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Java - Thread.sleep VS Awaitility.await()

Happy Friday everyone,
I'd like to replace Thread.sleep with Awaitility.await(),ideally with minimal changes, as I'm going over Sonar bugs in an older repository. I'm attempting to avoid usage of until of Awaitility, and it is not working out. I understand Awaitility is for async behavior and until function is a big part of it. I'm hoping someone with more experience with Awaitility could suggest a clean usage of it in this test scenario, much appreciate your input.
//Thread.sleep(1000);
Awaitility.await().atMost(Duration.ONE_SECOND);
list = client.getLocation();
Assertions.assertFalse(list.isEmpty());

Despite your intention, I encourage you to give Awaitility and until() another try. The example below test the same thing in a single one-liner (albeit it is written in several lines for improved readability):
#Test
void test_refresh() {
Awaitility
.await()
.atMost(5, TimeUnit.SECONDS)
.until(() -> {
List<Trp> trpList = client.getAllTrps();
return !trpList.isEmpty();
});
}
For more fine grained control of the polling you can take a look at methods like pollInterval() and pollDelay().

Though what you did can make sense from the first look, but with a better understanding of the design of the library there are two apparent principles that you have missed.
The Awaitility library introduces a functional style usage to define properties, conditions, accumulators and transformations.
1. Terminal Operations.
Awaitility
.await()
.atMost(Duration.TWO_SECONDS)
The above code will not execute, because internally what runs the chain is a call to Condition#await which is accessed only by caling ConditionFactory#until.
This call to until can be called a Terminal Operation
2. Intermediate Operations.
Operations like await, atMost , timeout and other operations only return the same instance of ConditionFactory, which will only define behaviour and actions in a lazy manner.
So in a nutshell the design expects you to:
Create an instance of ConditionFactory using Awaitility#await.
Define your await behaviour using the intermediate methods.
Execute and/or transform using until and its variants.

Output result of heavy calculation in Vert.x with back-pressure

The application in question handles requests from clients which then requires a lot of calculation on the server side. This calculation is done piece-by-piece, so if the client is slow to read, this calculation should not progress (the calculation should respond to back-pressure).
The calculation is now represented as a Supplier<Buffer>, in which the get() call might take a long time and needs to be called multiple times until it responds with null (no more data). The get() should be called in a separate thread-pool (which is shared with other requests), and should only be called if the client is really able to accept the data.
My current code is:
ReadStream<Buffer> readStream = new MyComplicatedReadStream(supplier, executor)
.exceptionHandler(request::fail)
.endHandler(x -> request.response().end());
Pump.pump(readStream, request.response())).start();
I've made a custom implementation of ReadStream to do this, which sort-of works, but is long, clunky and has synchronization issues.
Instead of fixing that, I wonder if there is a idiomatic way in vert.x / rx to implement / instantiate a MyComplicatedReadStream. So, for a Supplier<Buffer> and an ExecutorService get a ReadStream<Buffer> which executes get() with the given executor and doesn't generate if it is paused.

I have near 0 experience with vert.x but I do have some experience with rxjava. So there might be a better way to do this but from rxjava perspective you can make use of generate method to create 'cold' flowables which only generate items on demand. I believe in this case when the stream is paused, no additional calls to supplier.get() will be made as there is no 'demand'
using kotlin syntax here but I think you can derive the java version easily.
Flowable.generate<Buffer> { emitter ->
val nextValue = supplier.get()
if (nextValue == null) {
emitter.onComplete()
} else {
emitter.onNext(nextValue)
}
}.subscribeOn(Schedulers.from(executor)) // this will make the above callback run in the given executor
Since it seems that the supplier is holding some state, you may in some cases want to generate a 'new supplier' for each consumer, in which case you can use the overload of the generate method that allows specifying another callback to get an instance of the state (supplier in your case). http://reactivex.io/RxJava/2.x/javadoc/io/reactivex/Flowable.html#generate-java.util.concurrent.Callable-io.reactivex.functions.BiConsumer-
Looks like then you can convert the flowable to a read stream:
ReadStream<Buffer> readStream = FlowableHelper.toReadStream(observable);
based on the docs here: https://vertx.tk/docs/vertx-rx/java2/#_read_stream_support

Why does Java have no async/await?

Using async/await it is possible to code asynchronous functions in an imperative style. This can greatly facilitate asynchronous programming. After it was first introduced in C#, it was adopted by many languages such as JavaScript, Python, and Kotlin.
EA Async is a library that adds async/await like functionality to Java. The library abstracts away the complexity of working with CompletableFutures.
But why has async/await neither been added to Java SE, nor are there any plans to add it in the future?

The short answer is that the designers of Java try to eliminate the need for asynchronous methods instead of facilitating their use.
According to Ron Pressler's talk asynchronous programming using CompletableFuture causes three main problems.
branching or looping over the results of asynchronous method calls is not possible
stacktraces cannot be used to identify the source of errors, profiling becomes impossible
it is viral: all methods that do asynchronous calls have to be asynchronous as well, i.e. synchronous and asynchronous worlds don't mix
While async/await solves the first problem it can only partially solve the second problem and does not solve the third problem at all (e.g. all methods in C# doing an await have to be marked as async).
But why is asynchronous programming needed at all? Only to prevent the blocking of threads, because threads are expensive. Thus instead of introducing async/await in Java, in project Loom Java designers are working on virtual threads (aka fibers/lightweight threads) which will aim to significantly reduce the cost of threads and thus eliminate the need of asynchronous programming. This would make all three problems above also obsolete.

Better late than never!!!
Java is 10+ years late in trying to come up with lighter weight units of execution which can be executed in parallel. As a side note, Project loom also aims to expose in Java 'delimited continuation' which, I believe is nothing more than good old 'yield' keyword of C# (again almost 20 years late!!)
Java does recognize the need for solving the bigger problem solved by asyn await (or actually Tasks in C# which is the big idea. Async Await is more of a syntactical sugar. Highly significant improvement, but still not a necessity to solve the actual problem of OS mapped Threads being heavier than desired).
Look at the proposal for project loom here: https://cr.openjdk.java.net/~rpressler/loom/Loom-Proposal.html
and navigate to last section 'Other Approaches'. You will see why Java does not want to introduce async/await.
Having said this, I don't really agree with the reasoning being provided. Neither in this proposal nor in Stephan's answer.
First let us diagnose Stephan's answer
async await solves point 1 mentioned there. (Stephan also acknowledges it further down the answer)
It is extra work for sure on the part of the framework and tools but not at all on the part of the programmers. Even with async await, .Net debuggers are pretty good in this aspect.
This I only partially agree with. Whole purpose of async await is to elegantly mix asynchronous world with synchronous constructs. But yes, you either need to declare the caller also as async or deal directly with Task in the caller routine. However, project loom will not solve it either in a meaningful way. To fully benefit from the light weight virtual threads, even the caller routine must be getting executed on a virtual thread. Otherwise what's the benefit? You will end up blocking an OS backed thread!!! Hence even virtual threads need to be 'viral' in the code. On the contrary, it will be easier in Java to not notice that the routine you are calling is async and will block the calling thread (which will be concerning if the calling routine is itself not executing on a virtual thread). Async keyword in C# makes the intent very clear and forces you to decide (it is possible in C# to block as well if you want by asking for Task.Result. Most of the time the calling routine can just as easily be async itself).
Stephan is right when he says async programming is needed to prevent blocking of (OS) threads as (OS) threads are expensive. And that's precisely the whole reason why virtual threads (or C# tasks) are needed. You should be able to 'block' on these tasks without losing your sleep. Offcourse to not lose the sleep, either the calling routine itself should be a task or blocking should be on non-blocking IO, with framework being smart enough to not block the calling thread in that case (power of continuation).
C# supports this and proposed Java feature aims to support this.
According to the proposed Java api, blocking on virtual thread will require calling vThread.join() method in Java.
How is it really more beneficial than calling await workDoneByVThread()?
Now let us look at project loom proposal reasoning
Continuations and fibers dominate async/await in the sense that async/await is easily implemented with continuations (in fact, it can be implemented with a weak form of delimited continuations known as stackless continuations, that don't capture an entire call-stack but only the local context of a single subroutine), but not vice-versa
I don't simply understand this statement. If someone does, please let me know in the comments.
For me, async/await are implemented using continuations and as far as stack trace is concerned, since the fibres/virtual threads/tasks are within the virtual machine, it must be possible to manage that aspect. In-fact .net tools do manage that.
While async/await makes code simpler and gives it the appearance of normal, sequential code, like asynchronous code it still requires significant changes to existing code, explicit support in libraries, and does not interoperate well with synchronous code
I have already covered this. Not making significant changes to existing code and no explicit support in libraries will actually mean not using this feature effectively. Until and unless Java is aiming to transparently transform all the threads to virtual threads, which it can't and isn't, this statement does not make sense to me.
As a core idea, I find no real difference between Java virtual threads and C# tasks. To the point that project loom is also aiming for work-stealing scheduler as default, same as the scheduler used by .Net by default (https://learn.microsoft.com/en-us/dotnet/api/system.threading.tasks.taskscheduler?view=net-5.0, scroll to last remarks section ).
Only debate it seems is on what syntax should be adopted to consume these.
C# adopted
A distinct class and interface as compared to existing threads
Very helpful syntactical sugar for marrying async with sync
Java is aiming for:
Same familiar interface of Java Thread
No special constructs apart from try-with-resources support for ExecutorService so that the result for submitted tasks/virtual threads can be automatically waited for (thus blocking the calling thread, virtual/non-virtual).
IMHO, Java's choices are worse than those of C#. Having a separate interface and class actually makes it very clear that the behavior is a lot different. Retaining same old interface can lead to subtle bugs when a programmer does not realize that she is now dealing with something different or when a library implementation changes to take advantage of the new constructs but ends up blocking the calling (non-virtual) thread.
Also no special language syntax means that reading async code will remain difficult to understand and reason about (I don't know why Java thinks programmers are in love with Java's Thread syntax and they will be thrilled to know that instead of writing sync looking code they will be using the lovely Thread class)
Heck, even Javascript now has async await (with all its 'single-threadedness').

I release a new project JAsync implement async-await fashion in java which use Reactor as its low level framework. It is in the alpha stage. I need more suggest and test case.
This project makes the developer's asynchronous programming experience as close as possible to the usual synchronous programming, including both coding and debugging.
I think my project solves point 1 mentioned by Stephan.
Here is an example:
#RestController
#RequestMapping("/employees")
public class MyRestController {
#Inject
private EmployeeRepository employeeRepository;
#Inject
private SalaryRepository salaryRepository;
// The standard JAsync async method must be annotated with the Async annotation, and return a JPromise object.
#Async()
private JPromise<Double> _getEmployeeTotalSalaryByDepartment(String department) {
double money = 0.0;
// A Mono object can be transformed to the JPromise object. So we get a Mono object first.
Mono<List<Employee>> empsMono = employeeRepository.findEmployeeByDepartment(department);
// Transformed the Mono object to the JPromise object.
JPromise<List<Employee>> empsPromise = Promises.from(empsMono);
// Use await just like es and c# to get the value of the JPromise without blocking the current thread.
for (Employee employee : empsPromise.await()) {
// The method findSalaryByEmployee also return a Mono object. We transform it to the JPromise just like above. And then await to get the result.
Salary salary = Promises.from(salaryRepository.findSalaryByEmployee(employee.id)).await();
money += salary.total;
}
// The async method must return a JPromise object, so we use just method to wrap the result to a JPromise.
return JAsync.just(money);
}
// This is a normal webflux method.
#GetMapping("/{department}/salary")
public Mono<Double> getEmployeeTotalSalaryByDepartment(#PathVariable String department) {
// Use unwrap method to transform the JPromise object back to the Mono object.
return _getEmployeeTotalSalaryByDepartment(department).unwrap(Mono.class);
}
}
In addition to coding, JAsync also greatly improves the debugging experience of async code.
When debugging, you can see all variables in the monitor window just like when debugging normal code. I will try my best to solve point 2 mentioned by Stephan.
For point 3, I think it is not a big problem. Async/Await is popular in c# and es even if it is not satisfied with it.

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.

Relinquish the thread/CPU until async call completes in Akka and Java?

I'm looking for the Java/Akka equivalent of Python's yield from or gevent's monkey patch.
Update
There has been some confusion in the commets about what is question is asking so let me restate the question:
If I have a future, how do I wait for the future to compete without blocking the thread AND without returning to caller until the future is complete?
Lets say we have method that blocks:
public Object foo() {
Object result = someBlockingCall();
return doSomeThingWithResult(result);
}
To make this asynchronous, we would pass SomeBlockingCall() a callback:
public void foo() {
someAsyncCall(new Handler() {
public void onSuccess(Object result) {
message = doSomethingWithResult(result);
callerRef.tell(message, ActorRef.noSender());
}
});
}
The call to foo() now returns before the result is ready, so the caller no longer gets the result. We have to get the result back to the caller by passing a message. To convert synchronous code to asynchronous Akka code, a redesign of the caller is required.
What I'd like is async code that looks like synchronous code like Python's Gevent.
I want to write:
public Object foo() {
Future future = someAsyncCall();
// save stack frame, go back to the event loop and relinquish CPU
// so other events can use the thread,
// and come back when future is complete and restore stack frame
return yield(future);
}
This would allow me to make my synchronous code asynchronous without a redesign.
Is this posible?
Note:
The Play framework seems to fake this with async() and AsyncResult. But this won't work in general since I have to write the code that handles the AsyncResult which would look like the above callback handler.

I think trying to get back a more straightforward sync design, although an efficient one, is actually a good intention and a good idea (see for example here).
Quasar has facilities to obtain sync/blocking APIs that are still highly efficient from async APIs (see this blog post), which looks exactly what you're looking for.
The fundamental problem is not that the sync/blocking style itself is bad (actually async and sync are dual styles and can be transformed into one another, see for example here), but rather than blocking Java's heavyweight threads is not efficient: it is not an abstraction problem but an implementation problem, so instead of giving up the easier thread abstraction only because the implementation is inefficient, I agree it is better for the future of your code to try and look for more efficient thread implementations.
As Roland hinted, Quasar adds lightweight threads or fibers to the JVM, so you can get the same performance of async frameworks without giving up the thread abstraction and regular imperative control flow constructs (sequence, loops etc.) available in the language.
It also unifies JVM/JDK's threads and its fibers under a common strand interface, so they can interoperate seamlessly, and provides a porting of java.util.concurrent to this unified concept.
On top of strands (either fibers or regular threads) Quasar also offers fully-fledged Erlang-style actors, blocking Go-like channels and dataflow programming, so you can choose the concurrent programming paradigm that suits best your skills and needs without being forced into one.
It also provides bindings for popular and standard technologies (as part of the Comsat project), so you can preserve your code assets because the porting effort will be minimal (if any). For the same reason you can also opt-out easily, should you choose to.
Currently Quasar has binding for Java 7 and 8, Clojure under the Pulsar project and soon JetBrain's Kotlin. Being based on JVM bytecode instrumentation, Quasar can really work with any JVM language if an integration module is present, and it offers tools to build additional ones.

The answer to your question is “no”, and that is very much by design. Writing a method to be asynchronous means returning the Future as its result, the method itself will not perform the computation but will arrange for the result to be provided later. You can then pass this Future to the right place where it is further used, for example by transforming it using one of the many combinators (like map, recover, etc.).
Awaiting a strict result for the Future will have to block the current thread of execution, no matter which technology you use. With plain JVM threads you will block a real thread from the operating system, with Quasar you will block your Fiber, with Akka you will block your Actor (*); blocking means blocking, no way around that.
(*) In an Actor you would get the result via a message at a later point, and until that point you will have to switch the behavior such that new incoming messages are stashed, rejected or dropped, depending on your use-case.