andygrove commented on code in PR #2528: URL: https://github.com/apache/datafusion-comet/pull/2528#discussion_r2534533647
########## spark/src/test/scala/org/apache/comet/CometFuzzIcebergSuite.scala: ########## @@ -0,0 +1,235 @@ +/* + * 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.comet + +import scala.util.Random + +import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.internal.SQLConf.ParquetOutputTimestampType +import org.apache.spark.sql.types._ + +import org.apache.comet.DataTypeSupport.isComplexType +import org.apache.comet.testing.{DataGenOptions, FuzzDataGenerator, ParquetGenerator, SchemaGenOptions} + +class CometFuzzIcebergSuite extends CometFuzzIcebergBase { + + test("select *") { + val sql = s"SELECT * FROM $icebergTableName" + val (_, cometPlan) = checkSparkAnswer(sql) + assert(1 == collectIcebergNativeScans(cometPlan).length) + } + + test("select * with limit") { + val sql = s"SELECT * FROM $icebergTableName LIMIT 500" + val (_, cometPlan) = checkSparkAnswer(sql) + assert(1 == collectIcebergNativeScans(cometPlan).length) + } + + test("order by single column") { + val df = spark.table(icebergTableName) + for (col <- df.columns) { + val sql = s"SELECT $col FROM $icebergTableName ORDER BY $col" + // cannot run fully natively due to range partitioning and sort + val (_, cometPlan) = checkSparkAnswer(sql) + assert(1 == collectIcebergNativeScans(cometPlan).length) + } + } + + test("order by multiple columns") { + val df = spark.table(icebergTableName) + val allCols = df.columns.mkString(",") + val sql = s"SELECT $allCols FROM $icebergTableName ORDER BY $allCols" + // cannot run fully natively due to range partitioning and sort + val (_, cometPlan) = checkSparkAnswer(sql) + assert(1 == collectIcebergNativeScans(cometPlan).length) + } + + test("order by random columns") { + val df = spark.table(icebergTableName) + + for (_ <- 1 to 10) { + // We only do order by permutations of primitive types to exercise native shuffle's + // RangePartitioning which only supports those types. + // This used to be df.columns.slice(0, 14) but we're removing two binary columns in + // CometFuzzIcebergBase.beforeAll + val shuffledPrimitiveCols = Random.shuffle(df.columns.slice(0, 12).toList) + val randomSize = Random.nextInt(shuffledPrimitiveCols.length) + 1 + val randomColsSubset = shuffledPrimitiveCols.take(randomSize).toArray.mkString(",") + val sql = s"SELECT $randomColsSubset FROM $icebergTableName ORDER BY $randomColsSubset" + checkSparkAnswerAndOperator(sql) + } + } + + test("distribute by single column (complex types)") { + val df = spark.table(icebergTableName) + val columns = df.schema.fields.filter(f => isComplexType(f.dataType)).map(_.name) + for (col <- columns) { + // DISTRIBUTE BY is equivalent to df.repartition($col) and uses + val sql = s"SELECT $col FROM $icebergTableName DISTRIBUTE BY $col" + val resultDf = spark.sql(sql) + resultDf.collect() + // check for Comet shuffle + val plan = + resultDf.queryExecution.executedPlan.asInstanceOf[AdaptiveSparkPlanExec].executedPlan + val cometShuffleExchanges = collectCometShuffleExchanges(plan) + // Iceberg native scan supports complex types + assert(cometShuffleExchanges.length == 1) + } + } + + test("shuffle supports all types") { + val df = spark.table(icebergTableName) + val df2 = df.repartition(8, df.col("c0")).sort("c1") + df2.collect() + val cometShuffles = collectCometShuffleExchanges(df2.queryExecution.executedPlan) + // Iceberg native scan supports complex types + assert(cometShuffles.length == 2) + } + + test("join") { + val df = spark.table(icebergTableName) + df.createOrReplaceTempView("t1") + df.createOrReplaceTempView("t2") + // Filter out complex types - iceberg-rust can't create predicates for struct/array/map equality + val primitiveColumns = df.schema.fields.filterNot(f => isComplexType(f.dataType)).map(_.name) + for (col <- primitiveColumns) { + // cannot run fully native due to HashAggregate + val sql = s"SELECT count(*) FROM t1 JOIN t2 ON t1.$col = t2.$col" + val (_, cometPlan) = checkSparkAnswer(sql) + assert(2 == collectIcebergNativeScans(cometPlan).length) + } + } + + test("decode") { + val df = spark.table(icebergTableName) + // We want to make sure that the schema generator wasn't modified to accidentally omit + // BinaryType, since then this test would not run any queries and silently pass. + var testedBinary = false + for (field <- df.schema.fields if field.dataType == BinaryType) { + testedBinary = true + // Intentionally use odd capitalization of 'utf-8' to test normalization. + val sql = s"SELECT decode(${field.name}, 'utF-8') FROM $icebergTableName" + checkSparkAnswerAndOperator(sql) + } + assert(testedBinary) + } + + test("regexp_replace") { + withSQLConf(CometConf.COMET_REGEXP_ALLOW_INCOMPATIBLE.key -> "true") { + val df = spark.table(icebergTableName) + // We want to make sure that the schema generator wasn't modified to accidentally omit + // StringType, since then this test would not run any queries and silently pass. + var testedString = false + for (field <- df.schema.fields if field.dataType == StringType) { + testedString = true + val sql = s"SELECT regexp_replace(${field.name}, 'a', 'b') FROM $icebergTableName" + checkSparkAnswerAndOperator(sql) + } + assert(testedString) + } + } + + test("Iceberg temporal types written as INT96") { + testIcebergTemporalTypes(ParquetOutputTimestampType.INT96) + } + + test("Iceberg temporal types written as TIMESTAMP_MICROS") { + testIcebergTemporalTypes(ParquetOutputTimestampType.TIMESTAMP_MICROS) + } + + test("Iceberg temporal types written as TIMESTAMP_MILLIS") { + testIcebergTemporalTypes(ParquetOutputTimestampType.TIMESTAMP_MILLIS) + } + + private def testIcebergTemporalTypes( + outputTimestampType: ParquetOutputTimestampType.Value, + generateArray: Boolean = true, + generateStruct: Boolean = true): Unit = { + + val schema = FuzzDataGenerator.generateSchema( + SchemaGenOptions( + generateArray = generateArray, + generateStruct = generateStruct, + primitiveTypes = SchemaGenOptions.defaultPrimitiveTypes.filterNot { dataType => + // Disable decimals - iceberg-rust doesn't support FIXED_LEN_BYTE_ARRAY in page index yet + dataType.isInstanceOf[DecimalType] + })) + + val options = + DataGenOptions(generateNegativeZero = false) + + withTempPath { filename => + val random = new Random(42) + withSQLConf( + CometConf.COMET_ENABLED.key -> "false", + SQLConf.PARQUET_OUTPUT_TIMESTAMP_TYPE.key -> outputTimestampType.toString, + SQLConf.SESSION_LOCAL_TIMEZONE.key -> defaultTimezone) { + ParquetGenerator.makeParquetFile(random, spark, filename.toString, schema, 100, options) + } + + Seq(defaultTimezone, "UTC", "America/Denver").foreach { tz => + Seq(true, false).foreach { inferTimestampNtzEnabled => + Seq(true, false).foreach { int96TimestampConversion => + Seq(true, false).foreach { int96AsTimestamp => + withSQLConf( + CometConf.COMET_ENABLED.key -> "true", + SQLConf.SESSION_LOCAL_TIMEZONE.key -> tz, + SQLConf.PARQUET_INT96_AS_TIMESTAMP.key -> int96AsTimestamp.toString, + SQLConf.PARQUET_INT96_TIMESTAMP_CONVERSION.key -> int96TimestampConversion.toString, + SQLConf.PARQUET_INFER_TIMESTAMP_NTZ_ENABLED.key -> inferTimestampNtzEnabled.toString) { + + val df = spark.table(icebergTableName) + + Seq(defaultTimezone, "UTC", "America/Denver").foreach { tz => Review Comment: This answers my earlier question about timezone testing -- This is an automated message from the Apache Git Service. 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