1. Overview
In this tutorial, we’ll be looking at the ThreadLocal construct from the java.lang package. This gives us the ability to store data individually for the current thread and simply wrap it within a special type of object.
2. ThreadLocal API
The TheadLocal construct allows us to store data that will be accessible only by a specific thread.
Let’s say that we want to have an Integer value that will be bundled with the specific thread:
ThreadLocal<Integer> threadLocalValue = new ThreadLocal<>();
Next, when we want to use this value from a thread, we only need to call a get() or set() method. Simply put, we can imagine that ThreadLocal stores data inside of a map with the thread as the key.
As a result, when we call a get() method on the threadLocalValue, we’ll get an Integer value for the requesting thread:
threadLocalValue.set(1);
Integer result = threadLocalValue.get();
We can construct an instance of the ThreadLocal by using the withInitial() static method and passing a supplier to it:
ThreadLocal<Integer> threadLocal = ThreadLocal.withInitial(() -> 1);
To remove the value from the ThreadLocal, we can call the remove() method:
threadLocal.remove();
To see how to use the ThreadLocal properly, we’ll first look at an example that doesn’t use a ThreadLocal, and then we’ll rewrite our example to leverage that construct.
3. Storing User Data in a Map
Let’s consider a program that needs to store the user-specific Context data per given user id:
public class Context {
private String userName;
public Context(String userName) {
this.userName = userName;
}
}
We want to have one thread per user id. We’ll create a SharedMapWithUserContext class that implements the Runnable interface. The implementation in the run() method calls some database through the UserRepository class that returns a Context object for a given userId.
Next, we store that context in the ConcurentHashMap keyed by userId:
public class SharedMapWithUserContext implements Runnable {
public static Map<Integer, Context> userContextPerUserId
= new ConcurrentHashMap<>();
private Integer userId;
private UserRepository userRepository = new UserRepository();
@Override
public void run() {
String userName = userRepository.getUserNameForUserId(userId);
userContextPerUserId.put(userId, new Context(userName));
}
// standard constructor
}
We can easily test our code by creating and starting two threads for two different userIds, and asserting that we have two entries in the userContextPerUserId map:
SharedMapWithUserContext firstUser = new SharedMapWithUserContext(1);
SharedMapWithUserContext secondUser = new SharedMapWithUserContext(2);
new Thread(firstUser).start();
new Thread(secondUser).start();
assertEquals(SharedMapWithUserContext.userContextPerUserId.size(), 2);
4. Storing User Data in ThreadLocal
We can rewrite our example to store the user Context instance using a ThreadLocal. Each thread will have its own ThreadLocal instance.
When using ThreadLocal, we need to be very careful because every ThreadLocal instance is associated with a particular thread. In our example, we have a dedicated thread for each particular userId, and this thread is created by us, so we have full control over it.
The run() method will fetch the user context and store it into the ThreadLocal variable using the set() method:
public class ThreadLocalWithUserContext implements Runnable {
private static ThreadLocal<Context> userContext
= new ThreadLocal<>();
private Integer userId;
private UserRepository userRepository = new UserRepository();
@Override
public void run() {
String userName = userRepository.getUserNameForUserId(userId);
userContext.set(new Context(userName));
System.out.println("thread context for given userId: "
+ userId + " is: " + userContext.get());
}
// standard constructor
}
We can test it by starting two threads that will execute the action for a given userId:
ThreadLocalWithUserContext firstUser
= new ThreadLocalWithUserContext(1);
ThreadLocalWithUserContext secondUser
= new ThreadLocalWithUserContext(2);
new Thread(firstUser).start();
new Thread(secondUser).start();
After running this code, we’ll see on the standard output that ThreadLocal was set per given thread:
thread context for given userId: 1 is: Context{userNameSecret='18a78f8e-24d2-4abf-91d6-79eaa198123f'}
thread context for given userId: 2 is: Context{userNameSecret='e19f6a0a-253e-423e-8b2b-bca1f471ae5c'}
We can see that each of the users has its own Context.
*5. ThreadLocals and Thread Pools*
ThreadLocal provides an easy-to-use API to confine some values to each thread. This is a reasonable way of achieving thread-safety in Java. However, we should be extra careful when we’re using ThreadLocals and thread pools together.
In order to better understand this possible caveat, let’s consider the following scenario:
- First, the application borrows a thread from the pool.
- Then it stores some thread-confined values into the current thread’s ThreadLocal.
- Once the current execution finishes, the application returns the borrowed thread to the pool.
- After a while, the application borrows the same thread to process another request.
- Since the application didn’t perform the necessary cleanups last time, it may re-use the same ThreadLocal data for the new request.
This may cause surprising consequences in highly concurrent applications.
One way to solve this problem is to manually remove each ThreadLocal once we’re done using it. Because this approach needs rigorous code reviews, it can be error-prone.
5.1. Extending the ThreadPoolExecutor
As it turns out, it’s possible to extend the ThreadPoolExecutor class and provide a custom hook implementation for the beforeExecute() and afterExecute() methods. The thread pool will call the beforeExecute() method before running anything using the borrowed thread. On the other hand, it’ll call the afterExecute() method after executing our logic.
Therefore, we can extend the ThreadPoolExecutor class and remove the ThreadLocal data in the afterExecute() method:
public class ThreadLocalAwareThreadPool extends ThreadPoolExecutor {
@Override
protected void afterExecute(Runnable r, Throwable t) {
// Call remove on each ThreadLocal
}
}
If we submit our requests to this implementation of ExecutorService, then we can be sure that using ThreadLocal and thread pools won’t introduce safety hazards for our application.
6. Conclusion
In this brief article, we examined the ThreadLocal construct. We implemented the logic that uses ConcurrentHashMap that was shared between threads to store the context associated with a particular userId. Then we rewrote our example to leverage ThreadLocal to store data associated with a particular userId and a particular thread.
The implementation of all these examples and code snippets can be found over on GitHub.