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Posted to issues@flink.apache.org by "Fabian Hueske (JIRA)" <ji...@apache.org> on 2018/05/22 14:20:00 UTC
[jira] [Updated] (FLINK-9166) Performance issue with many
topologies in a single job
[ https://issues.apache.org/jira/browse/FLINK-9166?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Fabian Hueske updated FLINK-9166:
---------------------------------
Summary: Performance issue with many topologies in a single job (was: Performance issue with Flink SQL)
> 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 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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