Performance Characterization of Data Store Event Trigger Mechanisms for Serverless Computing

Serverless applications are composed of functions triggered by events. Data stores are a common source of event triggers in the cloud, even beyond serverless, such as in Kubernetes. We find trigger latency, the time from event generation to function invocation, to take up to 62% of execution time for common serverless applications. Even though event triggers play a crucial role in serverless performance, the mechanisms driving these triggers are ill-understood. In this paper, we analyze data store trigger mechanisms, define the features that make up these mechanisms, and characterize their performance with TriggerPerf, a benchmarking tool for data store triggers. We implement TriggerPerf on three AWS data stores with built-in trigger support: S3, DynamoDB, and AuroraDB. With TriggerPerf, we demonstrate significant latency, scalability, and elasticity bottlenecks across these data stores. We observe that the trigger latency of AWS data stores is up to 100 × higher compared to a reference etcd data store. Moreover, the median tail latency of S3 and AuroraDB is 10x higher when under high load, unlike DynamoDB. The observed variability in performance patterns significantly impacts the reliability of serverless and distributed systems that depend on them, highlighting the critical need for further research into the underlying mechanisms. The tool is open-sourced and is available at https://github.com/atlarge-research/trigger-perf.