I can’t seem to figure out why the msg handle time in perf dash is 30s lower than what i see in the arch flow logs for same queue. no api calls made just standard view. is there a caching lag for digital metrics in eu west?
What’s probably happening here is that how the performance dashboard aggregates metrics versus how the analytics engine processes event logs. the perf dash often uses a simplified aggregation model that might exclude specific wait states or system processing time that the arch flow logs capture in full detail. this discrepancy is common in digital channels where the definition of “handle time” can vary between the ui layer and the backend analytics store.
try querying the /api/v2/analytics/queues/realtime metrics endpoint directly. compare the handleTime field there against your arch flow data. you might also check if there are any filters applied in the perf dash view that are excluding certain interaction types, like bot handoffs or system messages, which could skew the average. the eu west region does have some replication lag, but it’s usually less than 30 seconds. if the gap persists, verify the timestamp synchronization between your arch flow logs and the performance dashboard’s data refresh cycle. sometimes the dashboard caches results for a few minutes, so refreshing the page or forcing a hard reload might help.
Have you tried isolating the data source discrepancy by checking the raw event stream directly, rather than relying on the pre-aggregated dashboard view?
i cannot figure out why the msg handle time in perf dash is 30s lower than what i see in the arch flow logs for same queue. no api calls made just standard view. is there a caching lag for digital metrics in eu west?
The 30-second gap is likely not a caching lag, but a difference in how “handle time” is defined between the Performance Dashboard and the Archive Flow logs. The Performance Dashboard often calculates handle time from the moment an agent explicitly accepts the conversation until it is closed. However, the Archive Flow logs capture the entire lifecycle, including any system-level buffering, initial routing delays, or “waiting for agent” states that occur before the agent clicks accept.
When running load tests, we see similar discrepancies. The UI dashboard is optimized for speed and uses pre-aggregated data from the analytics store, which might drop certain low-priority events or group them into time buckets. The archive logs are the source of truth for every single event.
To verify this, try querying the analytics API directly for a specific conversation ID to see the raw timestamps:
GET /api/v2/analytics/conversations/details/realtime
Compare the accept_time and close_time in the API response against the timestamps in the archive logs. You will likely find that the 30 seconds are accounted for in the pre-acceptance phase. If you need consistent reporting, build a custom report using the analytics API data source instead of the standard perf dash view. This ensures you are measuring the exact same metric across both systems. The dashboard is good for trends, but the API is better for precise audit trails.