Implementing Asynchronous Processing

1. Using Message Queues

ChoiceUse
RabbitMQWork queues, routing
SQSManaged, simple
KafkaStreams, ordered partitions
NATS JetStreamLightweight, persistent

2. Implementing Background Jobs

ToolDetail
Spring @Async / @ScheduledIn-process
QuartzDistributed cron
Sidekiq / Celery / BullMQPer ecosystem
PracticeIdempotent + retry + DLQ

3. Using Event-Driven Processing

AspectDetail
ProducersPublish facts
ConsumersReact independently
DecouplesTime and team

4. Implementing Worker Pools

ParamGuidance
Pool sizeCPU-bound: cores; IO-bound: higher
Queue boundAvoid OOM
Reject policyCallerRuns / abort + alert

5. Managing Long-Running Operations

PatternDetail
202 + status URLClient polls
WebhookPush when done
Operation resourceRFC operations/{id}
CancellableExpose DELETE

6. Using Webhooks

PracticeDetail
Sign payloadHMAC + timestamp
Retry w/ backoff2xx = success
At-least-onceReceivers must dedupe
Replay UIManual redelivery

7. Implementing Polling vs Push Mechanisms

ApproachWhen
PollingSimple, firewall-friendly
Long pollingLower latency than poll
SSEServer → client stream
WebSocketBidirectional
WebhookServer-to-server push

8. Managing Job Queues

ElementDetail
Priority queuesCritical first
Delayed jobsSchedule future
Visibility timeoutRe-deliver if not ack'd
DLQAfter N failures

9. Implementing Status Tracking

FieldDetail
Statequeued → running → done/failed
Progress0–100% optional
Result URLWhen complete

10. Handling Async Failures

StrategyDetail
Retry w/ jitterExp. backoff
DLQ + alertInspect & reprocess
Poison pillMove out of band
Compensating actionSaga step undo

11. Implementing Async Request-Reply

ElementDetail
Reply queuePer-client temp queue
Correlation IDMatch reply to request
TimeoutCancel waiter

12. Using Async Processing Tools

ToolUse
Temporal / CadenceDurable workflows
Step FunctionsAWS-managed orchestration
Camunda / ZeebeBPMN engines
Kafka Streams / FlinkStream processing