Watermarks: Time and progress in Apache Beam (incubating) and beyond
Watermarks are a system for measuring progress and completeness in out-of-order streaming systems and are utilized to emit correct results in a timely manner. Given the trend toward out-of-order processing in existing streaming systems, watermarks are an increasingly important tool when designing streaming pipelines. Slava Chernyak explains watermarks and explores real-world applications.
|Talk Title||Watermarks: Time and progress in Apache Beam (incubating) and beyond|
|Conference||Strata + Hadoop World|
|Conf Tag||Make Data Work|
|Location||New York, New York|
|Date||September 27-29, 2016|
Moving from batch to streaming involves changing how we think about time. Streaming data is neither bounded nor typically well ordered in time. However, to make streaming systems useful and deliver on the promise of low-latency results, we often want to know when we have all the data relevant to emitting a correct aggregation. Watermarks provide the foundation for making such decisions, enabling streaming systems to emit timely, correct results when processing out-of-order data. Given the trend toward out-of-order processing in existing streaming systems, understanding watermarks is an increasingly important skill when designing pipelines. This methodology, first discussed in the MillWheel paper and further explored in the Dataflow model paper, is now referred to as the Beam model. This approach is not limited to just Google’s stream processing efforts; rather, it is a solution to a general problem that must be addressed by any system that wishes to provide timely out-of-order distributed stream processing and has since been pursued by others such as Flink and Qubit (which built a watermark tracking system on top of Spark Streaming for its own internal use). Based on his experience developing and using watermarks at Google, Slava Chernyak discusses details of how watermarks are applied, as well as their strengths and limitations, and explores real-world use cases, providing a practical set of tools for understanding watermarks and time in out-of-order stream processing pipelines. Along the way, Slava also outlines some of the implementation challenges for computing watermarks with low latency in a highly distributed system.