amaliujia commented on code in PR #40783: URL: https://github.com/apache/spark/pull/40783#discussion_r1165933582
########## connector/connect/client/jvm/src/test/scala/org/apache/spark/sql/streaming/StreamingQuerySuite.scala: ########## @@ -0,0 +1,78 @@ +/* + * 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.scalatest.concurrent.Eventually.eventually +import org.scalatest.concurrent.Futures.timeout +import org.scalatest.time.SpanSugar._ + +import org.apache.spark.sql.SQLHelper +import org.apache.spark.sql.connect.client.util.RemoteSparkSession +import org.apache.spark.sql.functions.col +import org.apache.spark.sql.functions.window + +class StreamingQuerySuite extends RemoteSparkSession with SQLHelper { + + test("Streaming API with windowed aggregate query") { + // This verifies standard streaming API by starting a streaming query with windowed count. + withSQLConf( + "spark.sql.shuffle.partitions" -> "1" // Avoid too many reducers. + ) { + val readDF = spark + .readStream + .format("rate") + .option("rowsPerSecond", "10") + .option("numPartitions", "1") + .load() + + // Verify schema (results in RPC + assert(readDF.schema.toDDL == "timestamp TIMESTAMP,value BIGINT") + + val countsDF = readDF + .withWatermark("timestamp", "10 seconds") + .groupBy(window(col("timestamp"), "5 seconds")) + .count() + .selectExpr( + "window.start as timestamp", + "count as num_events" + ) + + assert(countsDF.schema.toDDL == "timestamp TIMESTAMP,num_events BIGINT NOT NULL") + + // Start the query + val queryName = "sparkConnectStreamingQuery" + + val query = countsDF + .writeStream + .format("memory") + .queryName(queryName) + .trigger(Trigger.ProcessingTime("1 second")) + .start() + + // Verify some of the API. + assert(query.isActive) + eventually(timeout(10.seconds)) { + assert(query.status.isDataAvailable) + assert(query.recentProgress.length > 0) + } + + // Don't wait for any processed data. Otherwise the test could take multiple seconds. + query.stop() Review Comment: Actually now because the underlying implementation of this is changed to send the `stop` through the gRPC so I am wondering if you know at high-level how does the gRPC approach impact the lifecycle management of a StreamingQuery? One example is some exception might come from gRPC directly. Another example is if `query.stop()` is idempotent (or call it exactly-once?) as the gRPC can be broken and re-connect and the command is issued twice. -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
