Workers recycling under memory pressure, and why the platform can't tell you which flow
MuleSoft
Docker
Kubernetes
RedisThe daily worker recycles stopped, and in-flight messages now survive a restart rather than being lost. Worker sizing was set from measured behavior instead of guesswork.
Overview
When a CloudHub worker recycles under memory pressure, the platform reports that it happened. It does not tell you which flow, which object store, or which connector leak caused it. That analysis is application-side, and it is where AceMQ works — we tune and debug what customers run on MuleSoft, we do not operate MuleSoft itself.
Challenge
Workers were recycling several times a day, dropping in-flight messages. The application used an in-memory object store for deduplication keys with no expiry, held cached lookup data that grew unbounded, and had a connector configuration that opened a new connection per request without pooling. Each cause on its own was survivable; together they crossed the worker's memory ceiling daily.
Environment
Mule applications on CloudHub across several environments, integrating point-of-sale, inventory, and ERP systems.
Approach
AceMQ correlated restart timestamps with application behavior and worker metrics to narrow the search, then reviewed object store configuration, caching scopes, and connector pooling as the three most common application-side memory sinks. Named senior engineers worked the issue directly with 24/7 coverage and a 15-minute emergency response SLA.
Solution
- 1Correlated worker recycle events with flow activity and memory metrics to isolate the contributing flows
- 2Moved unbounded in-memory object stores to persistent stores with explicit TTLs
- 3Bounded cache scopes and set eviction policies on lookup data that had grown without limit
- 4Enabled connection pooling on connectors that were opening a connection per request
- 5Added message-level durability so an unexpected recycle no longer drops in-flight work
- 6Right-sized worker allocation against measured steady-state and peak memory
Outcome
The daily worker recycles stopped, and in-flight messages now survive a restart rather than being lost. Worker sizing was set from measured behavior instead of guesswork.
Technologies
MuleSoftDockerKubernetesRedisRelated Use Cases
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Need Expert MuleSoft Support?
AceMQ's senior MuleSoft engineers have handled this exact type of engagement before. Whether you need architectural guidance, hands-on remediation, or an ongoing managed partnership, we're ready to help.