Package org.apache.commons.lang3.concurrent

Provides support classes for multi-threaded programming.

See: Description

Package org.apache.commons.lang3.concurrent Description

Provides support classes for multi-threaded programming. This package is intended to be an extension to java.util.concurrent. These classes are thread-safe.

A group of classes deals with the correct creation and initialization of objects that are accessed by multiple threads. All these classes implement the ConcurrentInitializer interface which provides just a single method:

 public interface ConcurrentInitializer<T> {
    T get() throws ConcurrentException;

A ConcurrentInitializer produces an object. By calling the get() method the object managed by the initializer can be obtained. There are different implementations of the interface available addressing various use cases:

ConstantInitializer is a very straightforward implementation of the ConcurrentInitializer interface: An instance is passed an object when it is constructed. In its get() method it simply returns this object. This is useful, for instance in unit tests or in cases when you want to pass a specific object to a component which expects a ConcurrentInitializer.

The LazyInitializer class can be used to defer the creation of an object until it is actually used. This makes sense, for instance, if the creation of the object is expensive and would slow down application startup or if the object is needed only for special executions. LazyInitializer implements the double-check idiom for an instance field as discussed in Joshua Bloch's "Effective Java", 2nd edition, item 71. It uses volatile fields to reduce the amount of synchronization. Note that this idiom is appropriate for instance fields only. For static fields there are superior alternatives.

We provide an example use case to demonstrate the usage of this class: A server application uses multiple worker threads to process client requests. If such a request causes a fatal error, an administrator is to be notified using a special messaging service. We assume that the creation of the messaging service is an expensive operation. So it should only be performed if an error actually occurs. Here is where LazyInitializer comes into play. We create a specialized subclass for creating and initializing an instance of our messaging service. LazyInitializer declares an abstract initialize() method which we have to implement to create the messaging service object:

 public class MessagingServiceInitializer extends LazyInitializer<MessagingService> {
   protected MessagingService initialize() throws ConcurrentException {
     // Do all necessary steps to create and initialize the service object
     MessagingService service = ...
     return service;

Now each server thread is passed a reference to a shared instance of our new MessagingServiceInitializer class. The threads run in a loop processing client requests. If an error is detected, the messaging service is obtained from the initializer, and the administrator is notified:

 public class ServerThread implements Runnable {
  // The initializer for obtaining the messaging service.
  private final ConcurrentInitializer<MessagingService> initializer;

  public ServerThread(ConcurrentInitializer<MessagingService> init) {
    initializer = init;

  public void run() {
    while (true) {
      try {
        // wait for request
        // process request
      } catch (FatalServerException ex) {
        // get messaging service
        try {
          MessagingService svc = initializer.get();
        } catch (ConcurrentException cex) {

The AtomicInitializer class is very similar to LazyInitializer. It serves the same purpose: to defer the creation of an object until it is needed. The internal structure is also very similar. Again there is an abstract initialize() method which has to be implemented by concrete subclasses in order to create and initialize the managed object. Actually, in our example above we can turn the MessagingServiceInitializer into an atomic initializer by simply changing the extends declaration to refer to AtomicInitializer<MessagingService> as super class.

With AtomicSafeInitializer there is yet another variant implementing the lazy initializing pattern. Its implementation is close to AtomicInitializer; it also uses atomic variables internally and therefore does not need synchronization. The name "Safe" is derived from the fact that it implements an additional check which guarantees that the initialize() method is called only once. So it behaves exactly in the same way as LazyInitializer.

Now, which one of the lazy initializer implementations should you use? First of all we have to state that is is problematic to give general recommendations regarding the performance of these classes. The initializers make use of low-level functionality whose efficiency depends on multiple factors including the target platform and the number of concurrent threads. So developers should make their own benchmarks in scenarios close to their specific use cases. The following statements are rules of thumb which have to be verified in practice.

AtomicInitializer is probably the most efficient implementation due to its lack of synchronization and further checks. Its main drawback is that the initialize() method can be called multiple times. In cases where this is not an issue AtomicInitializer is a good choice. AtomicSafeInitializer and LazyInitializer both guarantee that the initialization method is called only once. Because AtomicSafeInitializer does not use synchronization it is probably slightly more efficient than LazyInitializer, but the concrete numbers might depend on the level of concurrency.

Another implementation of the ConcurrentInitializer interface is BackgroundInitializer. It is again an abstract base class with an initialize() method that has to be defined by concrete subclasses. The idea of BackgroundInitializer is that it calls the initialize() method in a separate worker thread. An application creates a background initializer and starts it. Then it can continue with its work while the initializer runs in parallel. When the application needs the results of the initializer it calls its get() method. get() blocks until the initialization is complete. This is useful for instance at application startup. Here initialization steps (e.g. reading configuration files, opening a database connection, etc.) can be run in background threads while the application shows a splash screen and constructs its UI.

As a concrete example consider an application that has to read the content of a URL - maybe a page with news - which is to be displayed to the user after login. Because loading the data over the network can take some time a specialized implementation of BackgroundInitializer can be created for this purpose:

 public class URLLoader extends BackgroundInitializer<String> {
   // The URL to be loaded.
   private final URL url;

   public URLLoader(URL u) {
     url = u;

   protected String initialize() throws ConcurrentException {
     try {
       InputStream in = url.openStream();
       // read content into string
       return content;
     } catch (IOException ioex) {
       throw new ConcurrentException(ioex);

An application creates an instance of URLLoader and starts it. Then it can do other things. When it needs the content of the URL it calls the initializer's get() method:

 URL url = new URL("");
 URLLoader loader = new URLLoader(url);
 loader.start();  // this starts the background initialization

 // do other stuff
 // now obtain the content of the URL
 String content;
 try {
   content = loader.get();  // this may block
 } catch (ConcurrentException cex) {
   content = "Error when loading URL " + url;
 // display content

Related to BackgroundInitializer is the MultiBackgroundInitializer class. As the name implies, this class can handle multiplie initializations in parallel. The basic usage scenario is that a MultiBackgroundInitializer instance is created. Then an arbitrary number of BackgroundInitializer objects is added using the MultiBackgroundInitializer.addInitializer(String, BackgroundInitializer) method. When adding an initializer a string has to be provided which is later used to obtain the result for this initializer. When all initializers have been added the BackgroundInitializer.start() method is called. This starts processing of all initializers. Later the get() method can be called. It waits until all initializers have finished their initialization. get() returns an object of type MultiBackgroundInitializer.MultiBackgroundInitializerResults. This object provides information about all initializations that have been performed. It can be checked whether a specific initializer was successful or threw an exception. Of course, all initialization results can be queried.

With MultiBackgroundInitializer we can extend our example to perform multiple initialization steps. Suppose that in addition to loading a web site we also want to create a JPA entity manager factory and read a configuration file. We assume that corresponding BackgroundInitializer implementations exist. The following example fragment shows the usage of MultiBackgroundInitializer for this purpose:

 MultiBackgroundInitializer initializer = new MultiBackgroundInitializer();
 initializer.addInitializer("url", new URLLoader(url));
 initializer.addInitializer("jpa", new JPAEMFInitializer());
 initializer.addInitializer("config", new ConfigurationInitializer());
 initializer.start();  // start background processing

 // do other interesting things in parallel
 // evaluate the results of background initialization
 MultiBackgroundInitializer.MultiBackgroundInitializerResults results =
 String urlContent = (String) results.getResultObject("url");
 EntityManagerFactory emf =
 (EntityManagerFactory) results.getResultObject("jpa");

The child initializers are added to the multi initializer and are assigned a unique name. The object returned by the get() method is then queried for the single results using these unique names.

If background initializers - including MultiBackgroundInitializer - are created using the standard constructor, they create their own ExecutorService which is used behind the scenes to execute the worker tasks. It is also possible to pass in an ExecutorService when the initializer is constructed. That way client code can configure the ExecutorService according to its specific needs; for instance, the number of threads available could be limited.

Utility Classes

Another group of classes in the new concurrent package offers some generic functionality related to concurrency. There is the ConcurrentUtils class with a bunch of static utility methods. One focus of this class is dealing with exceptions thrown by JDK classes. Many JDK classes of the executor framework throw exceptions of type ExecutionException if something goes wrong. The root cause of these exceptions can also be a runtime exception or even an error. In typical Java programming you often do not want to deal with runtime exceptions directly; rather you let them fall through the hierarchy of method invocations until they reach a central exception handler. Checked exceptions in contrast are usually handled close to their occurrence. With ExecutionException this principle is violated. Because it is a checked exception, an application is forced to handle it even if the cause is a runtime exception. So you typically have to inspect the cause of the ExecutionException and test whether it is a checked exception which has to be handled. If this is not the case, the causing exception can be rethrown.

The ConcurrentUtils.extractCause(java.util.concurrent.ExecutionException) method does this work for you. It is passed an ExecutionException and tests its root cause. If this is an error or a runtime exception, it is directly rethrown. Otherwise, an instance of ConcurrentException is created and initialized with the root cause (ConcurrentException is a new exception class in the o.a.c.l.concurrent package). So if you get such a ConcurrentException, you can be sure that the original cause for the ExecutionException was a checked exception. For users who prefer runtime exceptions in general there is also an ConcurrentUtils.extractCauseUnchecked(java.util.concurrent.ExecutionException) method which behaves like extractCause(), but returns the unchecked exception ConcurrentRuntimeException instead.

In addition to the extractCause() methods there are corresponding ConcurrentUtils.handleCause(java.util.concurrent.ExecutionException) and ConcurrentUtils.handleCauseUnchecked(java.util.concurrent.ExecutionException) methods. These methods extract the cause of the passed in ExecutionException and throw the resulting ConcurrentException or ConcurrentRuntimeException. This makes it easy to transform an ExecutionException into a ConcurrentException ignoring unchecked exceptions:

 Future<Object> future = ...;
 try {
   Object result = future.get();
 } catch (ExecutionException eex) {

There is also some support for the concurrent initializers introduced in the last sub section. The initialize() method is passed a ConcurrentInitializer object and returns the object created by this initializer. It is null-safe. The initializeUnchecked() method works analogously, but a ConcurrentException throws by the initializer is rethrown as a ConcurrentRuntimeException. This is especially useful if the specific ConcurrentInitializer does not throw checked exceptions. Using this method the code for requesting the object of an initializer becomes less verbose. The direct invocation looks as follows:

 ConcurrentInitializer<MyClass> initializer = ...;
 try {
   MyClass obj = initializer.get();
   // do something with obj
 } catch (ConcurrentException cex) {
   // exception handling

Using the ConcurrentUtils.initializeUnchecked(ConcurrentInitializer) method, this becomes:

 ConcurrentInitializer<MyClass> initializer = ...;
 MyClass obj = ConcurrentUtils.initializeUnchecked(initializer);
 // do something with obj

Another utility class deals with the creation of threads. When using the Executor framework new in JDK 1.5 the developer usually does not have to care about creating threads; the executors create the threads they need on demand. However, sometimes it is desired to set some properties of the newly created worker threads. This is possible through the ThreadFactory interface; an implementation of this interface has to be created and passed to an executor on creation time. Currently, the JDK does not provide an implementation of ThreadFactory, so one has to start from scratch.

With BasicThreadFactory Commons Lang has an implementation of ThreadFactory that works out of the box for many common use cases. For instance, it is possible to set a naming pattern for the new threads, set the daemon flag and a priority, or install a handler for uncaught exceptions. Instances of BasicThreadFactory are created and configured using the nested BasicThreadFactory.Builder class. The following example shows a typical usage scenario:

 BasicThreadFactory factory = new BasicThreadFactory.Builder()
 ExecutorService exec = Executors.newSingleThreadExecutor(factory);

The nested Builder class defines some methods for configuring the new BasicThreadFactory instance. Objects of this class are immutable, so these attributes cannot be changed later. The naming pattern is a string which can be passed to String.format(). The placeholder %d is replaced by an increasing counter value. An instance can wrap another ThreadFactory implementation; this is achieved by calling the builder's wrappedFactory(ThreadFactory) method. This factory is then used for creating new threads; after that the specific attributes are applied to the new thread. If no wrapped factory is set, the default factory provided by the JDK is used.

Synchronization objects

The concurrent package also provides some support for specific synchronization problems with threads.

TimedSemaphore allows restricted access to a resource in a given time frame. Similar to a semaphore, a number of permits can be acquired. What is new is the fact that the permits available are related to a given time unit. For instance, the timed semaphore can be configured to allow 10 permits in a second. Now multiple threads access the semaphore and call its TimedSemaphore.acquire() method. The semaphore keeps track about the number of granted permits in the current time frame. Only 10 calls are allowed; if there are further callers, they are blocked until the time frame (one second in this example) is over. Then all blocking threads are released, and the counter of available permits is reset to 0. So the game can start anew.

What are use cases for TimedSemaphore? One example is to artificially limit the load produced by multiple threads. Consider a batch application accessing a database to extract statistical data. The application runs multiple threads which issue database queries in parallel and perform some calculation on the results. If the database to be processed is huge and is also used by a production system, multiple factors have to be balanced: On one hand, the time required for the statistical evaluation should not take too long. Therefore you will probably use a larger number of threads because most of its life time a thread will just wait for the database to return query results. On the other hand, the load on the database generated by all these threads should be limited so that the responsiveness of the production system is not affected. With a TimedSemaphore object this can be achieved. The semaphore can be configured to allow e.g. 100 queries per second. After these queries have been sent to the database the threads have to wait until the second is over - then they can query again. By fine-tuning the limit enforced by the semaphore a good balance between performance and database load can be established. It is even possible to chang? the number of available permits at runtime. So this number can be reduced during the typical working hours and increased at night.

The following code examples demonstrate parts of the implementation of such a scenario. First the batch application has to create an instance of TimedSemaphore and to initialize its properties with default values:

TimedSemaphore semaphore = new TimedSemaphore(1, TimeUnit.SECONDS, 100);

Here we specify that the semaphore should allow 100 permits in one second. This is effectively the limit of database queries per second in our example use case. Next the server threads issuing database queries and performing statistical operations can be initialized. They are passed a reference to the semaphore at creation time. Before they execute a query they have to acquire a permit.

 public class StatisticsTask implements Runnable {
 // The semaphore for limiting database load.
   private final TimedSemaphore semaphore;

   public StatisticsTask(TimedSemaphore sem, Connection con) {
     semaphore = sem;

   //The main processing method. Executes queries and evaluates their results.
   public void run() {
     try {
       while (!isDone()) {
         semaphore.acquire();    // enforce the load limit
     } catch (InterruptedException iex) {
       // fall through

The important line here is the call to semaphore.acquire(). If the number of permits in the current time frame has not yet been reached, the call returns immediately. Otherwise, it blocks until the end of the time frame. The last piece missing is a scheduler service which adapts the number of permits allowed by the semaphore according to the time of day. We assume that this service is pretty simple and knows only two different time slots: working shift and night shift. The service is triggered periodically. It then determines the current time slot and configures the timed semaphore accordingly.

 public class SchedulerService {
   // The semaphore for limiting database load.
   private final TimedSemaphore semaphore;

   // Configures the timed semaphore based on the current time of day. This method is called periodically.
   public void configureTimedSemaphore() {
      int limit;
      if (isWorkshift()) {
        limit = 50;    // low database load
      } else {
        limit = 250;   // high database load


With the TimedSemaphore.setLimit(int) method the number of permits allowed for a time frame can be changed. There are some other methods for querying the internal state of a timed semaphore. Also some statistical data is available, e.g. the average number of acquire() calls per time frame. When a timed semaphore is no more needed, its shutdown() method has to be called.

Copyright © 2001–2015 The Apache Software Foundation. All rights reserved.