HeartSaVioR commented on code in PR #40561: URL: https://github.com/apache/spark/pull/40561#discussion_r1153906537
########## 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", $"eventTime".cast("long").as[Long]) + + testStream(result, Append)( + // Advances watermark to 15 + AddData(inputData, "a" -> 17), + CheckNewAnswer("a" -> 17), + // expired time is set to 19 + assertNumStateRows(total = 1, updated = 1), + + // Watermark does not advance + AddData(inputData, "a" -> 16), + CheckNewAnswer(), + assertNumStateRows(total = 1, updated = 0), + + // Watermark does not advance + // Should not emit anything as data less than watermark + AddData(inputData, "a" -> 13), + CheckNewAnswer(), + assertNumStateRows(total = 1, updated = 0, droppedByWatermark = 1), + + // Advances watermark to 20. no-data batch drops state row ("a" -> 19) + AddData(inputData, "b" -> 22, "c" -> 21), + CheckNewAnswer("b" -> 22, "c" -> 21), + // expired time is set to 24 and 23 + assertNumStateRows(total = 2, updated = 2), + + // Watermark does not advance + AddData(inputData, "a" -> 21), + // "a" is identified as new event since previous batch dropped state row ("a" -> 19) + CheckNewAnswer("a" -> 21), + // expired time is set to 23 + assertNumStateRows(total = 3, updated = 1), + + // Advances watermark to 23. no-data batch drops state row ("a" -> 23), ("c" -> 23) + AddData(inputData, "d" -> 25), + CheckNewAnswer("d" -> 25), + assertNumStateRows(total = 2, updated = 1) + ) + } + + test("SPARK-39650: duplicate with specific keys should allow input to change schema") { + withTempDir { checkpoint => + val dedupeInputData = MemoryStream[(String, Int)] + val dedupe = dedupeInputData.toDS() + .withColumn("eventTime", timestamp_seconds($"_2")) + .withWatermark("eventTime", "10 second") + .dropDuplicatesWithinWatermark("_1") + .select($"_1", $"eventTime".cast("long").as[Long]) + + testStream(dedupe, Append)( + StartStream(checkpointLocation = checkpoint.getCanonicalPath), + + AddData(dedupeInputData, "a" -> 1), + CheckNewAnswer("a" -> 1), + + AddData(dedupeInputData, "a" -> 2, "b" -> 3), + CheckNewAnswer("b" -> 3) + ) + + val dedupeInputData2 = MemoryStream[(String, Int, String)] + val dedupe2 = dedupeInputData2.toDS() + .withColumn("eventTime", timestamp_seconds($"_2")) + .withWatermark("eventTime", "10 second") + .dropDuplicatesWithinWatermark(Seq("_1")) + .select($"_1", $"eventTime".cast("long").as[Long], $"_3") + + // initialize new memory stream with previously executed batches + dedupeInputData2.addData(("a", 1, "dummy")) + dedupeInputData2.addData(Seq(("a", 2, "dummy"), ("b", 3, "dummy"))) + + testStream(dedupe2, Append)( + StartStream(checkpointLocation = checkpoint.getCanonicalPath), + + AddData(dedupeInputData2, ("a", 5, "a"), ("b", 2, "b"), ("c", 9, "c")), + CheckNewAnswer(("c", 9, "c")) + ) + } + } + + // FIXME: test to disallow changing event time column between TimestampType vs TimestampNTZType Review Comment: cc. @gengliangwang since he made efforts for TimestampNTZ. My understanding is that the underlying long value of TimestampType is based on epoch (+00:00) so technically it should be already a form of NTZ, but semantic and expectation from users during dealing with two types may not be very clear. Did we go through this topic in any area (SQL batch query)? -- 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
