Develop Reference

How to perform an task in the background?

I want to use WorkRequest to run a task in the background on a daily basis. But I have two problems:
These codes are executed again each time when the app runs.
When the app is closed, the code will not be executed.
I also want the code to run when the program is closed

You can either use a Handler or you can use workManager.
WorkManager works on devices runnig Android 5.0 or higher.So, if your min sdk version is higher or equal to that , prefer using that.Work Manager is used for deferrable tasks generally like sending analytics event etc.

You can use WorkManager.enqueueUniquePeriodicWork() method to schedule your work. There will be only one instance of that work.
ExistingPeriodicWorkPolicy.KEEP keeps the existing work and ignore the new request each time the app is started.
Example:
WorkManager.getInstance(this).enqueueUniquePeriodicWork(
"UNIQUE_TAG",
ExistingPeriodicWorkPolicy.KEEP,
yourWorkRequest
)
Source: https://developer.android.com/topic/libraries/architecture/workmanager/how-to/managing-work#unique-work

There are several ways to perform a task in the background threads and not use it from the UI thread.
using the AysnkTask. Tutorial : AsyncTask
using RxJava. RxJava helps you to perform tasks in other threads and even manage the consequence of a task to perform in which thread. Tutorial RxJava

equivalent of an ExecutorService in D language?

The D documentation is a bit difficult to understand, how do I achieve the following Java code in D?
ExecutorService service = Executors.newFixedThreadPool(num_threads);
for (File f : files) {
service.execute(() -> process(f));
}
service.shutdown();
try {
service.awaitTermination(24, TimeUnit.HOURS);
} catch (InterruptedException e) {
e.printStackTrace();
}
Would I use std.parallelism or std.concurrency or is this functionality not available in the standard library.

The example you posted is best represented by std.parallelism. You can use the parallel helper function in there, which when used in a foreach it will automatically execute the body of the foreach loop in a thread pool with a thread number (worker size) of totalCPUs - 1. You can change this default value by setting defaultPoolThreads = x; before doing any parallel code (best done at the start of your main) or by using a custom taskPool.
basically then your code would translate to this:
foreach (f; files.parallel) {
process(f); // or just paste what should be done with f in here if it matters
}
std.parallelism is the high-level implementation of multithreading. If you want to just have a task pool you can create a new TaskPool() (with number of workers as optional argument) and then do the same as above using service.parallel(files).
Alternatively you could queue lots of tasks using
foreach (f; files) {
service.put!process(f);
}
service.finish(true); // true = blocking
// you could also do false here in a while true loop with sleeps to implement a timeout
which would then allow to implement a timeout.
Though I would recommend using parallel because it handles the code above for you + gives each thread a storage to access the local stack so you can use it just the same as a normal non-parallel foreach loop.
A side-note/explanation on the documentation:
The std.concurrency is also very useful, though not what you would use with your example. In it there is a spawn function which is spawning a new thread with the powerful messaging API. With the messaging API (send and receive) you can implement thread-safe value passing between threads without using sockets, files or other workarounds.
When you have a task (thread with messaging API) and call receive in it it will wait until the passed timeout is done or another thread calls the send function on the task. For example you could have a file loading queue task which always waits using receive and when e.g. the UI puts a file into the loading queue (just by calling send once or more) it can work on these files and send them back to the UI task which receives using a timeout in the main loop.
std.concurrency also has a FiberScheduler which can be used to do thread style programming in a single thread. For example if you have a UI which does drawing and input handling and all sorts of things it can then in the main loop on every tick call the FiberScheduler and all the currently running tasks will continue where they last stopped (by calling yield). This is useful when you have like an image generator which takes long to generate, but you don't want to block the UI for too long so you call yield() every iteration or so to halt the execution of the generator and do one step of the main loop.
When fibers aren't running they can even be passed around threads so you can have a thread pool from std.parallelism and a custom FiberScheduler implementation and do load balancing which could be useful in a web server for example.
If you want to create Fibers without a FiberScheduler and call them raw (and check their finish states and remove them from any custom scheduler implementation) you can inherit the Fiber class from core.thread, which works exactly the same as a Thread, you just need to call Fiber.yield() every time you wait or think you are in a CPU intensive section.
Though because most APIs aren't made for Fibers they will block and make Fibers seem kind of useless, so you definitely want to use some API which uses Fibers there. For example vibe.d has lots of fiber based functions, but a custom std.concurrency implementation so you need to look out for that.
But just to come back to your question, a TaskPool or in your particular case the parallel function is what you need.
https://dlang.org/phobos/std_parallelism.html#.parallel
https://dlang.org/phobos/std_parallelism.html#.TaskPool.parallel

Difference between Futures(Guava)/CompletableFuture and Observable(RxJava) [duplicate]

I would like to know the difference between
CompletableFuture,Future and Observable RxJava.
What I know is all are asynchronous but
Future.get() blocks the thread
CompletableFuture gives the callback methods
RxJava Observable --- similar to CompletableFuture with other benefits(not sure)
For example: if client needs to make multiple service calls and when we use Futures (Java) Future.get() will be executed sequentially...would like to know how its better in RxJava..
And the documentation http://reactivex.io/intro.html says
It is difficult to use Futures to optimally compose conditional asynchronous execution flows (or impossible, since latencies of each request vary at runtime). This can be done, of course, but it quickly becomes complicated (and thus error-prone) or it prematurely blocks on Future.get(), which eliminates the benefit of asynchronous execution.
Really interested to know how RxJava solves this problem. I found it difficult to understand from the documentation.

Futures
Futures were introduced in Java 5 (2004). They're basically placeholders for a result of an operation that hasn't finished yet. Once the operation finishes, the Future will contain that result. For example, an operation can be a Runnable or Callable instance that is submitted to an ExecutorService. The submitter of the operation can use the Future object to check whether the operation isDone(), or wait for it to finish using the blocking get() method.
Example:
/**
* A task that sleeps for a second, then returns 1
**/
public static class MyCallable implements Callable<Integer> {
#Override
public Integer call() throws Exception {
Thread.sleep(1000);
return 1;
}
}
public static void main(String[] args) throws Exception{
ExecutorService exec = Executors.newSingleThreadExecutor();
Future<Integer> f = exec.submit(new MyCallable());
System.out.println(f.isDone()); //False
System.out.println(f.get()); //Waits until the task is done, then prints 1
}
CompletableFutures
CompletableFutures were introduced in Java 8 (2014). They are in fact an evolution of regular Futures, inspired by Google's Listenable Futures, part of the Guava library. They are Futures that also allow you to string tasks together in a chain. You can use them to tell some worker thread to "go do some task X, and when you're done, go do this other thing using the result of X". Using CompletableFutures, you can do something with the result of the operation without actually blocking a thread to wait for the result. Here's a simple example:
/**
* A supplier that sleeps for a second, and then returns one
**/
public static class MySupplier implements Supplier<Integer> {
#Override
public Integer get() {
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
//Do nothing
}
return 1;
}
}
/**
* A (pure) function that adds one to a given Integer
**/
public static class PlusOne implements Function<Integer, Integer> {
#Override
public Integer apply(Integer x) {
return x + 1;
}
}
public static void main(String[] args) throws Exception {
ExecutorService exec = Executors.newSingleThreadExecutor();
CompletableFuture<Integer> f = CompletableFuture.supplyAsync(new MySupplier(), exec);
System.out.println(f.isDone()); // False
CompletableFuture<Integer> f2 = f.thenApply(new PlusOne());
System.out.println(f2.get()); // Waits until the "calculation" is done, then prints 2
}
RxJava
RxJava is whole library for reactive programming created at Netflix. At a glance, it will appear to be similar to Java 8's streams. It is, except it's much more powerful.
Similarly to Futures, RxJava can be used to string together a bunch of synchronous or asynchronous actions to create a processing pipeline. Unlike Futures, which are single-use, RxJava works on streams of zero or more items. Including never-ending streams with an infinite number of items. It's also much more flexible and powerful thanks to an unbelievably rich set of operators.
Unlike Java 8's streams, RxJava also has a backpressure mechanism, which allows it to handle cases in which different parts of your processing pipeline operate in different threads, at different rates.
The downside of RxJava is that despite the solid documentation, it is a challenging library to learn due to the paradigm shift involved. Rx code can also be a nightmare to debug, especially if multiple threads are involved, and even worse - if backpressure is needed.
If you want to get into it, there's a whole page of various tutorials on the official website, plus the official documentation and Javadoc. You can also take a look at some of the videos such as this one which gives a brief intro into Rx and also talks about the differences between Rx and Futures.
Bonus: Java 9 Reactive Streams
Java 9's Reactive Streams aka Flow API are a set of Interfaces implemented by various reactive streams libraries such as RxJava 2, Akka Streams, and Vertx. They allow these reactive libraries to interconnect, while preserving the all important back-pressure.

I have been working with Rx Java since 0.9, now at 1.3.2 and soon migrating to 2.x I use this in a private project where I already work on for 8 years.
I wouldn't program without this library at all anymore. In the beginning I was skeptic but it is a complete other state of mind you need to create. Quiete difficult in the beginning. I sometimes was looking at the marbles for hours.. lol
It is just a matter of practice and really getting to know the flow (aka contract of observables and observer), once you get there, you'll hate to do it otherwise.
For me there is not really a downside on that library.
Use case:
I have a monitor view that contains 9 gauges (cpu, mem, network, etc...). When starting up the view, the view subscribes itselfs to a system monitor class that returns an observable (interval) that contains all the data for the 9 meters.
It will push each second a new result to the view (so not polling !!!).
That observable uses a flatmap to simultaneously (async!) fetch data from 9 different sources and zips the result into a new model your view will get on the onNext().
How the hell you gonna do that with futures, completables etc ... Good luck ! :)
Rx Java solves many issues in programming for me and makes in a way a lot easier...
Advantages:
Statelss !!! (important thing to mention, most important maybe)
Thread management out of the box
Build sequences that have their own lifecycle
Everything are observables so chaining is easy
Less code to write
Single jar on classpath (very lightweight)
Highly concurrent
No callback hell anymore
Subscriber based (tight contract between consumer and producer)
Backpressure strategies (circuit breaker a like)
Splendid error handling and recovering
Very nice documentation (marbles <3)
Complete control
Many more ...
Disadvantages:
- Hard to test

Java's Future is a placeholder to hold something that will be completed in the future with a blocking API. You'll have to use its' isDone() method to poll it periodically to check if that task is finished. Certainly you can implement your own asynchronous code to manage the polling logic. However, it incurs more boilerplate code and debug overhead.
Java's CompletableFuture is innovated by Scala's Future. It carries an internal callback method. Once it is finished, the callback method will be triggered and tell the thread that the downstream operation should be executed. That's why it has thenApply method to do further operation on the object wrapped in the CompletableFuture.
RxJava's Observable is an enhanced version of CompletableFuture. It allows you to handle the backpressure. In the thenApply method (and even with its brothers thenApplyAsync) we mentioned above, this situation might happen: the downstream method wants to call an external service that might become unavailable sometimes. In this case, the CompleteableFuture will fail completely and you will have to handle the error by yourself. However, Observable allows you to handle the backpressure and continue the execution once the external service to become available.
In addition, there is a similar interface of Observable: Flowable. They are designed for different purposes. Usually Flowable is dedicated to handle the cold and non-timed operations, while Observable is dedicated to handle the executions requiring instant responses. See the official documents here: https://github.com/ReactiveX/RxJava#backpressure

All three interfaces serve to transfer values from producer to consumer. Consumers can be of 2 kinds:
synchronous: consumer makes blocking call which returns when the value is ready
asynchronous: when the value is ready, a callback method of the consumer is called
Also, communication interfaces differ in other ways:
able to transfer single value of multiple values
if multiple values, backpressure can be supported or not
As a result:
Future transferes single value using synchronous interface
CompletableFuture transferes single value using both synchronous and asynchronous interfaces
Rx transferes multiple values using asynchronous interface with backpressure
Also, all these communication facilities support transferring exceptions. This is not always the case. For example, BlockingQueue does not.

The main advantage of CompletableFuture over normal Future is that CompletableFuture takes advantage of the extremely powerful stream API and gives you callback handlers to chain your tasks, which is absolutely absent if you use normal Future. That along with providing asynchronous architecture, CompletableFuture is the way to go for handling computation heavy map-reduce tasks, without worrying much about application performance.

Java / Scala Future driven by a callback

Short Version:
How can I create a Promise<Result> which is completed on a trigger of a callback?
Long Version:
I am working on an application which deals with third-party SOAP services. A request from user delegates to multiple SOAP services simultaneously, aggregates the results and sends back to the user.
The system needs to be scalable and should allow multiple concurrent users. As each user requests ends up triggering about 10 web service calls and each call blocking for about 1 second, the system needs to be designed with non-blocking I/O.
I am using Apache CXF within Play Framework (Java) for this system. I have managed to generate the Asynchronous WS Client proxies and enable the async transport. What I am unable to figure out is how to return a Future to Play's Thread when I have delegated to multiple Web Service proxies and the results will be obtained as callbacks.
Option 1: Using async method calls returning Java Future.
As described in this scala.concurrent.Future wrapper for java.util.concurrent.Future thread, there is no way we can convert a Java Future to a Scala Future. Only way to get a result from the Future is to do Future.get() which blocks the caller. Since CXF's generated proxies return Java Future, this option is ruled out.
Option 2: Use Scala Future.
Since CXF generates the proxy interfaces, I am not sure if there is any way I can intervene and return a Scala Future (AFAIK Akka uses Scala Futures) instead of Java Future?
Option 3: Use the callback approach.
The async methods generated by CXF which return Java Future also takes a callback object which I suppose will provide a callback when result is ready. To use this approach, I will need to return a Future which will wait until I receive a callback.
I think Option 3 is most promising, although I have no ideas about how I can return a Promise which will be completed on receiving a callback. I could possibly have a thread waiting in a while(true) and waiting in between until result is available. Again, I don't know how I can go into wait without blocking the thread?
In a nutshell, I am trying to build a system which is making a lot of SOAP web service calls, where each call blocks for significant time. The system may easily run out of threads in case of lot of concurrent web service calls. I am working on finding a solution which is non-blocking I/O based which can allow many ongoing web service calls at the same time.

Option 3 looks good :) A couple of imports to start with...
import scala.concurrent.{Await, Promise}
import scala.concurrent.duration.Duration
and, just to illustrate the point, here's a mocked CXF API that takes the callback:
def fetch(url: String, callback: String => Unit) = {
callback(s"results for $url")
}
Create a promise, call API with promise as callback:
val promise = Promise[String]
fetch("http://corp/api", result => promise.success(result))
Then you can take promise.future which is an instance of Future into your Play app.
To test it, you can do this:
Await.result(promise.future, Duration.Inf)
which will block awaiting the result, at which point you should see "results for http://corp/api" in the console.

Techniques for modeling a dynamic dataflow with Java concurrency API

EDIT: This is basically a "how to properly implement a data flow engine in Java" question, and I feel this cannot be adequately answered in a single answer (it's like asking, "how to properly implement an ORM layer" and getting someone to write out the details of Hibernate or something), so consider this question "closed".
Is there an elegant way to model a dynamic dataflow in Java? By dataflow, I mean there are various types of tasks, and these tasks can be "connected" arbitrarily, such that when a task finishes, successor tasks are executed in parallel using the finished tasks output as input, or when multiple tasks finish, their output is aggregated in a successor task (see flow-based programming). By dynamic, I mean that the type and number of successors tasks when a task finishes depends on the output of that finished task, so for example, task A may spawn task B if it has a certain output, but may spawn task C if has a different output. Another way of putting it is that each task (or set of tasks) is responsible for determining what the next tasks are.
Sample dataflow for rendering a webpage: I have as task types: file downloader, HTML/CSS renderer, HTML parser/DOM builder, image renderer, JavaScript parser, JavaScript interpreter.
File downloader task for HTML file
HTML parser/DOM builder task
File downloader task for each embedded file/link
If image, image renderer
If external JavaScript, JavaScript parser
JavaScript interpreter
Otherwise, just store in some var/field in HTML parser task
JavaScript parser for each embedded script
JavaScript interpreter
Wait for above tasks to finish, then HTML/CSS renderer (obviously not optimal or perfectly correct, but this is simple)
I'm not saying the solution needs to be some comprehensive framework (in fact, the closer to the JDK API, the better), and I absolutely don't want something as heavyweight is say Spring Web Flow or some declarative markup or other DSL.
To be more specific, I'm trying to think of a good way to model this in Java with Callables, Executors, ExecutorCompletionServices, and perhaps various synchronizer classes (like Semaphore or CountDownLatch). There are a couple use cases and requirements:
Don't make any assumptions on what executor(s) the tasks will run on. In fact, to simplify, just assume there's only one executor. It can be a fixed thread pool executor, so a naive implementation can result in deadlocks (e.g. imagine a task that submits another task and then blocks until that subtask is finished, and now imagine several of these tasks using up all the threads).
To simplify, assume that the data is not streamed between tasks (task output->succeeding task input) - the finishing task and succeeding task don't have to exist together, so the input data to the succeeding task will not be changed by the preceeding task (since it's already done).
There are only a couple operations that the dataflow "engine" should be able to handle:
A mechanism where a task can queue more tasks
A mechanism whereby a successor task is not queued until all the required input tasks are finished
A mechanism whereby the main thread (or other threads not managed by the executor) blocks until the flow is finished
A mechanism whereby the main thread (or other threads not managed by the executor) blocks until certain tasks have finished
Since the dataflow is dynamic (depends on input/state of the task), the activation of these mechanisms should occur within the task code, e.g. the code in a Callable is itself responsible for queueing more Callables.
The dataflow "internals" should not be exposed to the tasks (Callables) themselves - only the operations listed above should be available to the task.
Note that the type of the data is not necessarily the same for all tasks, e.g. a file download task may accept a File as input but will output a String.
If a task throws an uncaught exception (indicating some fatal error requiring all dataflow processing to stop), it must propagate up to the thread that initiated the dataflow as quickly as possible and cancel all tasks (or something fancier like a fatal error handler).
Tasks should be launched as soon as possible. This along with the previous requirement should preclude simple Future polling + Thread.sleep().
As a bonus, I would like to dataflow engine itself to perform some action (like logging) every time task is finished or when no has finished in X time since last task has finished. Something like: ExecutorCompletionService<T> ecs; while (hasTasks()) { Future<T> future = ecs.poll(1 minute); some_action_like_logging(); if (future != null) { future.get() ... } ... }
Are there straightforward ways to do all this with Java concurrency API? Or if it's going to complex no matter what with what's available in the JDK, is there a lightweight library that satisfies the requirements? I already have a partial solution that fits my particular use case (it cheats in a way, since I'm using two executors, and just so you know, it's not related at all to the web browser example I gave above), but I'd like to see a more general purpose and elegant solution.

How about defining interface such as:
interface Task extends Callable {
boolean isReady();
}
Your "dataflow engine" would then just need to manage a collection of Task objects i.e. allow new Task objects to be queued for excecution and allow queries as to the status of a given task (so maybe the interface above needs extending to include id and/or type). When a task completes (and when the engine starts of course) the engine must just query any unstarted tasks to see if they are now ready, and if so pass them to be run on the executor. As you mention, any logging, etc. could also be done then.
One other thing that may help is to use Guice (http://code.google.com/p/google-guice/) or a similar lightweight DI framework to help wire up all the objects correctly (e.g. to ensure that the correct executor type is created, and to make sure that Tasks that need access to the dataflow engine (either for their isReady method or for queuing other tasks, say) can be provided with an instance without introducing complex circular relationships.
HTH, but please do comment if I've missed any key aspects...
Paul.

Look at https://github.com/rfqu/df4j — a simple but powerful dataflow library. If it lacks some desired features, they can be added easily.