Having some config trouble here… The custom Data Action in Architect keeps timing out with HTTP 408 when JMeter fires 200 concurrent requests. Using Python SDK 1.4.2 to hit the /api/v2/wfm/schedules endpoint. The payload is minimal, just updating shift segments. No 429s, just straight drops. Is there a hidden concurrency cap on the Data Action executor itself? Need to stabilize the load test.
This looks like a configuration mismatch rather than a hard limit. The Performance Dashboard shows queue activity spikes during these updates, suggesting the WFM integration is throttling to protect agent stability. Try reducing the batch size in the Architect flow to see if the timeouts resolve without impacting overall system health.
Have you tried implementing a retry mechanism with exponential backoff in your Python SDK call?
The 408 timeout usually means the WFM service is overwhelmed by the sheer volume of concurrent schedule writes. It is not necessarily a hard cap on the Data Action executor. It is a resource contention issue.
WFM schedules are complex objects. They involve shift segments, availability, and adherence rules. Processing 200 updates simultaneously creates a lock contention scenario. The database locks the schedule records to prevent race conditions. This causes the API layer to time out before the transaction completes.
Try splitting the bulk update into smaller chunks. Process 10 to 20 agents at a time. Add a small delay between batches. This gives the WFM engine time to commit the changes and release locks.
Here is a simple pattern using the time module and a loop.
import time
import requests
agents = get_agent_list() # Your list of 200 agents
batch_size = 15
for i in range(0, len(agents), batch_size):
batch = agents[i : i + batch_size]
for agent in batch:
try:
update_schedule(agent)
except requests.exceptions.Timeout:
# Exponential backoff
time.sleep(2 ** (retry_count))
retry_count += 1
update_schedule(agent)
# Pause between batches to let WFM catch up
time.sleep(1)
This approach mimics natural user behavior. It prevents the system from treating your test as a DDoS attack. The WFM module prioritizes agent self-service actions over bulk API calls. High concurrency triggers protective throttling.
Reducing the burst rate stabilizes the connection. You will still hit all 200 agents. It just takes a few seconds longer. This is the trade-off for system stability. The schedule publish window is critical for agent visibility. Protecting that integrity is worth the slight delay in test execution.