Sometimes compression can reduce message size, but there are various use cases that entail large message payloads where compression may not be enough. Often these use cases are related to image, video, or audio processing: image recognition, video analytics, audio analytics, etc.
How do I handle use cases where the event payload is too large to move through the event streaming platform as a single event?
Instead of storing the entire event as a single event in the event streaming platform, break it into chunks (an approach called "chunking") so that the large event is sent across as multiple smaller events. The producer can do the chunking when writing events into the event streaming platform. Downstream clients consume the chunks and, when all the smaller chunks have been received, recombine ("unchunk") them to restore the original event.
Use metadata to track each chunk so that they can be associated to their respective parent event:
Chunking places additional burden on client applications. First, implementing the chunking and unchunking logic requires more application development. Second, the consumer application needs to be able to cache the chunks as it waits to receive all the smaller chunks that comprise the original event. This, in turn, can have implications on memory fragmentation and longer garbage collection (GC). Mitigating this depends on the programming language: in Java, for example, the JVM heap size and GC can be tuned.
Client applications that are not aware of the protocol used for chunking events may not be able to reconstruct the original event accurately.