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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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