![]() One major limitation of structured streaming like this is that it is currently unable to handle multi-stage aggregations within a single pipeline.Īpache Flink is a data processing engine that incorporates many of the concepts from MillWheel streaming. Users need to manually scale their Spark clusters up and down. State management in Spark is similar to the original MillWheel concept of providing a coarse-grained persistence mechanism. Spark does have some limitations as far as its ability to handle late data, because its event processing capabilities (and thus garbage collection) are based on static thresholds rather than watermarks. ![]() Spark has native exactly once support, as well as support for event time processing. Spark has a rich ecosystem, including a number of tools for ML workloads. It does not natively support watermark semantics (though can support them through Kafka Streams) or autoscaling, and users must re-shard their application in order to scale the system up or down.Īpache Spark is a data processing engine that was (and still is) developed with many of the same goals as Google Flume and Dataflow-providing higher-level abstractions that hide underlying infrastructure from users. Kafka does support transactional interactions between two topics in order to provide exactly once communication between two systems that support these transactional semantics. These can be layered on top through abstractions like Kafka Streams. Because it is a message delivery system, Kafka does not have direct support for state storage for aggregates or timers. Here, we'll talk specifically about the core Kafka experience. We’re biased, of course, but we think that we've balanced these needs particularly well in Dataflow.Īpache Kafka is a very popular system for message delivery and subscription, and provides a number of extensions that increase its versatility and power. Each system that we talk about has a unique set of strengths and applications that it has been optimized for. To place Google Cloud’s stream and batch processing tool Dataflow in the larger ecosystem, we'll discuss how it compares to other data processing systems. ![]() Editor's note: This is the third blog in a three-part series examining the internal Google history that led to Dataflow, how Dataflow works as a Google Cloud service, and here, how it compares and contrasts with other products in the marketplace.
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