LinSimon-901101 commented on code in PR #6262: URL: https://github.com/apache/datafusion-comet/pull/6262#discussion_r4129316459
########## spark/src/test/scala/org/apache/comet/exec/CometInMemoryCachePruningSuite.scala: ########## @@ -0,0 +1,256 @@ +/* + * 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.exec + +import java.sql.Timestamp +import java.time.Instant + +import org.apache.spark.SparkConf +import org.apache.spark.sql.{CometTestBase, DataFrame, Row} +import org.apache.spark.sql.comet.{CometInMemoryTableScanExec, CometNativeScanExec} +import org.apache.spark.sql.execution.FileSourceScanExec +import org.apache.spark.sql.execution.columnar.CometInMemoryRelationHelper +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.types._ + +import org.apache.comet.CometConf + +class CometInMemoryCachePruningSuite extends CometTestBase { + + override protected def beforeAll(): Unit = { + CometInMemoryRelationHelper.clearSerializer() + super.beforeAll() + } + + override protected def afterAll(): Unit = { + try { + super.afterAll() + } finally { + CometInMemoryRelationHelper.clearSerializer() + } + } + + override protected def sparkConf: SparkConf = super.sparkConf + .set("spark.plugins", "org.apache.spark.CometPlugin") + .set( + "spark.sql.cache.serializer", + "org.apache.spark.sql.comet.execution.arrow.ArrowCachedBatchSerializer") + + private val schema = StructType( + Seq( + StructField("id", IntegerType, nullable = false), + StructField("d", DoubleType), + StructField("f", FloatType), + StructField("n", IntegerType), + StructField("s", StringType), + StructField("dec", DecimalType(20, 3)), + StructField("ts", TimestampType), + StructField("b", BooleanType))) + + private def fixture(): DataFrame = { + def repeated(d: Double): Seq[Double] = Seq.fill(4)(d) + // Every four rows form one batch in all three writers. Keep NaN-only, mixed finite/NaN, + // signed-zero-only, infinity and all-null batches separate so incorrect bounds lose rows. + val values = Seq( + repeated(Double.NegativeInfinity), + repeated(-100.0), + repeated(-2.0), + repeated(-0.0), + repeated(0.0), + repeated(0.25), + repeated(1.0), + repeated(2.0), + repeated(100.0), + repeated(Double.PositiveInfinity), + repeated(Double.NaN), + Seq(1.0, Double.NaN, 3.0, 2.0), + repeated(0.0), // all-null batch + Seq(-0.0, 0.0, -0.0, 0.0), + Seq(-3.0, -2.0, -1.0, 0.0), + Seq(Double.PositiveInfinity, Double.NaN, Double.PositiveInfinity, Double.NaN)) + val strings = Seq( + "", + "a", + "ab", + "b", + "\u007f", + "\u0080", + "\ue000", + "\ud800\udc00", + "é", + "中", + "prefix-a", + "prefix-z", + null, + "z", + "e\u0301", + "😀") + val rows = values.zipWithIndex.flatMap { case (batch, group) => + batch.zipWithIndex.map { case (d, offset) => + val isNull = group == 12 || (group == 11 && offset == 2) + Row( + group * 4 + offset, + if (isNull) null else Double.box(d), + if (isNull) null else Float.box(d.toFloat), + if (isNull) null else Int.box(group), + strings(group), + if (group == 12) null else new java.math.BigDecimal(s"${group - 8}.125"), + if (group == 12) null + else + Timestamp.from( + Instant + .parse("1960-01-01T00:00:00Z") + .plusSeconds(group * 86400L) + .plusNanos(offset * 1000L)), + if (group == 12) null else Boolean.box(group % 2 == 0)) + } + } + spark.createDataFrame(spark.sparkContext.parallelize(rows, 1), schema) + } + + private val predicates = Seq( + "d = CAST('NaN' AS DOUBLE)", + "f = CAST('NaN' AS FLOAT)", + "d > CAST('Infinity' AS DOUBLE)", + "f < CAST('NaN' AS FLOAT)", + "d = 0.0D", + "f = CAST('-0.0' AS FLOAT)", + "d >= CAST('-0.0' AS DOUBLE) AND d <= 0.0D", + "f >= CAST(0.0 AS FLOAT) AND f <= CAST('-0.0' AS FLOAT)", + "d = CAST('-Infinity' AS DOUBLE)", + "f >= CAST('Infinity' AS FLOAT)", + "d > -2.0D AND d < 2.0D", + "d IS NULL", + "d IS NOT NULL", + "n IS NULL", + "s = '中'", + "s >= '\ue000'", + "s < '\u0080'", + "s LIKE 'prefix%'", + "dec >= -1.125 AND dec < 2.125", + "ts < TIMESTAMP '1960-01-05 00:00:00'", + "b <=> true", + "id IN (1, 9, 49)", Review Comment: @andygrove Thanks for pointing this out. I'll add the `n = 5` coverage in a follow-up issue. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
