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[SPARK-46189][PS][SQL] Perform comparisons and arithmetic between sam…
…e types in various Pandas aggregate functions to avoid interpreted mode errors
### What changes were proposed in this pull request?
In various Pandas aggregate functions, remove each comparison or arithmetic operation between `DoubleType` and `IntergerType` in `evaluateExpression` and replace with a comparison or arithmetic operation between `DoubleType` and `DoubleType`.
Affected functions are `PandasStddev`, `PandasVariance`, `PandasSkewness`, `PandasKurtosis`, and `PandasCovar`.
### Why are the changes needed?
These functions fail in interpreted mode. For example, `evaluateExpression` in `PandasKurtosis` compares a double to an integer:
```
If(n < 4, Literal.create(null, DoubleType) ...
```
This results in a boxed double and a boxed integer getting passed to `SQLOrderingUtil.compareDoubles` which expects two doubles as arguments. The scala runtime tries to unbox the boxed integer as a double, resulting in an error.
Reproduction example:
```
spark.sql("set spark.sql.codegen.wholeStage=false")
spark.sql("set spark.sql.codegen.factoryMode=NO_CODEGEN")
import numpy as np
import pandas as pd
import pyspark.pandas as ps
pser = pd.Series([1, 2, 3, 7, 9, 8], index=np.random.rand(6), name="a")
psser = ps.from_pandas(pser)
psser.kurt()
```
See Jira (SPARK-46189) for the other reproduction cases.
This works fine in codegen mode because the integer is already unboxed and the Java runtime will implictly cast it to a double.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
New unit tests.
### Was this patch authored or co-authored using generative AI tooling?
No.
Closes #44099 from bersprockets/unboxing_error.
Authored-by: Bruce Robbins <bersprockets@gmail.com>
Signed-off-by: Ruifeng Zheng <ruifengz@apache.org>- Loading branch information
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77 changes: 77 additions & 0 deletions
77
...est/scala/org/apache/spark/sql/catalyst/expressions/aggregate/CentralMomentAggSuite.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,77 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package org.apache.spark.sql.catalyst.expressions.aggregate | ||
|
|
||
| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.AttributeReference | ||
| import org.apache.spark.sql.types.DoubleType | ||
|
|
||
| class CentralMomentAggSuite extends TestWithAndWithoutCodegen { | ||
| val input = AttributeReference("input", DoubleType, nullable = true)() | ||
|
|
||
| testBothCodegenAndInterpreted("SPARK-46189: pandas_kurtosis eval") { | ||
| val evaluator = DeclarativeAggregateEvaluator(PandasKurtosis(input), Seq(input)) | ||
| val buffer = evaluator.update( | ||
| InternalRow(1.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(3.0d), | ||
| InternalRow(7.0d), | ||
| InternalRow(9.0d), | ||
| InternalRow(8.0d)) | ||
| val result = evaluator.eval(buffer) | ||
| assert(result === InternalRow(-2.5772889417360285d)) | ||
| } | ||
|
|
||
| testBothCodegenAndInterpreted("SPARK-46189: pandas_skew eval") { | ||
| val evaluator = DeclarativeAggregateEvaluator(PandasSkewness(input), Seq(input)) | ||
| val buffer = evaluator.update( | ||
| InternalRow(1.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(100.0d)) | ||
| val result = evaluator.eval(buffer) | ||
| assert(result === InternalRow(2.4489389171333733d)) | ||
| } | ||
|
|
||
| testBothCodegenAndInterpreted("SPARK-46189: pandas_stddev eval") { | ||
| val evaluator = DeclarativeAggregateEvaluator(PandasStddev(input, 1), Seq(input)) | ||
| val buffer = evaluator.update( | ||
| InternalRow(1.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(3.0d), | ||
| InternalRow(7.0d), | ||
| InternalRow(9.0d), | ||
| InternalRow(8.0d)) | ||
| val result = evaluator.eval(buffer) | ||
| assert(result === InternalRow(3.40587727318528d)) | ||
| } | ||
|
|
||
| testBothCodegenAndInterpreted("SPARK-46189: pandas_variance eval") { | ||
| val evaluator = DeclarativeAggregateEvaluator(PandasVariance(input, 1), Seq(input)) | ||
| val buffer = evaluator.update( | ||
| InternalRow(1.0d), | ||
| InternalRow(2.0d), | ||
| InternalRow(3.0d), | ||
| InternalRow(7.0d), | ||
| InternalRow(9.0d), | ||
| InternalRow(8.0d)) | ||
| val result = evaluator.eval(buffer) | ||
| assert(result === InternalRow(11.6d)) | ||
| } | ||
| } |
39 changes: 39 additions & 0 deletions
39
...c/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/CovarianceAggSuite.scala
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|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package org.apache.spark.sql.catalyst.expressions.aggregate | ||
|
|
||
| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.catalyst.expressions.AttributeReference | ||
| import org.apache.spark.sql.types.DoubleType | ||
|
|
||
| class CovarianceAggSuite extends TestWithAndWithoutCodegen { | ||
| val a = AttributeReference("a", DoubleType, nullable = true)() | ||
| val b = AttributeReference("b", DoubleType, nullable = true)() | ||
|
|
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| testBothCodegenAndInterpreted("SPARK-46189: pandas_covar eval") { | ||
| val evaluator = DeclarativeAggregateEvaluator(PandasCovar(a, b, 1), Seq(a, b)) | ||
| val buffer = evaluator.update( | ||
| InternalRow(1.0d, 1.0d), | ||
| InternalRow(2.0d, 2.0d), | ||
| InternalRow(3.0d, 3.0d), | ||
| InternalRow(7.0d, 7.0d), | ||
| InternalRow(9.0, 9.0), | ||
| InternalRow(8.0d, 6.0)) | ||
| val result = evaluator.eval(buffer) | ||
| assert(result === InternalRow(10.4d)) | ||
| } | ||
| } |
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35
...scala/org/apache/spark/sql/catalyst/expressions/aggregate/TestWithAndWithoutCodegen.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package org.apache.spark.sql.catalyst.expressions.aggregate | ||
|
|
||
| import org.apache.spark.SparkFunSuite | ||
| import org.apache.spark.sql.catalyst.expressions.CodegenObjectFactoryMode | ||
| import org.apache.spark.sql.catalyst.plans.SQLHelper | ||
| import org.apache.spark.sql.internal.SQLConf | ||
|
|
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| trait TestWithAndWithoutCodegen extends SparkFunSuite with SQLHelper { | ||
| def testBothCodegenAndInterpreted(name: String)(f: => Unit): Unit = { | ||
| val modes = Seq(CodegenObjectFactoryMode.CODEGEN_ONLY, CodegenObjectFactoryMode.NO_CODEGEN) | ||
| for (fallbackMode <- modes) { | ||
| test(s"$name with $fallbackMode") { | ||
| withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> fallbackMode.toString) { | ||
| f | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } |