e) {
LOG.info("Skipping grouped aggregate pushdown: ", e);
return null;
}

if (evaluatorsByPartition.isEmpty()) {
return null;
}

for (AggregateEvaluator evaluator : evaluatorsByPartition.values()) {
if (!evaluator.allAggregatorsValid()) {
return null;
}
}

return evaluatorsByPartition;
}

private SparkLocalScan buildGroupedLocalScan(
List<Types.NestedField> groupByFields,
Map<List<Object>, AggregateEvaluator> evaluatorsByPartition) {
AggregateEvaluator firstEvaluator = evaluatorsByPartition.values().iterator().next();
List<Types.NestedField> resultFields = Lists.newArrayList();
int fieldId = 0;

for (Types.NestedField field : groupByFields) {
resultFields.add(Types.NestedField.optional(fieldId++, field.name(), field.type()));
}

for (Types.NestedField field : firstEvaluator.resultType().fields()) {
resultFields.add(Types.NestedField.optional(fieldId++, field.name(), field.type()));
}

Types.StructType resultType = Types.StructType.of(resultFields);
List<InternalRow> resultRows = Lists.newArrayList();

for (Map.Entry<List<Object>, AggregateEvaluator> entry : evaluatorsByPartition.entrySet()) {
List<Object> partitionValues = entry.getKey();
StructLike aggResult = entry.getValue().result();

Object[] combined = new Object[resultFields.size()];
for (int i = 0; i < partitionValues.size(); i++) {
combined[i] = partitionValues.get(i);
}

for (int i = 0; i < aggResult.size(); i++) {
combined[partitionValues.size() + i] = aggResult.get(i, Object.class);
}

resultRows.add(new StructInternalRow(resultType).setStruct(new ArrayStructLike(combined)));
}

StructType pushedSchema = SparkSchemaUtil.convert(new Schema(resultFields));
return new SparkLocalScan(
table(), pushedSchema, resultRows.toArray(new InternalRow[0]), filters());
}

private int findIdentityPartitionPosition(PartitionSpec spec, int sourceFieldId) {
List<PartitionField> fields = spec.fields();
for (int i = 0; i < fields.size(); i++) {
PartitionField field = fields.get(i);
if (field.sourceId() == sourceFieldId && field.transform().isIdentity()) {
return i;
}
}

return -1;
}

private boolean allGroupByAreIdentityPartitionFields(Aggregation aggregation) {
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@anuragmantri anuragmantri May 8, 2026

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allGroupByAreIdentityPartitionFields() and resolveGroupByFields() look very similar except

  • allGroupByAreIdentityPartitionFields additionally checks instanceof NamedReference
  • resolveGroupByFields additionally collects field IDs and fields into output lists
    Can we merge these two?

Or maybe let canPushDownAggregation() allow group by and then have the checks in this merged method? What do you think?

PartitionSpec spec = table().spec();
Schema tableSchema = table().schema();

for (org.apache.spark.sql.connector.expressions.Expression groupByExpr :
aggregation.groupByExpressions()) {
if (!(groupByExpr instanceof org.apache.spark.sql.connector.expressions.NamedReference)) {
return false;
}

String colName =
SparkUtil.toColumnName(
(org.apache.spark.sql.connector.expressions.NamedReference) groupByExpr);
Types.NestedField sourceField = tableSchema.findField(colName);
if (sourceField == null) {
return false;
}

if (findIdentityPartitionPosition(spec, sourceField.fieldId()) < 0) {
return false;
}
}

return true;
}

private static class ArrayStructLike implements StructLike {
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@anuragmantri anuragmantri May 8, 2026

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Can we use AggregateEvaluator.ArrayStructLike instead? May have to make it package-private.

private final Object[] values;

ArrayStructLike(Object[] values) {
this.values = values;
}

@Override
public int size() {
return values.length;
}

@Override
@SuppressWarnings("unchecked")
public <T> T get(int pos, Class<T> javaClass) {
return (T) values[pos];
}

@Override
public <T> void set(int pos, T value) {
values[pos] = value;
}
}

private boolean canPushDownAggregation(Aggregation aggregation) {
if (!isMainTable()) {
return false;
Expand All @@ -195,12 +412,12 @@ private boolean canPushDownAggregation(Aggregation aggregation) {
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("Skipping aggregate pushdown: group by aggregation push down is not supported");
return false;
if (!allGroupByAreIdentityPartitionFields(aggregation)) {
LOG.info(
"Skipping aggregate pushdown: group by columns must all be identity partition fields");
return false;
}
}

return true;
Expand Down
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