Reactive execution engine, thread pools and back pressure
How the Mule 4 reactive execution engine classifies work into processing types, runs it on the shared UBER pool, keeps transactions on one thread and applies back pressure, and what changes when you alter global or application-level thread pools or upgrade the runtime.
Describe the characteristics and implications of the Mule 4 reactive event processing model.
~6 min read · 11 practice questions
What the exam expects
- Describe the Mule 4 reactive, task-oriented execution model and the Proactor pattern behind it.
- Classify event processors as CPU-light, blocking I/O or CPU-intensive, and read those types in logs.
- Explain the scope of the UBER pool, the extra pools components create, and thread behavior inside a transaction.
- Predict the impact of global versus application-level thread pool configuration and plan testing for it.
- Explain what triggers back pressure and how each source reacts, and what to do with custom threading when upgrading.
Processing type: the kind of work an event processor tells Mule it does. UBER pool: Mule's single managed thread pool, shared by the applications of one Mule instance. Proactor pattern: an asynchronous design where tasks are submitted by category so threads return to the main loop quickly. Back pressure: what happens when Mule has no resources to accept a new event (Execution Engine).
Sources for this section (1)
Key facts
- Detail
- CPU Light when none is specified
- Detail
- One pool shared by all apps in a Mule instance, since 4.3
- Detail
- NIO selectors and recurring-task pools can exist beside UBER
- Detail
- Thread switches suspended; participants share one thread
- Detail
MULE_HOME/conf/schedulers-pools.conf, on premises
- Detail
workQueue.size=0by default
- Detail
- New pool set for the app; defaults unchanged
- Detail
- Busy threads or
maxConcurrencyexceeded
- Detail
- Connector-specific: 503, wait, drop or disconnect
- Detail
- Periodic, scheduled for February, from Mule 4.6
Sources for this section (2)
- When did the UBER pool become Mule's single thread pool?
- Roughly how long is a CPU-light operation expected to take?
- When the scheduler service stops, what does
gracefulShutdownTimeoutcontrol? - Why should app-level pool sizes on CloudHub take the worker size into account?
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