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# Tuning Recommendations

> For the full documentation index, see: https://docs.mulesoft.com/llms.txt

Mule 4 provides self-tuning execution, which adjusts itself for optimal performance according to the
underlying operating conditions of the environment where you deploy Mule runtime engine (Mule).  
Self-tuning allows you to focus on the specifics of your application rather than Mule internals,
which eases the optimization process. Additionally, Mule comes to you already tuned for optimal
performance for most common use cases, and changing any parameter is advised against unless you
fully understand the implications of doing so.

There are tuning recommendations that you can apply at the application level and benefit your
specific use case.

## Repeatable vs Non-repeatable Streaming

Understand which streaming strategy results in the best flow performance for your use case:

- A repeatable stream (read payload more than once)

- A non-repeatable stream (read payload only once)

See [Repeatable vs Non-repeatable Streaming](https://docs.mulesoft.com/mule-runtime/4.11/tuning-repeatable-nonrepeatable) for details.

## Back-Pressure and MaxConcurrency

Understand back-pressure and learn how to use the `maxConcurrency` parameter to tune the number of
concurrent messages sent to your flow.

See [Back-Pressure and MaxConcurrency](https://docs.mulesoft.com/mule-runtime/4.11/tuning-backpressure-maxconcurrency) for details.

## Backend Server Response Time

Understand if the average latency and throughput of backend servers is limiting the scalability or
performance of your application.

See [Backend Server Response Time](https://docs.mulesoft.com/mule-runtime/4.11/tuning-backend-server) for details.

## Caching

Understand when to use cache and which cache-strategy to follow, based on key aspects of your data.
Mule offers some customizable mechanisms, such as Cache Scope and the HTTP Caching API Gateway
policy, to enable cache according to your needs.

See [Caching](https://docs.mulesoft.com/mule-runtime/4.11/tuning-caching) for details.

## Pooling Profiles

Pooling components helps process simultaneous incoming requests. Understand how to add
`pooling-profile` to connectors when performance tests show that it is necessary.

See [Pooling Profiles](https://docs.mulesoft.com/mule-runtime/4.11/tuning-pooling-profiles) for details.

## Domains

Using domains provides a central repository for all the shared resources, facilitating the
class-loading process. Domains enhance performance when you deploy multiple services on the same
on-premises instance of Mule runtime engine.

See [Domains](https://docs.mulesoft.com/mule-runtime/4.11/shared-resources) for details.

## Logging

Understand why asynchronous logging performs better than synchronous logging.

See [Logging](https://docs.mulesoft.com/mule-runtime/4.11/tuning-logging) for details.

## Batch Processing

Mule can process messages in batches, but batch processing requires having enough available memory
to process the threads in parallel, which means moving the records from persistent storage into RAM
in a fixed-size block. Understand how to configure the batch block size property for your
application.

See [Batch Processing](https://docs.mulesoft.com/mule-runtime/4.11/tuning-batch-processing) for details.

## Application Design Best Practices

Following certain practices at the design phase helps you to achieve better performance for your
Mule apps.

See [Application Design](https://docs.mulesoft.com/mule-runtime/4.11/tuning-app-design) for details.

## See Also

- [Performance Tuning Guide](https://docs.mulesoft.com/mule-runtime/4.11/tuning-guide)