Worker Concurrency
Worker concurrency is the number of jobs that consumers may run at the same time. A limit protects the server and the services those jobs call.
queue depth + max active consumers + claim/lease + dependency capacity -> throughputqueue depth + max active consumers + claim/lease + dependency capacity -> throughputJOBAnother example
A receipt queue has 120 messages. Three consumers each take one available message, so at most three receipts render at once. An autoscaler can add consumers when the queue grows. The pool never passes the storage service's safe limit.
Main parts
- 01Queue depth
- 02Consumer pool
- 03Active-job limit
- 04Message claim or lease
- 05Downstream capacity
- 06Scale and stop signal
Use it when
Set a concurrency limit when separate queued jobs can run together. Use measured job cost, queue delay, and how much work the next service can safely handle.
Do not use it when
Do not keep adding workers when jobs share one unsafe resource or must stay in order. More workers can increase duplicate work and overload a dependency.
Name used in code
queue depth + max active consumers + claim/lease + dependency capacity -> throughput Before you ship
Check contracts and invariants, authorization, and input limits. Test retries, one-time effects, timeouts, and partial failures. Verify data integrity, compatibility, monitoring, recovery, and rollback.
Outcome: Design or review Worker Concurrency for this system while keeping data correct, secure, and easy to operate. User context: A receipt queue has 120 messages. Three consumers each take one available message, so at most three receipts render at once. An autoscaler can add consumers when the queue grows. The pool never passes the storage service's safe limit. Technical concept or control: Worker Concurrency. Why it fits: Set a concurrency limit when separate queued jobs can run together. Use measured job cost, queue delay, and how much work the next service can safely handle. Do not use it when: Do not keep adding workers when jobs share one unsafe resource or must stay in order. More workers can increase duplicate work and overload a dependency. Engineering requirements: Define inputs, outputs, contracts, and rules that must always hold. Set authorization and input checks. Define failures, timeouts, retries, one-time effects, data moves, and compatibility limits. Operations: Add useful signals and logs that hide sensitive data. Set capacity limits and write an operator guide. Test recovery and rollback. Acceptance criteria: Contract and failure-path tests pass. Data and authorization rules still hold during retries and partial failures. Check performance limits, monitoring, recovery, and rollback. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Where the code runs, data stores, traffic shape, consistency needs, and the security boundary. Also confirm the compatibility period, service goals, and rollback limits.
Check this request
Review the current use of Worker Concurrency. Definition: Worker concurrency is the number of jobs that consumers may run at the same time. A limit protects the server and the services those jobs call. Release checks: Check contracts and invariants, authorization, and input limits. Test retries, one-time effects, timeouts, and partial failures. Verify data integrity, compatibility, monitoring, recovery, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.