Apache Flink:运行许多作业时的性能问题

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如果Flink SQL查询数量众多(以下100个)，则Flink命令行客户端将在Yarn群集上失败，并显示"JobManager在600000毫秒内未响应"，即该作业从未在该群集上启动.

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, i.e. the job is never started on the cluster.

• 在最后一个TaskManager启动之后，JobManager日志什么都没有，除了 调试日志与"ID为5cd95f89ed7a66ec44f2d19eca0592f7的作业不 在JobManager中找到"，表明其可能卡住了(创建 ExecutionGraph?).
• 与本地独立Java程序相同 (最初是高CPU)
• 注意:structStream中的每一行都包含515 列(许多最终为空)，包括具有原始列的列 信息.
• 在YARN群集中，我们为TaskManager指定18GB，为18GB 对于JobManager，每个5个插槽，并行度为725(分区 在我们的Kafka资料中).

• 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, 5 slots each and parallelism of 725 (partitions in our Kafka source).

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

代码

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; } 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; }

推荐答案

在我看来，这似乎是JobManager重载了太多正在同时运行的作业.我建议将作业分配给更多的JobManagers/Flink群集.

This looks to me as if the JobManager is overloaded with too many concurrently running jobs. I'd suggest to distribute the jobs to more JobManagers / Flink clusters.