I got this question on an interview and I'm trying to learn from this.
Assuming that this repository is used in a concurrent context with billions of messages in the database.
public class MessageRepository {
public static final Map<String, Message> cache = new HashMap<>();
public Message findMessageById(String id) {
if(cache.containsKey(id)) {
return cache.get(id);
}
Message p = loadMessageFromDb(id);
cache.put(id, p);
return p;
}
Message loadMessageFromDb(String id) {
/* query DB and map row to a Message object */
}
}
What are possible problems with this approach?
One I can think of is HashMap not being a thread safe implementation of Map. Perhaps ConcurrentHashMap would be better for that.
I wasn't sure about any other of the possible answers which were:
1) Class MessageRepository is final meaning it's immutable, so it can't have a modifiable cache.
(AFAIK HashMap is mutable and it's composed into MessageRepository so this wouldn't be an issue).
2) Field cache is final meaning that it's immutable, so it can't be modified by put.
(final doesn't mean immutable so this wouldn't be an issue either)
3) Field cache is static meaning that it will be reset every time an instance of MessageRepository will be built.
(cache will be shared by all instances of MessageRepository so it shouldn't be a problem)
4) HashMap is synchronized, performances may be better without synchronization.
(I think SynchronizedHashMap is the synced implementation)
5) HashMap does not implement evict mechanism out of the box, it may cause memory problems.
(What kind of problems?)
I see two problems with this cache. If loadMessageFromDb() is an expensive operation, then two threads can initiate duplicate calculations. This isn't alleviated even if you use ConcurrentHashMap. A proper implementation of a cache that avoid this would be:
public class MessageRepository {
private static final Map<String, Future<Message>> CACHE = new ConcurrentHashMap<>();
public Message findMessageById(String id) throws ExecutionException, InterruptedException {
Future<Message> messageFuture = CACHE.get(id);
if (null == messageFuture) {
FutureTask<Message> ft = new FutureTask<>(() -> loadMessageFromDb(id));
messageFuture = CACHE.putIfAbsent(id, ft);
if (null == messageFuture) {
messageFuture = ft;
ft.run();
}
}
return messageFuture.get();
}
}
(Taken directly from JCIP By Brian Goetz et. al.)
In the cache above, when a thread starts a computation, it puts the Future into the cache and then patiently waits till the computation finishes. Any thread that comes in with the same id sees that a computation is already ongoing and will again wait on the same future. If two threads call exactly at the same time, putIfAbsent ensures that only one thread is able to initiate the computation.
Java does not have any SynchronizedHashMap class. You should use ConcurrentHashMap. You can do Collections.synchronisedMap(new HashMap<>()) but it has really bad performance.
A problem with the above cache is that it does not evict entries. Java provides LinkedHashMap that can help you create a LRU cache, but it is not synchronised. If you want both functionalities, you should try Guava cache.
Sorry for my English
I don't use any of fields for the locking because so I shouldn't think about could or couldn't some field have value null.
I always create special fields used only for locking in thread synchronization.
For example:
public class Worker {
private static final List<Toilet> TOILETS = Arrays.asList(
new Toilet(1),
new Toilet(2),
// ...
new Toilet(NUMBER_OF_FLOORS)
);
// here it is:
private static final List<String> LOCK_TOILETS = Arrays.asList(
"LOCK TOILET #1",
"LOCK TOILET #2",
// ...
"LOCK TOILET #" + NUMBER_OF_FLOORS
);
private final int floorNumber;
public void spendWorkingHours() {
for (int i = 0; i < X; ++i) {
doWork();
snackSomething();
String lockToilet = LOCK_TOILETS.get(floorNumber);
Toilet theOnlyToiletOnTheFloor = TOILETS.get(floorNumber);
synchronized (lockToilet) {
goToToilet(theOnlyToiletOnTheFloor);
}
}
}
}
You should not use Strings for lock objects especially not string literals.
String literals are from the String pool and each String literal which is the same string is the same reference. This means if 2 different threads use 2 "different" string literals are actually the same and hence deadlock can easily occur.
To demonstrate:
// Thread #1
String LOCK1 = "mylock";
synchronized (LOCK1) {
}
// Thread #2
String LOCK2 = "mylock";
synchronized (LOCK2) {
// This is actually the SAME lock,
// might cause deadlock between the 2 synchronized blocks!
// Because LOCK1==LOCK2!
}
Best would be to synchronize on private objects which are not accessible from the "outside". If you use an Object for lock which is visible from "outside" (or returned by a method), that object is available to anyone to also use as a lock which you have no control over and may cause a deadlock with your internal synchronized block.
For example you can synchronize on the object you whish to guard if it is private, or create a private, internal lock Object:
private final Object LOCK = new Object();
// Later:
synchronized (LOCK) {
// LOCK is not known to any "outsiders", safe to use it as internal lock
}
Using a String may not be the best idea, because this class gets a bit of special treatment, and strings with the same contents may be reused (so locking a toilet on the first floor would also lock the toilet with the same number on the other floors).
Your best choice here is locking the actual toilet.
There is no need for lockToilet why don't you just use a synchronized statement over each TOILET resource?
Toilet t;
syncrhonized(TOILETS)
{
t = = TOILETS.get(floorNumber);
}
synchronized (t) {
goToToilet(t);
}
syncrhonized In this code means that any use of the object between parentheses is thread exclussive within the scope between brakets thus this object becoming a lock.
Answers do cover your question regarding the use of Strings for locking (See String interning for more details) so I will just mention a few other considerations:
Although you have defined the List as final (Cannot assign another list instance) and initialized with .asList(..) (Cannot change size) this doesn't make read-only or thread-safe, so if someone changes elements in that list you might get into an unstable state. Consider using a read-only list.
You also need to clarify the scope of locking. What are you trying to lock against? If goToToilet changes the object attributes, then the point of synchronization would be better placed in the method that changes the state of the Object. (This is a design recommendation; The code would work but would also be prone to errors when changing the code in the future)
Finally, I would also have a look in java concurrent structures as you might find concurrent collections and locking mechanisms useful.
Is volatile redundant in this code?
public class Test {
private volatile Map<String, String> map = null;
public void resetMap() { map = new ConcurrentHashMap<>(); }
public Map<String, String> getMap() { return map; }
}
In other words, does map = new ConcurrentHashMap<>(); provide any visibility guarantees?
As far as I can see, the only guarantee provided by ConcurrentMap is:
Actions in a thread prior to placing an object into a ConcurrentMap as a key or value happen-before actions subsequent to the access or removal of that object from the ConcurrentMap in another thread.
How about other thread safe collections in java.util.concurrent (CopyOnWriteArrayList, etc.)?
volatile is not redundant as you are changing the reference to the map. i.e. ConcurrentMap only provides guarentees about the contents of the collection, not references to it.
An alternative would be
public class Test {
private final Map<String, String> map = new ConcurrentHashMap<>();
public void resetMap() { map.clear(); }
public Map<String, String> getMap() { return map; }
}
How about other thread safe collections in java.util.concurrent (CopyOnWriteArrayList, etc.)?
Only the behaviour of the collection is thread safe. Reference to the collection are not thread safe, elements in the collection are not made thread safe by adding them to the collection.
volatile is necessary here. It applies to the reference, not to what it references to. In other words it doesn't matter that an object is thread safe, other threads won't see the new value of map field (e.g. might see previously referenced concurrent map or null).
Moreover, even if your object was immutable (e.g. String) you would still need volatile, not to mention other thread-safe collections like CopyOnWriteArrayList.
This is not just about the references. Generally, without the volatile modifier other threads may observe a new reference to an object, but observe the object in a partially constructed state. In general, it is not easy to know, even after consulting the documentation, which objects are safe for publication by a data race. An interesting note is that the JLS does guarantee this for thread-safe immutable objects, so if the docs mention those two properties it should be enough.
ConcurrentHashMap is obviously not an immutable object, so that doesn't apply, and the docs don't mention anything about publication by a data race. By careful inspection of the source code we may conclude that it is indeed safe, however I wouldn't recommend relying on such findings without this property being clearly documented.
Memory Consistency Properties
A write to a volatile field happens-before every subsequent read of that same field. Writes and reads of volatile fields have similar memory consistency effects as entering and exiting monitors, but do not entail mutual exclusion locking.
Actions in a thread prior to placing an object into any concurrent collection happen-before actions subsequent to the access or removal of that element from the collection in another thread.
OK - I was able to construct an example that breaks (on my machine: JDK 1.7.06 / Win 7 64 bits) if the field is not volatile - the program never prints Loop exited if map is not volatile - it does print Loop exited if map is volatile. QED.
public class VolatileVisibility extends Thread {
Map<String, String> stop = null;
public static void main(String[] args) throws InterruptedException {
VolatileVisibility t = new VolatileVisibility();
t.start();
Thread.sleep(100);
t.stop = new ConcurrentHashMap<>(); //write of reference
System.out.println("In main: " + t.stop); // read of reference
System.out.println("Waiting for run to finish");
Thread.sleep(200);
System.out.println("Still waiting");
t.stop.put("a", "b"); //write to the map
Thread.sleep(200);
System.exit(0);
}
public void run() {
System.out.println("In run: " + stop); // read of reference
while (stop == null) {
}
System.out.println("Loop exited");
}
}
My impression is that Doug Lea's concurrent objects can be safely published by data race, so that they are "thread-safe" even if misused. Though he probably wouldn't advertise that publicly.
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.
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.