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https://issues.apache.org/jira/browse/FLINK-9166?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16902712#comment-16902712
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pj commented on FLINK-9166:
---------------------------
[~fhueske] [~rmetzger]
I have developed a rule engine based on flink sql(Flink streaming, single flink
job on yarn). All sqls in the rule engine query on the same table, one the
other word, they share common source. The number of sqls is about 200 or more,
split different sql to different flink application is unacceptable. Because if
we split 200 sqls to 10 per job, in the practice we need 20 or more jobs, the
operation cost is too high. So is there has any way to walk around it and let
the flink can run big jobgraph (I can give more slots and more memory to it) ?
> Performance issue with many topologies in a single job
> ------------------------------------------------------
>
> Key: FLINK-9166
> URL: https://issues.apache.org/jira/browse/FLINK-9166
> Project: Flink
> Issue Type: Bug
> Components: Table SQL / Legacy Planner
> 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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