Infinispan has quite a few spectacular ways of executing code in the grid. But I bet you haven't heard or aren't really familiar with those, which is disappointing. I hope to fix this, however, as we have added more information to the user guide and wanted to detail that here in this blog.
As I am sure you are aware Infinispan can be used in embedded (in your JVM) and remote (in a standalone server). Unfortunately, this means there are different ways of executing code based on which mode you are in.
One question that seems to come up more than others is how a user can perform cache operations on all data, such as remove all elements that match a given filter. If you are curious about this one, you should check out the Examples section with the example named "Remove specific entries" as it details how a user would do exactly that.
I should also point out the new Cluster Executor section, which is similar to Streams that replaced Map Reduce, is here to replace the old Distributed Executor. With Cluster Executor and Distributed Streams there is a clearer distinction between executing code on nodes (Cluster Executor) and executing code based on data (Distributed Streams).
Scripting is by far the easiest to use - just insert your script and execute - but has some limitations that we haven't been able to fix yet.
Server tasks can run pretty much any Java but require registering classes beforehand. Unfortunately, this section still needs to be filled in and should be added sometime in the near future. I would say, until then, if you are interested, you can look at some tests in github.
As I am sure you are aware Infinispan can be used in embedded (in your JVM) and remote (in a standalone server). Unfortunately, this means there are different ways of executing code based on which mode you are in.
Embedded
The embedded mode has the most features available and is the easiest to use. The appropriate section can be found here.One question that seems to come up more than others is how a user can perform cache operations on all data, such as remove all elements that match a given filter. If you are curious about this one, you should check out the Examples section with the example named "Remove specific entries" as it details how a user would do exactly that.
I should also point out the new Cluster Executor section, which is similar to Streams that replaced Map Reduce, is here to replace the old Distributed Executor. With Cluster Executor and Distributed Streams there is a clearer distinction between executing code on nodes (Cluster Executor) and executing code based on data (Distributed Streams).
Server
The server is a bit more interesting and usually requires configuration ahead of time, unlike Embedded. It can be found in this section. The benefit of the server is most of these can invoke embedded operations internally.Scripting is by far the easiest to use - just insert your script and execute - but has some limitations that we haven't been able to fix yet.
Server tasks can run pretty much any Java but require registering classes beforehand. Unfortunately, this section still needs to be filled in and should be added sometime in the near future. I would say, until then, if you are interested, you can look at some tests in github.
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