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!");
});

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.

AtomicReference to a mutable object and visibility

Say I have an AtomicReferenceto a list of objects:
AtomicReference<List<?>> batch = new AtomicReference<List<Object>>(new ArrayList<Object>());
Thread A adds elements to this list: batch.get().add(o);
Later, thread B takes the list and, for example, stores it in a DB: insertBatch(batch.get());
Do I have to do additional synchronization when writing (Thread A) and reading (Thread B) to ensure thread B sees the list the way A left it, or is this taken care of by the AtomicReference?
In other words: if I have an AtomicReference to a mutable object, and one thread changes that object, do other threads see this change immediately?
Edit:
Maybe some example code is in order:
public void process(Reader in) throws IOException {
List<Future<AtomicReference<List<Object>>>> tasks = new ArrayList<Future<AtomicReference<List<Object>>>>();
ExecutorService exec = Executors.newFixedThreadPool(4);
for (int i = 0; i < 4; ++i) {
tasks.add(exec.submit(new Callable<AtomicReference<List<Object>>>() {
#Override public AtomicReference<List<Object>> call() throws IOException {
final AtomicReference<List<Object>> batch = new AtomicReference<List<Object>>(new ArrayList<Object>(batchSize));
Processor.this.parser.parse(in, new Parser.Handler() {
#Override public void onNewObject(Object event) {
batch.get().add(event);
if (batch.get().size() >= batchSize) {
dao.insertBatch(batch.getAndSet(new ArrayList<Object>(batchSize)));
}
}
});
return batch;
}
}));
}
List<Object> remainingBatches = new ArrayList<Object>();
for (Future<AtomicReference<List<Object>>> task : tasks) {
try {
AtomicReference<List<Object>> remainingBatch = task.get();
remainingBatches.addAll(remainingBatch.get());
} catch (ExecutionException e) {
Throwable cause = e.getCause();
if (cause instanceof IOException) {
throw (IOException)cause;
}
throw (RuntimeException)cause;
}
}
// these haven't been flushed yet by the worker threads
if (!remainingBatches.isEmpty()) {
dao.insertBatch(remainingBatches);
}
}
What happens here is that I create four worker threads to parse some text (this is the Reader in parameter to the process() method). Each worker saves the lines it has parsed in a batch, and flushes the batch when it is full (dao.insertBatch(batch.getAndSet(new ArrayList<Object>(batchSize)));).
Since the number of lines in the text isn't a multiple of the batch size, the last objects end up in a batch that isn't flushed, since it's not full. These remaining batches are therefore inserted by the main thread.
I use AtomicReference.getAndSet() to replace the full batch with an empty one. It this program correct with regards to threading?

Um... it doesn't really work like this. AtomicReference guarantees that the reference itself is visible across threads i.e. if you assign it a different reference than the original one the update will be visible. It makes no guarantees about the actual contents of the object that reference is pointing to.
Therefore, read/write operations on the list contents require separate synchronization.
Edit: So, judging from your updated code and the comment you posted, setting the local reference to volatile is sufficient to ensure visibility.

I think that, forgetting all the code here, you exact question is this:
Do I have to do additional synchronization when writing (Thread A) and
reading (Thread B) to ensure thread B sees the list the way A left it,
or is this taken care of by the AtomicReference?
So, the exact response to that is: YES, atomic take care of visibility. And it is not my opinion but the JDK documentation one:
The memory effects for accesses and updates of atomics generally follow the rules for volatiles, as stated in The Java Language Specification, Third Edition (17.4 Memory Model).
I hope this helps.

Adding to Tudor's answer: You will have to make the ArrayList itself threadsafe or - depending on your requirements - even larger code blocks.
If you can get away with a threadsafe ArrayList you can "decorate" it like this:
batch = java.util.Collections.synchronizedList(new ArrayList<Object>());
But keep in mind: Even "simple" constructs like this are not threadsafe with this:
Object o = batch.get(batch.size()-1);

The AtomicReference will only help you with the reference to the list, it will not do anything to the list itself. More particularly, in your scenario, you will almost certainly run into problems when the system is under load where the consumer has taken the list while the producer is adding an item to it.
This sound to me like you should be using a BlockingQueue. You can then Limit the memory footprint if you producer is faster than your consumer and let the queue handle all contention.
Something like:
ArrayBlockingQueue<Object> queue = new ArrayBlockingQueue<Object> (50);
// ... Producer
queue.put(o);
// ... Consumer
List<Object> queueContents = new ArrayList<Object> ();
// Grab everything waiting in the queue in one chunk. Should never be more than 50 items.
queue.drainTo(queueContents);
Added
Thanks to #Tudor for pointing out the architecture you are using. ... I have to admit it is rather strange. You don't really need AtomicReference at all as far as I can see. Each thread owns its own ArrayList until it is passed on to dao at which point it is replaced so there is no contention at all anywhere.
I am a little concerned about you creating four parser on a single Reader. I hope you have some way of ensuring each parser does not affect the others.
I personally would use some form of producer-consumer pattern as I have described in the code above. Something like this perhaps.
static final int PROCESSES = 4;
static final int batchSize = 10;
public void process(Reader in) throws IOException, InterruptedException {
final List<Future<Void>> tasks = new ArrayList<Future<Void>>();
ExecutorService exec = Executors.newFixedThreadPool(PROCESSES);
// Queue of objects.
final ArrayBlockingQueue<Object> queue = new ArrayBlockingQueue<Object> (batchSize * 2);
// The final object to post.
final Object FINISHED = new Object();
// Start the producers.
for (int i = 0; i < PROCESSES; i++) {
tasks.add(exec.submit(new Callable<Void>() {
#Override
public Void call() throws IOException {
Processor.this.parser.parse(in, new Parser.Handler() {
#Override
public void onNewObject(Object event) {
queue.add(event);
}
});
// Post a finished down the queue.
queue.add(FINISHED);
return null;
}
}));
}
// Start the consumer.
tasks.add(exec.submit(new Callable<Void>() {
#Override
public Void call() throws IOException {
List<Object> batch = new ArrayList<Object>(batchSize);
int finishedCount = 0;
// Until all threads finished.
while ( finishedCount < PROCESSES ) {
Object o = queue.take();
if ( o != FINISHED ) {
// Batch them up.
batch.add(o);
if ( batch.size() >= batchSize ) {
dao.insertBatch(batch);
// If insertBatch takes a copy we could merely clear it.
batch = new ArrayList<Object>(batchSize);
}
} else {
// Count the finishes.
finishedCount += 1;
}
}
// Finished! Post any incopmplete batch.
if ( batch.size() > 0 ) {
dao.insertBatch(batch);
}
return null;
}
}));
// Wait for everything to finish.
exec.shutdown();
// Wait until all is done.
boolean finished = false;
do {
try {
// Wait up to 1 second for termination.
finished = exec.awaitTermination(1, TimeUnit.SECONDS);
} catch (InterruptedException ex) {
}
} while (!finished);
}

Synchronizing on String objects in Java

I have a webapp that I am in the middle of doing some load/performance testing on, particularily on a feature where we expect a few hundred users to be accessing the same page and hitting refresh about every 10 seconds on this page. One area of improvement that we found we could make with this function was to cache the responses from the web service for some period of time, since the data is not changing.
After implementing this basic caching, in some further testing I found out that I didn't consider how concurrent threads could access the Cache at the same time. I found that within the matter of ~100ms, about 50 threads were trying to fetch the object from the Cache, finding that it had expired, hitting the web service to fetch the data, and then putting the object back in the cache.
The original code looked something like this:
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
final String key = "Data-" + email;
SomeData[] data = (SomeData[]) StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data, CACHE_TIME);
}
else {
logger.debug("getSomeDataForEmail: using cached object");
}
return data;
}
So, to make sure that only one thread was calling the web service when the object at key expired, I thought I needed to synchronize the Cache get/set operation, and it seemed like using the cache key would be a good candidate for an object to synchronize on (this way, calls to this method for email b#b.com would not be blocked by method calls to a#a.com).
I updated the method to look like this:
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
SomeData[] data = null;
final String key = "Data-" + email;
synchronized(key) {
data =(SomeData[]) StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data, CACHE_TIME);
}
else {
logger.debug("getSomeDataForEmail: using cached object");
}
}
return data;
}
I also added logging lines for things like "before synchronization block", "inside synchronization block", "about to leave synchronization block", and "after synchronization block", so I could determine if I was effectively synchronizing the get/set operation.
However it doesn't seem like this has worked. My test logs have output like:
(log output is 'threadname' 'logger name' 'message')
http-80-Processor253 jsp.view-page - getSomeDataForEmail: about to enter synchronization block
http-80-Processor253 jsp.view-page - getSomeDataForEmail: inside synchronization block
http-80-Processor253 cache.StaticCache - get: object at key [SomeData-test#test.com] has expired
http-80-Processor253 cache.StaticCache - get: key [SomeData-test#test.com] returning value [null]
http-80-Processor263 jsp.view-page - getSomeDataForEmail: about to enter synchronization block
http-80-Processor263 jsp.view-page - getSomeDataForEmail: inside synchronization block
http-80-Processor263 cache.StaticCache - get: object at key [SomeData-test#test.com] has expired
http-80-Processor263 cache.StaticCache - get: key [SomeData-test#test.com] returning value [null]
http-80-Processor131 jsp.view-page - getSomeDataForEmail: about to enter synchronization block
http-80-Processor131 jsp.view-page - getSomeDataForEmail: inside synchronization block
http-80-Processor131 cache.StaticCache - get: object at key [SomeData-test#test.com] has expired
http-80-Processor131 cache.StaticCache - get: key [SomeData-test#test.com] returning value [null]
http-80-Processor104 jsp.view-page - getSomeDataForEmail: inside synchronization block
http-80-Processor104 cache.StaticCache - get: object at key [SomeData-test#test.com] has expired
http-80-Processor104 cache.StaticCache - get: key [SomeData-test#test.com] returning value [null]
http-80-Processor252 jsp.view-page - getSomeDataForEmail: about to enter synchronization block
http-80-Processor283 jsp.view-page - getSomeDataForEmail: about to enter synchronization block
http-80-Processor2 jsp.view-page - getSomeDataForEmail: about to enter synchronization block
http-80-Processor2 jsp.view-page - getSomeDataForEmail: inside synchronization block
I wanted to see only one thread at a time entering/exiting the synchronization block around the get/set operations.
Is there an issue in synchronizing on String objects? I thought the cache-key would be a good choice as it is unique to the operation, and even though the final String key is declared within the method, I was thinking that each thread would be getting a reference to the same object and therefore would synchronization on this single object.
What am I doing wrong here?
Update: after looking further at the logs, it seems like methods with the same synchronization logic where the key is always the same, such as
final String key = "blah";
...
synchronized(key) { ...
do not exhibit the same concurrency problem - only one thread at a time is entering the block.
Update 2: Thanks to everyone for the help! I accepted the first answer about intern()ing Strings, which solved my initial problem - where multiple threads were entering synchronized blocks where I thought they shouldn't, because the key's had the same value.
As others have pointed out, using intern() for such a purpose and synchronizing on those Strings does indeed turn out to be a bad idea - when running JMeter tests against the webapp to simulate the expected load, I saw the used heap size grow to almost 1GB in just under 20 minutes.
Currently I'm using the simple solution of just synchronizing the entire method - but I really like the code samples provided by martinprobst and MBCook, but since I have about 7 similar getData() methods in this class currently (since it needs about 7 different pieces of data from a web service), I didn't want to add almost-duplicate logic about getting and releasing locks to each method. But this is definitely very, very valuable info for future usage. I think these are ultimately the correct answers on how best to make an operation like this thread-safe, and I'd give out more votes to these answers if I could!

Without putting my brain fully into gear, from a quick scan of what you say it looks as though you need to intern() your Strings:
final String firstkey = "Data-" + email;
final String key = firstkey.intern();
Two Strings with the same value are otherwise not necessarily the same object.
Note that this may introduce a new point of contention, since deep in the VM, intern() may have to acquire a lock. I have no idea what modern VMs look like in this area, but one hopes they are fiendishly optimised.
I assume you know that StaticCache still needs to be thread-safe. But the contention there should be tiny compared with what you'd have if you were locking on the cache rather than just the key while calling getSomeDataForEmail.
Response to question update:
I think that's because a string literal always yields the same object. Dave Costa points out in a comment that it's even better than that: a literal always yields the canonical representation. So all String literals with the same value anywhere in the program would yield the same object.
Edit
Others have pointed out that synchronizing on intern strings is actually a really bad idea - partly because creating intern strings is permitted to cause them to exist in perpetuity, and partly because if more than one bit of code anywhere in your program synchronizes on intern strings, you have dependencies between those bits of code, and preventing deadlocks or other bugs may be impossible.
Strategies to avoid this by storing a lock object per key string are being developed in other answers as I type.
Here's an alternative - it still uses a singular lock, but we know we're going to need one of those for the cache anyway, and you were talking about 50 threads, not 5000, so that may not be fatal. I'm also assuming that the performance bottleneck here is slow blocking I/O in DoSlowThing() which will therefore hugely benefit from not being serialised. If that's not the bottleneck, then:
If the CPU is busy then this approach may not be sufficient and you need another approach.
If the CPU is not busy, and access to server is not a bottleneck, then this approach is overkill, and you might as well forget both this and per-key locking, put a big synchronized(StaticCache) around the whole operation, and do it the easy way.
Obviously this approach needs to be soak tested for scalability before use -- I guarantee nothing.
This code does NOT require that StaticCache is synchronized or otherwise thread-safe. That needs to be revisited if any other code (for example scheduled clean-up of old data) ever touches the cache.
IN_PROGRESS is a dummy value - not exactly clean, but the code's simple and it saves having two hashtables. It doesn't handle InterruptedException because I don't know what your app wants to do in that case. Also, if DoSlowThing() consistently fails for a given key this code as it stands is not exactly elegant, since every thread through will retry it. Since I don't know what the failure criteria are, and whether they are liable to be temporary or permanent, I don't handle this either, I just make sure threads don't block forever. In practice you may want to put a data value in the cache which indicates 'not available', perhaps with a reason, and a timeout for when to retry.
// do not attempt double-check locking here. I mean it.
synchronized(StaticObject) {
data = StaticCache.get(key);
while (data == IN_PROGRESS) {
// another thread is getting the data
StaticObject.wait();
data = StaticCache.get(key);
}
if (data == null) {
// we must get the data
StaticCache.put(key, IN_PROGRESS, TIME_MAX_VALUE);
}
}
if (data == null) {
// we must get the data
try {
data = server.DoSlowThing(key);
} finally {
synchronized(StaticObject) {
// WARNING: failure here is fatal, and must be allowed to terminate
// the app or else waiters will be left forever. Choose a suitable
// collection type in which replacing the value for a key is guaranteed.
StaticCache.put(key, data, CURRENT_TIME);
StaticObject.notifyAll();
}
}
}
Every time anything is added to the cache, all threads wake up and check the cache (no matter what key they're after), so it's possible to get better performance with less contentious algorithms. However, much of that work will take place during your copious idle CPU time blocking on I/O, so it may not be a problem.
This code could be commoned-up for use with multiple caches, if you define suitable abstractions for the cache and its associated lock, the data it returns, the IN_PROGRESS dummy, and the slow operation to perform. Rolling the whole thing into a method on the cache might not be a bad idea.

Synchronizing on an intern'd String might not be a good idea at all - by interning it, the String turns into a global object, and if you synchronize on the same interned strings in different parts of your application, you might get really weird and basically undebuggable synchronization issues such as deadlocks. It might seem unlikely, but when it happens you are really screwed. As a general rule, only ever synchronize on a local object where you're absolutely sure that no code outside of your module might lock it.
In your case, you can use a synchronized hashtable to store locking objects for your keys.
E.g.:
Object data = StaticCache.get(key, ...);
if (data == null) {
Object lock = lockTable.get(key);
if (lock == null) {
// we're the only one looking for this
lock = new Object();
synchronized(lock) {
lockTable.put(key, lock);
// get stuff
lockTable.remove(key);
}
} else {
synchronized(lock) {
// just to wait for the updater
}
data = StaticCache.get(key);
}
} else {
// use from cache
}
This code has a race condition, where two threads might put an object into the lock table after each other. This should however not be a problem, because then you only have one more thread calling the webservice and updating the cache, which shouldn't be a problem.
If you're invalidating the cache after some time, you should check whether data is null again after retrieving it from the cache, in the lock != null case.
Alternatively, and much easier, you can make the whole cache lookup method ("getSomeDataByEmail") synchronized. This will mean that all threads have to synchronize when they access the cache, which might be a performance problem. But as always, try this simple solution first and see if it's really a problem! In many cases it should not be, as you probably spend much more time processing the result than synchronizing.

Strings are not good candidates for synchronization. If you must synchronize on a String ID, it can be done by using the string to create a mutex (see "synchronizing on an ID"). Whether the cost of that algorithm is worth it depends on whether invoking your service involves any significant I/O.
Also:
I hope the StaticCache.get() and set() methods are threadsafe.
String.intern() comes at a cost (one that varies between VM implementations) and should be used with care.

Here is a safe short Java 8 solution that uses a map of dedicated lock objects for synchronization:
private static final Map<String, Object> keyLocks = new ConcurrentHashMap<>();
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
final String key = "Data-" + email;
synchronized (keyLocks.computeIfAbsent(key, k -> new Object())) {
SomeData[] data = StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data);
}
}
return data;
}
It has a drawback that keys and lock objects would retain in map forever.
This can be worked around like this:
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
final String key = "Data-" + email;
synchronized (keyLocks.computeIfAbsent(key, k -> new Object())) {
try {
SomeData[] data = StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data);
}
} finally {
keyLocks.remove(key); // vulnerable to race-conditions
}
}
return data;
}
But then popular keys would be constantly reinserted in map with lock objects being reallocated.
Update: And this leaves race condition possibility when two threads would concurrently enter synchronized section for the same key but with different locks.
So it may be more safe and efficient to use expiring Guava Cache:
private static final LoadingCache<String, Object> keyLocks = CacheBuilder.newBuilder()
.expireAfterAccess(10, TimeUnit.MINUTES) // max lock time ever expected
.build(CacheLoader.from(Object::new));
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
final String key = "Data-" + email;
synchronized (keyLocks.getUnchecked(key)) {
SomeData[] data = StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data);
}
}
return data;
}
Note that it's assumed here that StaticCache is thread-safe and wouldn't suffer from concurrent reads and writes for different keys.

Others have suggested interning the strings, and that will work.
The problem is that Java has to keep interned strings around. I was told it does this even if you're not holding a reference because the value needs to be the same the next time someone uses that string. This means interning all the strings may start eating up memory, which with the load you're describing could be a big problem.
I have seen two solutions to this:
You could synchronize on another object
Instead of the email, make an object that holds the email (say the User object) that holds the value of email as a variable. If you already have another object that represents the person (say you already pulled something from the DB based on their email) you could use that. By implementing the equals method and the hashcode method you can make sure Java considers the objects the same when you do a static cache.contains() to find out if the data is already in the cache (you'll have to synchronize on the cache).
Actually, you could keep a second Map for objects to lock on. Something like this:
Map<String, Object> emailLocks = new HashMap<String, Object>();
Object lock = null;
synchronized (emailLocks) {
lock = emailLocks.get(emailAddress);
if (lock == null) {
lock = new Object();
emailLocks.put(emailAddress, lock);
}
}
synchronized (lock) {
// See if this email is in the cache
// If so, serve that
// If not, generate the data
// Since each of this person's threads synchronizes on this, they won't run
// over eachother. Since this lock is only for this person, it won't effect
// other people. The other synchronized block (on emailLocks) is small enough
// it shouldn't cause a performance problem.
}
This will prevent 15 fetches on the same email address at one. You'll need something to prevent too many entries from ending up in the emailLocks map. Using LRUMaps from Apache Commons would do it.
This will need some tweaking, but it may solve your problem.
Use a different key
If you are willing to put up with possible errors (I don't know how important this is) you could use the hashcode of the String as the key. ints don't need to be interned.
Summary
I hope this helps. Threading is fun, isn't it? You could also use the session to set a value meaning "I'm already working on finding this" and check that to see if the second (third, Nth) thread needs to attempt to create the or just wait for the result to show up in the cache. I guess I had three suggestions.

You can use the 1.5 concurrency utilities to provide a cache designed to allow multiple concurrent access, and a single point of addition (i.e. only one thread ever performing the expensive object "creation"):
private ConcurrentMap<String, Future<SomeData[]> cache;
private SomeData[] getSomeDataByEmail(final WebServiceInterface service, final String email) throws Exception {
final String key = "Data-" + email;
Callable<SomeData[]> call = new Callable<SomeData[]>() {
public SomeData[] call() {
return service.getSomeDataForEmail(email);
}
}
FutureTask<SomeData[]> ft; ;
Future<SomeData[]> f = cache.putIfAbsent(key, ft= new FutureTask<SomeData[]>(call)); //atomic
if (f == null) { //this means that the cache had no mapping for the key
f = ft;
ft.run();
}
return f.get(); //wait on the result being available if it is being calculated in another thread
}
Obviously, this doesn't handle exceptions as you'd want to, and the cache doesn't have eviction built in. Perhaps you could use it as a basis to change your StaticCache class, though.

Use a decent caching framework such as ehcache.
Implementing a good cache is not as easy as some people believe.
Regarding the comment that String.intern() is a source of memory leaks, that is actually not true.
Interned Strings are garbage collected,it just might take longer because on certain JVM'S (SUN) they are stored in Perm space which is only touched by full GC's.

Your main problem is not just that there might be multiple instances of String with the same value. The main problem is that you need to have only one monitor on which to synchronize for accessing the StaticCache object. Otherwise multiple threads might end up concurrently modifying StaticCache (albeit under different keys), which most likely doesn't support concurrent modification.

The call:
final String key = "Data-" + email;
creates a new object every time the method is called. Because that object is what you use to lock, and every call to this method creates a new object, then you are not really synchronizing access to the map based on the key.
This further explain your edit. When you have a static string, then it will work.
Using intern() solves the problem, because it returns the string from an internal pool kept by the String class, that ensures that if two strings are equal, the one in the pool will be used. See
http://java.sun.com/j2se/1.4.2/docs/api/java/lang/String.html#intern()

This question seems to me a bit too broad, and therefore it instigated equally broad set of answers. So I'll try to answer the question I have been redirected from, unfortunately that one has been closed as duplicate.
public class ValueLock<T> {
private Lock lock = new ReentrantLock();
private Map<T, Condition> conditions = new HashMap<T, Condition>();
public void lock(T t){
lock.lock();
try {
while (conditions.containsKey(t)){
conditions.get(t).awaitUninterruptibly();
}
conditions.put(t, lock.newCondition());
} finally {
lock.unlock();
}
}
public void unlock(T t){
lock.lock();
try {
Condition condition = conditions.get(t);
if (condition == null)
throw new IllegalStateException();// possibly an attempt to release what wasn't acquired
conditions.remove(t);
condition.signalAll();
} finally {
lock.unlock();
}
}
Upon the (outer) lock operation the (inner) lock is acquired to get an exclusive access to the map for a short time, and if the correspondent object is already in the map, the current thread will wait,
otherwise it will put new Condition to the map, release the (inner) lock and proceed,
and the (outer) lock is considered obtained.
The (outer) unlock operation, first acquiring an (inner) lock, will signal on Condition and then remove the object from the map.
The class does not use concurrent version of Map, because every access to it is guarded by single (inner) lock.
Please notice, the semantic of lock() method of this class is different that of ReentrantLock.lock(), the repeated lock() invocations without paired unlock() will hang current thread indefinitely.
An example of usage that might be applicable to the situation, the OP described
ValueLock<String> lock = new ValueLock<String>();
// ... share the lock
String email = "...";
try {
lock.lock(email);
//...
} finally {
lock.unlock(email);
}

This is rather late, but there is quite a lot of incorrect code presented here.
In this example:
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
SomeData[] data = null;
final String key = "Data-" + email;
synchronized(key) {
data =(SomeData[]) StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data, CACHE_TIME);
}
else {
logger.debug("getSomeDataForEmail: using cached object");
}
}
return data;
}
The synchronization is incorrectly scoped. For a static cache that supports a get/put API, there should be at least synchronization around the get and getIfAbsentPut type operations, for safe access to the cache. The scope of synchronization will be the cache itself.
If updates must be made to the data elements themselves, that adds an additional layer of synchronization, which should be on the individual data elements.
SynchronizedMap can be used in place of explicit synchronization, but care must still be observed. If the wrong APIs are used (get and put instead of putIfAbsent) then the operations won't have the necessary synchronization, despite the use of the synchronized map. Notice the complications introduced by the use of putIfAbsent: Either, the put value must be computed even in cases when it is not needed (because the put cannot know if the put value is needed until the cache contents are examined), or requires a careful use of delegation (say, using Future, which works, but is somewhat of a mismatch; see below), where the put value is obtained on demand if needed.
The use of Futures is possible, but seems rather awkward, and perhaps a bit of overengineering. The Future API is at it's core for asynchronous operations, in particular, for operations which may not complete immediately. Involving Future very probably adds a layer of thread creation -- extra probably unnecessary complications.
The main problem of using Future for this type of operation is that Future inherently ties in multi-threading. Use of Future when a new thread is not necessary means ignoring a lot of the machinery of Future, making it an overly heavy API for this use.

Latest update 2019,
If you are searching for new ways of implementing synchronization in JAVA, this answer is for you.
I found this amazing blog by Anatoliy Korovin this will help you understand the syncronized deeply.
How to Synchronize Blocks by the Value of the Object in Java.
This helped me hope new developers will find this useful too.

Why not just render a static html page that gets served to the user and regenerated every x minutes?

I'd also suggest getting rid of the string concatenation entirely if you don't need it.
final String key = "Data-" + email;
Is there other things/types of objects in the cache that use the email address that you need that extra "Data-" at the beginning of the key?
if not, i'd just make that
final String key = email;
and you avoid all that extra string creation too.

In case others have a similar problem, the following code works, as far as I can tell:
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.function.Supplier;
public class KeySynchronizer<T> {
private Map<T, CounterLock> locks = new ConcurrentHashMap<>();
public <U> U synchronize(T key, Supplier<U> supplier) {
CounterLock lock = locks.compute(key, (k, v) ->
v == null ? new CounterLock() : v.increment());
synchronized (lock) {
try {
return supplier.get();
} finally {
if (lock.decrement() == 0) {
// Only removes if key still points to the same value,
// to avoid issue described below.
locks.remove(key, lock);
}
}
}
}
private static final class CounterLock {
private AtomicInteger remaining = new AtomicInteger(1);
private CounterLock increment() {
// Returning a new CounterLock object if remaining = 0 to ensure that
// the lock is not removed in step 5 of the following execution sequence:
// 1) Thread 1 obtains a new CounterLock object from locks.compute (after evaluating "v == null" to true)
// 2) Thread 2 evaluates "v == null" to false in locks.compute
// 3) Thread 1 calls lock.decrement() which sets remaining = 0
// 4) Thread 2 calls v.increment() in locks.compute
// 5) Thread 1 calls locks.remove(key, lock)
return remaining.getAndIncrement() == 0 ? new CounterLock() : this;
}
private int decrement() {
return remaining.decrementAndGet();
}
}
}
In the case of the OP, it would be used like this:
private KeySynchronizer<String> keySynchronizer = new KeySynchronizer<>();
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
String key = "Data-" + email;
return keySynchronizer.synchronize(key, () -> {
SomeData[] existing = (SomeData[]) StaticCache.get(key);
if (existing == null) {
SomeData[] data = service.getSomeDataForEmail(email);
StaticCache.set(key, data, CACHE_TIME);
return data;
}
logger.debug("getSomeDataForEmail: using cached object");
return existing;
});
}
If nothing should be returned from the synchronized code, the synchronize method can be written like this:
public void synchronize(T key, Runnable runnable) {
CounterLock lock = locks.compute(key, (k, v) ->
v == null ? new CounterLock() : v.increment());
synchronized (lock) {
try {
runnable.run();
} finally {
if (lock.decrement() == 0) {
// Only removes if key still points to the same value,
// to avoid issue described below.
locks.remove(key, lock);
}
}
}
}

I've added a small lock class that can lock/synchronize on any key, including strings.
See implementation for Java 8, Java 6 and a small test.
Java 8:
public class DynamicKeyLock<T> implements Lock
{
private final static ConcurrentHashMap<Object, LockAndCounter> locksMap = new ConcurrentHashMap<>();
private final T key;
public DynamicKeyLock(T lockKey)
{
this.key = lockKey;
}
private static class LockAndCounter
{
private final Lock lock = new ReentrantLock();
private final AtomicInteger counter = new AtomicInteger(0);
}
private LockAndCounter getLock()
{
return locksMap.compute(key, (key, lockAndCounterInner) ->
{
if (lockAndCounterInner == null) {
lockAndCounterInner = new LockAndCounter();
}
lockAndCounterInner.counter.incrementAndGet();
return lockAndCounterInner;
});
}
private void cleanupLock(LockAndCounter lockAndCounterOuter)
{
if (lockAndCounterOuter.counter.decrementAndGet() == 0)
{
locksMap.compute(key, (key, lockAndCounterInner) ->
{
if (lockAndCounterInner == null || lockAndCounterInner.counter.get() == 0) {
return null;
}
return lockAndCounterInner;
});
}
}
#Override
public void lock()
{
LockAndCounter lockAndCounter = getLock();
lockAndCounter.lock.lock();
}
#Override
public void unlock()
{
LockAndCounter lockAndCounter = locksMap.get(key);
lockAndCounter.lock.unlock();
cleanupLock(lockAndCounter);
}
#Override
public void lockInterruptibly() throws InterruptedException
{
LockAndCounter lockAndCounter = getLock();
try
{
lockAndCounter.lock.lockInterruptibly();
}
catch (InterruptedException e)
{
cleanupLock(lockAndCounter);
throw e;
}
}
#Override
public boolean tryLock()
{
LockAndCounter lockAndCounter = getLock();
boolean acquired = lockAndCounter.lock.tryLock();
if (!acquired)
{
cleanupLock(lockAndCounter);
}
return acquired;
}
#Override
public boolean tryLock(long time, TimeUnit unit) throws InterruptedException
{
LockAndCounter lockAndCounter = getLock();
boolean acquired;
try
{
acquired = lockAndCounter.lock.tryLock(time, unit);
}
catch (InterruptedException e)
{
cleanupLock(lockAndCounter);
throw e;
}
if (!acquired)
{
cleanupLock(lockAndCounter);
}
return acquired;
}
#Override
public Condition newCondition()
{
LockAndCounter lockAndCounter = locksMap.get(key);
return lockAndCounter.lock.newCondition();
}
}
Java 6:
public class DynamicKeyLock implements Lock
{
private final static ConcurrentHashMap locksMap = new ConcurrentHashMap();
private final T key;
public DynamicKeyLock(T lockKey) {
this.key = lockKey;
}
private static class LockAndCounter {
private final Lock lock = new ReentrantLock();
private final AtomicInteger counter = new AtomicInteger(0);
}
private LockAndCounter getLock()
{
while (true) // Try to init lock
{
LockAndCounter lockAndCounter = locksMap.get(key);
if (lockAndCounter == null)
{
LockAndCounter newLock = new LockAndCounter();
lockAndCounter = locksMap.putIfAbsent(key, newLock);
if (lockAndCounter == null)
{
lockAndCounter = newLock;
}
}
lockAndCounter.counter.incrementAndGet();
synchronized (lockAndCounter)
{
LockAndCounter lastLockAndCounter = locksMap.get(key);
if (lockAndCounter == lastLockAndCounter)
{
return lockAndCounter;
}
// else some other thread beat us to it, thus try again.
}
}
}
private void cleanupLock(LockAndCounter lockAndCounter)
{
if (lockAndCounter.counter.decrementAndGet() == 0)
{
synchronized (lockAndCounter)
{
if (lockAndCounter.counter.get() == 0)
{
locksMap.remove(key);
}
}
}
}
#Override
public void lock()
{
LockAndCounter lockAndCounter = getLock();
lockAndCounter.lock.lock();
}
#Override
public void unlock()
{
LockAndCounter lockAndCounter = locksMap.get(key);
lockAndCounter.lock.unlock();
cleanupLock(lockAndCounter);
}
#Override
public void lockInterruptibly() throws InterruptedException
{
LockAndCounter lockAndCounter = getLock();
try
{
lockAndCounter.lock.lockInterruptibly();
}
catch (InterruptedException e)
{
cleanupLock(lockAndCounter);
throw e;
}
}
#Override
public boolean tryLock()
{
LockAndCounter lockAndCounter = getLock();
boolean acquired = lockAndCounter.lock.tryLock();
if (!acquired)
{
cleanupLock(lockAndCounter);
}
return acquired;
}
#Override
public boolean tryLock(long time, TimeUnit unit) throws InterruptedException
{
LockAndCounter lockAndCounter = getLock();
boolean acquired;
try
{
acquired = lockAndCounter.lock.tryLock(time, unit);
}
catch (InterruptedException e)
{
cleanupLock(lockAndCounter);
throw e;
}
if (!acquired)
{
cleanupLock(lockAndCounter);
}
return acquired;
}
#Override
public Condition newCondition()
{
LockAndCounter lockAndCounter = locksMap.get(key);
return lockAndCounter.lock.newCondition();
}
}
Test:
public class DynamicKeyLockTest
{
#Test
public void testDifferentKeysDontLock() throws InterruptedException
{
DynamicKeyLock<Object> lock = new DynamicKeyLock<>(new Object());
lock.lock();
AtomicBoolean anotherThreadWasExecuted = new AtomicBoolean(false);
try
{
new Thread(() ->
{
DynamicKeyLock<Object> anotherLock = new DynamicKeyLock<>(new Object());
anotherLock.lock();
try
{
anotherThreadWasExecuted.set(true);
}
finally
{
anotherLock.unlock();
}
}).start();
Thread.sleep(100);
}
finally
{
Assert.assertTrue(anotherThreadWasExecuted.get());
lock.unlock();
}
}
#Test
public void testSameKeysLock() throws InterruptedException
{
Object key = new Object();
DynamicKeyLock<Object> lock = new DynamicKeyLock<>(key);
lock.lock();
AtomicBoolean anotherThreadWasExecuted = new AtomicBoolean(false);
try
{
new Thread(() ->
{
DynamicKeyLock<Object> anotherLock = new DynamicKeyLock<>(key);
anotherLock.lock();
try
{
anotherThreadWasExecuted.set(true);
}
finally
{
anotherLock.unlock();
}
}).start();
Thread.sleep(100);
}
finally
{
Assert.assertFalse(anotherThreadWasExecuted.get());
lock.unlock();
}
}
}

In your case you could use something like this (this doesn't leak any memory):
private Synchronizer<String> synchronizer = new Synchronizer();
private SomeData[] getSomeDataByEmail(WebServiceInterface service, String email) {
String key = "Data-" + email;
return synchronizer.synchronizeOn(key, () -> {
SomeData[] data = (SomeData[]) StaticCache.get(key);
if (data == null) {
data = service.getSomeDataForEmail(email);
StaticCache.set(key, data, CACHE_TIME);
} else {
logger.debug("getSomeDataForEmail: using cached object");
}
return data;
});
}
to use it you just add a dependency:
compile 'com.github.matejtymes:javafixes:1.3.0'

You should be very careful using short lived objects with synchronization. Every Java object has an attached monitor and by default this monitor is deflated; however if 2 threads contend on acquiring the monitor, the monitor gets inflated. If the object would be long lived, this isn't a problem. However if the object is short lived, then cleaning up this inflated monitor can be a serious hit on GC times (so higher latencies and reduced throughput). And it can even be tricky to spot on the GC times since it isn't always listed.
If you do want to synchronize, you could use a java.util.concurrent.Lock. Or make use of a manually crafted striped lock and use the hash of the string as an index on that striped lock. This striped lock you keep around so you don't get the GC problems.
So something like this:
static final Object[] locks = newLockArray();
Object lock = locks[hashToIndex(key.hashcode(),locks.length];
synchronized(lock){
....
}
int hashToIndex(int hash, int length) {
if (hash == Integer.MIN_VALUE return 0;
return abs(hash) % length;
}

other way synchronizing on string object :
String cacheKey = ...;
Object obj = cache.get(cacheKey)
if(obj==null){
synchronized (Integer.valueOf(Math.abs(cacheKey.hashCode()) % 127)){
obj = cache.get(cacheKey)
if(obj==null){
//some cal obtain obj value,and put into cache
}
}
}

You can safely use String.intern for synchronize if you can reasonably guarantee that the string value is unique across your system. UUIDS are a good way to approach this. You can associate a UUID with your actual string key, either via a cache, a map, or maybe even store the uuid as a field on your entity object.
#Service
public class MySyncService{
public Map<String, String> lockMap=new HashMap<String, String>();
public void syncMethod(String email) {
String lock = lockMap.get(email);
if(lock==null) {
lock = UUID.randomUUID().toString();
lockMap.put(email, lock);
}
synchronized(lock.intern()) {
//do your sync code here
}
}