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[NSE-762] Add complex types support for ColumnarSortExec (#763)
* Add complex types support for ColumnarSortExec * Add struct/map support * Fix clang format * Add Key check and fix Attr parse issue * Fix remaining UTs * Correct typo * Correct Typos * Add enable/disable config in ColumnarSort * Correct Error message
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native-sql-engine/core/src/test/scala/com/intel/oap/execution/PayloadSuite.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. | ||
| */ | ||
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| package com.intel.oap.execution | ||
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| import java.nio.file.Files | ||
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| import com.intel.oap.tpc.util.TPCRunner | ||
| import org.apache.log4j.{Level, LogManager} | ||
| import org.apache.spark.SparkConf | ||
| import org.apache.spark.sql.QueryTest | ||
| import org.apache.spark.sql.execution.ColumnarShuffleExchangeExec | ||
| import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec | ||
| import org.apache.spark.sql.functions.{col, expr} | ||
| import org.apache.spark.sql.test.SharedSparkSession | ||
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| class PayloadSuite extends QueryTest with SharedSparkSession { | ||
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| private val MAX_DIRECT_MEMORY = "5000m" | ||
| private var runner: TPCRunner = _ | ||
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| private var lPath: String = _ | ||
| private var rPath: String = _ | ||
| private val scale = 100 | ||
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| override protected def sparkConf: SparkConf = { | ||
| val conf = super.sparkConf | ||
| conf.set("spark.memory.offHeap.size", String.valueOf(MAX_DIRECT_MEMORY)) | ||
| .set("spark.plugins", "com.intel.oap.GazellePlugin") | ||
| .set("spark.sql.codegen.wholeStage", "false") | ||
| .set("spark.sql.sources.useV1SourceList", "") | ||
| .set("spark.oap.sql.columnar.tmp_dir", "/tmp/") | ||
| .set("spark.sql.columnar.sort.broadcastJoin", "true") | ||
| .set("spark.storage.blockManagerSlaveTimeoutMs", "3600000") | ||
| .set("spark.executor.heartbeatInterval", "3600000") | ||
| .set("spark.network.timeout", "3601s") | ||
| .set("spark.oap.sql.columnar.preferColumnar", "true") | ||
| .set("spark.oap.sql.columnar.sortmergejoin", "true") | ||
| .set("spark.sql.columnar.codegen.hashAggregate", "false") | ||
| .set("spark.sql.columnar.sort", "true") | ||
| .set("spark.sql.columnar.window", "true") | ||
| .set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.ColumnarShuffleManager") | ||
| .set("spark.unsafe.exceptionOnMemoryLeak", "false") | ||
| .set("spark.network.io.preferDirectBufs", "false") | ||
| .set("spark.sql.sources.useV1SourceList", "arrow,parquet") | ||
| .set("spark.sql.autoBroadcastJoinThreshold", "-1") | ||
| .set("spark.oap.sql.columnar.sortmergejoin.lazyread", "true") | ||
| .set("spark.oap.sql.columnar.autorelease", "false") | ||
| .set("spark.sql.shuffle.partitions", "50") | ||
| .set("spark.sql.adaptive.coalescePartitions.initialPartitionNum", "5") | ||
| .set("spark.oap.sql.columnar.shuffledhashjoin.buildsizelimit", "200m") | ||
| // .set("spark.oap.sql.columnar.rowtocolumnar", "false") | ||
| // .set("spark.oap.sql.columnar.columnartorow", "false") | ||
| return conf | ||
| } | ||
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| override def beforeAll(): Unit = { | ||
| super.beforeAll() | ||
| LogManager.getRootLogger.setLevel(Level.WARN) | ||
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| val lfile = Files.createTempFile("", ".parquet").toFile | ||
| lfile.deleteOnExit() | ||
| lPath = lfile.getAbsolutePath | ||
| spark.range(2).select(col("id"), expr("1").as("kind"), | ||
| expr("1").as("key"), | ||
| expr("array(1, 2)").as("arr_field"), | ||
| expr("array(array(1, 2), array(3, 4))").as("arr_arr_field"), | ||
| expr("array(struct(1, 2), struct(1, 2))").as("arr_struct_field"), | ||
| expr("array(map(1, 2), map(3,4))").as("arr_map_field"), | ||
| expr("struct(1, 2)").as("struct_field"), | ||
| expr("struct(1, struct(1, 2))").as("struct_struct_field"), | ||
| expr("struct(1, array(1, 2))").as("struct_array_field"), | ||
| expr("map(1, 2)").as("map_field"), | ||
| expr("map(1, map(3,4))").as("map_map_field"), | ||
| expr("map(1, array(1, 2))").as("map_arr_field"), | ||
| expr("map(struct(1, 2), 2)").as("map_struct_field")) | ||
| .coalesce(1) | ||
| .write | ||
| .format("parquet") | ||
| .mode("overwrite") | ||
| .parquet(lPath) | ||
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| val rfile = Files.createTempFile("", ".parquet").toFile | ||
| rfile.deleteOnExit() | ||
| rPath = rfile.getAbsolutePath | ||
| spark.range(2).select(col("id"), expr("id % 2").as("kind"), | ||
| expr("id % 2").as("key"), | ||
| expr("array(1, 2)").as("arr_field"), | ||
| expr("struct(1, 2)").as("struct_field")) | ||
| .coalesce(1) | ||
| .write | ||
| .format("parquet") | ||
| .mode("overwrite") | ||
| .parquet(rPath) | ||
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| spark.catalog.createTable("ltab", lPath, "arrow") | ||
| spark.catalog.createTable("rtab", rPath, "arrow") | ||
| } | ||
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| test("Test Array in Sort") { | ||
| // spark.sql("SELECT * FROM ltab").printSchema() | ||
| val df = spark.sql("SELECT ltab.arr_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count == 2) | ||
| } | ||
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| test("Test Nest Array in Sort") { | ||
| val df = spark.sql("SELECT ltab.arr_arr_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count == 2) | ||
| } | ||
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| test("Test Nest Array in multi-keys Sort") { | ||
| val df = spark.sql("SELECT ltab.arr_arr_field FROM ltab order by ltab.kind, ltab.key") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count == 2) | ||
| } | ||
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| test("Test Struct in Sort") { | ||
| val df = spark.sql("SELECT ltab.struct_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
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| test("Test Nest Struct in Sort") { | ||
| val df = spark.sql("SELECT ltab.struct_struct_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
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| test("Test Struct_Array in Sort") { | ||
| val df = spark.sql("SELECT ltab.struct_array_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
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| test("Test Map in Sort") { | ||
| val df = spark.sql("SELECT ltab.map_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
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| test("Test Nest Map in Sort") { | ||
| val df = spark.sql("SELECT ltab.map_map_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
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| test("Test Map_Array in Sort") { | ||
| val df = spark.sql("SELECT ltab.map_arr_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
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| test("Test Map_Struct in Sort") { | ||
| val df = spark.sql("SELECT ltab.map_struct_field FROM ltab order by ltab.kind") | ||
| df.explain(false) | ||
| df.show() | ||
| assert(df.queryExecution.executedPlan.find(_.isInstanceOf[ColumnarSortExec]).isDefined) | ||
| assert(df.count() == 2) | ||
| } | ||
|
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| override def afterAll(): Unit = { | ||
| super.afterAll() | ||
| } | ||
| } |
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