Develop Reference

Use of a key to synchronize access to code block

Normally I would lock on a critical section like the following.
public class Cache {
private Object lockObject = new Object();
public Object getFromCache(String key) {
synchronized(lockObject) {
if (cache.containsKey(key)) {
// key found in cache - return cache value
}
else {
// retrieve cache value from source, cache it and return
}
}
}
}
The idea being I avoid a race condition which could result in the data source being hit multiple times and the key being added to the cache multiple times.
Right now if two threads come in at about the same time for different cache keys, I will still block one.
Assuming the keys are unique - will the lock still work by locking on the key?
I think it won't work because I understand that the object reference should be the same for the lock to come into effect. I guess this comes down to how it checks for equality.
public class Cache {
public Object getFromCache(String key) {
synchronized(key) {
if (cache.containsKey(key)) {
// key found in cache - return cache value
}
else {
// retrieve cache value from source, cache it and return
}
}
}
}

public class Cache {
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 Object getFromCache(String key) throws InterruptedException {
try {
lock(key);
if (cache.containsKey(key)) {
// key found in cache - return cache value
}
else {
// retrieve cache value from source, cache it and return
}
} finally {
unlock(key);
}
}
}
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.

Each object has an implicit monitor upon which synchronization works. String object may be created in heap and also may be different for same set of characters (if created by using new) or may be from pool. Two threads will acess the critical section with synchronized block only if they synchronize on same object.
Synchronizing on String literal is really a bad idea. String literal from pool are shared. Just imagine if at two different parts of your code you are having two synchronized sections and you synchronize on two references of String but initilized with string with same set of characters, if String from pool is used then both the places it will be the same object. Even though both the places may have different business context but still you may end up in your application being hanged. It will be very difficult to debug too.
For the specific to the question of will the purpose be solved if synchronization is done on keys.
You want to avoid two threads trying to write without reading the latest value of cache. You will have different key for each entry. Suppose a thread1 and thread2 wants to access the same key then synchronization on the same key object will prevent both of them to enter the synchronized block. Meanwhile if a thread3 wants to access another different key then it can very well do so. Here we see the read and writes are faster as compared to single common object for reads and writes for all keys. So far so good but the problem will arise if suppose you are keeping an array or any other similar non thread safe data structure for storing the cached values. Simultaneous writes (for two or more different keys) can result in one write being overwritten by another at same index.
So it depends upon the implementation of cache data structure how best you can prepare it for faster read and writes in a multi threaded enviornment.

Java synchronization on Collection with expensive operations

I have a list that I synchronize on named synchronizedMap in my function doMapOperation. In this function, I need to add/remove items from a map and perform expensive operations on these objects. I know that I don't want to call an expensive operation in a synchronized block, but I don't know how to make sure that the map is in a consistent state while I do these operations. What is the right way to do this?
This is my initial layout which I am sure is wrong because you want to avoid calling an expensive operation in a synchronized block:
public void doMapOperation(Object key1, Object key2) {
synchronized (synchronizedMap) {
// Remove key1 if it exists.
if (synchronizedMap.containsKey(key1)) {
Object value = synchronizedMap.get(key1);
value.doExpensiveOperation(); // Shouldn't be in synchronized block.
synchronizedMap.remove(key1);
}
// Add key2 if necessary.
Object value = synchronizedMap.get(key2);
if (value == null) {
Object value = new Object();
synchronizedMap.put(key2, value);
}
value.doOtherExpensiveOperation(); // Shouldn't be in synchronized block.
} // End of synchronization.
}
I guess as a continuation of this question, how would you do this in a loop?
public void doMapOperation(Object... keys) {
synchronized (synchronizedMap) {
// Loop through keys and remove them.
for (Object key : keys) {
// Check if map has key, remove if key exists, add if key doesn't.
if (synchronizedMap.containsKey(key)) {
Object value = synchronizedMap.get(key);
value.doExpensiveOperation(); // Shouldn't be here.
synchronizedMap.remove(key);
} else {
Object value = new Object();
value.doAnotherExpensiveOperation(); // Shouldn't here.
synchronizedMap.put(key, value);
}
}
} // End of synchronization block.
}
Thanks for the help.

You can do the expensive operations outside your synchronized block like so:
public void doMapOperation(Object... keys) {
ArrayList<Object> contained = new ArrayList<Object>();
ArrayList<Object> missing = new ArrayList<Object>();
synchronized (synchronizedMap) {
if (synchronizedMap.containsKey(key)) {
contained.add(synchronizedMap.get(key));
synchronizedMap.remove(key);
} else {
missing.add(synchronizedMap.get(key));
synchronizedMap.put(key, value);
}
}
for (Object o : contained)
o.doExpensiveOperation();
for (Object o : missing)
o.doAnotherExpensiveOperation();
}
The only disadvantage is you may be performing operations on values after they are removed from the synchronizedMap.

You can create a wrapper for your synchronizedMap and make sure the operations like containsKey, remove, and put are synchronized methods. Then only access to the map will be synchronized, while your expensive operations can take place outside the synchronized block.
Another advantage is by keeping your expensive operations outside the synchronized block you avoid a possible deadlock risk if the operations call another synchronized map method.

In the first snippet: Declare the two values out of the if-clause, and just assign them in the if-clause. Make the if-clause synchronized, and invoke the expensive operations outside.
In the 2nd case do the same, but inside the loop. (synchronized inside the loop). You can, of course, have only one synchronized statement, outside the loop, and simply fill a List of objects on which to invoke the expensive operation. Then, in a 2nd loop, outside the synchronized block, invoke that operations on all values in the list.

We should forget about small efficiencies, say about 97% of the time:
premature optimization is the root of all evil. Yet we should not pass
up our opportunities in that critical 3%. A good programmer will not
be lulled into complacency by such reasoning, he will be wise to look
carefully at the critical code; but only after that code has been
identified. — Donald Knuth
You have a single method, doMapOperation(). What is your performance if this method continues to be block-synchronized? If you don't know then how will you know when you've got a good performing solution? Are you prepared to handle multiple calls to your expensive operations even after they have been removed from the map?
I'm not trying to be condescending, since maybe you understand the problem at hand better than you've conveyed, but it seems like you're jumping into a level of optimization for which you may not be prepared and may not be necessary.

You can actually do it all with only one synchronization hit. The first remove is probably the easiest. If you know the object exists, and you know remove is atomic, why not just remove it and if what is returned is not null invoke the expensive operations?
// Remove key1 if it exists.
if (synchronizedMap.containsKey(key1)) {
Object value = synchronizedMap.remove(key1);
if(value != null){ //thread has exclusive access to value
value.doExpensiveOperation();
}
}
For the put, since it is expensive and should be atomic you are pretty much out of luck and need to synchronize access. I would recommend using some kind of a computing map. Take a look at google-collections and MapMaker
You can create a ConcurrentMap that will build the expensive object based on your key for example
ConcurrentMap<Key, ExpensiveObject> expensiveObjects = new MapMaker()
.concurrencyLevel(32)
.makeComputingMap(
new Function<Key, ExpensiveObject>() {
public ExpensiveObject apply(Key key) {
return createNewExpensiveObject(key);
}
});
This is simlpy a form of memoization
In both of these cases, you don't need to use synchronized at all (at least explicitly)

If you don't have null values in the Map, you don't need the containsKey() call at all: you can use Map.remove() to both remove the item and tell you whether it was there. So the true content of your synchronized block only needs to be this:
Object value = Map.remove(key);
if (value != null)
value.doExpensiveOperation();
else
{
value = new Value();
value.doExpensiveOperation();
map.put(key,value);
}
If the expensive operation itself doesn't need to be synchronized, i.e. if you don't mind other clients of the Map seeing the value while it is being operated on, you can further simplify to this:
Object value = Map.remove(key);
if (value == null)
{
value = new Value();
map.put(key,value);
}
value.doExpensiveOperation();
and the synchronized block can terminate before the expensive operation.

Synchronize access to given key in a ConcurrentMap

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

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

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

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

Multi-thread object→object cache map in Java?

I want a collection in Java which:
maps arbitrary Objects to Objects (not String or otherwise restricted keys only)
will be used as a cache; if the key is not in the cache, a value will be computed (this doesn't have to be built into the collection)
will be accessed from multiple threads simultaneously
will never have items removed from it
must be very efficient to read (cache hit); not necessarily efficient to write (cache miss)
It's OK if cache misses simultaneously in multiple threads cause redundant computations; the typical case is that the cache is mostly filled by one thread at first.
A synchronized block around a thread-unsafe hashtable fails the efficient-to-read criterion. Thread-local caches would be straightforward but mean that new threads are expensive since they have full copies of the cache.
Java 1.5 built-ins, or one-or-few class files we can copy into our MIT-licensed project, are preferred, rather than large external libraries.

Use the java concurrent hashmap
ConcurrentHashMap<object, object> table;
public object getFromCache(object key)
{
value = table.get(key);
if (value == null)
{
//key isn't a key into this table, ie. it's not in the cache
value = calculateValueForKey(key)
object fromCache = table.putIfAbsent(key, value);
}
return value;
}
/**
* This calculates a new value to put into the cache
*/
public abstract object calculateValueForKey(object key);
N.b. This is not longer a general solution for multithreaded caching, since it relies on the stated fact that objects are immutable, and thus object equivalence is not important.

This is my own idea for a solution, but I'm not an expert on threaded programming, so please comment/vote/compare to other answers as you feel appropriate.
Use a thread-local variable (java.lang.ThreadLocal) which contains a per-thread hashtable used as a first-level cache. If the key is not found in this table, synchronized access is done to a second-level cache, which is a synchronized-access hashtable shared by all threads. In this way, the calculation of the cache value is only ever done once, and it is shared among all threads, but each thread has a local copy of the mapping from keys to values, so there is some memory cost (but less than having independent per-thread caches), but reads are efficient.

How about something like this SingletonCache class from one of my projects?
public abstract class SingletonCache<K, V> {
private final ConcurrentMap<K, V> cache = new ConcurrentHashMap<K, V>();
public V get(K key) {
V value = cache.get(key);
if (value == null) {
cache.putIfAbsent(key, newInstance(key));
value = cache.get(key);
}
return value;
}
protected abstract V newInstance(K key);
}
To use it, you extend it and implement the newInstance method which creates a new value in case of a cache miss. Then call the get method with a key to get an instance which corresponds that key. Here is an example of how it is used.
That class guarantees that for each key, only one instance is returned, but the newInstance method may be called multiple times, in which case the first computed instance is used and the rest are discarded. Also note that this cache does not remove old instances, but stores all values indefinitely (in my use case there is a limited number of instances that must be cached). Reading from a ConcurrentHashMap does not use locking, so it should satisfy your efficiency requirement.

How about the cache described in section 5.6 of Concurrency In Practice, by Brian Goetz? It's outlined here.
It only uses classes from the java.util.concurrent package.
The linked article builds up a cache and describes the weaknesses of each version, until final version is an efficient cache in which only one concurrent thread will compute an absent entry.
I've cut and pasted the final code below, but its worth reading through the article and thinking about the issues outlined. Or even better - buy the book - it's great.
import java.util.concurrent.*;
public class Memoizer<A, V> implements Computable<A, V> {
private final ConcurrentMap<A, Future<V>> cache
= new ConcurrentHashMap<A, Future<V>>();
private final Computable<A, V> c;
public Memoizer(Computable<A, V> c) { this.c = c; }
public V compute(final A arg) throws InterruptedException {
while (true) {
Future<V> f = cache.get(arg);
if (f == null) {
Callable<V> eval = new Callable<V>() {
public V call() throws InterruptedException {
return c.compute(arg);
}
};
FutureTask<V> ft = new FutureTask<V>(eval);
f = cache.putIfAbsent(arg, ft);
if (f == null) {
f = ft;
ft.run();
}
}
try {
return f.get();
} catch (CancellationException e) {
cache.remove(arg, f);
} catch (ExecutionException e) {
// Kabutz: this is my addition to the code...
try {
throw e.getCause();
} catch (RuntimeException ex) {
throw ex;
} catch (Error ex) {
throw ex;
} catch (Throwable t) {
throw new IllegalStateException("Not unchecked", t);
}
}
}
}
}

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
}
}