How to Avoid OutOfMemoryError with Kafka Streams Hopping Windows скачать в хорошем качестве

How to Avoid OutOfMemoryError with Kafka Streams Hopping Windows

Learn how to manage the memory consumption of Kafka Streams Hopping Windows when trying to calculate moving windows effectively without hitting `OutOfMemoryError` issues. --- This video is based on the question https://stackoverflow.com/q/76282603/ asked by the user 'ashur' ( https://stackoverflow.com/u/1165499/ ) and on the answer https://stackoverflow.com/a/76286510/ provided by the user 'maow' ( https://stackoverflow.com/u/6564620/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Kafka Streams Hopping Window generates thousands of windows Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Managing Memory with Kafka Streams Hopping Windows When working with Apache Kafka Streams, developers sometimes encounter challenges that can lead to significant memory consumption. One such issue arises when trying to implement a hopping window that aggregates data over a rolling time frame. In this guide, we'll explore the challenges of implementing hopping windows in Kafka Streams, particularly when you want to calculate a moving window for every second across the last 24 hours, and how to address the related OutOfMemoryError (OOM). The Problem: Excessive Window Generation In the scenario presented, the goal is to create a hopping window of 24 hours that generates new windows every second. Unfortunately, this approach results in the creation of 86,400 windows (as there are 86,400 seconds in 24 hours) that need to be maintained simultaneously. Here’s a breakdown of what happens: Each event is added to every single one of the 86,400 windows. Every time an event is processed, entries that are outside the window need to be removed. Despite the majority of events being similar across these windows, Kafka Streams struggles to manage this efficiently. As a result, the memory requirements skyrocket, leading to an OOM error in systems with limited resources. If your application processes data at even a modest rate (e.g., 1 MB of data per day), you could quickly consume over 86.4 GB of RAM. Solution: Reducing Memory Consumption To tackle the issues described above, consider the following strategies: 1. Reduce Cardinality The most straightforward way to overcome the limitations is by controlling the number of windows you're generating. Here are a couple of alternatives: Multi-level Aggregation: Instead of directly aggregating on a per-second basis, aggregate at various levels like daily, hourly, and then combine results: Monthly Aggregates Daily Aggregates Hourly Aggregates This method incurs additional complexity and some latency, but reduces the overall number of active windows considerably. 2. Use Tumbling Windows with Suppression An alternative approach is to use tumbling windows aggregated per second, combined with the suppress method. This involves: Setting the tumbling window for a second interval. Using suppression to emit only a single event at the end of each interval. While this won’t drastically reduce RAM consumption, it will significantly lower computation overhead. 3. Seek Feedback on Requirements Before implementing such intensive solutions, consult with your product manager (PM) or stakeholders to verify if the granularity of every second is truly necessary. Propose a slight adjustment to 15 seconds or more to reduce load without sacrificing too much detail. Conclusion In summary, working with Apache Kafka Streams for hopping windows can lead to excessive memory usage and OOM errors if not managed carefully. By reducing the cardinality of your windowing strategy, leveraging a combination of tumbling windows with suppression, and aligning with your team on the desired granularity, you can create a more efficient and manageable streaming application. Remember, the best approach often requires balancing the need for data accuracy with performance limitations, ensuring a smooth and efficient handling of your big data applications.