Functional Map API: Working with single entries
In this blog post we’ll continue with the introduction of the experimental Functional Map API, which was released as part of Infinispan 8.0.0.Final, focusing on how to manipulate data using single-key operations.
As mentioned in the Functional Map API introduction, there are three types of operations that can be executed against a functional map: read-only operations (executed via ReadOnlyMap), write-only operations (executed via WriteOnlyMap), and read-write operations (executed via ReadWriteMap) and .
Next, let’s see all three types of operations in action, chaining them to store a single key/value pair along with some metadata, then read it and finally delete a returning the previously stored data:
This example demonstrates some of the key aspects of working with single entries using the Functional Map API:
Single entry methods are asynchronous returning CompletableFuture instances which provide methods to compose and chain operations so that it can feel is they’re being executed sequentially. Unfortunately Java does not have Haskell’s do notation or Scala’s for comprehensions to make it more palatable, but it’s great news that Java finally offers mechanisms to work with CompletableFutures in a non-blocking way, even if they’re a bit more verbose than what’s proposed in other languages.
All data-handling methods for WriteOnlyMap return CompletableFuture<Void>, meaning that the user can find out when the operation has completed but nothing else, because there’s nothing the function can provide that could not be computed in advance or outside the function.
The return type for most of the data handling methods in ReadOnlyMap (and ReadWriteMap) are quite flexible. So, a function can decide to return value information, or metadata, or for convenience, it can also return the ReadEntryView it receives as parameter. This can be useful for users wanting to return both value and metadata parameter information.
The read-write operation demonstrated above showed how to remove an entry and return the previously associated value. In this particular case, we know there’s a value associated with the entry and hence we called ReadEntryView.get() directly, but if we were not sure if the value is present or not, ReadEntryView.find() should be called and return the Optional instance instead.
In the example, Lifespan metadata parameter is constructed using the new Java Time API available in Java 8, but it could have been done equally with java.util.concurrent.TimeUnit as long as the conversion was done to number of milliseconds during which the entry should be accessible.
Lifespan-based expiration works just as it does with other Infinispan APIs, so you can easily modify the example to lower the lifespan, wait for duration to pass and then verify that the value is not present any more.
If storing a constant value, WriteOnlyMap.eval(K, Consumer) could be used instead of WriteOnlyMap.eval(K, V, Consumer), making the code clearer, but if the value is variable, WriteOnlyMap.eval(K, V, Consumer) should be used to avoid, as much as possible, functions capturing external variables. Clearly, operations exposed by functional map can’t cover all scenarios and there might be situations where external variables are captured by functions, but these should in general, should be a minority. Here is as example showing how to implement ConcurrentMap.replace(K, V, V) where external variable capturing is required:
The reason we didn’t add a WriteOnly.eval(K, V, V, Consumer) to the API is because value-equality-based replace comparisons are just one type of replace operations that could be executed. In other cases, metadata parameter based comparison might be more suitable, e.g. Hot Rod replace operation where version (a type of metadata parameter) equality is the deciding factor to determine whether the replace should happen or not.
In the next blog post, we’ll be looking at how to work with multiple entries using the Functional Map API.
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