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144 changes: 144 additions & 0 deletions
144
...mon-cluster-test/src/test/scala/org/apache/spark/sql/common/util/DataSourceTestUtil.scala
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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. | ||
*/ | ||
package org.apache.spark.sql.common.util | ||
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import java.io.File | ||
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import scala.collection.JavaConverters._ | ||
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import org.apache.spark.sql.carbondata.execution.datasources.CarbonFileIndexReplaceRule | ||
import org.apache.spark.sql.{DataFrame, Row, SparkSession} | ||
import org.apache.spark.sql.catalyst.plans.logical | ||
import org.apache.spark.sql.catalyst.util.sideBySide | ||
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import org.apache.carbondata.core.constants.CarbonCommonConstants | ||
import org.apache.carbondata.core.datastore.impl.FileFactory | ||
import org.apache.carbondata.core.util.CarbonProperties | ||
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object DataSourceTestUtil { | ||
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val rootPath = new File(this.getClass.getResource("/").getPath | ||
+ "../../../..").getCanonicalPath | ||
val warehouse1 = FileFactory.getPath(s"$rootPath/integration/spark-datasource/target/warehouse") | ||
.toString | ||
val resource = s"$rootPath/integration/spark-datasource/src/test/resources" | ||
val metaStoreDB1 = s"$rootPath/integration/spark-datasource/target" | ||
val spark = SparkSession | ||
.builder() | ||
.enableHiveSupport() | ||
.master("local") | ||
.config("spark.sql.warehouse.dir", warehouse1) | ||
.config("spark.driver.host", "localhost") | ||
.config("spark.sql.crossJoin.enabled", "true") | ||
.config("spark.sql.hive.caseSensitiveInferenceMode", "INFER_AND_SAVE") | ||
.getOrCreate() | ||
spark.sparkContext.setLogLevel("ERROR") | ||
if (!spark.sparkContext.version.startsWith("2.1")) { | ||
spark.experimental.extraOptimizations = Seq(new CarbonFileIndexReplaceRule) | ||
} | ||
CarbonProperties.getInstance() | ||
.addProperty(CarbonCommonConstants.CARBON_MINMAX_ALLOWED_BYTE_COUNT, "40") | ||
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def checkAnswer(df: DataFrame, expectedAnswer: java.util.List[Row]): Unit = { | ||
checkAnswer(df, expectedAnswer.asScala) | ||
} | ||
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def checkExistence(df: DataFrame, exists: Boolean, keywords: String*) { | ||
val outputs = df.collect().map(_.mkString).mkString | ||
for (key <- keywords) { | ||
if (exists) { | ||
assert(outputs.contains(key), s"Failed for $df ($key doesn't exist in result)") | ||
} else { | ||
assert(!outputs.contains(key), s"Failed for $df ($key existed in the result)") | ||
} | ||
} | ||
} | ||
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def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = { | ||
checkAnswer(df, expectedAnswer.collect()) | ||
} | ||
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/** | ||
* Runs the plan and makes sure the answer matches the expected result. | ||
* If there was exception during the execution or the contents of the DataFrame does not | ||
* match the expected result, an error message will be returned. Otherwise, a [[None]] will | ||
* be returned. | ||
* | ||
* @param df the [[DataFrame]] to be executed | ||
* @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. | ||
*/ | ||
def checkAnswer(df: DataFrame, expectedAnswer: Seq[Row]): Unit = { | ||
val isSorted = df.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty | ||
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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. | ||
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 | ||
case d: Double => | ||
if (!d.isInfinite && !d.isNaN) { | ||
var bd = BigDecimal(d) | ||
bd = bd.setScale(5, BigDecimal.RoundingMode.UP) | ||
bd.doubleValue() | ||
} | ||
else { | ||
d | ||
} | ||
case o => o | ||
}) | ||
} | ||
if (!isSorted) converted.sortBy(_.toString()) else converted | ||
} | ||
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val sparkAnswer = try df.collect().toSeq catch { | ||
case e: Exception => | ||
val errorMessage = | ||
s""" | ||
|Exception thrown while executing query: | ||
|${ df.queryExecution } | ||
|== Exception == | ||
|$e | ||
|${ org.apache.spark.sql.catalyst.util.stackTraceToString(e) } | ||
""".stripMargin | ||
return Some(errorMessage) | ||
} | ||
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if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) { | ||
val errorMessage = | ||
s""" | ||
|Results do not match for query: | ||
|${ df.queryExecution } | ||
|== Results == | ||
|${ | ||
sideBySide( | ||
s"== Correct Answer - ${ expectedAnswer.size } ==" +: | ||
prepareAnswer(expectedAnswer).map(_.toString()), | ||
s"== Spark Answer - ${ sparkAnswer.size } ==" +: | ||
prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n") | ||
} | ||
""".stripMargin | ||
assert(false, errorMessage) | ||
} | ||
} | ||
} |
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