How to deal with back pressure issues in Flink?

The issue of backpressure in Flink occurs when the speed of data generation exceeds the speed of data processing, leading to data accumulating in the system, resulting in wasted resources and increased delays. To address the backpressure issue in Flink, several methods can be implemented:

1. Dynamically adjust parallelism: Tasks can be dynamically adjusted based on system load to allocate them to more computing nodes, improving the system’s processing capability.
2. Repartitioning: By repartitioning, data can be evenly distributed across different partitions to reduce data skewness and improve parallelism.
3. Using a buffer: By utilizing a buffer, data can be temporarily stored to prevent data loss and delays when the system’s processing capacity is insufficient.
4. Increase computing resources: can add computing resources, including increasing computing nodes, adding CPUs and memory, etc., to enhance the system’s processing capabilities.
5. Adjusting task chain: Task chains can be adjusted based on the dependency relationships of tasks and the allocation of resources to improve task parallelism and system processing capabilities.
6. Controlling data generation speed with watermarks: Watermarks can be used to control the speed at which data is generated. If the data generation speed surpasses the system processing speed, adjusting the watermark position can help regulate data generation speed and prevent wasting system resources and increasing delays.

Here are some common methods for dealing with Flink backpressure issues, choose the appropriate method based on the specific situation to resolve the problem.