2020-08-17 18:00:00 +0000

In this series so far, we have learned about Resilience4j and its Retry and RateLimiter modules. In this article, we will continue exploring Resilience4j with a look into the TimeLimiter. We will find out what problem it solves, when and how to use it, and also look at a few examples.

Code Example

This article is accompanied by a working code example on GitHub.

What is Resilience4j?

Please refer to the description in the previous article for a quick intro into how Resilience4j works in general.

What is Time Limiting?

Setting a limit on the amount of time we are willing to wait for an operation to complete is called time limiting. If the operation does not complete within the time we specified, we want to be notified about it with a timeout error.

Sometimes, this is also referred to as “setting a deadline”.

One main reason why we would do this is to ensure that we don’t make users or clients wait indefinitely. A slow service that does not give any feedback can be frustrating to the user.

Another reason we set time limits on operations is to make sure we don’t hold up server resources indefinitely. The timeout value that we specify when using Spring’s @Transactional annotation is an example - we don’t want to hold up database resources for long in this case.

When to Use the Resilience4j TimeLimiter?

Resilience4j’s TimeLimiter can be used to set time limits (timeouts) on asynchronous operations implemented with CompleteableFutures.

The CompletableFuture class introduced in Java 8 makes asynchronous, non-blocking programming easier. A slow method can be executed on a different thread, freeing up the current thread to handle other tasks. We can provide a callback to be executed when slowMethod() returns:

int slowMethod() {
    // time-consuming computation or remote operation
  return 42;


The slowMethod() here could be some computation or remote operation. Usually, we want to set a time limit when making an asynchronous call like this. We don’t want to wait indefinitely for slowMethod() to return. If slowMethod() takes more than a second, for example, we may want to return a previously computed, cached value or maybe even error out.

In Java 8’s CompletableFuture there’s no easy way to set a time limit on an asynchronous operation. CompletableFuture implements the Future interface and Future has an overloaded get() method to specify how long we can wait:

CompletableFuture<Integer> completableFuture = CompletableFuture
Integer result = completableFuture.get(3000, TimeUnit.MILLISECONDS);

But there’s a problem here - the get() method is a blocking call. So it defeats the purpose of using CompletableFuture in the first place, which was to free up the current thread.

This is the problem that Resilience4j’s TimeLimiter solves - it lets us set a time limit on the asynchronous operation while retaining the benefit of being non-blocking when working with CompletableFuture in Java 8.

This limitation of CompletableFuture has been addressed in Java 9. We can set time limits directly using methods like orTimeout() or completeOnTimeout() on CompletableFuture in Java 9 and above. With Resilience4J’s metrics and events, it still provides added value compared to the plain Java 9 solution, however.

Resilience4j TimeLimiter Concepts

The TimeLimiter supports both Future and CompletableFuture. But using it with Future is equivalent to a Future.get(long timeout, TimeUnit unit). So we will focus on the CompletableFuture in the remainder of this article.

Like the other Resilience4j modules, the TimeLimiter works by decorating our code with the required functionality - returning a TimeoutException if an operation did not complete in the specified timeoutDuration in this case.

We provide the TimeLimiter a timeoutDuration, a ScheduledExecutorService and the asynchronous operation itself expressed as a Supplier of a CompletionStage. It returns a decorated Supplier of a CompletionStage.

Internally, it uses the scheduler to schedule a timeout task - the task of completing the CompletableFuture by throwing a TimeoutException. If the operation finishes first, the TimeLimiter cancels the internal timeout task.

Along with the timeoutDuration, there is another configuration cancelRunningFuture associated with a TimeLimiter. This configuration applies to Future only and not CompletableFuture. When a timeout occurs, it cancels the running Future before throwing a TimeoutException.

Using the Resilience4j TimeLimiter Module

TimeLimiterRegistry, TimeLimiterConfig, and TimeLimiter are the main abstractions in resilience4j-timelimiter.

TimeLimiterRegistry is a factory for creating and managing TimeLimiter objects.

TimeLimiterConfig encapsulates the timeoutDuration and cancelRunningFuture configurations. Each TimeLimiter object is associated with a TimeLimiterConfig.

TimeLimiter provides helper methods to create or execute decorators for Future and CompletableFuture Suppliers.

Let’s see how to use the various features available in the TimeLimiter module. We will use the same example as the previous articles in this series. Assume that we are building a website for an airline to allow its customers to search for and book flights. Our service talks to a remote service encapsulated by the class FlightSearchService.

The first step is to create a TimeLimiterConfig:

TimeLimiterConfig config = TimeLimiterConfig.ofDefaults();

This creates a TimeLimiterConfig with default values for timeoutDuration (1000ms) and cancelRunningFuture (true).

Let’s say we want to set a timeout value of 2s instead of the default:

TimeLimiterConfig config = TimeLimiterConfig.custom()

We then create a TimeLimiter:

TimeLimiterRegistry registry = TimeLimiterRegistry.of(config);
TimeLimiter limiter = registry.timeLimiter("flightSearch");

We want to asynchronously call FlightSearchService.searchFlights() which returns a List<Flight>. Let’s express this as a Supplier<CompletionStage<List<Flight>>>:

Supplier<List<Flight>> flightSupplier = () -> service.searchFlights(request);
Supplier<CompletionStage<List<Flight>>> origCompletionStageSupplier = 
() -> CompletableFuture.supplyAsync(flightSupplier);

We can then decorate the Supplier using the TimeLimiter:

ScheduledExecutorService scheduler = 
Supplier<CompletionStage<List<Flight>>> decoratedCompletionStageSupplier =  
  limiter.decorateCompletionStage(scheduler, origCompletionStageSupplier);

Finally, let’s call the decorated asynchronous operation:

decoratedCompletionStageSupplier.get().whenComplete((result, ex) -> {
  if (ex != null) {
  if (result != null) {

Here’s sample output for a successful flight search that took less than the 2s timeoutDuration we specified:

Searching for flights; current time = 19:25:09 783; current thread = ForkJoinPool.commonPool-worker-3
Flight search successful
[Flight{flightNumber='XY 765', flightDate='08/30/2020', from='NYC', to='LAX'}, Flight{flightNumber='XY 746', flightDate='08/30/2020', from='NYC', to='LAX'}] on thread ForkJoinPool.commonPool-worker-3

And this is sample output for a flight search that timed out:

Exception java.util.concurrent.TimeoutException: TimeLimiter 'flightSearch' recorded a timeout exception on thread pool-1-thread-1 at 19:38:16 963
Searching for flights; current time = 19:38:18 448; current thread = ForkJoinPool.commonPool-worker-3
Flight search successful at 19:38:18 461

The timestamps and thread names above show that the calling thread got a TimeoutException even as the asynchronous operation completed later on the other thread.

We would use decorateCompletionStage() if we wanted to create a decorator and re-use it at a different place in the codebase. If we want to create it and immediately execute the Supplier<CompletionStage>, we can use executeCompletionStage() instance method instead:

CompletionStage<List<Flight>> decoratedCompletionStage =  
  limiter.executeCompletionStage(scheduler, origCompletionStageSupplier);

TimeLimiter Events

TimeLimiter has an EventPublisher which generates events of the types TimeLimiterOnSuccessEvent, TimeLimiterOnErrorEvent, and TimeLimiterOnTimeoutEvent. We can listen for these events and log them, for example:

TimeLimiter limiter = registry.timeLimiter("flightSearch");
limiter.getEventPublisher().onSuccess(e -> System.out.println(e.toString()));
limiter.getEventPublisher().onError(e -> System.out.println(e.toString()));
limiter.getEventPublisher().onTimeout(e -> System.out.println(e.toString()));

The sample output shows what’s logged:

2020-08-07T11:31:48.181944: TimeLimiter 'flightSearch' recorded a successful call.
... other lines omitted ...
2020-08-07T11:31:48.582263: TimeLimiter 'flightSearch' recorded a timeout exception.

TimeLimiter Metrics

TimeLimiter tracks the number of successful, failed, and timed-out calls.

First, we create TimeLimiterConfig, TimeLimiterRegistry, and TimeLimiter as usual. Then, we create a MeterRegistry and bind the TimeLimiterRegistry to it:

MeterRegistry meterRegistry = new SimpleMeterRegistry();

After running the time-limited operation a few times, we display the captured metrics:

Consumer<Meter> meterConsumer = meter -> {
  String desc = meter.getId().getDescription();
  String metricName = meter.getId().getName();
  String metricKind = meter.getId().getTag("kind");
  Double metricValue = 
    StreamSupport.stream(meter.measure().spliterator(), false)
    .filter(m -> m.getStatistic().name().equals("COUNT"))
  System.out.println(desc + " - " + 
                     metricName + 
                     "(" + metricKind + ")" + 
                     ": " + metricValue);

Here’s some sample output:

The number of timed out calls - resilience4j.timelimiter.calls(timeout): 6.0
The number of successful calls - resilience4j.timelimiter.calls(successful): 4.0
The number of failed calls - resilience4j.timelimiter.calls(failed): 0.0

In a real application, we would export the data to a monitoring system periodically and analyze it on a dashboard.

Gotchas and Good Practices When Implementing Time Limiting

Usually, we deal with two kinds of operations - queries (or reads) and commands (or writes). It is safe to time-limit queries because we know that they don’t change the state of the system. The searchFlights() operation we saw was an example of a query operation.

Commands usually change the state of the system. A bookFlights() operation would be an example of a command. When time-limiting a command we have to keep in mind that the command is most likely still running when we timeout. A TimeoutException on a bookFlights() call for example doesn’t necessarily mean that the command failed.

We need to manage the user experience in such cases - perhaps on timeout, we can notify the user that the operation is taking longer than we expected. We can then query the upstream to check the status of the operation and notify the user later.


In this article, we learned how we can use Resilience4j’s TimeLimiter module to set a time limit on asynchronous, non-blocking operations. We learned when to use it and how to configure it with some practical examples.

You can play around with a complete application illustrating these ideas using the code on GitHub.

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