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Unifying batch and stream processing

More recent work has enabled the benefits of the lambda architecture to be enjoyed without its downsides, by allowing both batch computations (reprocessing historical data) and stream computations (processing events as they arrive) to be implemented in the same system [15].

Unifying batch and stream processing in one system requires the following features, which are becoming increasingly widely available:

  • • The ability to replay historical events through the same processing engine that handles the stream of recent events. For example, log-based message brokers have the ability to replay messages (see “Replaying old messages” on page 451), and some stream processors can read input from a distributed filesystem like HDFS.
  • • Exactly-once semantics for stream processors—that is, ensuring that the output is the same as if no faults had occurred, even if faults did in fact occur (see “Fault Tolerance” on page 476). Like with batch processing, this requires discarding the partial output of any failed tasks.
  • • Tools for windowing by event time, not by processing time, since processing time is meaningless when reprocessing historical events (see “Reasoning About Time” on page 468). For example, Apache Beam provides an API for expressing such computations, which can then be run using Apache Flink or Google Cloud Dataflow.
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