Monday, 01 February 2016
The recording of the talk presented last Wednesday in London is available! Thanks to everyone who joined and to the London JBUG organizers for the awesome new venue!
Tags: functional presentations spark hadoop streams video
Monday, 12 October 2015
We continue with the blog series on the experimental Functional Map API which was released as part of Infinispan 8.0.0.Final. In this blog post we’ll be focusing on how to listen for Functional Map events. For reference, here are the previous entries in the series:
The first thing to notice about Functional Map listeners is that they only send events post-event, so that means the events are received after the event has happened. In contrast with Infinispan Cache listeners, there are no pre-event listener invocations. The reason pre-events are not available is because listeners are meant to be an opportunity to find out what has happened, and having pre-events can sometimes hint as if the listener was able to alter the execution of the operation, for which the listener is not really suited. If interested in pre-events or potentially altering the execution, plugging custom interceptors is the recommended solution.
Functional Map offers two type of event listeners: write-only operation listeners and read-write operation listeners.
Write listeners enable users to register listeners for any cache entry write events that happen in either a read-write or write-only functional map.
Listeners for write events cannot distinguish between cache entry created and cache entry modify/update events because they don’t have access to the previous value. All they know is that a new non-null entry has been written. However, write event listeners can distinguish between entry removals and cache entry create/modify-update events because they can query what the new entry’s value via ReadEntryView.find() method.
Adding a write listener is done via the WriteListeners interface which is accessible via both ReadWriteMap.listeners() and WriteOnlyMap.listeners() method. A write listener implementation can be defined either passing a function to onWrite(Consumer<ReadEntryView<K, V>>) method, or passing a WriteListener implementation to add(WriteListener<K, V>) method. Either way, all these methods return an AutoCloseable instance that can be used to de-register the function listener. Example and expected output:
Read-write listeners enable users to register listeners for cache entry created, modified and removed events, and also register listeners for any cache entry write events. Entry created, modified and removed events can only be fired when these originate on a read-write functional map, since this is the only one that guarantees that the previous value has been read, and hence the differentiation between create, modified and removed can be fully guaranteed.
Adding a read-write listener is done via the ReadWriteListeners interface which is accessible via ReadWriteMap.listeners() method. If interested in only one of the event types, the simplest way to add a listener is to pass a function to either onCreate, onModify or onRemove methods. Otherwise, if interested in multiple type of events, passing a ReadWriteListener implementation via add(ReadWriteListener<K, V>) is the easiest. As with write-listeners, all these methods return an AutoCloseable instance that can be used to de-register the listener.
Here’s an example of adding a ReadWriteListener that handles multiple type of events:
More listener event types are yet to be implemented for Functional API, such as expiration events or passivation/activation events. We are capturing this future work and other improvements under the ISPN-5704 issue.
We’d love to hear from you on how you are finding this new API. To provide feedback or report any problems with it, head to our user forums and create a post there.
In next blog post in the series, we’ll be looking into how to pass per-invocation parameters to tweak operations.
Tags: functional listeners API
Wednesday, 02 September 2015
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.
Tags: functional introduction API lambda