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Support group-limit optimization for ROW_NUMBER #11886

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merged 2 commits into from
Dec 19, 2024
+12 −39
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Support group-limit optimization for ROW_NUMBER #11886

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dbbd070
Support group-limit optimization for `ROW_NUMBER`
mythrocks Dec 17, 2024
b483ac2
Reformatted exception message.
mythrocks Dec 18, 2024
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37 changes: 3 additions & 34 deletions integration_tests/src/main/python/window_function_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2107,7 +2107,9 @@ def assert_query_runs_on(exec, conf):
'DENSE_RANK() OVER (PARTITION BY a ORDER BY b, c) ',
'RANK() OVER (ORDER BY a,b,c) ',
'DENSE_RANK() OVER (ORDER BY a,b,c) ',
])
# ROW_NUMBER() on an un-partitioned window does not invoke WindowGroupLimit optimization.
'ROW_NUMBER() OVER (PARTITION BY a ORDER BY b,c) ',
])
def test_window_group_limits_for_ranking_functions(data_gen, batch_size, rank_clause):
"""
This test verifies that window group limits are applied for queries with ranking-function based
Expand All @@ -2133,39 +2135,6 @@ def test_window_group_limits_for_ranking_functions(data_gen, batch_size, rank_cl
conf=conf)


@allow_non_gpu('WindowGroupLimitExec')
@pytest.mark.skipif(condition=not (is_spark_350_or_later() or is_databricks133_or_later()),
reason="WindowGroupLimit not available for spark.version < 3.5 "
" and Databricks version < 13.3")
@ignore_order(local=True)
@approximate_float
def test_window_group_limits_fallback_for_row_number():
"""
This test verifies that window group limits are applied for queries with ranking-function based
row filters.
This test covers RANK() and DENSE_RANK(), for window function with and without `PARTITIONED BY`
clauses.
"""
conf = {'spark.rapids.sql.batchSizeBytes': '1g',
'spark.rapids.sql.castFloatToDecimal.enabled': True}

data_gen = _grpkey_longs_with_no_nulls
query = """
SELECT * FROM (
SELECT *, ROW_NUMBER() OVER (PARTITION BY a ORDER BY b, c) AS rnk
FROM window_agg_table
)
WHERE rnk < 3
"""

assert_gpu_sql_fallback_collect(
lambda spark: gen_df(spark, data_gen, length=512),
cpu_fallback_class_name="WindowGroupLimitExec",
table_name="window_agg_table",
sql=query,
conf=conf)


def test_lru_cache_datagen():
# log cache info at the end of integration tests, not related to window functions
info = gen_df_help.cache_info()
Expand Down
14 changes: 9 additions & 5 deletions ...gin/src/main/spark341db/scala/com/nvidia/spark/rapids/shims/GpuWindowGroupLimitExec.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -30,12 +30,12 @@ import ai.rapids.cudf.GroupByOptions
import com.nvidia.spark.rapids.{BaseExprMeta, DataFromReplacementRule, GpuBindReferences, GpuBoundReference, GpuColumnVector, GpuExec, GpuExpression, GpuMetric, GpuOverrides, GpuProjectExec, RapidsConf, RapidsMeta, SparkPlanMeta, SpillableColumnarBatch, SpillPriorities}
import com.nvidia.spark.rapids.Arm.withResource
import com.nvidia.spark.rapids.RmmRapidsRetryIterator.{splitSpillableInHalfByRows, withRetry}
import com.nvidia.spark.rapids.window.{GpuDenseRank, GpuRank}
import com.nvidia.spark.rapids.window.{GpuDenseRank, GpuRank, GpuRowNumber}

import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{Attribute, DenseRank, Expression, Rank, SortOrder}
import org.apache.spark.sql.catalyst.expressions.{Attribute, DenseRank, Expression, Rank, RowNumber, SortOrder}
import org.apache.spark.sql.execution.SparkPlan
import org.apache.spark.sql.execution.window.{Final, Partial, WindowGroupLimitExec, WindowGroupLimitMode}
import org.apache.spark.sql.types.DataType
Expand All @@ -44,6 +44,7 @@ import org.apache.spark.sql.vectorized.ColumnarBatch
sealed trait RankFunctionType
case object RankFunction extends RankFunctionType
case object DenseRankFunction extends RankFunctionType
case object RowNumberFunction extends RankFunctionType

class GpuWindowGroupLimitExecMeta(limitExec: WindowGroupLimitExec,
conf: RapidsConf,
Expand All @@ -63,8 +64,8 @@ class GpuWindowGroupLimitExecMeta(limitExec: WindowGroupLimitExec,
wrapped.rankLikeFunction match {
case DenseRank(_) =>
case Rank(_) =>
// case RowNumber() => // TODO: Future.
case _ => willNotWorkOnGpu("Only Rank() and DenseRank() are " +
case RowNumber() =>
case _ => willNotWorkOnGpu("Only rank, dense_rank and row_number are " +
"currently supported for window group limits")
}

Expand Down Expand Up @@ -166,6 +167,7 @@ class GpuWindowGroupLimitingIterator(input: Iterator[ColumnarBatch],
rankFunctionType match {
case RankFunction => GpuRank(sortColumns)
case DenseRankFunction => GpuDenseRank(sortColumns)
case RowNumberFunction => GpuRowNumber
case _ => throw new IllegalArgumentException("Unexpected ranking function")
}
}
Expand Down Expand Up @@ -300,8 +302,10 @@ case class GpuWindowGroupLimitExec(
private def getRankFunctionType(expr: Expression): RankFunctionType = expr match {
case GpuRank(_) => RankFunction
case GpuDenseRank(_) => DenseRankFunction
case GpuRowNumber => RowNumberFunction
case _ =>
throw new UnsupportedOperationException("Only Rank() is currently supported for group limits")
throw new UnsupportedOperationException("Only rank, dense_rank and row_number are " +
"currently supported for group limits")
}

override protected def internalDoExecuteColumnar(): RDD[ColumnarBatch] = {
Expand Down
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