jerrypeng commented on code in PR #55420: URL: https://github.com/apache/spark/pull/55420#discussion_r3151685686
########## connector/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/RTMKafkaKafkaBenchmarkSuite.scala: ########## @@ -0,0 +1,296 @@ +/* + * 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.kafka010 + +import java.nio.file.Files +import java.util.{Properties, Timer, TimerTask} +import java.util.concurrent.{CountDownLatch, TimeUnit} +import java.util.concurrent.atomic.AtomicLong + +import scala.concurrent.duration._ + +import org.apache.kafka.clients.producer.{Callback, KafkaProducer, Producer, ProducerRecord, RecordMetadata} +import org.scalatest.BeforeAndAfterEach +import org.scalatest.matchers.should.Matchers + +import org.apache.spark.{SparkContext, ThreadAudit} +import org.apache.spark.sql.Column +import org.apache.spark.sql.execution.streaming.RealTimeTrigger +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.streaming.StreamingQueryListener +import org.apache.spark.sql.test.TestSparkSession + +/** + * Stateless Kafka-to-Kafka RTM benchmark. Reads from an input Kafka topic, applies a + * stateless transformation, and writes results to an output Kafka topic using + * [[RealTimeTrigger]]. After the run it reports e2e latency percentiles. + * + * This benchmark intentionally runs a real local-cluster and a live Kafka broker, so it + * is slow. Run it explicitly when measuring RTM throughput and latency for the stateless path. + */ +class RTMKafkaKafkaBenchmarkSuite + extends KafkaSourceTest + with ThreadAudit + with BeforeAndAfterEach + with Matchers { + + override protected def createSparkSession = new TestSparkSession( + new SparkContext( + "local-cluster[3, 5, 1024]", + "microbatch-context", + sparkConf + )) + + test("RTM stateless kafka-to-kafka benchmark") { + benchmark(15.seconds.toMillis, 4) + } + + def benchmark(longRunningBatchDurationMs: Long, numBatches: Long): Unit = { + val inputTopic = newTopic() + testUtils.createTopic(inputTopic, partitions = 5) + + val outputTopic = newTopic() + testUtils.createTopic(outputTopic, partitions = 5) + + spark.conf.set(SQLConf.STREAMING_POLLING_DELAY.key, 10) + + val kafkaStream = spark.readStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("subscribe", inputTopic) + .option("kafka.fetch.max.wait.ms", "10") + .option("kafka.max.partition.fetch.bytes", "10485760") // 10MB + .load() + + val currentTimestampUDF = udf(() => System.currentTimeMillis()) + + val streamWithObserved = kafkaStream + .withColumn("value", base64(col("value"))) + .withColumn( + "headers", + array( + struct( + lit("source-timestamp") as "key", + unix_millis(col("timestamp")).cast("STRING").cast("BINARY") as "value"))) + .withColumn("temp-timestamp", currentTimestampUDF()) + .withColumn( + "latency", + col("temp-timestamp").cast("long") - unix_millis(col("timestamp")).cast("long")) + .observe( + name = "observedLatency", + avg(col("latency")).as("avg"), + max(col("latency")).as("max"), + percentile_approx(col("latency"), lit(0.99), lit(10000)).as("p99"), + percentile_approx(col("latency"), lit(0.5), lit(10000)).as("p50")) + .drop(col("latency")) + .drop(col("temp-timestamp")) + .drop(col("timestamp")) + + val query = streamWithObserved.writeStream + .format("kafka") + .option("kafka.bootstrap.servers", testUtils.brokerAddress) + .option("topic", outputTopic) + .option("checkpointLocation", Files.createTempDirectory("some-prefix").toFile.getName) + .option("kafka.buffer.memory", "67108864") // 64MB + .option("kafka.compression.type", "snappy") + .outputMode("update") + .queryName("rtm-kafka-kafka") + .trigger(RealTimeTrigger.apply(s"${longRunningBatchDurationMs} milliseconds")) + .start() + + val dataGenThread = new Thread(() => { + genData(testUtils.brokerAddress, inputTopic, 1000) + }) + dataGenThread.start() + + val latch = new CountDownLatch(1) + + spark.streams.addListener(new StreamingQueryListener { + override def onQueryStarted( + event: StreamingQueryListener.QueryStartedEvent): Unit = {} + + override def onQueryTerminated( + event: StreamingQueryListener.QueryTerminatedEvent): Unit = {} + + override def onQueryProgress(event: StreamingQueryListener.QueryProgressEvent): Unit = { + if (event.progress.batchId == numBatches - 1) { + latch.countDown() + } + } + }) + + val timeoutMs = numBatches * longRunningBatchDurationMs * 2 + 60 * 1000 + val completed = latch.await(timeoutMs, TimeUnit.MILLISECONDS) + query.stop() + dataGenThread.interrupt() + if (!completed) { + throw new RuntimeException( + s"Benchmark timed out waiting for $numBatches batches to complete after ${timeoutMs}ms.") + } + + getLatencies(longRunningBatchDurationMs, numBatches, outputTopic) + } + + private def genData(url: String, topicName: String, throughput: Long): Unit = { Review Comment: done -- This is an automated message from the Apache Git Service. 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