This article is a part of CQK Top 10 series.

Modern applications process their work in multiple thread pools. Knowing where they are and how to set them up correctly is crucial for making sure applications behave well in production. We observe that stability and peformance issues are often a result of mistakes in this area.

Do you know which thread pools handle your requests?

A typical server-side application processes work in multiple thread pools. Depending on your technology choices, things may start in an I/O pool (for reading from and writing to sockets) and worker pool (for request handling) managed by your web server. You may also introduce additional thread pools either directly by yourself or through popular tools like Hystrix or RxJava.

It’s important to be aware of all the thread pools on the request handling path, as well as which parts of the code are executed in which pools. Misconfigured thread pools can easily become performance bottlenecks or affect application stability.

Do you bound your task queues?

Thread pools are most commonly implemented with a task queue in front of them, so when all the threads are busy at the time the task is submitted, it can wait for execution. If tasks arrive faster than they can be executed, this queue will keep increasing in size.

Many commonly used thread pools, e.g. Executors.newFixedThreadPool() or those used by Spring’s ThreadPoolTaskExecutor, use a LinkedBlockingQueue as their task queue implementation. Such a queue is not limited in size. If tasks are consumed slowly, it can grow in size until it fills all the available memory and causes the application instance to crash with an OutOfMemoryError. This in turn can increase the load on the remaining application instances and make them crash even faster.

Queues should be limited in size. If a certain number of tasks already wait to be executed, any new arriving tasks should be quickly rejected, e.g. with an error response served to the caller. This way an overloaded service can correctly process some part of the requests, as opposed to crashing and not processing any at all. Watch out for retries performed by the caller though! Clients shouldn’t retry on errors returned this way, as it would increase the load of the already struggling service.

You can bound your queues e.g. by using an ArrayBlockingQueue instead of the default LinkedBlockingQueue. Here’s an example using raw JDK API:

ThreadPoolExecutor executorService =
    new ThreadPoolExecutor(..., ..., ..., ..., new ArrayBlockingQueue(100));

Attempt to submit a task to a full queue will cause a RejectedExecutionException to be thrown by default. You can override this behaviour by providing a custom RejectedExecutionHandler implementation when constructing a ThreadPoolExecutor.

Do you isolate your critical thread pools?

Applications commonly provide multiple functionalities, some of which are more critical than others. Problems with one of the functionalities shouldn’t cause unavailability of others. It may happen though, if functionalities execute on shared thread pools.

Issues with one of the functionalities, e.g. increased processing time due to an increased latency of one of the dependencies, may cause the pools on which this functionality is operating to saturate. If some healthy functionalities need to execute on the same pools, they are denied access to resources and become unavailable as well. This way an issue with some less important functionality may cause an outage of a crucial one.

The solution is to keep resource pools separate, especially for the crucial functionalities. In practice, this means each asychronous processing job should be done in its dedicated thread pool. If you use a technology which abstracts asynchronous processing for you, make sure you are aware where the thread pools are exactly and if they are shared.

Take Spring’s @Async annotation for example, which delegates the execution of a method to a task executor. If you annotate a method without specifying the task executor on which it should be executed, then the default one will be used – the same for all the methods annotated this way. Specifying a dedicated task executor for each method can mitigate this problem.

Do you name your threads?

Default thread names in pools from the JDK make it hard to tell which pool they belong to. When you are in the middle of investigating an outage, e.g. viewing a thread dump to discover the state of an application, that’s a piece of information you really need.

Guava makes naming threads very easy by providing a ThreadFactoryBuilder:

ThreadFactory namedThreadsFactory = new ThreadFactoryBuilder().setNameFormat("my-pool-%d").build;
Executors.newFixedThreadPool(..., namedThreadsFactory);

With threads configured this way it’s going to be easy to discover e.g. that all the threads from my-pool are blocked by some I/O operation.

Do your monitor your thread pools?

It’s hard to reason about performance bottlenecks or to locate the causes of stability issues without proper metrics. You should measure at least the number of active threads in the pool and the task queue size. Here’s how you could do it:

MetricRegistry registry = ...;  // e.g. when using dropwizard-metrics
BlockingQueue<Runnable> q = new ArrayBlockingQueue<>(...);
ThreadPoolExecutor executorService = ...;

// we can measure either queue utilization ...
        (Gauge<Double>) () -> {q.size() / (double) (q.size() + q.remainingCapacity())}

// ... or queue size
registry.register("my-thread-pool.queue-size", (Gauge<Integer>) q::size});

// active threads count
        (Gauge) () -> executorService.getActiveCount()

Are your threadpools’ parameters tuned according to your needs?

There are many parameters configurable for thread pools, most importantly the thread pool and task queue sizes. Finding the right values for your requirements needs proper measurements during load tests and in production.

Setting thread pool size too low will cause your tasks to queue up even though enough CPU power may be available. Setting it too high results in wasted resources and may actually slow processing down due to thread context switches. For queue size, setting it too low will result in tasks being rejected when even a small burst of requests arrives. Setting it too high on the other hand will require large amounts of memory when tasks arrive too quickly for the thread pool to keep up, as well as increasing the time it takes for a task to get processed in such scenario – perhaps way above your SLA. In such scenario if there is a time constraint on how quickly tasks need to be processed, e.g. a client waiting for a response until a fixed timeout, after some point all tasks are guaranteed to fall into this timeout and the system overall is not making any progress. Smaller queue would decrease processing time below client timeout and allow for some progress despite system overload.


Setting up thread pools correctly is not a trivial task and understanding the setup is essential for investigating production problems. Hopefully, with these tips, you’ll be able to successfully run and manage your application in production.