rangadi commented on code in PR #40561: URL: https://github.com/apache/spark/pull/40561#discussion_r1153915130
########## 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") { + val inputData = MemoryStream[(String, Int)] + val result = inputData.toDS() + .withColumn("eventTime", timestamp_seconds($"_2")) + .withWatermark("eventTime", "2 seconds") + .dropDuplicatesWithinWatermark("_1") + .select($"_1", $"eventTime".cast("long").as[Long]) + + testStream(result, Append)( + // Advances watermark to 15 + AddData(inputData, "a" -> 17), + CheckNewAnswer("a" -> 17), + // expired time is set to 19 + assertNumStateRows(total = 1, updated = 1), + + // Watermark does not advance + AddData(inputData, "a" -> 16), + CheckNewAnswer(), + assertNumStateRows(total = 1, updated = 0), + + // Watermark does not advance + // Should not emit anything as data less than watermark + AddData(inputData, "a" -> 13), + CheckNewAnswer(), + assertNumStateRows(total = 1, updated = 0, droppedByWatermark = 1), + + // Advances watermark to 20. no-data batch drops state row ("a" -> 19) + AddData(inputData, "b" -> 22, "c" -> 21), + CheckNewAnswer("b" -> 22, "c" -> 21), + // expired time is set to 24 and 23 + assertNumStateRows(total = 2, updated = 2), + + // Watermark does not advance + AddData(inputData, "a" -> 21), + // "a" is identified as new event since previous batch dropped state row ("a" -> 19) + CheckNewAnswer("a" -> 21), + // expired time is set to 23 + assertNumStateRows(total = 3, updated = 1), + + // Advances watermark to 23. no-data batch drops state row ("a" -> 23), ("c" -> 23) + AddData(inputData, "d" -> 25), + CheckNewAnswer("d" -> 25), + assertNumStateRows(total = 2, updated = 1) Review Comment: > 1 entry should be always left as we have no mechanism to advance watermark without input. 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