HeartSaVioR commented on code in PR #40561: URL: https://github.com/apache/spark/pull/40561#discussion_r1153931344
########## sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamingDeduplicationWithinWatermarkSuite.scala: ########## @@ -0,0 +1,184 @@ +/* + * 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.streaming + +import org.apache.spark.sql.{AnalysisException, Dataset, SaveMode} +import org.apache.spark.sql.catalyst.streaming.InternalOutputModes.Append +import org.apache.spark.sql.execution.streaming.MemoryStream +import org.apache.spark.sql.functions.timestamp_seconds + +class StreamingDeduplicationWithinWatermarkSuite extends StateStoreMetricsTest { + + import testImplicits._ + + test("deduplicate without event time column") { + def testAndVerify(df: Dataset[_]): Unit = { + val exc = intercept[AnalysisException] { + df.writeStream.format("noop").start() + } + + assert(exc.getMessage.contains("dropDuplicatesWithinWatermark is not supported")) + assert(exc.getMessage.contains("streaming DataFrames/DataSets without watermark")) + } + + val inputData = MemoryStream[String] + val result = inputData.toDS().dropDuplicatesWithinWatermark() + testAndVerify(result) + + val result2 = inputData.toDS().withColumn("newcol", $"value") + .dropDuplicatesWithinWatermark("newcol") + testAndVerify(result2) + + val inputData2 = MemoryStream[(String, Int)] + val otherSideForJoin = inputData2.toDF() + .select($"_1" as "key", timestamp_seconds($"_2") as "time") + .withWatermark("Time", "10 seconds") + + val result3 = inputData.toDS() + .select($"value".as("key")) + // there are two streams which one stream only defines the watermark. the stream which + // contains dropDuplicatesWithinWatermark does not define the watermark, which is not + // supported. + .dropDuplicatesWithinWatermark() + .join(otherSideForJoin, "key") + testAndVerify(result3) + } + + test("deduplicate with all columns with event time column") { + val inputData = MemoryStream[Int] + val result = inputData.toDS() + .withColumn("eventTime", timestamp_seconds($"value")) + .withWatermark("eventTime", "10 seconds") + .dropDuplicatesWithinWatermark() + .select($"eventTime".cast("long").as[Long]) + + testStream(result, Append)( + // Advance watermark to 5 secs, no-data-batch does not drop state rows + AddData(inputData, (1 to 5).flatMap(_ => (10 to 15)): _*), + CheckAnswer(10 to 15: _*), + assertNumStateRows(total = 6, updated = 6), + + // Advance watermark to 7 secs, no-data-batch does not drop state rows + AddData(inputData, (13 to 17): _*), + // 13 to 15 are duplicated + CheckNewAnswer(16, 17), + assertNumStateRows(total = 8, updated = 2), + + AddData(inputData, 5), // Should not emit anything as data less than watermark + CheckNewAnswer(), + assertNumStateRows(total = 8, updated = 0, droppedByWatermark = 1), + + // Advance watermark to 25 secs, no-data-batch drops state rows having expired time <= 25 + AddData(inputData, 35), + CheckNewAnswer(35), + assertNumStateRows(total = 3, updated = 1), + + // Advance watermark to 45 seconds, no-data-batch drops state rows having expired time <= 45 + AddData(inputData, 55), + CheckNewAnswer(55), + assertNumStateRows(total = 1, updated = 1) + ) + } + + test("deduplicate with some columns with event time column") { Review Comment: That's actually not the main purpose (just wanted to test with subset of columns) but it's also good to frame like that. -- 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: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
