Transitioning from a monolithic application to a set of microservices can help increase performance and scalability, but it can also drastically increase complexity. Layers of inter-service network calls for add latency and an increasing risk of failure where previously only local function calls existed.

In my talk from Scala Days 2016 in New York, I spoke about how to tame this complexity using Apache Kafka and Reactive Streams to:

  • Extract non-critical processing from the critical path of your application to reduce request latency
  • Provide back-pressure to handle both slow and fast producers/consumers
  • Maintain high availability, high performance, and reliable messaging
  • Evolve message payloads while maintaining backwards and forwards compatibility.



About the Author
Jim Jim is a Senior Engineer on Hootsuite’s Platform team. When he’s not working on building a rapidly expanding collection of Scala/Akka micro services, he likes cycling and writing/playing music. Follow him on Twitter @jimriecken.

Hootsuite sends millions of messages to social networks every day. This is our bread and butter, so any unexpected errors sending these messages requires that we quickly detect, diagnose and fix the problem. For example, was it because of a recently deployed code change, or a change in behaviour on the social network? Logs are an invaluable tool for debugging issues like this in production, but without easily searchable information about the context of the error, we could find ourselves looking in the wrong direction.

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