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[SPARK-8446] [SQL] Add helper functions for testing SparkPlan physical operators #6885
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/* | ||
* 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. | ||
*/ | ||
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package org.apache.spark.sql.execution | ||
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import org.apache.spark.sql.catalyst.dsl.expressions._ | ||
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class SortSuite extends SparkPlanTest { | ||
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// This test was originally added as an example of how to use [[SparkPlanTest]]; | ||
// it's not designed to be a comprehensive test of ExternalSort. | ||
test("basic sorting using ExternalSort") { | ||
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val input = Seq( | ||
("Hello", 4, 2.0), | ||
("Hello", 1, 1.0), | ||
("World", 8, 3.0) | ||
) | ||
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checkAnswer( | ||
input.toDF("a", "b", "c"), | ||
ExternalSort('a.asc :: 'b.asc :: Nil, global = false, _: SparkPlan), | ||
input.sorted) | ||
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checkAnswer( | ||
input.toDF("a", "b", "c"), | ||
ExternalSort('b.asc :: 'a.asc :: Nil, global = false, _: SparkPlan), | ||
input.sortBy(t => (t._2, t._1))) | ||
} | ||
} |
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/* | ||
* 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. | ||
*/ | ||
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package org.apache.spark.sql.execution | ||
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import scala.language.implicitConversions | ||
import scala.reflect.runtime.universe.TypeTag | ||
import scala.util.control.NonFatal | ||
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import org.apache.spark.SparkFunSuite | ||
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import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute | ||
import org.apache.spark.sql.catalyst.expressions.BoundReference | ||
import org.apache.spark.sql.catalyst.util._ | ||
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import org.apache.spark.sql.test.TestSQLContext | ||
import org.apache.spark.sql.{DataFrameHolder, Row, DataFrame} | ||
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/** | ||
* Base class for writing tests for individual physical operators. For an example of how this | ||
* class's test helper methods can be used, see [[SortSuite]]. | ||
*/ | ||
class SparkPlanTest extends SparkFunSuite { | ||
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/** | ||
* Creates a DataFrame from a local Seq of Product. | ||
*/ | ||
implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = { | ||
TestSQLContext.implicits.localSeqToDataFrameHolder(data) | ||
} | ||
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/** | ||
* Runs the plan and makes sure the answer matches the expected result. | ||
* @param input the input data to be used. | ||
* @param planFunction a function which accepts the input SparkPlan and uses it to instantiate | ||
* the physical operator that's being tested. | ||
* @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. | ||
*/ | ||
protected def checkAnswer( | ||
input: DataFrame, | ||
planFunction: SparkPlan => SparkPlan, | ||
expectedAnswer: Seq[Row]): Unit = { | ||
SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer) match { | ||
case Some(errorMessage) => fail(errorMessage) | ||
case None => | ||
} | ||
} | ||
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/** | ||
* Runs the plan and makes sure the answer matches the expected result. | ||
* @param input the input data to be used. | ||
* @param planFunction a function which accepts the input SparkPlan and uses it to instantiate | ||
* the physical operator that's being tested. | ||
* @param expectedAnswer the expected result in a [[Seq]] of [[Product]]s. | ||
*/ | ||
protected def checkAnswer[A <: Product : TypeTag]( | ||
input: DataFrame, | ||
planFunction: SparkPlan => SparkPlan, | ||
expectedAnswer: Seq[A]): Unit = { | ||
val expectedRows = expectedAnswer.map(Row.fromTuple) | ||
SparkPlanTest.checkAnswer(input, planFunction, expectedRows) match { | ||
case Some(errorMessage) => fail(errorMessage) | ||
case None => | ||
} | ||
} | ||
} | ||
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/** | ||
* Helper methods for writing tests of individual physical operators. | ||
*/ | ||
object SparkPlanTest { | ||
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/** | ||
* Runs the plan and makes sure the answer matches the expected result. | ||
* @param input the input data to be used. | ||
* @param planFunction a function which accepts the input SparkPlan and uses it to instantiate | ||
* the physical operator that's being tested. | ||
* @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. | ||
*/ | ||
def checkAnswer( | ||
input: DataFrame, | ||
planFunction: SparkPlan => SparkPlan, | ||
expectedAnswer: Seq[Row]): Option[String] = { | ||
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val outputPlan = planFunction(input.queryExecution.sparkPlan) | ||
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// A very simple resolver to make writing tests easier. In contrast to the real resolver | ||
// this is always case sensitive and does not try to handle scoping or complex type resolution. | ||
val resolvedPlan = outputPlan transform { | ||
case plan: SparkPlan => | ||
val inputMap = plan.children.flatMap(_.output).zipWithIndex.map { | ||
case (a, i) => | ||
(a.name, BoundReference(i, a.dataType, a.nullable)) | ||
}.toMap | ||
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plan.transformExpressions { | ||
case UnresolvedAttribute(Seq(u)) => | ||
inputMap.getOrElse(u, | ||
sys.error(s"Invalid Test: Cannot resolve $u given input $inputMap")) | ||
} | ||
} | ||
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def prepareAnswer(answer: Seq[Row]): Seq[Row] = { | ||
// Converts data to types that we can do equality comparison using Scala collections. | ||
// For BigDecimal type, the Scala type has a better definition of equality test (similar to | ||
// Java's java.math.BigDecimal.compareTo). | ||
// For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for | ||
// equality test. | ||
// This function is copied from Catalyst's QueryTest | ||
val converted: Seq[Row] = answer.map { s => | ||
Row.fromSeq(s.toSeq.map { | ||
case d: java.math.BigDecimal => BigDecimal(d) | ||
case b: Array[Byte] => b.toSeq | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. The equality check of Array[Byte] will be fixed by #6876 There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Ah, gotcha. I'd be happy to block on this. One consideration, though: we might want to backport this test helper to some of our maintenance branches so that we don't get build failures when backporting regression tests which use There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Instead of blocking lets just make a note in the JIRA to remove these hacks if possible later. I'd like to get this in today and I agree with you backporting concerns. |
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case o => o | ||
}) | ||
} | ||
converted.sortBy(_.toString()) | ||
} | ||
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val sparkAnswer: Seq[Row] = try { | ||
resolvedPlan.executeCollect().toSeq | ||
} catch { | ||
case NonFatal(e) => | ||
val errorMessage = | ||
s""" | ||
| Exception thrown while executing Spark plan: | ||
| $outputPlan | ||
| == Exception == | ||
| $e | ||
| ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)} | ||
""".stripMargin | ||
return Some(errorMessage) | ||
} | ||
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if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) { | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Should we use ``!==` here? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I don't think so, since we want to throw our own custom error message rather than letting ScalaTest do it. |
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val errorMessage = | ||
s""" | ||
| Results do not match for Spark plan: | ||
| $outputPlan | ||
| == Results == | ||
| ${sideBySide( | ||
s"== Correct Answer - ${expectedAnswer.size} ==" +: | ||
prepareAnswer(expectedAnswer).map(_.toString()), | ||
s"== Spark Answer - ${sparkAnswer.size} ==" +: | ||
prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")} | ||
""".stripMargin | ||
return Some(errorMessage) | ||
} | ||
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None | ||
} | ||
} | ||
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What's the problem with java.math.BigDecimal?
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I'm not sure; as the comment for
converted
notes, this is duplicated from Catalyst'sQueryTest
.