This book is about building business systems with stream processing tools, so it is useful to have an appreciation for where stream processing came from. The maturation of this toolset, in the world of real-time analytics, has heavily influenced the way we build event-driven systems today.
Figure 2-1 shows a stream processing system used to ingest data from several hundred thousand mobile devices. Each device sends small JSON messages to denote applications on each mobile phone that are being opened, being closed, or crashing. This can be used to look for instability—that is, where the ratio of crashes to usage is comparatively high.
Figure 2-1. A typical streaming application that ingests data from mobile devices into Kafka, processes it in a streaming layer, and then pushes the result to a serving layer where it can be queried
The mobile devices land their data into Kafka, which buffers it until it can be extracted by the various applications that need to put it to further use. For this type of workload the cluster would be relatively large; as a ballpark figure Kafka ingests data at network speed, but the overhead of replication typically divides that by three (so a three-node 10 GbE cluster will ingest around 1 GB/s in practice).
To the right of Kafka in Figure 2-1 sits the stream processing layer. This is a clustered application, where ...
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