Develop Reference

Atomic updates of values in concurrent hash map - how to?

Task is to keep track of some running processes. Keeping that information in memory is just fine, so I'm using a concurrent hash map to store that data:
ConcurrentHashMap<String, ProcessMetaData> RUNNING_PROCESSES = new ConcurrentHashMap();
It's all good and fine with safely putting new objects in the map, problem is that state of those processes change so I have to update ProcessMetaData from time to time. I made ProcessMetaData immutable and use ConcurrentHashMap's compute() method to update values, but now the problem is ProcessMetaData gets more complicated and keeping it immutable gets hardly manageable. The question is - as long as I only update ProcessMetaData in atomic (as per javadoc) compute() method - the object may be mutable and overall things will still be thread-safe? Is my assumption correct?

As long as you only access the value within the function passed to compute, modifications made in that function are safe.
This, however, is a pointless theoretical view. The purpose of storing values into a collection or map, is to eventually retrieve and use them. And this is where the problems start.
The compute method returns the result value just like get returns the currently stored value. Once a caller starts using that value, this use may be concurrent to subsequent compute operations on the map. The get method may even retrieve the value while a compute operation is in progress. Allowing non-blocking retrieval operation is one of ConcurrentHashMap’s main features. Therefore, all kind of race conditions may occur.
So, using a mutable object and modifying an already stored value in compute is only safe, when you use the map as write-only memory, which is a far-fetched scenario. It might work when you use a different thread safe mechanism to ensure that all updates have been completed before starting to read the map, but your use case seems to be different.

Is Hashmap's containsKey method threadsafe if the map is initialized once, and is never modified again

Can we use Hashmap's containsKey() method without synchronizing in an multi-threaded environment?
Note: Threads are only going to read the Hashmap. The map is initialized once, and is never modified again.

It really depends on how/when your map is accessed.
Assuming the map is initialized once, and never modified again, then methods that don't modify the internal state like containsKey() should be safe.
In this case though, you should make sure your map really is immutable, and is published safely.
Now if in your particular case the state does change during the course of your program, then no, it is not safe.
From the documentation:
Note that this implementation is not synchronized.
If multiple threads access a hash map concurrently, and at least one of the threads modifies the map structurally, it must be synchronized externally.
In this case, you should use ConcurrentHashMap, or synchronize externally.

You shouldn't look at a single method this way. A HashMap is not meant to be used in a multi-threaded setup.
Having said that, the one exception would be: a map that gets created once (single threaded), and afterwards is "read" only. In other words: if a map doesn't get changed anymore, then you can have as many threads reading it as you want.
From that point of view, just containsKey() calls shouldn't call a problem. The problem arises when the information that this method relies on changes over time.

No, it is not thread-safe for any operations. You need to synchronise all access or use something like ConcurrentHashMap.
My favourite production system troubleshooting horror story is when we found that HashMap.get went into an infinite loop locking up 100% CPU forever because of missing synchronisation. This happened because the linked lists that were used within each bucket got into an inconsistent state. The same could happen with containsKey.
You should be safe if no one modifies the HashMap after it has been initially published, but better use an implementation that guarantees this explicitly (such as ImmutableMap or, again, a ConcurrentMap).

No. (No it is not. Not at all. 30 characters?)

It's complicated, but, mostly, no.
The spec of HashMap makes no guarantees whatsoever. It therefore reserves the right to blast yankee doodle dandy from your speakers if you try: You're just not supposed to use it that way.
... however, in practice, whilst the API of HashMap makes no guarantees, generally it works out. But, mind the horror story of #Thilo's answer.
... buuut, the Java Memory Model works like this: You should consider that each thread gets an individual copy of each and every field across the entire heap of the VM. These individual copies are then synced up at indeterminate times. That means that all sorts of code simply isn't going to work right; you add an entry to the map from one thread, and if you then access that map from another you won't see it even though a lot of time has passed – that's theoretically possible. Also, internally, map uses multiple fields and presumably these fields must be consistent with each other or you'll get weird behaviours (exceptions and wrong results). The JMM makes no guarantees about consistency either. The way out of this dilemma is that the JMM offers these things called 'comes-before/comes-after' relationships which give you guarantees that changes have been synced up. Using the 'synchronized' keyword is one easy way to get such relationships going.
Why not use a ConcurrentHashMap which has all the bells and whistles built in and does in fact guarantee that adding an entry from thread A and then querying it via containsKey from thread B will get you a consistent answer (which might still be 'no, that key is not in the map', because perhaps thread B got there slightly before thread A or slightly after but there's no way for you to know. It won't throw any exceptions or do something really bizarre such as returning 'false' for things you added ages ago all of a sudden).
So, whilst it's complicated, the answer is basically: Don't do that; either use a synchronized guard, or, probably the better choice: ConcurrentHashMap.

No, Read the bold part of HashMap documentation:
Note that this implementation is not synchronized.
So you should handle it:
If multiple threads access a hash map concurrently, and at least one of the threads modifies the map structurally, it must be synchronized externally.
And suggested solutions:
This is typically accomplished by synchronizing on some object that naturally encapsulates the map. If no such object exists, the map should be "wrapped" using the Collections.synchronizedMap method

#user7294900 is right.
If your application does not modifies the HashMap structurally which is build thread-safely and your application just invoke containsKey method, it's thread safe.
For instance, I've used HashMap like this:
#Component
public class SpringSingletonBean {
private Map<String, String> map = new HashMap<>();
public void doSomething() {
//
if (map.containsKey("aaaa")) {
//do something
}
}
#PostConstruct
public void init() {
// do something to initialize the map
}
}
It works well.

How do I get the latest view of a ConcurrentHashMap?

You can ensure that changes one thread makes to a variable can be seen on other threads by making the variable volatile, or by having both threads synchronize on something. If the thing being changed is a java.util.ConcurrentHashMap, does it make sense to create a memory barrier by declaring the type of the variable holding this map as volatile, or are readers accessing the map (say via myMap.values()) going to get the latest possible view anyway? For context I have a heavy reading, light writing scenario where I am switching my lock free read solution to a ConcurrentHashMap.

ConcurrentHashMap guarantees that there is a happens-before relationship between writes and subsequent reads. So yes, when you read (get), you will see the most recent changes that have been "committed" (put has returned).
Note: this does not apply to iterators as explained in the javadoc.

The variable "holding" the map is a reference or pointer to the map object (respectively (simplified) to the memory address where the map is stored). Making it volatile would only affect the pointer, not the map object itself. As long as you always use the same Map-Object and ensure that the map is fully initialized before the threads use it, you don't have to use "volatile references" to it. The concurrency is handled transparently inside the concurrent hash map.

Yes, ConcurrentHashMap gives the latest views. If you refer the javadocs at http://docs.oracle.com/javase/6/docs/api/java/util/concurrent/ConcurrentHashMap.html#get(java.lang.Object)
it is clearly written that
Retrievals reflect the results of the most recently completed update
operations holding upon their onset
It has some more details and I would suggest you go and read it.
Besides, as already noted using volatile is not what you want as it will only affect the pointer and not the actual contents of the map.

All you need to do is make sure that the reference holding the map is final, so you get a final field fence that guarantees you see a properly initialised map and that the reference itself is not changed.
As others point out, ConcurrentHashMap will guarantee visibility/happens-before of writes internally, as all of the java.util.concurrent.* collections do. You should however use the conditional writes exposed on the ConcurrentMap interface to avoid data-races in your writes.

In Java can I depend on reference assignment being atomic to implement copy on write?

If I have an unsynchronized java collection in a multithreaded environment, and I don't want to force readers of the collection to synchronize[1], is a solution where I synchronize the writers and use the atomicity of reference assignment feasible? Something like:
private Collection global = new HashSet(); // start threading after this
void allUpdatesGoThroughHere(Object exampleOperand) {
// My hypothesis is that this prevents operations in the block being re-ordered
synchronized(global) {
Collection copy = new HashSet(global);
copy.remove(exampleOperand);
// Given my hypothesis, we should have a fully constructed object here. So a
// reader will either get the old or the new Collection, but never an
// inconsistent one.
global = copy;
}
}
// Do multithreaded reads here. All reads are done through a reference copy like:
// Collection copy = global;
// for (Object elm: copy) {...
// so the global reference being updated half way through should have no impact
Rolling your own solution seems to often fail in these type of situations, so I'd be interested in knowing other patterns, collections or libraries I could use to prevent object creation and blocking for my data consumers.
[1] The reasons being a large proportion of time spent in reads compared to writes, combined with the risk of introducing deadlocks.
Edit: A lot of good information in several of the answers and comments, some important points:
A bug was present in the code I posted. Synchronizing on global (a badly named variable) can fail to protect the syncronized block after a swap.
You could fix this by synchronizing on the class (moving the synchronized keyword to the method), but there may be other bugs. A safer and more maintainable solution is to use something from java.util.concurrent.
There is no "eventual consistency guarantee" in the code I posted, one way to make sure that readers do get to see the updates by writers is to use the volatile keyword.
On reflection the general problem that motivated this question was trying to implement lock free reads with locked writes in java, however my (solved) problem was with a collection, which may be unnecessarily confusing for future readers. So in case it is not obvious the code I posted works by allowing one writer at a time to perform edits to "some object" that is being read unprotected by multiple reader threads. Commits of the edit are done through an atomic operation so readers can only get the pre-edit or post-edit "object". When/if the reader thread gets the update, it cannot occur in the middle of a read as the read is occurring on the old copy of the "object". A simple solution that had probably been discovered and proved to be broken in some way prior to the availability of better concurrency support in java.

Rather than trying to roll out your own solution, why not use a ConcurrentHashMap as your set and just set all the values to some standard value? (A constant like Boolean.TRUE would work well.)
I think this implementation works well with the many-readers-few-writers scenario. There's even a constructor that lets you set the expected "concurrency level".
Update: Veer has suggested using the Collections.newSetFromMap utility method to turn the ConcurrentHashMap into a Set. Since the method takes a Map<E,Boolean> my guess is that it does the same thing with setting all the values to Boolean.TRUE behind-the-scenes.
Update: Addressing the poster's example
That is probably what I will end up going with, but I am still curious about how my minimalist solution could fail. – MilesHampson
Your minimalist solution would work just fine with a bit of tweaking. My worry is that, although it's minimal now, it might get more complicated in the future. It's hard to remember all of the conditions you assume when making something thread-safe—especially if you're coming back to the code weeks/months/years later to make a seemingly insignificant tweak. If the ConcurrentHashMap does everything you need with sufficient performance then why not use that instead? All the nasty concurrency details are encapsulated away and even 6-months-from-now you will have a hard time messing it up!
You do need at least one tweak before your current solution will work. As has already been pointed out, you should probably add the volatile modifier to global's declaration. I don't know if you have a C/C++ background, but I was very surprised when I learned that the semantics of volatile in Java are actually much more complicated than in C. If you're planning on doing a lot of concurrent programming in Java then it'd be a good idea to familiarize yourself with the basics of the Java memory model. If you don't make the reference to global a volatile reference then it's possible that no thread will ever see any changes to the value of global until they try to update it, at which point entering the synchronized block will flush the local cache and get the updated reference value.
However, even with the addition of volatile there's still a huge problem. Here's a problem scenario with two threads:
We begin with the empty set, or global={}. Threads A and B both have this value in their thread-local cached memory.
Thread A obtains obtains the synchronized lock on global and starts the update by making a copy of global and adding the new key to the set.
While Thread A is still inside the synchronized block, Thread B reads its local value of global onto the stack and tries to enter the synchronized block. Since Thread A is currently inside the monitor Thread B blocks.
Thread A completes the update by setting the reference and exiting the monitor, resulting in global={1}.
Thread B is now able to enter the monitor and makes a copy of the global={1} set.
Thread A decides to make another update, reads in its local global reference and tries to enter the synchronized block. Since Thread B currently holds the lock on {} there is no lock on {1} and Thread A successfully enters the monitor!
Thread A also makes a copy of {1} for purposes of updating.
Now Threads A and B are both inside the synchronized block and they have identical copies of the global={1} set. This means that one of their updates will be lost! This situation is caused by the fact that you're synchronizing on an object stored in a reference that you're updating inside your synchronized block. You should always be very careful which objects you use to synchronize. You can fix this problem by adding a new variable to act as the lock:
private volatile Collection global = new HashSet(); // start threading after this
private final Object globalLock = new Object(); // final reference used for synchronization
void allUpdatesGoThroughHere(Object exampleOperand) {
// My hypothesis is that this prevents operations in the block being re-ordered
synchronized(globalLock) {
Collection copy = new HashSet(global);
copy.remove(exampleOperand);
// Given my hypothesis, we should have a fully constructed object here. So a
// reader will either get the old or the new Collection, but never an
// inconsistent one.
global = copy;
}
}
This bug was insidious enough that none of the other answers have addressed it yet. It's these kinds of crazy concurrency details that cause me to recommend using something from the already-debugged java.util.concurrent library rather than trying to put something together yourself. I think the above solution would work—but how easy would it be to screw it up again? This would be so much easier:
private final Set<Object> global = Collections.newSetFromMap(new ConcurrentHashMap<Object,Boolean>());
Since the reference is final you don't need to worry about threads using stale references, and since the ConcurrentHashMap handles all the nasty memory model issues internally you don't have to worry about all the nasty details of monitors and memory barriers!

According to the relevant Java Tutorial,
We have already seen that an increment expression, such as c++, does not describe an atomic action. Even very simple expressions can define complex actions that can decompose into other actions. However, there are actions you can specify that are atomic:
Reads and writes are atomic for reference variables and for most primitive variables (all types except long and double).
Reads and writes are atomic for all variables declared volatile (including long and double variables).
This is reaffirmed by Section §17.7 of the Java Language Specification
Writes to and reads of references are always atomic, regardless of whether they are implemented as 32-bit or 64-bit values.
It appears that you can indeed rely on reference access being atomic; however, recognize that this does not ensure that all readers will read an updated value for global after this write -- i.e. there is no memory ordering guarantee here.
If you use an implicit lock via synchronized on all access to global, then you can forge some memory consistency here... but it might be better to use an alternative approach.
You also appear to want the collection in global to remain immutable... luckily, there is Collections.unmodifiableSet which you can use to enforce this. As an example, you should likely do something like the following...
private volatile Collection global = Collections.unmodifiableSet(new HashSet());
... that, or using AtomicReference,
private AtomicReference<Collection> global = new AtomicReference<>(Collections.unmodifiableSet(new HashSet()));
You would then use Collections.unmodifiableSet for your modified copies as well.
// ... All reads are done through a reference copy like:
// Collection copy = global;
// for (Object elm: copy) {...
// so the global reference being updated half way through should have no impact
You should know that making a copy here is redundant, as internally for (Object elm : global) creates an Iterator as follows...
final Iterator it = global.iterator();
while (it.hasNext()) {
Object elm = it.next();
}
There is therefore no chance of switching to an entirely different value for global in the midst of reading.
All that aside, I agree with the sentiment expressed by DaoWen... is there any reason you're rolling your own data structure here when there may be an alternative available in java.util.concurrent? I figured maybe you're dealing with an older Java, since you use raw types, but it won't hurt to ask.
You can find copy-on-write collection semantics provided by CopyOnWriteArrayList, or its cousin CopyOnWriteArraySet (which implements a Set using the former).
Also suggested by DaoWen, have you considered using a ConcurrentHashMap? They guarantee that using a for loop as you've done in your example will be consistent.
Similarly, Iterators and Enumerations return elements reflecting the state of the hash table at some point at or since the creation of the iterator/enumeration.
Internally, an Iterator is used for enhanced for over an Iterable.
You can craft a Set from this by utilizing Collections.newSetFromMap like follows:
final Set<E> safeSet = Collections.newSetFromMap(new ConcurrentHashMap<E, Boolean>());
...
/* guaranteed to reflect the state of the set at read-time */
for (final E elem : safeSet) {
...
}

I think your original idea was sound, and DaoWen did a good job getting the bugs out. Unless you can find something that does everything for you, it's better to understand these things than hope some magical class will do it for you. Magical classes can make your life easier and reduce the number of mistakes, but you do want to understand what they are doing.
ConcurrentSkipListSet might do a better job for you here. It could get rid of all your multithreading problems.
However, it is slower than a HashSet (usually--HashSets and SkipLists/Trees hard to compare). If you are doing a lot of reads for every write, what you've got will be faster. More importantly, if you update more than one entry at a time, your reads could see inconsistent results. If you expect that whenever there is an entry A there is an entry B, and vice versa, the skip list could give you one without the other.
With your current solution, to the readers, the contents of the map are always internally consistent. A read can be sure there's an A for every B. It can be sure that the size() method gives the precise number of elements that will be returned by the iterator. Two iterations will return the same elements in the same order.
In other words, allUpdatesGoThroughHere and ConcurrentSkipListSet are two good solutions to two different problems.

Can you use the Collections.synchronizedSet method? From HashSet Javadoc http://docs.oracle.com/javase/6/docs/api/java/util/HashSet.html
Set s = Collections.synchronizedSet(new HashSet(...));

Replace the synchronized by making global volatile and you'll be alright as far as the copy-on-write goes.
Although the assignment is atomic, in other threads it is not ordered with the writes to the object referenced. There needs to be a happens-before relationship which you get with a volatile or synchronising both reads and writes.
The problem of multiple updates happening at once is separate - use a single thread or whatever you want to do there.
If you used a synchronized for both reads and writes then it'd be correct but the performance may not be great with reads needing to hand-off. A ReadWriteLock may be appropriate, but you'd still have writes blocking reads.
Another approach to the publication issue is to use final field semantics to create an object that is (in theory) safe to be published unsafely.
Of course, there are also concurrent collections available.

Why ConcurrentHashMap.putifAbsent is safe?

I have been reading for concurency since yesterday and i dont know much things... However some things are starting to getting clear...
I understand why double check locking isnt safe (i wonder what is the propability the rare condition to occur) but volatile fixes the issue in 1.5 +....
But i wonder if this occurs with putifAbsent
like...
myObj = new myObject("CodeMonkey");
cHashM.putIfAbsent("keyy",myObj);
Then does this ensures that myObj would be 100% intialiased when another thread does a cHashM.get() ??? Because it could have a reference isnt completely initialised (the double check lock problem)

If you invoke concurrentHashMap.get(key) and it returns an object, that object is guaranteed to be fully initialized. Each put (or putIfAbsent) will obtain a bucket specific lock and will append the element to the bucket's entries.
Now you may go through the code and notice that the get method doesnt obtain this same lock. So you can argue that there can be an out of date read, that isn't true either. The reason here is that value within the entry itself is volatile. So you will be sure to get the most up to date read.

putIfAbsent method in ConcurrentHashMap is check-if-absent-then-set method. It's an atomic operation. But to answer the following part: "Then does this ensures that myObj would be 100% intialiased when another thread does a cHashM.get() ", it would depend on when the object is put into the HashMap. Usually there is a happens-before precedence, i.e., if the caller gets first before the object is placed in the map, then null would be returned, else the value would be returned.

The relevant part of the documentation is this:
Memory consistency effects: As with
other concurrent collections, actions
in a thread prior to placing an object
into a ConcurrentMap as a key or value
happen-before actions subsequent to
the access or removal of that object
from the ConcurrentMap in another
thread.
-- java.util.ConcurrentMap
So, yes you have your happens-before relationship.

I'm not an expert on this, but looking at the implementation of Segment in ConcurrentHashMap I see that the volatile field count appears to be used to ensure proper visibility between threads. All read operations have to read the count field and all write operations have to write to it. From comments in the class:
Read operations can thus proceed without locking, but rely
on selected uses of volatiles to ensure that completed
write operations performed by other threads are
noticed. For most purposes, the "count" field, tracking the
number of elements, serves as that volatile variable
ensuring visibility. This is convenient because this field
needs to be read in many read operations anyway:
- All (unsynchronized) read operations must first read the
"count" field, and should not look at table entries if
it is 0.
- All (synchronized) write operations should write to
the "count" field after structurally changing any bin.
The operations must not take any action that could even
momentarily cause a concurrent read operation to see
inconsistent data. This is made easier by the nature of
the read operations in Map. For example, no operation
can reveal that the table has grown but the threshold
has not yet been updated, so there are no atomicity
requirements for this with respect to reads.