Overview
Federated query latency problems cross system boundaries, which makes them hard to own internally — the Trino team blames the source, the source team blames Trino, and nobody has the full picture. AceMQ provides named senior engineers who can read both sides.
Challenge
Latency regressions in Starburst are often environmental rather than structural: a source database changed an index, statistics went stale after a bulk load, a connector upgrade changed pushdown behavior, or coordinator queueing grew because concurrency crept up. Each of these looks identical from the BI tool — the dashboard is slow — and diagnosing them requires access and expertise on both sides of the connector.
Environment
Starburst Enterprise or Galaxy deployments on Kubernetes, VMs, or managed cloud, federating multiple production sources.
Approach
AceMQ engineers work from query event history, coordinator queue metrics, and source-side execution plans together, so a regression can be attributed correctly instead of argued about. Immediate mitigation may be a resource group change or a query rewrite; the follow-up addresses the statistics, connector configuration, or source-side index that caused it.
Solution
- 15-minute emergency SLA with named senior engineers, 24/7, no tier-1 triage
- Query event history and coordinator queue analysis to separate execution slowness from admission delay
- Source-side plan comparison to determine whether the regression originated in Trino or the underlying system
- Connector upgrade and configuration regression review after Starburst or source version changes
- Statistics freshness checks and ANALYZE scheduling for tables that changed shape after bulk loads
- Concurrency and resource group adjustment for BI tool query storms
Outcome
Latency regressions get attributed to the right system quickly, which shortens resolution and ends the cross-team standoff that usually accompanies federated query problems.
Technologies
Starburst
Trino
Apache Hive
Kubernetes
PostgreSQL
Apache Spark
S3
Prometheus
