Hi, I am Hwanju at AWS Kinesis Analytics. We would like to start a discussion thread about a project we consider for Flink operational improvement in production. We would like to start conversation early before detailed design, so any high-level feedback would welcome.
For service providers who operate Flink in a multi-tenant environment, such as AWS Kinesis Data Analytics, it is crucial to measure application health and clearly differentiate application unavailability issue caused by Flink framework or service environment from the ones caused by application code. The current metrics of Flink represent overall job availability in time, it still needs to be improved to give Flink operators better insight for the detailed application availability. The current availability metrics such as uptime and downtime measures the time based on the running state of a job, which does not necessarily represent actual running state of a job (after a job transitions to running, each task should still be scheduled/deployed in order to run user-defined functions). The detailed view should enable operators to have visibility on 1) how long each specific stage takes (e.g., task scheduling or deployment), 2) what failure is introduced in which stage leading to job downtime, 3) whether such failure is classified to user code error (e.g., uncaught exception from user-defined function) or platform/environmental errors (e.g., checkpointing issue, unhealthy nodes hosting job/task managers, Flink bug). The last one is particularly needed to allow Flink operators to define SLA where only a small fraction of downtime should be introduced by service fault. All of these visibility enhancements can help community detect and fix Flink runtime issues quickly, whereby Flink can become more robust operating system for hosting data analytics applications. The current proposal is as follows. Firstly, we need to account time for each stage of task execution such as scheduling, deploying, and running, to enable better visibility of how long a job takes in which stage while not running user functions. Secondly, any downtime in each stage can be associated with a failure cause, which could be identified by Java exception notified to job manager on task failure or unhealthy task manager (Flink already maintains a cause but it can be associated with an execution stage for causal tracking). Thirdly, downtime reason should be classified into user- or system-induced failure. This needs exception classifier by drawing the line between user-defined functions (or public API) and Flink runtime — This is particularly challenging to have 100% accuracy at one-shot due to empirical nature and custom logic injection like serialization, so pluggable classifier filters are must-have to enable incremental improvement. Fourthly, stuck progress, where task is apparently running but not being able to process data generally manifesting itself as long backpressure, can be monitored as higher level job availability and the runtime can determine whether the reason to be stuck is caused by user (e.g., under-provisioned resource, user function bug) or system (deadlock or livelock in Flink runtime). Finally, all the detailed tracking information and metrics are exposed via REST and Flink metrics, so that Flink dashboard can have enhanced information about job execution/availability and operators can set alarm appropriately on metrics. Best, Hwanju
