Hi team I'm working on a project that involves streaming upserts into PostgreSQL and ingesting data from Kafka. My requirements are as follows: Join 10 Kafka topics Process over 10,000 records and update 4 tables every second I've implemented this using Apache Flink for the streaming process, but the data processing time is very high. I need a solution for performance tuning and reducing the processing time. Could you provide guidance on optimizing the Flink streaming process?

My 2 cents on performance improvement : 1. Flink Configuration Tuning • Task Slots and Parallelism: Ensure that the number of task slots and the parallelism of your Flink job are configured appropriately. Generally, the parallelism should be set to the number of CPU cores available across your cluster.

taskmanager.numberOfTaskSlots: <number_of_slots>
parallelism.default: <desired_parallelism>

• Buffer Timeout: Adjust the buffer timeout to balance between latency and throughput.

execution.buffer-timeout: 100

2. State Management: Choose an efficient state backend. RocksDB is often preferred for large state sizes. If you are using something else configure it accordingly 3. Checkpointing: Enable and properly configure checkpointing to ensure fault tolerance without impacting performance.

state.checkpoints.dir: hdfs:///flink-checkpoints
execution.checkpointing.interval: 10000

Play around with

execution.checkpointing.interval

. Try to find the best interval that suits your use case. 4. I Dont know what processing logic you are implementing so check if Data Processing in job has any scope of optimization. 5. Resource Allocation • Cluster Resources: Ensure that your Flink cluster has enough resources (CPU, memory, network bandwidth) to handle the load. • TaskManager Memory: Tune the TaskManager memory settings to allocate sufficient memory for Flink processes.

taskmanager.memory.process.size: <total_memory>
taskmanager.memory.flink.size: <flink_memory>

6. Kafka Optimization • Kafka Partitions: If Possible, Increase the number of partitions in Kafka topics to parallelize the consumption. Again be cautious if increasing partition may not help if postgress is bottleneck • Kafka Consumer Configuration: Adjust Kafka consumer settings for better performance.

max.poll.records: <value>
fetch.max.bytes: <value>

7. PostgreSQL Optimization • Batch Inserts: Use batch inserts to reduce the number of database transactions. • Connection Pooling: Use connection pooling libraries like HikariCP to manage database connections efficiently. • Indexing: Ensure that the relevant columns in your PostgreSQL tables are indexed to speed up updates. Example Code Adjustments Here's an example of how to adjust some of these configurations in your Flink job:

java


StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(4); // Set parallelism

env.enableCheckpointing(10000); // Enable checkpointing
env.getCheckpointConfig().setCheckpointTimeout(60000);

DataStream<String> kafkaStream1 = env.addSource(new FlinkKafkaConsumer<>("topic1", new SimpleStringSchema(), properties));
DataStream<String> kafkaStream2 = env.addSource(new FlinkKafkaConsumer<>("topic2", new SimpleStringSchema(), properties));

// Example of keyed stream for efficient processing
DataStream<ProcessedRecord> processedStream = kafkaStream1
    .union(kafkaStream2)
    .keyBy(record -> record.getKey())
    .process(new MyKeyedProcessFunction());

processedStream.addSink(new MyPostgresSink());

Thank you

Thank you