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Iterating over single List in parallel without duplicates in Java

I have an ArrayList filled with 'someObject'. I need to iterate over this list, with 4 different threads (using Futures & Callables). The threads will keep the top 5 valued objects it comes across. I first tried creating a parallel stream, but that didn't work out so well. Is there some obvious thing I'm not thinking of, so each thread can iterate over the objects, without possibly grabbing the same object twice?

You can use an AtomicInteger to iterate over the list:
class MyRunnable implements Runnable {
final List<SomeObject> list;
final AtomicInteger counter; // initialize to 0
public void run() {
while(true) {
int index = counter.getAndIncrement();
if(index < list.size()) {
do something with list.get(index);
} else {
return;
}
}
}
}
So long as each MyRunnable has the same AtomicInteger reference they won't duplicate indices

You don't need AtomicInteger or any other synchronization for that matter.
You should simply logically partition your list (whose size is known upfront) based on the number of processing threads (whose number is also known upfront) and let each of them operate on its own section of [from, to) of the list.
This avoid the need for any synchronization at all (even if it's just an optimized one such as AtomicInteger) which is what you should always strive for (as long as it's safe).
Pseudo code
class Worker<T> implements Runnable {
final List<T> toProcess;
protected Worker(List<T> list, int fromInc, int toExcl){
// note this does not allow passing an empty list or specifying an empty work section but you can relax that if you wish
// this also implicitly checks the list for null
Preconditions.checkArgument(fromInc >= 0 && fromInc < list.size());
Preconditions.checkArgument(toExcl > 0 && fromInc <= list.size());
// note: this does not create a copy, but only a view so it's very cheap
toProcess = list.subList(fromInc, toExcl);
}
#Override
public final void run() {
for(final T t : toProcess) {
process(t);
}
}
protected abstract process(T t);
}
As with the AtomicInteger solution (really any solution which does not involve copying the list), this solution also assumes that you will not be modifying the list once you have handed it off to each thread and processing has commenced. Modifying the list while processing is in progress will result in undefined behavior.

How java AtomicReference works under the hood

How java AtomicReference works under the hood? I tried looking over the code but is based on sun.misc.Unsafe so probably another question is how Unsafe works?

This is specific to the current implementation and can change but isn't necessarily documents
How java AtomicReference works under the hood
There are two operations. Single read/writes or atomic swaps.
Single read/writes are simple volatile loads or stores.
The atomic swaps need processor level instructions. The most common implementations are Compare and Swap (CAS) found on sparc-TSO, x86, and ia64 and LL/SC found on arm, ppc and alpha. I am sure there are more that I am missing out but this gives you an idea of the scope.
another question is how Unsafe works?
Unsafe works via native methods leveraging processor instructions.
Sources:
http://gee.cs.oswego.edu/dl/jmm/cookbook.html

Some important elementary facts are as follows. 1> Different threads can only contend for instance and static member variables in the heap space. 2> Volatile read or write are completely atomic and serialized/happens before and only done from memory. By saying this I mean that any read will follow the previous write in memory. And any write will follow the previous read from memory. So any thread working with a volatile will always see the most up-to-date value. AtomicReference uses this property of volatile.
Following are some of the source code of AtomicReference. AtomicReference refers to an object reference. This reference is a volatile member variable in the AtomicReference instance as below.
private volatile V value;
get() simply returns the latest value of the variable (as volatiles do in a "happens before" manner).
public final V get()
Following is the most important method of AtomicReference.
public final boolean compareAndSet(V expect, V update) {
return unsafe.compareAndSwapObject(this, valueOffset, expect, update);
}
The compareAndSet(expect,update) method calls the compareAndSwapObject() method of the unsafe class of Java. This method call of unsafe invokes the native call, which invokes a single instruction to the processor. "expect" and "update" each reference an object.
If and only if the AtomicReference instance member variable "value" refers to the same object is referred to by "expect", "update" is assigned to this instance variable now, and "true" is returned. Or else, false is returned. The whole thing is done atomically. No other thread can intercept in between. As this is a single processor operation (magic of modern computer architecture), it's often faster than using a synchronized block. But remember that when multiple variables need to be updated atomically, AtomicReference won't help.
I would like to add a full fledged running code, which can be run in eclipse. It would clear many confusion. Here 22 users (MyTh threads) are trying to book 20 seats. Following is the code snippet followed by the full code.
Code snippet where 22 users are trying to book 20 seats.
for (int i = 0; i < 20; i++) {// 20 seats
seats.add(new AtomicReference<Integer>());
}
Thread[] ths = new Thread[22];// 22 users
for (int i = 0; i < ths.length; i++) {
ths[i] = new MyTh(seats, i);
ths[i].start();
}
Following is the full running code.
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicReference;
public class Solution {
static List<AtomicReference<Integer>> seats;// Movie seats numbered as per
// list index
public static void main(String[] args) throws InterruptedException {
// TODO Auto-generated method stub
seats = new ArrayList<>();
for (int i = 0; i < 20; i++) {// 20 seats
seats.add(new AtomicReference<Integer>());
}
Thread[] ths = new Thread[22];// 22 users
for (int i = 0; i < ths.length; i++) {
ths[i] = new MyTh(seats, i);
ths[i].start();
}
for (Thread t : ths) {
t.join();
}
for (AtomicReference<Integer> seat : seats) {
System.out.print(" " + seat.get());
}
}
/**
* id is the id of the user
*
* #author sankbane
*
*/
static class MyTh extends Thread {// each thread is a user
static AtomicInteger full = new AtomicInteger(0);
List<AtomicReference<Integer>> l;//seats
int id;//id of the users
int seats;
public MyTh(List<AtomicReference<Integer>> list, int userId) {
l = list;
this.id = userId;
seats = list.size();
}
#Override
public void run() {
boolean reserved = false;
try {
while (!reserved && full.get() < seats) {
Thread.sleep(50);
int r = ThreadLocalRandom.current().nextInt(0, seats);// excludes
// seats
//
AtomicReference<Integer> el = l.get(r);
reserved = el.compareAndSet(null, id);// null means no user
// has reserved this
// seat
if (reserved)
full.getAndIncrement();
}
if (!reserved && full.get() == seats)
System.out.println("user " + id + " did not get a seat");
} catch (InterruptedException ie) {
// log it
}
}
}
}

AtomicReference has two fields:-
* value, which is the reference
* valueOffset, which is the position of value in bytes from 'this', i.e. the AtomicReference
In compareAndSwap(expected, updated), the object at this-location + valueOffset is compared using == semantics with "expected", and if ==, then updated with "updated".
This is a single hardware instruction, and thus guaranteed to update or fail with false return atomically.
Read Unsafe source code from openJDK.

How to populate concurrenthashmap from multiple threads?

I have a ConcurrentHashMap which I am populating from multiple threads.
private static Map<DataCode, Long> errorMap = new ConcurrentHashMap<DataCode, Long>();
public static void addError(DataCode error) {
if (errorMap.keySet().contains(error)) {
errorMap.put(error, errorMap.get(error) + 1);
} else {
errorMap.put(error, 1L);
}
}
My above addError method is called from multiple threads which populates errorMap. I am not sure whether this is thread safe? Is there anything wrong I am doing here?
Any explanation of why it can skip updates will help me to understand better.

Whether this is safe depends on what you mean. It won't throw exceptions or corrupt the map, but it can skip updates. Consider:
Thread1: errorMap.get(error) returns 1
Thread2: errorMap.get(error) returns 1
Thread1: errorMap.put(error, 1+1);
Thread2: errorMap.put(error, 1+1);
A similar race exists around the keySet().contains(error) operation. To fix this you'll need to use atomic operations to update the map.
On Java 8, this is easy:
errorMap.compute(error, oldValue -> oldValue == null ? 1L : oldValue + 1L);
On older versions of Java you need to use a compare-and-update loop:
Long prevValue;
boolean done;
do {
prevValue = errorMap.get(error);
if (prevValue == null) {
done = errorMap.putIfAbsent(error, 1L);
} else {
done = errorMap.replace(error, prevValue, newValue);
}
} while (!done);
With this code, if two threads race one may end up retrying its update, but they'll get the right value in the end.
Alternately, you can also use Guava's AtomicLongMap which does all the thread-safety magic for you and gets higher performance (by avoiding all those boxing operations, among other things):
errorAtomicLongMap.incrementAndGet(error);

Atomically incrementing counters stored in ConcurrentHashMap

I would like to collect some metrics from various places in a web app. To keep it simple, all these will be counters and therefore the only modifier operation is to increment them by 1.
The increments will be concurrent and often. The reads (dumping the stats) is a rare operation.
I was thinking to use a ConcurrentHashMap. The issue is how to increment the counters correctly. Since the map doesn't have an "increment" operation, I need to read the current value first, increment it than put the new value in the map. Without more code, this is not an atomic operation.
Is it possible to achieve this without synchronization (which would defeat the purpose of the ConcurrentHashMap)? Do I need to look at Guava ?
Thanks for any pointers.
P.S.
There is a related question on SO (Most efficient way to increment a Map value in Java) but focused on performance and not multi-threading
UPDATE
For those arriving here through searches on the same topic: besides the answers below, there's a useful presentation which incidentally covers the same topic. See slides 24-33.

In Java 8:
ConcurrentHashMap<String, LongAdder> map = new ConcurrentHashMap<>();
map.computeIfAbsent("key", k -> new LongAdder()).increment();

Guava's new AtomicLongMap (in release 11) might address this need.

You're pretty close. Why don't you try something like a ConcurrentHashMap<Key, AtomicLong>?
If your Keys (metrics) are unchanging, you could even just use a standard HashMap (they are threadsafe if readonly, but you'd be well advised to make this explicit with an ImmutableMap from Google Collections or Collections.unmodifiableMap, etc.).
This way, you can use map.get(myKey).incrementAndGet() to bump statistics.

Other than going with AtomicLong, you can do the usual cas-loop thing:
private final ConcurrentMap<Key,Long> counts =
new ConcurrentHashMap<Key,Long>();
public void increment(Key key) {
if (counts.putIfAbsent(key, 1)) == null) {
return;
}
Long old;
do {
old = counts.get(key);
} while (!counts.replace(key, old, old+1)); // Assumes no removal.
}
(I've not written a do-while loop for ages.)
For small values the Long will probably be "cached". For longer values, it may require allocation. But the allocations are actually extremely fast (and you can cache further) - depends upon what you expect, in the worst case.

Got a necessity to do the same.
I'm using ConcurrentHashMap + AtomicInteger.
Also, ReentrantRW Lock was introduced for atomic flush(very similar behavior).
Tested with 10 Keys and 10 Threads per each Key. Nothing was lost.
I just haven't tried several flushing threads yet, but hope it will work.
Massive singleusermode flush is torturing me...
I want to remove RWLock and break down flushing into small pieces. Tomorrow.
private ConcurrentHashMap<String,AtomicInteger> counters = new ConcurrentHashMap<String, AtomicInteger>();
private ReadWriteLock rwLock = new ReentrantReadWriteLock();
public void count(String invoker) {
rwLock.readLock().lock();
try{
AtomicInteger currentValue = counters.get(invoker);
// if entry is absent - initialize it. If other thread has added value before - we will yield and not replace existing value
if(currentValue == null){
// value we want to init with
AtomicInteger newValue = new AtomicInteger(0);
// try to put and get old
AtomicInteger oldValue = counters.putIfAbsent(invoker, newValue);
// if old value not null - our insertion failed, lets use old value as it's in the map
// if old value is null - our value was inserted - lets use it
currentValue = oldValue != null ? oldValue : newValue;
}
// counter +1
currentValue.incrementAndGet();
}finally {
rwLock.readLock().unlock();
}
}
/**
* #return Map with counting results
*/
public Map<String, Integer> getCount() {
// stop all updates (readlocks)
rwLock.writeLock().lock();
try{
HashMap<String, Integer> resultMap = new HashMap<String, Integer>();
// read all Integers to a new map
for(Map.Entry<String,AtomicInteger> entry: counters.entrySet()){
resultMap.put(entry.getKey(), entry.getValue().intValue());
}
// reset ConcurrentMap
counters.clear();
return resultMap;
}finally {
rwLock.writeLock().unlock();
}
}

I did a benchmark to compare the performance of LongAdder and AtomicLong.
LongAdder had a better performance in my benchmark: for 500 iterations using a map with size 100 (10 concurrent threads), the average time for LongAdder was 1270ms while that for AtomicLong was 1315ms.

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
}
}