[id='performance-clustered-execution_{context}']
= Code execution and data processing

One of the benefits of distributed caching is that you can leverage compute resources from each host to perform large scale data processing more efficiently.
By executing your processing logic directly on {brandname} you spread the workload across multiple JVM instances.
Your code also runs in the same memory space where {brandname} stores your data, meaning that you can iterate over entries much faster.

In terms of performance impact to your {brandname} deployment, that entirely depends on your code execution.
More complex processing operations have higher performance penalties so you should approach running any code on {brandname} clusters with careful planning.
Start out by testing your code and performing multiple execution runs on a smaller, sample data set.
After you gather some metrics you can start identifying optimizations and understanding what performance implications of the code you're running.

One definite consideration is that long running processes can start having a negative impact on normal read and write operations.
So it is imperative that you monitor your deployment over time and continually assess performance.

[discrete]
== Embedded caches

With embedded caches, {brandname} provides two APIs that let you execute code in the same memory space as your data.

`ClusterExecutor` API:: Lets you perform any operation with the Cache Manager, including iterating over the entries of one or more caches, and gives you processing based on {brandname} nodes.
`CacheStream` API:: Lets you perform operations on collections and gives you processing based on data.

If you want to run an operation on a single node, a group of nodes, or all nodes in a certain geographic region, then you should use clustered execution.
If you want to run an operation that guarantees a correct result for your entire data set, then using distributed streams is a more effective option.

.Cluster execution
[source,java,options="nowrap",subs=attributes+,role="primary"]
----
include::code_examples/ClusterExecutor.java[]
----

.CacheStream
[source,java,options="nowrap",subs=attributes+,role="secondary"]
----
include::code_examples/Stream.java[]
----

[role="_additional-resources"]
.Additional resources
* link:../../apidocs/org/infinispan/manager/ClusterExecutor.html[org.infinispan.manager.ClusterExecutor]
* link:../../apidocs/org/infinispan/CacheStream.html[org.infinispan.CacheStream]

[discrete]
== Remote caches

For remote caches, {brandname} provides a `ServerTask` API that lets you register custom Java implementations with {brandname} Server and execute tasks programmatically by calling the `execute()` method over Hot Rod or by using the {brandname} Command Line Interface (CLI).
You can execute tasks on one {brandname} Server instance only or all server instances in the cluster.
