Schedule Adherence API latency during peak publishing hours

General Discussion
1

Quick question about the /api/v2/wfm/scheduling/schedule-adherence endpoint performance. We are noticing significant lag when pulling adherence data for agents who have utilized the self-service shift swap feature within the last 24 hours. The response time jumps from roughly 200ms to over 4 seconds, causing timeouts in our custom reporting dashboard.

This happens specifically on Tuesday mornings after the weekly schedule publish, which aligns with our highest volume of shift trades. The data returns correctly eventually, but the initial fetch fails with a 504 Gateway Timeout. I have verified that the agent availability and shift swap records are consistent in the database, so it is not a data integrity issue.

Has anyone else experienced this bottleneck with the WFM adherence API during high-traffic scheduling windows? We are using the standard SDK version 1.2.4. Should we be implementing a retry logic with exponential backoff, or is there a specific cache invalidation step we are missing in the publishing workflow?

2

It varies, but usually the latency stems from the synchronous recalculation of adherence rules triggered by shift swaps. Implementing a polling mechanism with exponential backoff or caching results on your end prevents dashboard timeouts during these peak windows.

{
 "polling_interval_ms": 1000,
 "max_retries": 5,
 "cache_ttl_seconds": 30
}
3

This is actually a known issue with synchronous adherence recalculations during high-traffic shift swap windows.

The problem isn’t just the API call itself, but the backend processing queue getting saturated when multiple agents trigger rule recalculations simultaneously. Instead of simple polling, try implementing a JMeter script that simulates the exact load pattern of your Tuesday morning peak. This helps identify if the latency is due to API rate limits or database locks. You can use this configuration to stagger requests and avoid hitting the wfm:admin scope limits too hard:

<ThreadGroup>
 <stringProp name="ThreadGroup.num_threads">50</stringProp>
 <stringProp name="ThreadGroup.ramp_time">60</stringProp>
 <boolProp name="ThreadGroup.scheduler">true</boolProp>
 <stringProp name="ThreadGroup.duration">3600</stringProp>
</ThreadGroup>

Staggering the initial burst prevents the queue from backing up, keeping response times under 500ms even during peak publishing hours.

4

Depends on your setup, but generally the latency stems from synchronous recalculation during peak swap windows. Caching adherence payloads prevents timeout errors in custom dashboards. See KB-8921 for implementation details.