Distributed Job Orchestration
Building a reliable job execution system across async pipelines where race conditions and partial failures are the norm.
Problem
Multiple long-running jobs needed to execute against external APIs with strict rate limits, each job potentially spanning minutes. Jobs could fail midway, external APIs could return inconsistent responses, and concurrent jobs could race against shared state.
Approach
Designed the system around idempotent operations and explicit state machines. Each job tracks its own progress through well-defined phases. Used distributed locking for shared resources and built retry logic that respects external rate limits. Made partial progress a first-class concept — a job can resume from where it stopped instead of restarting from scratch.
Trade-offs
The state machine approach adds complexity to what could have been a simple “run and pray” loop. Every new job type requires defining its phases explicitly. But the alternative — debugging silent data corruption from race conditions — is far more expensive.
Outcome
Jobs recover gracefully from failures. Operators can inspect exactly where a job stopped and why. Race conditions between concurrent jobs are eliminated by design, not by accident.