damccorm commented on code in PR #29860: URL: https://github.com/apache/beam/pull/29860#discussion_r1480057309
########## website/www/site/content/en/blog/apache-beam-flink-and-kubernetes-part3.md: ########## @@ -173,6 +173,18 @@ In the kubernetes world, the call will look like this for a scale up: Apache Flink will handle the rest of the work needed to scale up. +## Maintaining State for Stateful Streaming Application with Autoscaling + +Adapting Apache Flink's state recovery mechanisms for autoscaling involves leveraging its robust features like max parallelism, checkpointing, and the Adaptive Scheduler to ensure efficient and resilient stream processing, even as the system dynamically adjusts to varying loads. Here's how these components work together in an autoscaling context: + +1. **Max Parallelism** sets an upper limit on how much a job can scale out, ensuring that state can be redistributed across a larger or smaller number of nodes without exceeding predefined boundaries. This is crucial for autoscaling because it allows Flink to manage state effectively, even as the number of task slots changes to accommodate varying workloads. +2. **Checkpointing** is at the heart of Flink's fault tolerance mechanism, periodically saving the state of each job to a durable storage(In our case it is GCS bucket). In an autoscaling scenario, checkpointing enables Flink to recover to a consistent state after scaling operations. When the system scales out (adds more resources) or scales in (removes resources), Flink can restore the state from these checkpoints, ensuring data integrity and processing continuity without losing critical information. In scale down or up situations there could be a moment to reprocess data from last checkpoint. To reduce that amount we reduce checkpointing interval to 10 seconds. Review Comment: ```suggestion 2. **Checkpointing** is at the heart of Flink's fault tolerance mechanism, periodically saving the state of each job to a durable storage (in our case it is GCS bucket). In an autoscaling scenario, checkpointing enables Flink to recover to a consistent state after scaling operations. When the system scales out (adds more resources) or scales in (removes resources), Flink can restore the state from these checkpoints, ensuring data integrity and processing continuity without losing critical information. In scale down or up situations there could be a moment to reprocess data from last checkpoint. To reduce that amount we reduce the checkpointing interval to 10 seconds. ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
