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...ark-datasource/hudi-spark/src/test/java/org/apache/hudi/TestDecimalTypeDataWorkflow.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.hudi | ||
|
|
||
| import org.apache.hudi.DataSourceWriteOptions._ | ||
| import org.apache.hudi.common.config.{HoodieReaderConfig, HoodieStorageConfig} | ||
| import org.apache.hudi.config.HoodieWriteConfig | ||
| import org.apache.hudi.testutils.SparkClientFunctionalTestHarness | ||
| import org.apache.spark.sql.types.{Decimal, DecimalType, IntegerType, StructField, StructType} | ||
| import org.apache.spark.sql.{DataFrame, Row, SaveMode} | ||
| import org.junit.jupiter.api.Assertions.assertTrue | ||
| import org.junit.jupiter.api.Test | ||
| import org.junit.jupiter.params.ParameterizedTest | ||
| import org.junit.jupiter.params.provider.CsvSource | ||
|
|
||
| class TestDecimalTypeDataWorkflow extends SparkClientFunctionalTestHarness{ | ||
| val sparkOpts: Map[String, String] = Map( | ||
| HoodieStorageConfig.LOGFILE_DATA_BLOCK_FORMAT.key -> "parquet", | ||
| HoodieWriteConfig.RECORD_MERGE_IMPL_CLASSES.key -> classOf[DefaultSparkRecordMerger].getName) | ||
| val fgReaderOpts: Map[String, String] = Map( | ||
| HoodieReaderConfig.FILE_GROUP_READER_ENABLED.key -> "true", | ||
| HoodieReaderConfig.MERGE_USE_RECORD_POSITIONS.key -> "true") | ||
| val opts = sparkOpts ++ fgReaderOpts | ||
|
|
||
| @ParameterizedTest | ||
| @CsvSource(value = Array("10,2", "15,5", "20,10", "38,18", "5,0")) | ||
| def testDecimalInsertUpdateDeleteRead(precision: String, scale: String): Unit = { | ||
| // Create schema | ||
| val schema = StructType(Seq( | ||
| StructField("id", IntegerType, nullable = true), | ||
| StructField( | ||
| "decimal_col", | ||
| DecimalType(Integer.valueOf(precision), Integer.valueOf(scale)), | ||
| nullable = true))) | ||
| // Build data conforming to the schema. | ||
| val tablePath = basePath | ||
| val data: Seq[(Int, Decimal)] = Seq( | ||
| (1, Decimal("123.45")), | ||
| (2, Decimal("987.65")), | ||
| (3, Decimal("-10.23")), | ||
| (4, Decimal("0.01")), | ||
| (5, Decimal("1000.00"))) | ||
| val rows = data.map{ | ||
| case (id, decimalVal) => Row(id, decimalVal.toJavaBigDecimal)} | ||
| val rddData = spark.sparkContext.parallelize(rows) | ||
|
|
||
| // Insert. | ||
| val insertDf: DataFrame = spark.sqlContext.createDataFrame(rddData, schema) | ||
| .toDF("id", "decimal_col").sort("id") | ||
| insertDf.write.format("hudi") | ||
| .option(RECORDKEY_FIELD.key(), "id") | ||
| .option(PRECOMBINE_FIELD.key(), "decimal_col") | ||
| .option(TABLE_TYPE.key, "MERGE_ON_READ") | ||
| .option(TABLE_NAME.key, "test_table") | ||
| .options(opts) | ||
| .mode(SaveMode.Overwrite) | ||
| .save(tablePath) | ||
|
|
||
| // Update. | ||
| val update: Seq[(Int, Decimal)] = Seq( | ||
| (1, Decimal("543.21")), | ||
| (2, Decimal("111.11")), | ||
| (6, Decimal("1001.00"))) | ||
| val updateRows = update.map { | ||
| case (id, decimalVal) => Row(id, decimalVal.toJavaBigDecimal) | ||
| } | ||
| val rddUpdate = spark.sparkContext.parallelize(updateRows) | ||
| val updateDf: DataFrame = spark.createDataFrame(rddUpdate, schema) | ||
| .toDF("id", "decimal_col").sort("id") | ||
| updateDf.write.format("hudi") | ||
| .option(OPERATION.key(), "upsert") | ||
| .options(opts) | ||
| .mode(SaveMode.Append) | ||
| .save(tablePath) | ||
|
|
||
| // Delete. | ||
| val delete: Seq[(Int, Decimal)] = Seq( | ||
| (3, Decimal("543.21")), | ||
| (4, Decimal("111.11"))) | ||
| val deleteRows = delete.map { | ||
| case (id, decimalVal) => Row(id, decimalVal.toJavaBigDecimal) | ||
| } | ||
| val rddDelete = spark.sparkContext.parallelize(deleteRows) | ||
| val deleteDf: DataFrame = spark.createDataFrame(rddDelete, schema) | ||
| .toDF("id", "decimal_col").sort("id") | ||
| deleteDf.write.format("hudi") | ||
| .option(OPERATION.key(), "delete") | ||
| .options(opts) | ||
| .mode(SaveMode.Append) | ||
| .save(tablePath) | ||
|
|
||
| // Asserts | ||
| val actual = spark.read.format("hudi").load(tablePath).select("id", "decimal_col") | ||
| val expected: Seq[(Int, Decimal)] = Seq( | ||
| (1, Decimal("543.21")), | ||
| (2, Decimal("987.65")), | ||
| (5, Decimal("1000.00")), | ||
| (6, Decimal("1001.00"))) | ||
| val expectedRows = expected.map { | ||
| case (id, decimalVal) => Row(id, decimalVal.toJavaBigDecimal) | ||
| } | ||
| val rddExpected = spark.sparkContext.parallelize(expectedRows) | ||
| val expectedDf: DataFrame = spark.createDataFrame(rddExpected, schema) | ||
| .toDF("id", "decimal_col").sort("id") | ||
| val expectedMinusActual = expectedDf.except(actual) | ||
| val actualMinusExpected = actual.except(expectedDf) | ||
| expectedDf.show(false) | ||
| actual.show(false) | ||
| expectedMinusActual.show(false) | ||
| actualMinusExpected.show(false) | ||
| assertTrue(expectedMinusActual.isEmpty && actualMinusExpected.isEmpty) | ||
| } | ||
| } |