HeartSaVioR commented on code in PR #52428: URL: https://github.com/apache/spark/pull/52428#discussion_r2375142301
########## sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/EventTimeWatermarkWithWatermarkDefInSelectSuite.scala: ########## @@ -0,0 +1,267 @@ +/* + * 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.execution.streaming + +import java.{util => ju} +import java.text.SimpleDateFormat + +import org.scalatest.BeforeAndAfter + +import org.apache.spark.internal.Logging +import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.catalyst.util.DateTimeTestUtils.UTC +import org.apache.spark.sql.execution.streaming.runtime.MemoryStream +import org.apache.spark.sql.functions.{col, timestamp_seconds} +import org.apache.spark.sql.streaming.StateStoreMetricsTest + +class EventTimeWatermarkWithWatermarkDefInSelectSuite + extends StateStoreMetricsTest + with BeforeAndAfter + with Logging { + + import testImplicits._ + + after { + sqlContext.streams.active.foreach(_.stop()) + } + + test("event time and watermark metrics with watermark in select DML - case 1") { + // All event time metrics where watermarking is set + val inputData = MemoryStream[Int] + val df = inputData.toDF() + .withColumn("eventTime", timestamp_seconds(col("value"))) + df.createOrReplaceTempView("stream_src") + val aggWithWatermark = spark.sql( + """ + |SELECT + | CAST(window.start AS LONG), CAST(count(*) AS LONG) AS count + |FROM + | stream_src WATERMARK eventTime DELAY OF INTERVAL 10 seconds + |GROUP BY window(eventTime, '5 seconds') + |""".stripMargin) + + testWindowedAggregation(inputData, aggWithWatermark) + } + + test("event time and watermark metrics with watermark in select DML - case 2") { + // All event time metrics where watermarking is set + val inputData = MemoryStream[Int] + val df = inputData.toDF() + df.createOrReplaceTempView("stream_src") + val aggWithWatermark = spark.sql( + """ + |SELECT + | CAST(window.start AS LONG), CAST(count(*) AS LONG) AS count + |FROM + | stream_src + | WATERMARK timestamp_seconds(value) AS eventTime DELAY OF INTERVAL 10 seconds + |GROUP BY window(eventTime, '5 seconds') + |""".stripMargin) + + testWindowedAggregation(inputData, aggWithWatermark) + } + + private def testWindowedAggregation( + inputData: MemoryStream[Int], + dataFrame: DataFrame): Unit = { + testStream(dataFrame)( + AddData(inputData, 15), + CheckAnswer(), + assertEventStats(min = 15, max = 15, avg = 15, wtrmark = 0), + AddData(inputData, 10, 12, 14), + CheckAnswer(), + assertEventStats(min = 10, max = 14, avg = 12, wtrmark = 5), + AddData(inputData, 25), + CheckAnswer((10, 3)), + assertEventStats(min = 25, max = 25, avg = 25, wtrmark = 5) + ) + } + + test("stream-stream join with watermark in select DML - case 1") { + val leftInput = MemoryStream[(Int, Int)] + val rightInput = MemoryStream[(Int, Int)] + + val df1 = leftInput.toDF().toDF("leftKey", "time") + .select($"leftKey", timestamp_seconds($"time") as "leftTime", + ($"leftKey" * 2) as "leftValue") + val df2 = rightInput.toDF().toDF("rightKey", "time") + .select($"rightKey", timestamp_seconds($"time") as "rightTime", + ($"rightKey" * 3) as "rightValue") + + df1.createOrReplaceTempView("stream_left") + df2.createOrReplaceTempView("stream_right") + + val joined = spark.sql( + """ + |SELECT + | leftKey, rightKey, CAST(leftTime AS INTEGER), CAST(rightTime AS INTEGER) + |FROM + | stream_left WATERMARK leftTime DELAY OF INTERVAL 0 second + |FULL OUTER JOIN + | stream_right WATERMARK rightTime DELAY OF INTERVAL 0 second Review Comment: No, that's just for simplification. You can have different definition of watermark including delay threshold. e.g. you can define watermark delay to 10 minutes in stream_left while defining delay to 5 minutes in stream_right. The corresponding DataFrame query is following: stream_left <- streaming DF stream_right <- streaming DF ``` val leftDf = stream_left.withWatermark("leftTime, "0 second") val rightDf = stream_right.withWatermark("rightTime", "0 second") val query = stream.left.join(stream_right, expr("leftKey = rightKey AND leftTime BETWEEN rightTime - INTERVAL 5 SECONDS"), "full_outer") query.writeStream.blabla.start() ``` -- 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. 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