Develop Reference

How to synchronize multiple threads from accessing some common data

I have three different threads which creates three different objects to read/manipulate some data which is common for all the threads. Now, I need to ensure that we are giving an access only to one thread at a time.
The example goes something like this.
public interface CommonData {
public void addData(); // adds data to the cache
public void getDataAccessKey(); // Key that will be common across different threads for each data type
}
/*
* Singleton class
*/
public class CommonDataCache() {
private final Map dataMap = new HashMap(); // this takes keys and values as custom objects
}
The implementation class of the interface would look like this
class CommonDataImpl implements CommonData {
private String key;
public CommonDataImpl1(String key) {
this.key = key;
}
public void addData() {
// access the singleton cache class and add
}
public void getDataAccessKey() {
return key;
}
}
Each thread will be invoked as follows:
CommonData data = new CommonDataImpl("Key1");
new Thread(() -> data.addData()).start();
CommonData data1 = new CommonDataImpl("Key1");
new Thread(() -> data1.addData()).start();
CommonData data2 = new CommonDataImpl("Key1");
new Thread(() -> data2.addData()).start();
Now, I need to synchronize those threads if and only if the keys of the data object (passed on to the thread) is the same.
My thought process so far:
I tried to have a class that provides the lock on the fly for a given key which looks something like this.
/*
* Singleton class
*/
public class DataAccessKeyToLockProvider {
private volatile Map<String, ReentrantLock> accessKeyToLockHolder = new ConcurrentHashMap<>();
private DataAccessKeyToLockProvider() {
}
public ReentrantLock getLock(String key) {
return accessKeyToLockHolder.putIfAbsent(key, new ReentrantLock());
}
public void removeLock(BSSKey key) {
ReentrantLock removedLock = accessKeyToLockHolder.remove(key);
}
}
So each thread would call this class and get the lock and use it and remove it once the processing is done. But this can so result in a case where the second thread could get the lock object that was inserted by the first thread and waiting for the first thread to release the lock. Once the first thread removes the lock, now the third thread would get a different lock altogether, so the 2nd thread and the 3rd thread are not in sync anymore.
Something like this:
new Thread(() -> {
ReentrantLock lock = DataAccessKeyToLockProvider.get(data.getDataAccessKey());
lock.lock();
data.addData();
lock.unlock();
DataAccessKeyToLockProvider.remove(data.getDataAccessKey());
).start();
Please let me know if you need any additional details to help me resolve my problem
P.S: Removing the key from the lock provider is kind of mandatory as i will be dealing with some millions of keys (not necessarily strings), so I don't want the lock provider to eat up my memory
Inspired the solution provided #rzwitserloot, I have tried to put some generic code that waits for the other thread to complete its processing before giving the access to the next thread.
public class GenericKeyToLockProvider<K> {
private volatile Map<K, ReentrantLock> keyToLockHolder = new ConcurrentHashMap<>();
public synchronized ReentrantLock getLock(K key) {
ReentrantLock existingLock = keyToLockHolder.get(key);
try {
if (existingLock != null && existingLock.isLocked()) {
existingLock.lock(); // Waits for the thread that acquired the lock previously to release it
}
return keyToLockHolder.put(key, new ReentrantLock()); // Override with the new lock
} finally {
if (existingLock != null) {
existingLock.unlock();
}
}
}
}
But looks like the entry made by the last thread wouldn't be removed. Anyway to solve this?

First, a clarification: You either use ReentrantLock, OR you use synchronized. You don't synchronized on a ReentrantLock instance (you synchronize on any object you want) – or, if you want to go the lock route, you can call the lock lock method on your lock object, using a try/finally guard to always ensure you call unlock later (and don't use synchronized at all).
synchronized is low-level API. Lock, and all the other classes in the java.util.concurrent package are higher level and offer far more abstractions. It's generally a good idea to just peruse the javadoc of all the classes in the j.u.c package from time to time, very useful stuff in there.
The key issue is to remove all references to a lock object (thus ensuring it can be garbage collected), but not until you are certain there are zero active threads locking on it. Your current approach does not know how many classes are waiting. That needs to be fixed. Once you return an instance of a Lock object, it's 'out of your hands' and it is not possible to track if the caller is ever going to call lock on it. Thus, you can't do that. Instead, call lock as part of the job; the getLock method should actually do the locking as part of the operation. That way, YOU get to control the process flow. However, let's first take a step back:
You say you'll have millions of keys. Okay; but it is somewhat unlikely you'll have millions of threads. After all, a thread requires a stack, and even using the -Xss parameter to reduce the stack size to the minimum of 128k or so, a million threads implies you're using up 128GB of RAM just for stacks; seems unlikely.
So, whilst you might have millions of keys, the number of 'locked' keys is MUCH smaller. Let's focus on those.
You could make a ConcurrentHashMap which maps your string keys to lock objects. Then:
To acquire a lock:
Create a new lock object (literally: Object o = new Object(); - we are going to be using synchronized) and add it to the map using putIfAbsent. If you managed to create the key/value pair (compare the returned object using == to the one you made; if they are the same, you were the one to add it), you got it, go, run the code. Once you're done, acquire the sync lock on your object, send a notification, release, and remove:
public void doWithLocking(String key, Runnable op) {
Object locker = new Object();
Object o = concurrentMap.putIfAbsent(key, locker);
if (o == locker) {
op.run();
synchronized (locker) {
locker.notifyAll(); // wake up everybody waiting.
concurrentMap.remove(key); // this has to be inside!
}
} else {
...
}
}
To wait until the lock is available, first acquire a lock on the locker object, THEN check if the concurrentMap still contains it. If not, you're now free to retry this operation. If it's still in, then we now wait for a notification. In any case we always just retry from scratch. Thus:
public void performWithLocking(String key, Runnable op) throws InterruptedException {
while (true) {
Object locker = new Object();
Object o = concurrentMap.putIfAbsent(key, locker);
if (o == locker) {
try {
op.run();
} finally {
// We want to lock even if the operation throws!
synchronized (locker) {
locker.notifyAll(); // wake up everybody waiting.
concurrentMap.remove(key); // this has to be inside!
}
}
return;
} else {
synchronized (o) {
if (concurrentMap.containsKey(key)) o.wait();
}
}
}
}
}
Instead of this setup where you pass the operation to execute along with the lock key, you could have tandem 'lock' and 'unlock' methods but now you run the risk of writing code that forgets to call unlock. Hence why I wouldn't advise it!
You can call this with, for example:
keyedLockSupportThingie.doWithLocking("mykey", () -> {
System.out.println("Hello, from safety!");
});

Key based Semaphores with Guava vs Semaphores in a ConcurrentHashMap

I need a key based Semaphore mechanism in my application and stumbled upon Striped.semaphore(int, int) by Guava. However, it does not behave as expected.
Using the following code, fetch sometimes returns null. Both methods are accessed by different threads. I expect the thread that calls fetch to wait until a Blubb is available in the map.
private final Striped<Semaphore> semaphores = Striped.semaphore(64, 0);
private final Map<String, Blubb> blubbs = Collections.synchronizedMap(new HashMap<String, Blubb>());
private Semaphore getSemaphore(final String key) {
return semaphores.get(key);
}
#Override
public void put(String key, Blubb blubb) {
blubb.put(key, blubb);
final Semaphore semaphore = getSemaphore(toUser);
semaphore.release();
}
#Override
public blubb fetch(final String key) {
try {
final Semaphore semaphore = getSemaphore(key);
final boolean acquired = semaphore.tryAcquire(30, TimeUnit.SECONDS);
return blubbs.get(key);
} catch (final InterruptedException e) {
e.printStackTrace();
}
return null;
}
If I switch back to basic Java with the following code everything works as expected.
private final Map<String, Semaphore> semaphoresMap = new ConcurrentHashMap<String, Semaphore>();
private Semaphore getSemaphore(final String key) {
Semaphore semaphore = semaphoresMap.get(key);
if (semaphore == null) {
semaphore = new Semaphore(0);
semaphoresMap.put(key, semaphore);
}
return semaphore;
}
What am I missing here? Thanks

Guava's Striped specifies that multiple keys may potentially map to the same semaphore. From the Javadoc:
The guarantee provided by this class is that equal keys lead to the same lock (or semaphore), i.e. if (key1.equals(key2)) then striped.get(key1) == striped.get(key2) (assuming Object.hashCode() is correctly implemented for the keys). Note that if key1 is not equal to key2, it is not guaranteed that striped.get(key1) != striped.get(key2); the elements might nevertheless be mapped to the same lock. The lower the number of stripes, the higher the probability of this happening.
The underlying assumption in your code seems to be that if the semaphore associated with a particular object has a permit, then that object has an entry in the map, but that's not the case -- if there is an entry in the map for another object that happens to be associated with the same Semaphore, then that permit might be taken by a fetch on a completely different object, which does not actually have an entry in the map.

The 'basic java' example has a potential race condition, computeIfAbsent is an atomic operation and solves this:
private final Map<String, Semaphore> semaphoresMap = new ConcurrentHashMap<String, Semaphore>();
private Semaphore getSemaphore(final String key) {
return semaphoresMap.computeIfAbsent(key, (String absentKey) -> new Semaphore(0));
}

Java Concurrency: thread-safe modification of values in maps

I'm having a bit of trouble concerning concurrency and maps in Java.
Basically I have multiple threads using (reading and modifying) their own maps, however each of these maps is a part of a larger map which is being read and modified by a further thread:
My main method creates all threads, the threads create their respective maps which are then put into the "main" map:
Map<String, MyObject> mainMap = new HashMap<String, Integer>();
FirstThread t1 = new FirstThread();
mainMap.putAll(t1.getMap());
t1.start();
SecondThread t2 = new SecondThread();
mainMap.putAll(t2.getMap());
t2.start();
ThirdThread t3 = new ThirdThread(mainMap);
t3.start();
The problem I'm facing now is that the third (main) thread sees arbitrary values in the map, depending on when one or both of the other threads update "their" items.
I must however guarantee that the third thread can iterate over - and use the values of - the map without having to fear that a part of what is being read is "old":
FirstThread (analogue to SecondThread):
for (MyObject o : map.values()) {
o.setNewValue(getNewValue());
}
ThirdThread:
for (MyObject o : map.values()) {
doSomethingWith(o.getNewValue());
}
Any ideas? I've considered using a globally accessible (static final Object through a static class) lock which will be synchronized in each thread when the map must be modified.
Or are there specific Map implementations that assess this particular problem which I could use?
Thanks in advance!
Edit:
As suggested by #Pyranja, it would be possible to synchronize the getNewValue() method. However I forgot to mention that I am in fact trying to do something along the lines of transactions, where t1 and t2 modify multiple values before/after t3 works with said values. t3 is implemented in such a way that doSomethingWith() will not actually do anything with the value if it hasn't changed.

To synchronize at a higher level than the individual value objects, you need locks to handle the synchronization between the various threads. One way to do this, without changing your code too much, is a ReadWriteLock. Thread 1 and Thread 2 are writers, Thread 3 is a reader.
You can either do this with two locks, or one. I've sketched out below doing it with one lock, two writer threads, and one reader thread, without worrying about what happens with an exception during data update (ie, transaction rollback...).
All that said, this sounds like a classic producer-consumer scenario. You should consider using something like a BlockingQueue for communication between threads, as is outlined in this question.
There's other things you may want to consider changing as well, like using Runnable instead of extending Thread.
private static final class Value {
public void update() {
}
}
private static final class Key {
}
private final class MyReaderThread extends Thread {
private final Map<Key, Value> allValues;
public MyReaderThread(Map<Key, Value> allValues) {
this.allValues = allValues;
}
#Override
public void run() {
while (!isInterrupted()) {
readData();
}
}
private void readData() {
readLock.lock();
try {
for (Value value : allValues.values()) {
// Do something
}
}
finally {
readLock.unlock();
}
}
}
private final class WriterThread extends Thread {
private final Map<Key, Value> data = new HashMap<Key, Value>();
#Override
public void run() {
while (!isInterrupted()) {
writeData();
}
}
private void writeData() {
writeLock.lock();
try {
for (Value value : data.values()) {
value.update();
}
}
finally {
writeLock.unlock();
}
}
}
private final ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
private final ReadLock readLock;
private final WriteLock writeLock;
public Thing() {
readLock = lock.readLock();
writeLock = lock.writeLock();
}
public void doStuff() {
WriterThread thread1 = new WriterThread();
WriterThread thread2 = new WriterThread();
Map<Key, Value> allValues = new HashMap<Key, Value>();
allValues.putAll(thread1.data);
allValues.putAll(thread2.data);
MyReaderThread thread3 = new MyReaderThread(allValues);
thread1.start();
thread2.start();
thread3.start();
}

ConcurrentHashMap from java.util.concurrent - a thread-safe implementation of Map, which provides a much higher degree of concurrency than synchronizedMap. Just a lot of reads can almost always be performed in parallel, simultaneous reads and writes can usually be done in parallel, and multiple simultaneous recordings can often be done in parallel. (The class ConcurrentReaderHashMap offers a similar parallelism for multiple read operations, but allows only one active write operation.) ConcurrentHashMapis designed to optimize the retrieval operations.

Your example code may be misleading. In your first example you create a HashMap<String,Integer> but the second part iterates the map values which in this case are MyObject. The key to synchronization is to understand where and which mutable state is shared.
An Integer is immutable. It can be shared freely (but the reference to an Integer is mutable - it must be safely publicated and/or synchronized). But your code example suggests that the maps are populated with mutable MyObject instances.
Given that the map entries (key -> MyObject references) are not changed by any thread and all maps are created and safely publicated before any thread starts it would be in my opinion sufficient to synchronize the modification of MyObject. E.g.:
public class MyObject {
private Object value;
synchronized Object getNewValue() {
return value;
}
synchronized void setNewValue(final Object newValue) {
this.value = newValue;
}
}
If my assumptions are not correct, clarify your question / code example and also consider #jacobm's comment and #Alex answer.

Java synchronizing based on a parameter (named mutex/lock)

I'm looking for a way to synchronize a method based on the parameter it receives, something like this:
public synchronized void doSomething(name){
//some code
}
I want the method doSomething to be synchronized based on the name parameter like this:
Thread 1: doSomething("a");
Thread 2: doSomething("b");
Thread 3: doSomething("c");
Thread 4: doSomething("a");
Thread 1 , Thread 2 and Thread 3 will execute the code without being synchronized , but Thread 4 will wait until Thread 1 has finished the code because it has the same "a" value.
Thanks
UPDATE
Based on Tudor explanation I think I'm facing another problem:
here is a sample of the new code:
private HashMap locks=new HashMap();
public void doSomething(String name){
locks.put(name,new Object());
synchronized(locks.get(name)) {
// ...
}
locks.remove(name);
}
The reason why I don't populate the locks map is because name can have any value.
Based on the sample above , the problem can appear when adding / deleting values from the hashmap by multiple threads in the same time, since HashMap is not thread-safe.
So my question is if I make the HashMap a ConcurrentHashMap which is thread safe, will the synchronized block stop other threads from accessing locks.get(name) ??

TL;DR:
I use ConcurrentReferenceHashMap from the Spring Framework. Please check the code below.
Although this thread is old, it is still interesting. Therefore, I would like to share my approach with Spring Framework.
What we are trying to implement is called named mutex/lock. As suggested by Tudor's answer, the idea is to have a Map to store the lock name and the lock object. The code will look like below (I copy it from his answer):
Map<String, Object> locks = new HashMap<String, Object>();
locks.put("a", new Object());
locks.put("b", new Object());
However, this approach has 2 drawbacks:
The OP already pointed out the first one: how to synchronize the access to the locks hash map?
How to remove some locks which are not necessary anymore? Otherwise, the locks hash map will keep growing.
The first problem can be solved by using ConcurrentHashMap. For the second problem, we have 2 options: manually check and remove locks from the map, or somehow let the garbage collector knows which locks are no longer used and the GC will remove them. I will go with the second way.
When we use HashMap, or ConcurrentHashMap, it creates strong references. To implement the solution discussed above, weak references should be used instead (to understand what is a strong/weak reference, please refer to this article or this post).
So, I use ConcurrentReferenceHashMap from the Spring Framework. As described in the documentation:
A ConcurrentHashMap that uses soft or weak references for both keys
and values.
This class can be used as an alternative to
Collections.synchronizedMap(new WeakHashMap<K, Reference<V>>()) in
order to support better performance when accessed concurrently. This
implementation follows the same design constraints as
ConcurrentHashMap with the exception that null values and null keys
are supported.
Here is my code. The MutexFactory manages all the locks with <K> is the type of the key.
#Component
public class MutexFactory<K> {
private ConcurrentReferenceHashMap<K, Object> map;
public MutexFactory() {
this.map = new ConcurrentReferenceHashMap<>();
}
public Object getMutex(K key) {
return this.map.compute(key, (k, v) -> v == null ? new Object() : v);
}
}
Usage:
#Autowired
private MutexFactory<String> mutexFactory;
public void doSomething(String name){
synchronized(mutexFactory.getMutex(name)) {
// ...
}
}
Unit test (this test uses the awaitility library for some methods, e.g. await(), atMost(), until()):
public class MutexFactoryTests {
private final int THREAD_COUNT = 16;
#Test
public void singleKeyTest() {
MutexFactory<String> mutexFactory = new MutexFactory<>();
String id = UUID.randomUUID().toString();
final int[] count = {0};
IntStream.range(0, THREAD_COUNT)
.parallel()
.forEach(i -> {
synchronized (mutexFactory.getMutex(id)) {
count[0]++;
}
});
await().atMost(5, TimeUnit.SECONDS)
.until(() -> count[0] == THREAD_COUNT);
Assert.assertEquals(count[0], THREAD_COUNT);
}
}

Use a map to associate strings with lock objects:
Map<String, Object> locks = new HashMap<String, Object>();
locks.put("a", new Object());
locks.put("b", new Object());
// etc.
then:
public void doSomething(String name){
synchronized(locks.get(name)) {
// ...
}
}

The answer of Tudor is fine, but it's static and not scalable. My solution is dynamic and scalable, but it goes with increased complexity in the implementation. The outside world can use this class just like using a Lock, as this class implements the interface. You get an instance of a parameterized lock by the factory method getCanonicalParameterLock.
package lock;
import java.lang.ref.Reference;
import java.lang.ref.WeakReference;
import java.util.Map;
import java.util.WeakHashMap;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
import java.util.concurrent.locks.ReentrantLock;
public final class ParameterLock implements Lock {
/** Holds a WeakKeyLockPair for each parameter. The mapping may be deleted upon garbage collection
* if the canonical key is not strongly referenced anymore (by the threads using the Lock). */
private static final Map<Object, WeakKeyLockPair> locks = new WeakHashMap<>();
private final Object key;
private final Lock lock;
private ParameterLock (Object key, Lock lock) {
this.key = key;
this.lock = lock;
}
private static final class WeakKeyLockPair {
/** The weakly-referenced parameter. If it were strongly referenced, the entries of
* the lock Map would never be garbage collected, causing a memory leak. */
private final Reference<Object> param;
/** The actual lock object on which threads will synchronize. */
private final Lock lock;
private WeakKeyLockPair (Object param, Lock lock) {
this.param = new WeakReference<>(param);
this.lock = lock;
}
}
public static Lock getCanonicalParameterLock (Object param) {
Object canonical = null;
Lock lock = null;
synchronized (locks) {
WeakKeyLockPair pair = locks.get(param);
if (pair != null) {
canonical = pair.param.get(); // could return null!
}
if (canonical == null) { // no such entry or the reference was cleared in the meantime
canonical = param; // the first thread (the current thread) delivers the new canonical key
pair = new WeakKeyLockPair(canonical, new ReentrantLock());
locks.put(canonical, pair);
}
}
// the canonical key is strongly referenced now...
lock = locks.get(canonical).lock; // ...so this is guaranteed not to return null
// ... but the key must be kept strongly referenced after this method returns,
// so wrap it in the Lock implementation, which a thread of course needs
// to be able to synchronize. This enforces a thread to have a strong reference
// to the key, while it isn't aware of it (as this method declares to return a
// Lock rather than a ParameterLock).
return new ParameterLock(canonical, lock);
}
#Override
public void lock() {
lock.lock();
}
#Override
public void lockInterruptibly() throws InterruptedException {
lock.lockInterruptibly();
}
#Override
public boolean tryLock() {
return lock.tryLock();
}
#Override
public boolean tryLock(long time, TimeUnit unit) throws InterruptedException {
return lock.tryLock(time, unit);
}
#Override
public void unlock() {
lock.unlock();
}
#Override
public Condition newCondition() {
return lock.newCondition();
}
}
Of course you'd need a canonical key for a given parameter, otherwise threads would not be synchronized as they would be using a different Lock. Canonicalization is the equivalent of the internalization of Strings in Tudor's solution. Where String.intern() is itself thread-safe, my 'canonical pool' is not, so I need extra synchronization on the WeakHashMap.
This solution works for any type of Object. However, make sure to implement equals and hashCode correctly in custom classes, because if not, threading issues will arise as multiple threads could be using different Lock objects to synchronize on!
The choice for a WeakHashMap is explained by the ease of memory management it brings. How else could one know that no thread is using a particular Lock anymore? And if this could be known, how could you safely delete the entry out of the Map? You would need to synchronize upon deletion, because you have a race condition between an arriving thread wanting to use the Lock, and the action of deleting the Lock from the Map. All these things are just solved by using weak references, so the VM does the work for you, and this simplifies the implementation a lot. If you inspected the API of WeakReference, you would find that relying on weak references is thread-safe.
Now inspect this test program (you need to run it from inside the ParameterLock class, due to private visibility of some fields):
public static void main(String[] args) {
Runnable run1 = new Runnable() {
#Override
public void run() {
sync(new Integer(5));
System.gc();
}
};
Runnable run2 = new Runnable() {
#Override
public void run() {
sync(new Integer(5));
System.gc();
}
};
Thread t1 = new Thread(run1);
Thread t2 = new Thread(run2);
t1.start();
t2.start();
try {
t1.join();
t2.join();
while (locks.size() != 0) {
System.gc();
System.out.println(locks);
}
System.out.println("FINISHED!");
} catch (InterruptedException ex) {
// those threads won't be interrupted
}
}
private static void sync (Object param) {
Lock lock = ParameterLock.getCanonicalParameterLock(param);
lock.lock();
try {
System.out.println("Thread="+Thread.currentThread().getName()+", lock=" + ((ParameterLock) lock).lock);
// do some work while having the lock
} finally {
lock.unlock();
}
}
Chances are very high that you would see that both threads are using the same lock object, and so they are synchronized. Example output:
Thread=Thread-0, lock=java.util.concurrent.locks.ReentrantLock#8965fb[Locked by thread Thread-0]
Thread=Thread-1, lock=java.util.concurrent.locks.ReentrantLock#8965fb[Locked by thread Thread-1]
FINISHED!
However, with some chance it might be that the 2 threads do not overlap in execution, and therefore it is not required that they use the same lock. You could easily enforce this behavior in debugging mode by setting breakpoints at the right locations, forcing the first or second thread to stop wherever necessary. You will also notice that after the Garbage Collection on the main thread, the WeakHashMap will be cleared, which is of course correct, as the main thread waited for both worker threads to finish their job by calling Thread.join() before calling the garbage collector. This indeed means that no strong reference to the (Parameter)Lock can exist anymore inside a worker thread, so the reference can be cleared from the weak hashmap. If another thread now wants to synchronize on the same parameter, a new Lock will be created in the synchronized part in getCanonicalParameterLock.
Now repeat the test with any pair that has the same canonical representation (= they are equal, so a.equals(b)), and see that it still works:
sync("a");
sync(new String("a"))
sync(new Boolean(true));
sync(new Boolean(true));
etc.
Basically, this class offers you the following functionality:
Parameterized synchronization
Encapsulated memory management
The ability to work with any type of object (under the condition that equals and hashCode is implemented properly)
Implements the Lock interface
This Lock implementation has been tested by modifying an ArrayList concurrently with 10 threads iterating 1000 times, doing this: adding 2 items, then deleting the last found list entry by iterating the full list. A lock is requested per iteration, so in total 10*1000 locks will be requested. No ConcurrentModificationException was thrown, and after all worker threads have finished the total amount of items was 10*1000. On every single modification, a lock was requested by calling ParameterLock.getCanonicalParameterLock(new String("a")), so a new parameter object is used to test the correctness of the canonicalization.
Please note that you shouldn't be using String literals and primitive types for parameters. As String literals are automatically interned, they always have a strong reference, and so if the first thread arrives with a String literal for its parameter then the lock pool will never be freed from the entry, which is a memory leak. The same story goes for autoboxing primitives: e.g. Integer has a caching mechanism that will reuse existing Integer objects during the process of autoboxing, also causing a strong reference to exist. Addressing this, however, this is a different story.

Check out this framework. Seems you're looking for something like this.
public class WeatherServiceProxy {
...
private final KeyLockManager lockManager = KeyLockManagers.newManager();
public void updateWeatherData(String cityName, Date samplingTime, float temperature) {
lockManager.executeLocked(cityName, new LockCallback() {
public void doInLock() {
delegate.updateWeatherData(cityName, samplingTime, temperature);
}
});
}
https://code.google.com/p/jkeylockmanager/

I've created a tokenProvider based on the IdMutexProvider of McDowell.
The manager uses a WeakHashMap which takes care of cleaning up unused locks.
You could find my implementation here.

I've found a proper answer through another stackoverflow question: How to acquire a lock by a key
I copied the answer here:
Guava has something like this being released in 13.0; you can get it out of HEAD if you like.
Striped more or less allocates a specific number of locks, and then assigns strings to locks based on their hash code. The API looks more or less like
Striped<Lock> locks = Striped.lock(stripes);
Lock l = locks.get(string);
l.lock();
try {
// do stuff
} finally {
l.unlock();
}
More or less, the controllable number of stripes lets you trade concurrency against memory usage, because allocating a full lock for each string key can get expensive; essentially, you only get lock contention when you get hash collisions, which are (predictably) rare.

Just extending on to Triet Doan's answer, we also need to take care of if the MutexFactory can be used at multiple places, as with currently suggested code we will end up with same MutexFactory at all places of its usage.
For example:-
#Autowired
MutexFactory<CustomObject1> mutexFactory1;
#Autowired
MutexFactory<CustomObject2> mutexFactory2;
Both mutexFactory1 & mutexFactory2 will refer to the same instance of factory even if their type differs, this is due to the fact that a single instance of MutexFactory is created by spring during application startup and same is used for both mutexFactory1 & mutexFactory2.
So here is the extra Scope annotation that needs to be put in to avoid above case-
#Component
#Scope(ConfigurableBeanFactory.SCOPE_PROTOTYPE)
public class MutexFactory<K> {
private ConcurrentReferenceHashMap<K, Object> map;
public MutexFactory() {
this.map = new ConcurrentReferenceHashMap<>();
}
public Object getMutex(K key) {
return this.map.compute(key, (k, v) -> v == null ? new Object() : v);
}
}

I've used a cache to store lock objects. The my cache will expire objects after a period, which really only needs to be longer that the time it takes the synchronized process to run
`
import com.google.common.cache.Cache;
import com.google.common.cache.CacheBuilder;
...
private final Cache<String, Object> mediapackageLockCache = CacheBuilder.newBuilder().expireAfterWrite(DEFAULT_CACHE_EXPIRE, TimeUnit.SECONDS).build();
...
public void doSomething(foo) {
Object lock = mediapackageLockCache.getIfPresent(foo.toSting());
if (lock == null) {
lock = new Object();
mediapackageLockCache.put(foo.toString(), lock);
}
synchronized(lock) {
// execute code on foo
...
}
}
`

I have a much simpler, scalable implementation akin to #timmons post taking advantage of guavas LoadingCache with weakValues. You will want to read the help files on "equality" to understand the suggestion I have made.
Define the following weakValued cache.
private final LoadingCache<String,String> syncStrings = CacheBuilder.newBuilder().weakValues().build(new CacheLoader<String, String>() {
public String load(String x) throws ExecutionException {
return new String(x);
}
});
public void doSomething(String x) {
x = syncStrings.get(x);
synchronized(x) {
..... // whatever it is you want to do
}
}
Now! As a result of the JVM, we do not have to worry that the cache is growing too large, it only holds the cached strings as long as necessary and the garbage manager/guava does the heavy lifting.

Synchronize access to given key in a ConcurrentMap

I often enough want to access (and possibly add/remove) elements of a given ConcurrentMap so that only one thread can access any single key at a time. What is the best way to do this? Synchronizing on the key itself doesn't work: other threads might access the same key via an equal instance.
It's good enough if the answer only works with the maps built by guava MapMaker.

See a simple solution here Simple Java name based locks?
EDIT: This solution has a clear happens-before relation from unlock to lock. However, the next solution, now withdrawn, doesn't. The ConcurrentMap javadoc is too light to guaranteed that.
(Withdrawn) If you want to reuse your map as a lock pool,
private final V LOCK = ...; // a fake value
// if a key is mapped to LOCK, that means the key is locked
ConcurrentMap<K,V> map = ...;
V lock(key)
V value;
while( (value=map.putIfAbsent(key, LOCK))==LOCK )
// another thread locked it before me
wait();
// now putIfAbsent() returns a real value, or null
// and I just sucessfully put LOCK in it
// I am now the lock owner of this key
return value; // for caller to work on
// only the lock owner of the key should call this method
unlock(key, value)
// I put a LOCK on the key to stall others
// now I just need to swap it back with the real value
if(value!=null)
map.put(key, value);
else // map doesn't accept null value
map.remove(key)
notifyAll();
test()
V value = lock(key);
// work on value
// unlock.
// we have a chance to specify a new value here for the next worker
newValue = ...; // null if we want to remove the key from map
unlock(key, newValue); // in finally{}
This is quite messy because we reuse the map for two difference purposes. It's better to have lock pool as a separate data structure, leave map simply as the k-v storage.

private static final Set<String> lockedKeys = new HashSet<>();
private void lock(String key) throws InterruptedException {
synchronized (lockedKeys) {
while (!lockedKeys.add(key)) {
lockedKeys.wait();
}
}
}
private void unlock(String key) {
synchronized (lockedKeys) {
lockedKeys.remove(key);
lockedKeys.notifyAll();
}
}
public void doSynchronouslyOnlyForEqualKeys(String key) throws InterruptedException {
try {
lock(key);
//Put your code here.
//For different keys it is executed in parallel.
//For equal keys it is executed synchronously.
} finally {
unlock(key);
}
}
key can be not only a 'String' but any class with correctly overridden 'equals' and 'hashCode' methods.
try-finally - is very important - you must guarantee to unlock waiting threads after your operation even if your operation threw exception.
It will not work if your back-end is distributed across multiple servers/JVMs.

Can't you just create you own class that extends concurrentmap.
Override the get(Object key) method, so it checks if the requested key object is already 'checked out' by another thread ?
You'll also have to make a new method in your concurrentmap that 'returns' the items to the map, so they are available again to another thread.