AWS Under Real Load: Event Notification Fan-Out Storms in Amazon S3
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A production-grade diagnostic and prevention guide for cascading compute bursts and system instability caused by high-volume S3 event notifications.
Problem
A system that relies on S3 event notifications begins experiencing:
- Sudden Lambda concurrency spikes
- Increased SQS queue depth
- Rising processing latency
- Downstream timeouts
- Unexpected cost surges
- No visible S3 errors
PUT and DELETE operations succeed.
But the compute layer destabilizes.
The storage tier looks healthy.
The event-driven tier is overwhelmed.
Clarifying the Issue
S3 Event Notifications trigger downstream services for object events such as:
s3:ObjectCreated:*s3:ObjectRemoved:*s3:ObjectRestore:*
Under light traffic, this works seamlessly.
Under heavy object churn, each object operation generates an event.
High ingestion rates or mass deletes create:
- One object → one event
- 10,000 objects → 10,000 events
- 1 million objects → 1 million events
S3 does not batch its own event notifications.
Fan-out amplifies instantly.
If events trigger:
- AWS Lambda
- SQS
- SNS
- EventBridge
Each layer adds processing overhead.
This is not an S3 failure.
đź“Ś It is event amplification under load.
Why It Matters
Event fan-out storms can:
- Exhaust Lambda concurrency
- Trigger account-level throttling
- Increase SQS processing lag
- Create retry loops
- Inflate CloudWatch logging
- Cascade failures into dependent systems
Storage remains stable.
Compute collapses.
Under real load, event-driven architecture must scale with ingestion physics.
Key Terms
Event Fan-Out – One object operation triggering downstream compute
Concurrency Spike – Sudden surge in parallel compute execution
Retry Amplification – Downstream retries increasing effective workload
Backpressure Mismatch – Storage tier stable, compute tier saturated
Churn-Driven Events – Large-scale PUT/DELETE operations generating event floods
Steps at a Glance
- Correlate object operation rate with compute spikes
- Measure Lambda concurrency and throttling
- Inspect SQS queue depth and retry behavior
- Evaluate event filtering rules
- Introduce buffering and rate control
- Retest under controlled object churn
Detailed Steps
Step 1: Correlate Object Operations With Compute Load
Overlay:
- PUT rate
- DELETE rate
- Event invocation count
- Lambda concurrency
If compute spikes align with object churn, the system is experiencing event amplification.
Every object operation is a trigger.
Step 2: Measure Lambda Concurrency
Inspect:
- Concurrent executions
- Throttles
- Duration increases
- Error rates
If concurrency approaches account limits, downstream stability degrades.
Reserved Concurrency acts as an emergency brake. It prevents an S3-triggered event storm from consuming all available Lambda concurrency across your AWS account and impacting unrelated services.
Provisioned Concurrency improves latency predictability.
Reserved Concurrency protects system stability.
Step 3: Inspect Queue Behavior
If using SQS:
- Monitor queue depth
- Check message age
- Inspect visibility timeout behavior
- Identify retry amplification
Retries are inevitable during event storms.
If messages reappear faster than they are processed, fan-out cascades.
All event-driven processing must be idempotent to prevent duplicate side effects under load.
Step 4: Evaluate Event Filtering
Confirm whether events are overly broad.
Common anti-pattern:
- Triggering on all
ObjectCreatedevents - Triggering on deletes during cleanup
- No prefix filtering
- No suffix filtering
Mitigation:
- Filter by specific prefixes
- Filter by object type
- Avoid delete-triggered compute unless required
Not every object needs downstream processing.
Step 5: Introduce Buffering and Rate Control
Instead of direct S3-to-Lambda triggers:
- Route events to SQS
- Use controlled batch sizes
- Apply reserved concurrency limits
- Implement exponential backoff with jitter
Buffering transforms uncontrolled push into controlled pull.
Compute should shape itself to event velocity.
Do not allow ingestion to dictate concurrency.
Step 6: Retest Under Controlled Churn
Simulate:
- Gradual object ramp
- Burst uploads
- Delete storms
Measure:
- Lambda concurrency
- Queue stability
- Downstream latency
If smoothing ingestion reduces compute instability, the issue was fan-out amplification.
Pro Tips
- Every object operation can become compute.
- Storage scaling does not guarantee compute scaling.
- Reserved Concurrency protects the rest of your account from event storms.
- Retries are inevitable; idempotency is mandatory.
- Delete storms trigger event storms.
- Buffer before you process.
Conclusion
Event Notification Fan-Out Storms occur when object churn outpaces downstream compute capacity.
When:
- PUT and DELETE operations surge
- Events trigger unfiltered compute
- Concurrency is unconstrained
- Retries amplify load
Compute destabilizes while storage remains healthy.
Once:
- Event filtering is tightened
- Buffering is introduced
- Concurrency is controlled
- Processing is idempotent
The system stabilizes.
S3 scales smoothly.
Event-driven compute must scale deliberately.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.
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