Working with complex data events can be a challenge designing Storm topologies for real-time data processing. In such cases emitting single values for multiple and varying event characteristics soon reveals it’s limitations. For message serialization Storm leverages the Kryo serialization framework used by many other projects. Kryo keeps a registry of serializers being used for corresponding Class types. Mappings in that registry can be overridden or added making the framework extendable to diverse type serializations.
On the other hand Avro is a very popular “data serialization system” that bridges between many different programming languages and tools. While the fact that data objects can be described in JSON makes it really easy to use, Avro is often being used for it’s support of schema evolution. With support for schema evolution the same implementation (Storm topology) could be capable of reading different versions of the same data event without adaptation. This makes it a very good fit for Storm as a intermediator between data ingestion points and data storage in today’s Enterprise Data Architectures.
The example here does not provide complex event samples to illustrated that point, but it gives an end to end implementation of a Storm topology where events get send to a Kafka queue as Avro objects processesed natively by a real-time processing topology. The example can be found here. It’s a simple Hive Streaming example where stock events are read from a CSV file and send to Kafka. Stock events are a flat, none complex data type as already mentioned, but we’ll still use it to demo serialization with using Avro. Continue reading “Storm Serialization with Avro (using Kryo Serializer)”