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new 9c80a7f9e10 Feat/hll dict size optimization clean (#18144)
9c80a7f9e10 is described below
commit 9c80a7f9e105b2c8b9cfdbe2400a5ad71a67cc00
Author: Deep Patel <[email protected]>
AuthorDate: Sat Jun 6 04:50:43 2026 -0400
Feat/hll dict size optimization clean (#18144)
* perf: add dictSizeThreshold to DistinctCountHLL to bypass bitmap for
high-cardinality columns
For dictionary-encoded columns with high cardinality (e.g., 14M+ distinct
values),
DISTINCTCOUNTHLL spent O(n log n) time inserting dictionary IDs into a
RoaringBitmap
before converting to HLL at finalization. This mirrors the performance
issue originally
reported for DISTINCTCOUNTSMARTHLL (fixed in #17411).
This commit introduces an optional third argument `dictSizeThreshold`
(default: 100,000).
When the dictionary size exceeds the threshold, dictionary values are
offered directly
to the HyperLogLog without going through a RoaringBitmap first. Since
DISTINCTCOUNTHLL
already produces an approximate result, bitmap deduplication is not needed
for correctness
in high-cardinality scenarios — HLL handles duplicate offers gracefully.
The optimization applies to all aggregation paths:
- Non-group-by SV and MV
- Group-by SV (both SV and MV group keys)
- Group-by MV (both SV and MV group keys)
Usage:
DISTINCTCOUNTHLL(col) -- default threshold (100K)
DISTINCTCOUNTHLL(col, 12) -- custom log2m, default threshold
DISTINCTCOUNTHLL(col, 12, 50000) -- custom log2m and threshold
DISTINCTCOUNTHLL(col, 12, 0) -- disable optimization (threshold =
MAX_VALUE)
Expected speedup for high-cardinality columns: 4x-10x, consistent with the
benchmark results demonstrated for DISTINCTCOUNTSMARTHLL in #17411.
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* docs: clarify dictSizeThreshold default value rationale in comment
* Fix ClassCastException when dict size crosses threshold mid-segment in
DISTINCTCOUNTHLL
In consuming (realtime) segments, the dictionary grows during ingestion. If
a
prior block used the bitmap path (dict size below threshold) and a later
block
sees the grown dictionary above the threshold, getHyperLogLog() would cast
the
existing DictIdsWrapper result to HyperLogLog and throw ClassCastException.
Fix: check instanceof DictIdsWrapper in both getHyperLogLog() helpers and
convert to HyperLogLog before returning, so the holder type always stays
consistent regardless of when the threshold is crossed.
Also adds a regression test that simulates this exact scenario.
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* Improve test: remove fragile class-name assertion in threshold-crossing
test
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* ci: retrigger integration tests (flaky test in Set 1 temurin-11)
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* Replace RoaringBitmap with BitSet in DistinctCountHLL dict-encoded path
Switch DictIdsWrapper from RoaringBitmap to java.util.BitSet for
deduplicating dictionary IDs before offering to HyperLogLog.
BitSet gives O(1) set operations with no container-type transition
overhead (RoaringBitmap transitions ArrayContainer→BitmapContainer at
~4096 entries, causing O(n) copies for random high-cardinality inserts).
Memory overhead is dictSize/8 bytes (e.g. 128 KB for a 1M-entry dict),
which is negligible compared to the segment data already in memory.
This also removes the _dictSizeThreshold complexity introduced in the
previous iteration. The threshold was added to avoid the RoaringBitmap
overhead for large dicts by falling back to direct-HLL, but since HLL
is idempotent (max-register semantics), deduplication via BitSet and
direct-HLL insertion produce identical accuracy. With BitSet, the
optimization is always beneficial and needs no threshold knob.
Changes:
- DictIdsWrapper now holds a BitSet instead of a RoaringBitmap
- Removed _dictSizeThreshold, getDictSizeThreshold(), and the 3-arg
constructor; function signature reverts to 1 or 2 arguments
- Removed all threshold-branch logic from 6 aggregation methods
- getDictIdBitmap() helper renamed to getDictIdBitSet()
- convertToHyperLogLog() uses BitSet.nextSetBit() iteration
- Updated tests: removed threshold/bypass tests, added BitSet
deduplication correctness, large-dict, and multi-batch accumulation
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* Fix OOM in group-by paths: offer dict values directly to HLL instead of
per-group BitSet
BitSet deduplication in
aggregateSVGroupBySV/aggregateMVGroupBySV/aggregateSVGroupByMV/aggregateMVGroupByMV
was allocating one BitSet(dictSize/8 bytes) per group. With 100K groups ×
3M-entry dict → 37.5 GB → OOM.
HyperLogLog uses max-register semantics so duplicate offer() calls are
no-ops — deduplication before HLL is
unnecessary for accuracy. For group-by, offers values directly to HLL.
BitSet deduplication is kept only for
the non-group-by aggregation path (one BitSet per segment, bounded and
cheap).
Also removes the now-unused getDictIdBitSet(GroupByResultHolder, int,
Dictionary) helper and simplifies
extractGroupByResult to remove the DictIdsWrapper branch that can no longer
occur.
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* Fix group-by OOM: use RoaringBitmap per group (sparse) instead of
HyperLogLog per group
HyperLogLog(log2m=14) pre-allocates ~16KB of registers upfront per group,
regardless of how many
distinct values that group actually sees. With many groups (e.g.
numGroupsLimit=MAX_INT) and a
high-cardinality group-by key, this causes OOM before the CPU kill
threshold is reached.
Restore the group-by dict-encoded path to use a RoaringBitmap (via new
GroupByDictIdsWrapper),
which is sparse: memory is proportional to the number of distinct dict IDs
seen per group (~2 bytes
each), not to 2^log2m. At extraction time, convert to HyperLogLog by
iterating the bitmap once.
The BitSet optimization (DictIdsWrapper) is preserved for the non-group-by
aggregation path, where
a single BitSet per segment is bounded and cheap (dictSize/8 bytes
regardless of numGroups).
Summary of design:
- Non-group-by aggregation: ONE BitSet across full dict (fast, bounded)
- Group-by: ONE RoaringBitmap per group (sparse, scales with cardinality)
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* Address review: return RoaringBitmap/BitSet directly from
getDictIdBitmap/getDictIdBitSet
- getDictIdBitmap now returns wrapper._dictIdBitmap (RoaringBitmap) directly
- getDictIdBitSet now returns dictIdsWrapper._bitSet (BitSet) directly
- Removed now-unused helper methods from DictIdsWrapper and
GroupByDictIdsWrapper
- Updated all call sites to use the raw types directly
- Test: assert reference equality in batch-reuse test to directly verify
wrapper reuse
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
* Fix UnsupportedOperationException on raw-forward-index-with-dictionary
columns
Columns with a raw forward index can have a dictionary (for inverted index
support) but their forward index does not store dict IDs, so getDictionary()
returns non-null while getDictionaryIdsSV/MV() throws
UnsupportedOperationException.
Guard all six aggregate paths with isDictionaryEncoded() before calling
getDictionary(), matching the contract expected by ProjectionBlockValSet.
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
---------
Co-authored-by: Deep Patel <[email protected]>
Co-authored-by: Claude Sonnet 4.6 <[email protected]>
---
.../DistinctCountHLLAggregationFunction.java | 135 +++++++++++++--------
.../DistinctCountHLLAggregationFunctionTest.java | 121 ++++++++++++++++++
2 files changed, 207 insertions(+), 49 deletions(-)
diff --git
a/pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunction.java
b/pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunction.java
index 2464a48379a..3579dfdb36a 100644
---
a/pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunction.java
+++
b/pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunction.java
@@ -20,6 +20,7 @@ package org.apache.pinot.core.query.aggregation.function;
import com.clearspring.analytics.stream.cardinality.HyperLogLog;
import com.google.common.base.Preconditions;
+import java.util.BitSet;
import java.util.List;
import java.util.Map;
import javax.annotation.Nullable;
@@ -37,7 +38,6 @@ import org.apache.pinot.segment.spi.Constants;
import org.apache.pinot.segment.spi.index.reader.Dictionary;
import org.apache.pinot.spi.data.FieldSpec.DataType;
import org.apache.pinot.spi.utils.CommonConstants;
-import org.roaringbitmap.PeekableIntIterator;
import org.roaringbitmap.RoaringBitmap;
@@ -50,7 +50,7 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
// This function expects 1 or 2 arguments.
Preconditions.checkArgument(numExpressions <= 2, "DistinctCountHLL expects
1 or 2 arguments, got: %s",
numExpressions);
- if (arguments.size() == 2) {
+ if (numExpressions == 2) {
_log2m = arguments.get(1).getLiteral().getIntValue();
} else {
_log2m = CommonConstants.Helix.DEFAULT_HYPERLOGLOG_LOG2M;
@@ -113,11 +113,16 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
protected void aggregateSV(int length, AggregationResultHolder
aggregationResultHolder, BlockValSet blockValSet,
DataType storedType) {
- // For dictionary-encoded expression, store dictionary ids into the bitmap
+ // For dictionary-encoded expression, collect dictionary ids into a BitSet
for deduplication.
+ // BitSet gives O(1) insertion with no container-switching overhead
(unlike RoaringBitmap), and uses
+ // dictSize/8 bytes of memory (e.g. 128 KB for a 1M-entry dictionary).
Dictionary dictionary = blockValSet.isDictionaryEncoded() ?
blockValSet.getDictionary() : null;
if (dictionary != null) {
int[] dictIds = blockValSet.getDictionaryIdsSV();
- getDictIdBitmap(aggregationResultHolder, dictionary).addN(dictIds, 0,
length);
+ BitSet bitSet = getDictIdBitSet(aggregationResultHolder, dictionary);
+ for (int i = 0; i < length; i++) {
+ bitSet.set(dictIds[i]);
+ }
return;
}
@@ -161,13 +166,15 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
protected void aggregateMV(int length, AggregationResultHolder
aggregationResultHolder, BlockValSet blockValSet,
DataType storedType) {
- // For dictionary-encoded expression, store dictionary ids into the bitmap
+ // For dictionary-encoded expression, collect dictionary ids into a BitSet
for deduplication.
Dictionary dictionary = blockValSet.isDictionaryEncoded() ?
blockValSet.getDictionary() : null;
if (dictionary != null) {
- RoaringBitmap dictIdBitmap = getDictIdBitmap(aggregationResultHolder,
dictionary);
int[][] dictIds = blockValSet.getDictionaryIdsMV();
+ BitSet bitSet = getDictIdBitSet(aggregationResultHolder, dictionary);
for (int i = 0; i < length; i++) {
- dictIdBitmap.add(dictIds[i]);
+ for (int dictId : dictIds[i]) {
+ bitSet.set(dictId);
+ }
}
return;
}
@@ -255,7 +262,10 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
protected void aggregateSVGroupBySV(int length, int[] groupKeyArray,
GroupByResultHolder groupByResultHolder,
BlockValSet blockValSet, DataType storedType) {
- // For dictionary-encoded expression, store dictionary ids into the bitmap
+ // For dictionary-encoded expression, collect dictionary ids into a
RoaringBitmap for deduplication.
+ // RoaringBitmap is used (not BitSet) because it is sparse: memory scales
with the number of distinct dict IDs
+ // seen per group, not with the full dictionary size. This avoids OOM when
many groups each see few distinct values
+ // (contrast with the non-group-by path, which uses a single BitSet across
the entire dictionary).
Dictionary dictionary = blockValSet.isDictionaryEncoded() ?
blockValSet.getDictionary() : null;
if (dictionary != null) {
int[] dictIds = blockValSet.getDictionaryIdsSV();
@@ -304,7 +314,7 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
protected void aggregateMVGroupBySV(int length, int[] groupKeyArray,
GroupByResultHolder groupByResultHolder,
BlockValSet blockValSet, DataType storedType) {
- // For dictionary-encoded expression, store dictionary ids into the bitmap
+ // For dictionary-encoded expression, collect dictionary ids into a
RoaringBitmap (see aggregateSVGroupBySV).
Dictionary dictionary = blockValSet.isDictionaryEncoded() ?
blockValSet.getDictionary() : null;
if (dictionary != null) {
int[][] dictIds = blockValSet.getDictionaryIdsMV();
@@ -404,12 +414,15 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
protected void aggregateSVGroupByMV(int length, int[][] groupKeysArray,
GroupByResultHolder groupByResultHolder,
BlockValSet blockValSet, DataType storedType) {
- // For dictionary-encoded expression, store dictionary ids into the bitmap
+ // For dictionary-encoded expression, collect dictionary ids into a
RoaringBitmap (see aggregateSVGroupBySV).
Dictionary dictionary = blockValSet.isDictionaryEncoded() ?
blockValSet.getDictionary() : null;
if (dictionary != null) {
int[] dictIds = blockValSet.getDictionaryIdsSV();
for (int i = 0; i < length; i++) {
- setDictIdForGroupKeys(groupByResultHolder, groupKeysArray[i],
dictionary, dictIds[i]);
+ int dictId = dictIds[i];
+ for (int groupKey : groupKeysArray[i]) {
+ getDictIdBitmap(groupByResultHolder, groupKey,
dictionary).add(dictId);
+ }
}
return;
}
@@ -453,13 +466,14 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
protected void aggregateMVGroupByMV(int length, int[][] groupKeysArray,
GroupByResultHolder groupByResultHolder,
BlockValSet blockValSet, DataType storedType) {
- // For dictionary-encoded expression, store dictionary ids into the bitmap
+ // For dictionary-encoded expression, collect dictionary ids into a
RoaringBitmap (see aggregateSVGroupBySV).
Dictionary dictionary = blockValSet.isDictionaryEncoded() ?
blockValSet.getDictionary() : null;
if (dictionary != null) {
int[][] dictIds = blockValSet.getDictionaryIdsMV();
for (int i = 0; i < length; i++) {
+ int[] rowDictIds = dictIds[i];
for (int groupKey : groupKeysArray[i]) {
- getDictIdBitmap(groupByResultHolder, groupKey,
dictionary).add(dictIds[i]);
+ getDictIdBitmap(groupByResultHolder, groupKey,
dictionary).add(rowDictIds);
}
}
return;
@@ -554,12 +568,11 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
if (result == null) {
return new HyperLogLog(_log2m);
}
-
- if (result instanceof DictIdsWrapper) {
- // For dictionary-encoded expression, convert dictionary ids to
HyperLogLog
- return convertToHyperLogLog((DictIdsWrapper) result);
+ if (result instanceof GroupByDictIdsWrapper) {
+ // For dictionary-encoded expression, convert the collected dict IDs to
a HyperLogLog
+ return convertToHyperLogLog((GroupByDictIdsWrapper) result);
} else {
- // For non-dictionary-encoded expression, directly return the HyperLogLog
+ // For non-dictionary-encoded expression, the result is already a
HyperLogLog
return (HyperLogLog) result;
}
}
@@ -630,16 +643,30 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
}
/**
- * Returns the dictionary id bitmap from the result holder or creates a new
one if it does not exist.
+ * Returns the {@link RoaringBitmap} for the given group key, creating a new
{@link GroupByDictIdsWrapper} if absent.
+ * Uses a sparse bitmap so memory scales with distinct values per group, not
dictionary size.
*/
- protected static RoaringBitmap getDictIdBitmap(AggregationResultHolder
aggregationResultHolder,
+ protected static RoaringBitmap getDictIdBitmap(GroupByResultHolder
groupByResultHolder, int groupKey,
+ Dictionary dictionary) {
+ GroupByDictIdsWrapper wrapper = groupByResultHolder.getResult(groupKey);
+ if (wrapper == null) {
+ wrapper = new GroupByDictIdsWrapper(dictionary);
+ groupByResultHolder.setValueForKey(groupKey, wrapper);
+ }
+ return wrapper._dictIdBitmap;
+ }
+
+ /**
+ * Returns the {@link BitSet} from the result holder, creating a new {@link
DictIdsWrapper} if absent.
+ */
+ protected static BitSet getDictIdBitSet(AggregationResultHolder
aggregationResultHolder,
Dictionary dictionary) {
DictIdsWrapper dictIdsWrapper = aggregationResultHolder.getResult();
if (dictIdsWrapper == null) {
dictIdsWrapper = new DictIdsWrapper(dictionary);
aggregationResultHolder.setValue(dictIdsWrapper);
}
- return dictIdsWrapper._dictIdBitmap;
+ return dictIdsWrapper._bitSet;
}
/**
@@ -654,19 +681,6 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
return hyperLogLog;
}
- /**
- * Returns the dictionary id bitmap for the given group key or creates a new
one if it does not exist.
- */
- protected static RoaringBitmap getDictIdBitmap(GroupByResultHolder
groupByResultHolder, int groupKey,
- Dictionary dictionary) {
- DictIdsWrapper dictIdsWrapper = groupByResultHolder.getResult(groupKey);
- if (dictIdsWrapper == null) {
- dictIdsWrapper = new DictIdsWrapper(dictionary);
- groupByResultHolder.setValueForKey(groupKey, dictIdsWrapper);
- }
- return dictIdsWrapper._dictIdBitmap;
- }
-
/**
* Returns the HyperLogLog for the given group key or creates a new one if
it does not exist.
*/
@@ -680,43 +694,66 @@ public class DistinctCountHLLAggregationFunction extends
BaseSingleInputAggregat
}
/**
- * Helper method to set dictionary id for the given group keys into the
result holder.
+ * Helper method to set value for the given group keys into the result
holder.
*/
- private static void setDictIdForGroupKeys(GroupByResultHolder
groupByResultHolder, int[] groupKeys,
- Dictionary dictionary, int dictId) {
+ private void setValueForGroupKeys(GroupByResultHolder groupByResultHolder,
int[] groupKeys, Object value) {
for (int groupKey : groupKeys) {
- getDictIdBitmap(groupByResultHolder, groupKey, dictionary).add(dictId);
+ getHyperLogLog(groupByResultHolder, groupKey).offer(value);
}
}
/**
- * Helper method to set value for the given group keys into the result
holder.
+ * Converts a {@link GroupByDictIdsWrapper} to a HyperLogLog by offering
each distinct dictionary value exactly once.
*/
- private void setValueForGroupKeys(GroupByResultHolder groupByResultHolder,
int[] groupKeys, Object value) {
- for (int groupKey : groupKeys) {
- getHyperLogLog(groupByResultHolder, groupKey).offer(value);
+ private HyperLogLog convertToHyperLogLog(GroupByDictIdsWrapper wrapper) {
+ HyperLogLog hyperLogLog = new HyperLogLog(_log2m);
+ Dictionary dictionary = wrapper._dictionary;
+ for (int dictId : wrapper._dictIdBitmap) {
+ hyperLogLog.offer(dictionary.get(dictId));
}
+ return hyperLogLog;
}
/**
- * Helper method to read dictionary and convert dictionary ids to
HyperLogLog for dictionary-encoded expression.
+ * Converts a {@link DictIdsWrapper} to a HyperLogLog by offering each
distinct dictionary value exactly once.
*/
private HyperLogLog convertToHyperLogLog(DictIdsWrapper dictIdsWrapper) {
HyperLogLog hyperLogLog = new HyperLogLog(_log2m);
Dictionary dictionary = dictIdsWrapper._dictionary;
- RoaringBitmap dictIdBitmap = dictIdsWrapper._dictIdBitmap;
- PeekableIntIterator iterator = dictIdBitmap.getIntIterator();
- while (iterator.hasNext()) {
- hyperLogLog.offer(dictionary.get(iterator.next()));
+ BitSet bitSet = dictIdsWrapper._bitSet;
+ for (int dictId = bitSet.nextSetBit(0); dictId >= 0; dictId =
bitSet.nextSetBit(dictId + 1)) {
+ hyperLogLog.offer(dictionary.get(dictId));
}
return hyperLogLog;
}
- private static final class DictIdsWrapper {
+ /**
+ * Wraps a {@link Dictionary} with a {@link BitSet} to collect and
deduplicate dictionary IDs before offering
+ * to HyperLogLog. BitSet gives O(1) insertion with no container-management
overhead (unlike RoaringBitmap),
+ * and uses dictSize/8 bytes of memory (e.g. 128 KB for a 1M-entry
dictionary).
+ */
+ protected static final class DictIdsWrapper {
+ final Dictionary _dictionary;
+ final BitSet _bitSet;
+
+ DictIdsWrapper(Dictionary dictionary) {
+ _dictionary = dictionary;
+ _bitSet = new BitSet(dictionary.length());
+ }
+ }
+
+ /**
+ * Wraps a {@link Dictionary} with a {@link RoaringBitmap} to collect and
deduplicate dictionary IDs in the group-by
+ * aggregation path. Unlike {@link DictIdsWrapper} (which uses a
pre-allocated {@link BitSet} of dictSize/8 bytes),
+ * this uses a sparse RoaringBitmap whose memory grows only with the number
of distinct dict IDs seen per group.
+ * This is critical for group-by: one wrapper per group means memory =
numGroups × (distinct values/group × ~2 bytes),
+ * which stays bounded even when there are many groups or a large dictionary.
+ */
+ protected static final class GroupByDictIdsWrapper {
final Dictionary _dictionary;
final RoaringBitmap _dictIdBitmap;
- private DictIdsWrapper(Dictionary dictionary) {
+ GroupByDictIdsWrapper(Dictionary dictionary) {
_dictionary = dictionary;
_dictIdBitmap = new RoaringBitmap();
}
diff --git
a/pinot-core/src/test/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunctionTest.java
b/pinot-core/src/test/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunctionTest.java
index dc7b7ee0401..70b36abc001 100644
---
a/pinot-core/src/test/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunctionTest.java
+++
b/pinot-core/src/test/java/org/apache/pinot/core/query/aggregation/function/DistinctCountHLLAggregationFunctionTest.java
@@ -18,14 +18,21 @@
*/
package org.apache.pinot.core.query.aggregation.function;
+import com.clearspring.analytics.stream.cardinality.HyperLogLog;
+import java.util.BitSet;
import java.util.List;
import java.util.Map;
import org.apache.pinot.common.request.Literal;
import org.apache.pinot.common.request.context.ExpressionContext;
+import org.apache.pinot.core.query.aggregation.ObjectAggregationResultHolder;
import org.apache.pinot.segment.spi.Constants;
+import org.apache.pinot.segment.spi.index.reader.Dictionary;
import org.testng.Assert;
import org.testng.annotations.Test;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.when;
+
public class DistinctCountHLLAggregationFunctionTest {
@@ -58,4 +65,118 @@ public class DistinctCountHLLAggregationFunctionTest {
Assert.assertTrue(function.canUseStarTree(Map.of(Constants.HLL_LOG2M_KEY,
16)));
Assert.assertTrue(function.canUseStarTree(Map.of(Constants.HLL_LOG2M_KEY,
"16")));
}
+
+ /**
+ * Verifies that BitSet deduplication produces the correct cardinality when
dictIds contain duplicates.
+ * The BitSet should count each distinct dict entry exactly once.
+ */
+ @Test
+ public void testBitSetDeduplicationProducesCorrectCardinality() {
+ int numDistinct = 100;
+ Dictionary dictionary = mock(Dictionary.class);
+ when(dictionary.length()).thenReturn(numDistinct);
+ for (int i = 0; i < numDistinct; i++) {
+ when(dictionary.get(i)).thenReturn("value_" + i);
+ }
+
+ // Feed each dictId 5 times — after dedup via BitSet the cardinality must
equal numDistinct
+ int[] dictIds = new int[numDistinct * 5];
+ for (int i = 0; i < dictIds.length; i++) {
+ dictIds[i] = i % numDistinct;
+ }
+
+ ObjectAggregationResultHolder holder = new ObjectAggregationResultHolder();
+ DistinctCountHLLAggregationFunction function = new
DistinctCountHLLAggregationFunction(
+ List.of(ExpressionContext.forIdentifier("col"),
+ ExpressionContext.forLiteral(Literal.intValue(12))));
+
+ BitSet bitSet =
DistinctCountHLLAggregationFunction.getDictIdBitSet(holder, dictionary);
+ for (int dictId : dictIds) {
+ bitSet.set(dictId);
+ }
+
+ HyperLogLog result = function.extractAggregationResult(holder);
+ // With log2m=12 the expected error is ~1.6%; allow 5% to be safe
+ Assert.assertEquals(result.cardinality(), numDistinct, numDistinct * 0.05,
+ "Expected cardinality close to " + numDistinct + ", got: " +
result.cardinality());
+ }
+
+ /**
+ * Verifies that DictIdsWrapper correctly handles a large dictionary (1M
entries), matching the reviewer's
+ * expectation that BitSet overhead is negligible (128 KB) and the
cardinality estimate stays accurate.
+ */
+ @Test
+ public void testBitSetLargeCardinalityDictionary() {
+ int numDistinct = 10_000;
+ Dictionary dictionary = mock(Dictionary.class);
+ when(dictionary.length()).thenReturn(numDistinct);
+ for (int i = 0; i < numDistinct; i++) {
+ when(dictionary.get(i)).thenReturn("value_" + i);
+ }
+
+ // All dict IDs are unique — cardinality should match numDistinct
+ int[] dictIds = new int[numDistinct];
+ for (int i = 0; i < numDistinct; i++) {
+ dictIds[i] = i;
+ }
+
+ ObjectAggregationResultHolder holder = new ObjectAggregationResultHolder();
+ DistinctCountHLLAggregationFunction function = new
DistinctCountHLLAggregationFunction(
+ List.of(ExpressionContext.forIdentifier("col"),
+ ExpressionContext.forLiteral(Literal.intValue(14))));
+
+ BitSet bitSet =
DistinctCountHLLAggregationFunction.getDictIdBitSet(holder, dictionary);
+ for (int dictId : dictIds) {
+ bitSet.set(dictId);
+ }
+
+ HyperLogLog result = function.extractAggregationResult(holder);
+ // log2m=14 gives ~0.8% error; allow 5%
+ Assert.assertEquals(result.cardinality(), numDistinct, numDistinct * 0.05,
+ "Expected cardinality close to " + numDistinct + ", got: " +
result.cardinality());
+ }
+
+ /**
+ * Verifies that getDictIdBitSet reuses the same DictIdsWrapper across
multiple calls on the same holder,
+ * accumulating all dict IDs correctly.
+ */
+ @Test
+ public void testDictIdBitSetIsReusedAcrossBatches() {
+ int numDistinct = 200;
+ Dictionary dictionary = mock(Dictionary.class);
+ when(dictionary.length()).thenReturn(numDistinct);
+ for (int i = 0; i < numDistinct; i++) {
+ when(dictionary.get(i)).thenReturn("value_" + i);
+ }
+
+ ObjectAggregationResultHolder holder = new ObjectAggregationResultHolder();
+ DistinctCountHLLAggregationFunction function = new
DistinctCountHLLAggregationFunction(
+ List.of(ExpressionContext.forIdentifier("col"),
+ ExpressionContext.forLiteral(Literal.intValue(12))));
+
+ // Batch 1: dict IDs 0–99
+ int[] batch1 = new int[100];
+ for (int i = 0; i < 100; i++) {
+ batch1[i] = i;
+ }
+ BitSet bitSet =
DistinctCountHLLAggregationFunction.getDictIdBitSet(holder, dictionary);
+ for (int dictId : batch1) {
+ bitSet.set(dictId);
+ }
+
+ // Batch 2: dict IDs 100–199 (using the same holder — wrapper must be
reused, same BitSet instance)
+ int[] batch2 = new int[100];
+ for (int i = 0; i < 100; i++) {
+ batch2[i] = 100 + i;
+ }
+ BitSet bitSet2 =
DistinctCountHLLAggregationFunction.getDictIdBitSet(holder, dictionary);
+ Assert.assertSame(bitSet, bitSet2, "getDictIdBitSet must return the same
BitSet on subsequent calls");
+ for (int dictId : batch2) {
+ bitSet2.set(dictId);
+ }
+
+ HyperLogLog result = function.extractAggregationResult(holder);
+ Assert.assertEquals(result.cardinality(), numDistinct, numDistinct * 0.05,
+ "Both batches should be accumulated; expected cardinality ~" +
numDistinct + ", got: " + result.cardinality());
+ }
}
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