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https://issues.apache.org/jira/browse/FLINK-9166?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16443376#comment-16443376
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Robert Metzger commented on FLINK-9166:
---------------------------------------
I think the problem is actually that this code is creating one big job with a
lot of operators (mostly depending on the SQL queries).
Most likely the RPC system (akka) fails to submit the jobgraph to the
JobManager, because the submission message is bigger than the
{{akka.framesize}}.
> Performance issue with Flink SQL
> --------------------------------
>
> Key: FLINK-9166
> URL: https://issues.apache.org/jira/browse/FLINK-9166
> Project: Flink
> Issue Type: Bug
> Components: Table API & SQL
> Affects Versions: 1.4.2
> Reporter: SUBRAMANYA SURESH
> Priority: Major
> Labels: flink, graph, performance, sql, yarn
>
> With a high number of Flink SQL queries (100 of below), the Flink command
> line client fails with a "JobManager did not respond within 600000 ms" on a
> Yarn cluster.
> * JobManager logs has nothing after the last TaskManager started except
> DEBUG logs with "job with ID 5cd95f89ed7a66ec44f2d19eca0592f7 not found in
> JobManager", indicating its likely stuck (creating the ExecutionGraph?).
> * The same works as standalone java program locally (high CPU initially)
> * Note: Each Row in structStream contains 515 columns (many end up null)
> including a column that has the raw message.
> * In the YARN cluster we specify 18GB for TaskManager, 18GB for the
> JobManager, 145 TaskManagers with 5 slots each and parallelism of 725
> (partitions in our Kafka source).
> *Query:*
> {code:java}
> select count (*), 'idnumber' as criteria, Environment, CollectedTimestamp,
> EventTimestamp, RawMsg, Source
> from structStream
> where Environment='MyEnvironment' and Rule='MyRule' and LogType='MyLogType'
> and Outcome='Success'
> group by tumble(proctime, INTERVAL '1' SECOND), Environment,
> CollectedTimestamp, EventTimestamp, RawMsg, Source
> {code}
> *Code:*
> {code:java}
> public static void main(String[] args) throws Exception {
>
> FileSystems.newFileSystem(KafkaReadingStreamingJob.class.getResource(WHITELIST_CSV).toURI(),
> new HashMap<>());
> final StreamExecutionEnvironment streamingEnvironment =
> getStreamExecutionEnvironment();
> final StreamTableEnvironment tableEnv =
> TableEnvironment.getTableEnvironment(streamingEnvironment);
> final DataStream<Row> structStream =
> getKafkaStreamOfRows(streamingEnvironment);
> tableEnv.registerDataStream("structStream", structStream);
> tableEnv.scan("structStream").printSchema();
> for (int i = 0; i < 100; i++){
> for (String query : Queries.sample){
> // Queries.sample has one query that is above.
> Table selectQuery = tableEnv.sqlQuery(query);
> DataStream<Row> selectQueryStream = tableEnv.toAppendStream(selectQuery,
> Row.class);
> selectQueryStream.print();
> }
> }
> // execute program
> streamingEnvironment.execute("Kafka Streaming SQL");
> }
> private static DataStream<Row>
> getKafkaStreamOfRows(StreamExecutionEnvironment environment) throws Exception
> {
> Properties properties = getKafkaProperties();
> // TestDeserializer deserializes the JSON to a ROW of string columns (515)
> // and also adds a column for the raw message.
> FlinkKafkaConsumer011 consumer = new
> FlinkKafkaConsumer011(KAFKA_TOPIC_TO_CONSUME, new
> TestDeserializer(getRowTypeInfo()), properties);
> DataStream<Row> stream = environment.addSource(consumer);
> return stream;
> }
> private static RowTypeInfo getRowTypeInfo() throws Exception {
> // This has 515 fields.
> List<String> fieldNames = DDIManager.getDDIFieldNames();
> fieldNames.add("rawkafka"); // rawMessage added by TestDeserializer
> fieldNames.add("proctime");
> // Fill typeInformationArray with StringType to all but the last field which
> is of type Time
> .....
> return new RowTypeInfo(typeInformationArray, fieldNamesArray);
> }
> private static StreamExecutionEnvironment getStreamExecutionEnvironment()
> throws IOException {
> final StreamExecutionEnvironment env =
> StreamExecutionEnvironment.getExecutionEnvironment();
> env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);
> env.enableCheckpointing(60000);
> env.setStateBackend(new FsStateBackend(CHECKPOINT_DIR));
> env.setParallelism(725);
> return env;
> }
> {code}
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