Load Shedding Tuning Workflow
Add load shedding so overload drops low-priority work early instead of collapsing the whole service.
Objective
Protect critical traffic by shedding lower-priority load when the service is overloaded.
Environment
- Priority classification for requests
- Metrics for queue depth and latency
- A shedding mechanism
Steps
1. Classify request priority
Define which requests are critical and which can be deferred or dropped.
Expected state: Traffic has an explicit priority order.
2. Set shedding thresholds
Trigger shedding when queue depth or latency crosses a threshold.
Expected state: Shedding activates before full collapse.
3. Shed lower priority first
Drop or defer low-priority work while serving critical traffic.
Expected state: Critical requests keep succeeding under overload.
4. Verify graceful degradation
Load the service past capacity and confirm it degrades rather than fails.
Expected state: The service sheds load and recovers, clients get clear errors.
Decision points
Shed or queue?
- Shed low priority early
- Queue everything
Shed early; unbounded queues convert overload into timeout failures.
Who defines priority?
- Product and engineering together
- Engineering alone
Product defines what matters most; engineering implements the mechanism.
Expected outcome
Under overload the service sheds low-priority work and continues serving critical requests.
Verification
Confirm that past capacity, critical requests still succeed and shed requests receive a clear retryable error.
Cleanup
Document the priority rules and thresholds and tune from incident data.
Overview
Protect critical traffic by shedding lower-priority load when the service is overloaded.
This workflow is reusable across teams and environments; adapt the commands to your tooling while keeping the sequence and checkpoints intact.
Environment and prerequisites
- Priority classification for requests
- Metrics for queue depth and latency
- A shedding mechanism
Procedure
1. Classify request priority
Define which requests are critical and which can be deferred or dropped.
Expected state: Traffic has an explicit priority order.
2. Set shedding thresholds
Trigger shedding when queue depth or latency crosses a threshold.
Expected state: Shedding activates before full collapse.
3. Shed lower priority first
Drop or defer low-priority work while serving critical traffic.
Expected state: Critical requests keep succeeding under overload.
4. Verify graceful degradation
Load the service past capacity and confirm it degrades rather than fails.
Expected state: The service sheds load and recovers, clients get clear errors.
# shed when queue depth exceeds capacity
if queue.depth > 1000 { rejectLowPriority() }Verification
Confirm that past capacity, critical requests still succeed and shed requests receive a clear retryable error.
Decision points
Shed or queue?
- Shed low priority early
- Queue everything
Recommendation: Shed early; unbounded queues convert overload into timeout failures.
Who defines priority?
- Product and engineering together
- Engineering alone
Recommendation: Product defines what matters most; engineering implements the mechanism.
Common failures
- No shedding means overload collapses everything.
- Unbounded queues turn overload into timeouts.
- Shedding critical traffic harms the users who matter most.