Yan Cui on Serverless, including Orchestration/Choreography, Distributed Tracing, & more

Today on the InfoQ Podcast, Yan Cui (a long time AWS Lambda user and consultant) and Wes Reisz discuss serverless architectures. The conversation starts by focusing on architectural patterns around choreography and orchestration. From there, the two move into updates on the current state of serverless cold start times, distributed tracing, and state. Today’s podcast, while not specific to AWS, does lean heavily on Yan’s expertise with AWS and AWS Lambda.

Key Takeaways

• When we talk about choreography in the context of serverless, we’re talking about what we traditionally think about with an event-driven system. For orchestration, we’re more reliant on state. Both have use cases in developing serverless architectures.
• When designing business workflows, a good rule of thumb is to think about the service’s bounded context. Within the bounded context of a microservice, prefer orchestration. Between the bounded contexts, the event-driven nature of choreography is often a good choice.
• Distributed tracing remains one of the difficulties when developing event-driven systems with AWS lambda. Tools that can help in the space include Xray, Lumigo, Epsagon, and Thundra.
• Cold start time (or the time it takes to spin up the function to handle requests) in languages like Java and .NET Core are one of the areas to consider when developing Lambda applications. For context, cold start times with Node or Python are typically 300-800 ms, where a Java one today is typically 500-1000ms.
• Files of over 500mb have traditionally been a challenge with Lambda functions. Larger file sizes are now supported by mounting an EFS volume. However, there are still some challenges around latency when loading these larger files.

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