Related
Following up on this question (Java thread safety - multiple atomic operations?), I don't want to add more questions to it, but now I have this doubt:
private final Map<String, Set<String>> data = Maps.newConcurrentMap();
... then in a method ...
if (data.containsKey("A")) {
data.get("A").add("B");
}
It should be something like this:
synchronized(data) {
if (data.containsKey("A")) {
data.get("A").add("B");
}
}
In order to be thread-safe. Is that correct?
So operations are atomic, but combining them would require synchronisation, is that right? At that point, would it make sense to just use a simple HashMap instead of a concurrent one, as we're manually handling sync?
Is there any method in CHM to make this work atomically?
In your specific case, you might want to use computeIfPresent method of ConcurrentHashMap:
data.computeIfPresent("A", (k, v) -> { v.add("B"); return v; } );
From the javadocs:
If the value for the specified key is present, attempts to compute a new mapping given the key and its current mapped value. The entire method invocation is performed atomically.
So there's no need for explicit synchronization.
synchronised(data) {
if (data.containsKey("A")) {
data.get("A").add("B");
}
}
You probably need to show more code.
Looking only at this, the only possible issue is that someone removes the Set found at "A" after your if check. If you don't ever remove map entries you need no synchronization at all.
If you do remove map entries concurrently, you could use computeIfPresent to arrive at the updated map.
You could also do
Set<String> set = data.get("A");
if (set != null) set.add("B");
Since you are not actually producting a new Set, I find this more idiomatic than computeIfPresent (which should compute a new value).
Note that you need to make all these Sets thread-safe as well.
I am a bit confused regarding one pattern I have seen in some legacy code of ours.
The controller uses a map as a cache, with an approach that should be thread safe, however I am still not confident it indeed is. We have a map, which is properly synchronized during addition and retrieval, however, there is a bit of logic outside of the synchronized block, that does some additional filtering.
(the map itself and the lists are never accessed outside of this method, so concurrent modification is not an issue; the map holds some stable parameters, which basically never change, but are used often).
The code looks like the following sample:
public class FooBarController {
private final Map<String, List<FooBar>> fooBarMap =
new HashMap<String, List<FooBar>>();
public FooBar getFooBar(String key, String foo, String bar) {
List<FooBar> foobarList;
synchronized (fooBarMap) {
if (fooBarMap.get(key) == null) {
foobarList = queryDbByKey(key);
fooBarMap.put(key, foobarList);
} else {
foobarList = fooBarMap.get(key);
}
}
for(FooBar fooBar : foobarList) {
if(foo.equals(fooBar.getFoo()) && bar.equals(fooBar.getBar()))
return fooBar;
}
return null;
}
private List<FooBar> queryDbByKey(String key) {
// ... (simple Hibernate-query)
}
// ...
}
Based on what I know about the JVM memory model, this should be fine, since if one thread populates a list, another one can only retrieve it from the map with proper synchronization in place, ensuring that the entries of the list is visible. (putting the list happens-before getting it)
However, we keep seeing cases, where an entry expected to be in the map is not found, combined with the typical notorious symptoms of concurrency issues (e.g. intermittent failures in production, which I cannot reproduce in my development environment; different threads can properly retrieve the value etc.)
I am wondering if iterating through the elements of the List like this is thread-safe?
The code you provided is correct in terms of concurrency. Here are the guarantees:
only one thread at a time adds values to map, because of synchronization on map object
values added by thread become visible for all other threads, that enter synchronized block
Given that, you can be sure that all threads that iterate a list see the same elements. The issues you described are indeed strange but I doubt they're related to the code you provided.
It could be thread safe only if all access too fooBarMap are synchronized. A little out of scope, but safer may be to use a ConcurrentHashmap.
There is a great article on how hashmaps can be synchronized here.
In situation like this it's best option to use ConcurrentHashMap.
Verify if all Update-Read are in order.
As I understood from your question. There are fix set of params which never changes. One of the ways I preferred in situation like this is:
I. To create the map cache during start up and keep only one instance of it.
II. Read the map Instance anytime anywhere in the application.
In the for loop you are returning reference to fooBar objects in the foobarList.
So the method calling getFooBar() has access to the Map through this fooBar reference object.
try to clone fooBar before returning from getFooBar()
EDIT: I've reorganized this question to reflect the new information that since became available.
This question is based on the responses to a question by Viliam concerning Guava Maps' use of lazy eviction: Laziness of eviction in Guava's maps
Please read this question and its response first, but essentially the conclusion is that Guava maps do not asynchronously calculate and enforce eviction. Given the following map:
ConcurrentMap<String, MyObject> cache = new MapMaker()
.expireAfterAccess(10, TimeUnit.MINUTES)
.makeMap();
Once ten minutes has passed following access to an entry, it will still not be evicted until the map is "touched" again. Known ways to do this include the usual accessors - get() and put() and containsKey().
The first part of my question [solved]: what other calls cause the map to be "touched"? Specifically, does anyone know if size() falls into this category?
The reason for wondering this is that I've implemented a scheduled task to occasionally nudge the Guava map I'm using for caching, using this simple method:
public static void nudgeEviction() {
cache.containsKey("");
}
However I'm also using cache.size() to programmatically report the number of objects contained in the map, as a way to confirm this strategy is working. But I haven't been able to see a difference from these reports, and now I'm wondering if size() also causes eviction to take place.
Answer: So Mark has pointed out that in release 9, eviction is invoked only by the get(), put(), and replace() methods, which would explain why I wasn't seeing an effect for containsKey(). This will apparently change with the next version of guava which is set for release soon, but unfortunately my project's release is set sooner.
This puts me in an interesting predicament. Normally I could still touch the map by calling get(""), but I'm actually using a computing map:
ConcurrentMap<String, MyObject> cache = new MapMaker()
.expireAfterAccess(10, TimeUnit.MINUTES)
.makeComputingMap(loadFunction);
where loadFunction loads the MyObject corresponding to the key from a database. It's starting to look like I have no easy way of forcing eviction until r10. But even being able to reliably force eviction is put into doubt by the second part of my question:
The second part of my question [solved]: In reaction to one of the responses to the linked question, does touching the map reliably evict all expired entries? In the linked answer, Niraj Tolia indicates otherwise, saying eviction is potentially only processed in batches, which would mean multiple calls to touch the map might be needed to ensure all expired objects were evicted. He did not elaborate, however this seems related to the map being split into segments based on concurrency level. Assuming I used r10, in which a containsKey("") does invoke eviction, would this then be for the entire map, or only for one of the segments?
Answer: maaartinus has addressed this part of the question:
Beware that containsKey and other reading methods only run postReadCleanup, which does nothing but on each 64th invocation (see DRAIN_THRESHOLD). Moreover, it looks like all cleanup methods work with single Segment only.
So it looks like calling containsKey("") wouldn't be a viable fix, even in r10. This reduces my question to the title: How can I reliably force eviction to occur?
Note: Part of the reason my web app is noticeably affected by this issue is that when I implemented caching I decided to use multiple maps - one for each class of my data objects. So with this issue there is the possibility that one area of code is executed, causing a bunch of Foo objects to be cached, and then the Foo cache isn't touched again for a long time so it doesn't evict anything. Meanwhile Bar and Baz objects are being cached from other areas of code, and memory is being eaten. I'm setting a maximum size on these maps, but this is a flimsy safeguard at best (I'm assuming its effect is immediate - still need to confirm this).
UPDATE 1: Thanks to Darren for linking the relevant issues - they now have my votes. So it looks like a resolution is in the pipeline, but seems unlikely to be in r10. In the meantime, my question remains.
UPDATE 2: At this point I'm just waiting for a Guava team member to give feedback on the hack maaartinus and I put together (see answers below).
LAST UPDATE: feedback received!
I just added the method Cache.cleanUp() to Guava. Once you migrate from MapMaker to CacheBuilder you can use that to force eviction.
I was wondering the about the same issue you described in the first part of your question. From what I can tell from looking at the source code for Guava's CustomConcurrentHashMap (release 9), it appears that entries are evicted on the get(), put(), and replace() methods. The containsKey() method does not appear to invoke eviction. I'm not 100% sure because I took a quick pass at the code.
Update:
I also found a more recent version of the CustomConcurrentHashmap in Guava's git repository and it looks like containsKey() has been updated to invoke eviction.
Both release 9 and the latest version I just found do not invoke eviction when size() is called.
Update 2:
I recently noticed that Guava r10 (yet to be released) has a new class called CacheBuilder. Basically this class is a forked version of the MapMaker but with caching in mind. The documentation suggests that it will support some of the eviction requirements you are looking for.
I reviewed the updated code in r10's version of the CustomConcurrentHashMap and found what looks like a scheduled map cleaner. Unfortunately, that code appears unfinished at this point but r10 looks more and more promising each day.
Beware that containsKey and other reading methods only run postReadCleanup, which does nothing but on each 64th invocation (see DRAIN_THRESHOLD). Moreover, it looks like all cleanup methods work with single Segment only.
The easiest way to enforce eviction seems to be to put some dummy object into each segment. For this to work, you'd need to analyze CustomConcurrentHashMap.hash(Object), which is surely no good idea, as this method may change anytime. Moreover, depending on the key class it may be hard to find a key with a hashCode ensuring it lands in a given segment.
You could use reads instead, but would have to repeat them 64 times per segment. Here, it'd easy to find a key with an appropriate hashCode, since here any object is allowed as an argument.
Maybe you could hack into the CustomConcurrentHashMap source code instead, it could be as trivial as
public void runCleanup() {
final Segment<K, V>[] segments = this.segments;
for (int i = 0; i < segments.length; ++i) {
segments[i].runCleanup();
}
}
but I wouldn't do it without a lot of testing and/or an OK by a guava team member.
Yep, we've gone back and forth a few times on whether these cleanup tasks should be done on a background thread (or pool), or should be done on user threads. If they were done on a background thread, this would eventually happen automatically; as it is, it'll only happen as each segment gets used. We're still trying to come up with the right approach here - I wouldn't be surprised to see this change in some future release, but I also can't promise anything or even make a credible guess as to how it will change. Still, you've presented a reasonable use case for some kind of background or user-triggered cleanup.
Your hack is reasonable, as long as you keep in mind that it's a hack, and liable to break (possibly in subtle ways) in future releases. As you can see in the source, Segment.runCleanup() calls runLockedCleanup and runUnlockedCleanup: runLockedCleanup() will have no effect if it can't lock the segment, but if it can't lock the segment it's because some other thread has the segment locked, and that other thread can be expected to call runLockedCleanup as part of its operation.
Also, in r10, there's CacheBuilder/Cache, analogous to MapMaker/Map. Cache is the preferred approach for many current users of makeComputingMap. It uses a separate CustomConcurrentHashMap, in the common.cache package; depending on your needs, you may want your GuavaEvictionHacker to work with both. (The mechanism is the same, but they're different Classes and therefore different Methods.)
I'm not a big fan of hacking into or forking external code until absolutely necessary. This problem occurs in part due to an early decision for MapMaker to fork ConcurrentHashMap, thereby dragging in a lot of complexity that could have been deferred until after the algorithms were worked out. By patching above MapMaker, the code is robust to library changes so that you can remove your workaround on your own schedule.
An easy approach is to use a priority queue of weak reference tasks and a dedicated thread. This has the drawback of creating many stale no-op tasks, which can become excessive in due to the O(lg n) insertion penalty. It works reasonably well for small, less frequently used caches. It was the original approach taken by MapMaker and its simple to write your own decorator.
A more robust choice is to mirror the lock amortization model with a single expiration queue. The head of the queue can be volatile so that a read can always peek to determine if it has expired. This allows all reads to trigger an expiration and an optional clean-up thread to check regularly.
By far the simplest is to use #concurrencyLevel(1) to force MapMaker to use a single segment. This reduces the write concurrency, but most caches are read heavy so the loss is minimal. The original hack to nudge the map with a dummy key would then work fine. This would be my preferred approach, but the other two options are okay if you have high write loads.
I don't know if it is appropriate for your use case, but your main concern about the lack of background cache eviction seems to be memory consumption, so I would have thought that using softValues() on the MapMaker to allow the Garbage Collector to reclaim entries from the cache when a low memory situation occurs. Could easily be the solution for you. I have used this on a subscription-server (ATOM) where entries are served through a Guava cache using SoftReferences for values.
Based on maaartinus's answer, I came up with the following code which uses reflection rather than directly modifying the source (If you find this useful please upvote his answer!). While it will come at a performance penalty for using reflection, the difference should be negligible since I'll run it about once every 20 minutes for each caching Map (I'm also caching the dynamic lookups in the static block which will help). I have done some initial testing and it appears to work as intended:
public class GuavaEvictionHacker {
//Class objects necessary for reflection on Guava classes - see Guava docs for info
private static final Class<?> computingMapAdapterClass;
private static final Class<?> nullConcurrentMapClass;
private static final Class<?> nullComputingConcurrentMapClass;
private static final Class<?> customConcurrentHashMapClass;
private static final Class<?> computingConcurrentHashMapClass;
private static final Class<?> segmentClass;
//MapMaker$ComputingMapAdapter#cache points to the wrapped CustomConcurrentHashMap
private static final Field cacheField;
//CustomConcurrentHashMap#segments points to the array of Segments (map partitions)
private static final Field segmentsField;
//CustomConcurrentHashMap$Segment#runCleanup() enforces eviction on the calling Segment
private static final Method runCleanupMethod;
static {
try {
//look up Classes
computingMapAdapterClass = Class.forName("com.google.common.collect.MapMaker$ComputingMapAdapter");
nullConcurrentMapClass = Class.forName("com.google.common.collect.MapMaker$NullConcurrentMap");
nullComputingConcurrentMapClass = Class.forName("com.google.common.collect.MapMaker$NullComputingConcurrentMap");
customConcurrentHashMapClass = Class.forName("com.google.common.collect.CustomConcurrentHashMap");
computingConcurrentHashMapClass = Class.forName("com.google.common.collect.ComputingConcurrentHashMap");
segmentClass = Class.forName("com.google.common.collect.CustomConcurrentHashMap$Segment");
//look up Fields and set accessible
cacheField = computingMapAdapterClass.getDeclaredField("cache");
segmentsField = customConcurrentHashMapClass.getDeclaredField("segments");
cacheField.setAccessible(true);
segmentsField.setAccessible(true);
//look up the cleanup Method and set accessible
runCleanupMethod = segmentClass.getDeclaredMethod("runCleanup");
runCleanupMethod.setAccessible(true);
}
catch (ClassNotFoundException cnfe) {
throw new RuntimeException("ClassNotFoundException thrown in GuavaEvictionHacker static initialization block.", cnfe);
}
catch (NoSuchFieldException nsfe) {
throw new RuntimeException("NoSuchFieldException thrown in GuavaEvictionHacker static initialization block.", nsfe);
}
catch (NoSuchMethodException nsme) {
throw new RuntimeException("NoSuchMethodException thrown in GuavaEvictionHacker static initialization block.", nsme);
}
}
/**
* Forces eviction to take place on the provided Guava Map. The Map must be an instance
* of either {#code CustomConcurrentHashMap} or {#code MapMaker$ComputingMapAdapter}.
*
* #param guavaMap the Guava Map to force eviction on.
*/
public static void forceEvictionOnGuavaMap(ConcurrentMap<?, ?> guavaMap) {
try {
//we need to get the CustomConcurrentHashMap instance
Object customConcurrentHashMap;
//get the type of what was passed in
Class<?> guavaMapClass = guavaMap.getClass();
//if it's a CustomConcurrentHashMap we have what we need
if (guavaMapClass == customConcurrentHashMapClass) {
customConcurrentHashMap = guavaMap;
}
//if it's a NullConcurrentMap (auto-evictor), return early
else if (guavaMapClass == nullConcurrentMapClass) {
return;
}
//if it's a computing map we need to pull the instance from the adapter's "cache" field
else if (guavaMapClass == computingMapAdapterClass) {
customConcurrentHashMap = cacheField.get(guavaMap);
//get the type of what we pulled out
Class<?> innerCacheClass = customConcurrentHashMap.getClass();
//if it's a NullComputingConcurrentMap (auto-evictor), return early
if (innerCacheClass == nullComputingConcurrentMapClass) {
return;
}
//otherwise make sure it's a ComputingConcurrentHashMap - error if it isn't
else if (innerCacheClass != computingConcurrentHashMapClass) {
throw new IllegalArgumentException("Provided ComputingMapAdapter's inner cache was an unexpected type: " + innerCacheClass);
}
}
//error for anything else passed in
else {
throw new IllegalArgumentException("Provided ConcurrentMap was not an expected Guava Map: " + guavaMapClass);
}
//pull the array of Segments out of the CustomConcurrentHashMap instance
Object[] segments = (Object[])segmentsField.get(customConcurrentHashMap);
//loop over them and invoke the cleanup method on each one
for (Object segment : segments) {
runCleanupMethod.invoke(segment);
}
}
catch (IllegalAccessException iae) {
throw new RuntimeException(iae);
}
catch (InvocationTargetException ite) {
throw new RuntimeException(ite.getCause());
}
}
}
I'm looking for feedback on whether this approach is advisable as a stopgap until the issue is resolved in a Guava release, particularly from members of the Guava team when they get a minute.
EDIT: updated the solution to allow for auto-evicting maps (NullConcurrentMap or NullComputingConcurrentMap residing in a ComputingMapAdapter). This turned out to be necessary in my case, since I'm calling this method on all of my maps and a few of them are auto-evictors.
I'm the following situation.
At web application startup I need to load a Map which is thereafter used by multiple incoming threads. That is, requests comes in and the Map is used to find out whether it contains a particular key and if so the value (the object) is retrieved and associated to another object.
Now, at times the content of the Map changes. I don't want to restart my application to reload the new situation. Instead I want to do this dynamically.
However, at the time the Map is re-loading (removing all items and replacing them with the new ones), concurrent read requests on that Map still arrive.
What should I do to prevent all read threads from accessing that Map while it's being reloaded ? How can I do this in the most performant way, because I only need this when the Map is reloading which will only occur sporadically (each every x weeks) ?
If the above is not an option (blocking) how can I make sure that while reloading my read request won't suffer from unexpected exceptions (because a key is no longer there, or a value is no longer present or being reloaded) ?
I was given the advice that a ReadWriteLock might help me out. Can you someone provide me an example on how I should use this ReadWriteLock with my readers and my writer ?
Thanks,
E
I suggest to handle this as follow:
Have your map accessible at a central place (could be a Spring singleton, a static ...).
When starting to reload, let the instance as is, work in a different Map instance.
When that new map is filled, replace the old map with this new one (that's an atomic operation).
Sample code:
static volatile Map<U, V> map = ....;
// **************************
Map<U, V> tempMap = new ...;
load(tempMap);
map = tempMap;
Concurrency effects :
volatile helps with visibility of the variable to other threads.
While reloading the map, all other threads see the old value undisturbed, so they suffer no penalty whatsoever.
Any thread that retrieves the map the instant before it is changed will work with the old values.
It can ask several gets to the same old map instance, which is great for data consistency (not loading the first value from the older map, and others from the newer).
It will finish processing its request with the old map, but the next request will ask the map again, and will receive the newer values.
If the client threads do not modify the map, i.e. the contents of the map is solely dependent on the source from where it is loaded, you can simply load a new map and replace the reference to the map your client threads are using once the new map is loaded.
Other then using twice the memory for a short time, no performance penalty is incurred.
In case the map uses too much memory to have 2 of them, you can use the same tactic per object in the map; iterate over the map, construct a new mapped-to object and replace the original mapping once the object is loaded.
Note that changing the reference as suggested by others could cause problems if you rely on the map being unchanged for a while (e.g. if (map.contains(key)) {V value = map.get(key); ...}. If you need that, you should keep a local reference to the map:
static Map<U,V> map = ...;
void do() {
Map<U,V> local = map;
if (local.contains(key)) {
V value = local.get(key);
...
}
}
EDIT:
The assumption is that you don't want costly synchronization for your client threads. As a trade-off, you allow client threads to finish their work that they've already begun before your map changed - ignoring any changes to the map that happened while it is running. This way, you can safely made some assumptions about your map - e.g. that a key is present and always mapped to the same value for the duration of a single request. In the example above, if your reader thread changed the map just after a client called map.contains(key), the client might get null on map.get(key) - and you'd almost certainly end this request with a NullPointerException. So if you're doing multiple reads to the map and need to do some assumptions as the one mentioned before, it's easiest to keep a local reference to the (maybe obsolete) map.
The volatile keyword isn't strictly necessary here. It would just make sure that the new map is used by other threads as soon as you changed the reference (map = newMap). Without volatile, a subsequent read (local = map) could still return the old reference for some time (we're talking about less than a nanosecond though) - especially on multicore systems if I remember correctly. I wouldn't care about it, but f you feel a need for that extra bit of multi-threading beauty, your free to use it of course ;)
I like the volatile Map solution from KLE a lot and would go with that. Another idea that someone might find interesting is to use the map equivalent of a CopyOnWriteArrayList, basically a CopyOnWriteMap. We built one of these internally and it is non-trivial but you might be able to find a COWMap out in the wild:
http://old.nabble.com/CopyOnWriteMap-implementation-td13018855.html
This is the answer from the JDK javadocs for ReentrantReadWriteLock implementation of ReadWriteLock. A few years late but still valid, especially if you don't want to rely only on volatile
class RWDictionary {
private final Map<String, Data> m = new TreeMap<String, Data>();
private final ReentrantReadWriteLock rwl = new ReentrantReadWriteLock();
private final Lock r = rwl.readLock();
private final Lock w = rwl.writeLock();
public Data get(String key) {
r.lock();
try { return m.get(key); }
finally { r.unlock(); }
}
public String[] allKeys() {
r.lock();
try { return m.keySet().toArray(); }
finally { r.unlock(); }
}
public Data put(String key, Data value) {
w.lock();
try { return m.put(key, value); }
finally { w.unlock(); }
}
public void clear() {
w.lock();
try { m.clear(); }
finally { w.unlock(); }
}
}
This question already has an answer here:
Closed 10 years ago.
Possible Duplicate:
What is the best way to increase number of locks in java
Suppose I want to lock based on an integer id value. In this case, there's a function that pulls a value from a cache and does a fairly expensive retrieve/store into the cache if the value isn't there.
The existing code isn't synchronized and could potentially trigger multiple retrieve/store operations:
//psuedocode
public Page getPage (Integer id){
Page p = cache.get(id);
if (p==null)
{
p=getFromDataBase(id);
cache.store(p);
}
}
What I'd like to do is synchronize the retrieve on the id, e.g.
if (p==null)
{
synchronized (id)
{
..retrieve, store
}
}
Unfortunately this won't work because 2 separate calls can have the same Integer id value but a different Integer object, so they won't share the lock, and no synchronization will happen.
Is there a simple way of insuring that you have the same Integer instance? For example, will this work:
syncrhonized (Integer.valueOf(id.intValue())){
The javadoc for Integer.valueOf() seems to imply that you're likely to get the same instance, but that doesn't look like a guarantee:
Returns a Integer instance
representing the specified int value.
If a new Integer instance is not
required, this method should generally
be used in preference to the
constructor Integer(int), as this
method is likely to yield
significantly better space and time
performance by caching frequently
requested values.
So, any suggestions on how to get an Integer instance that's guaranteed to be the same, other than the more elaborate solutions like keeping a WeakHashMap of Lock objects keyed to the int? (nothing wrong with that, it just seems like there must be an obvious one-liner than I'm missing).
You really don't want to synchronize on an Integer, since you don't have control over what instances are the same and what instances are different. Java just doesn't provide such a facility (unless you're using Integers in a small range) that is dependable across different JVMs. If you really must synchronize on an Integer, then you need to keep a Map or Set of Integer so you can guarantee that you're getting the exact instance you want.
Better would be to create a new object, perhaps stored in a HashMap that is keyed by the Integer, to synchronize on. Something like this:
public Page getPage(Integer id) {
Page p = cache.get(id);
if (p == null) {
synchronized (getCacheSyncObject(id)) {
p = getFromDataBase(id);
cache.store(p);
}
}
}
private ConcurrentMap<Integer, Integer> locks = new ConcurrentHashMap<Integer, Integer>();
private Object getCacheSyncObject(final Integer id) {
locks.putIfAbsent(id, id);
return locks.get(id);
}
To explain this code, it uses ConcurrentMap, which allows use of putIfAbsent. You could do this:
locks.putIfAbsent(id, new Object());
but then you incur the (small) cost of creating an Object for each access. To avoid that, I just save the Integer itself in the Map. What does this achieve? Why is this any different from just using the Integer itself?
When you do a get() from a Map, the keys are compared with equals() (or at least the method used is the equivalent of using equals()). Two different Integer instances of the same value will be equal to each other. Thus, you can pass any number of different Integer instances of "new Integer(5)" as the parameter to getCacheSyncObject and you will always get back only the very first instance that was passed in that contained that value.
There are reasons why you may not want to synchronize on Integer ... you can get into deadlocks if multiple threads are synchronizing on Integer objects and are thus unwittingly using the same locks when they want to use different locks. You can fix this risk by using the
locks.putIfAbsent(id, new Object());
version and thus incurring a (very) small cost to each access to the cache. Doing this, you guarantee that this class will be doing its synchronization on an object that no other class will be synchronizing on. Always a Good Thing.
Use a thread-safe map, such as ConcurrentHashMap. This will allow you to manipulate a map safely, but use a different lock to do the real computation. In this way you can have multiple computations running simultaneous with a single map.
Use ConcurrentMap.putIfAbsent, but instead of placing the actual value, use a Future with computationally-light construction instead. Possibly the FutureTask implementation. Run the computation and then get the result, which will thread-safely block until done.
Integer.valueOf() only returns cached instances for a limited range. You haven't specified your range, but in general, this won't work.
However, I would strongly recommend you not take this approach, even if your values are in the correct range. Since these cached Integer instances are available to any code, you can't fully control the synchronization, which could lead to a deadlock. This is the same problem people have trying to lock on the result of String.intern().
The best lock is a private variable. Since only your code can reference it, you can guarantee that no deadlocks will occur.
By the way, using a WeakHashMap won't work either. If the instance serving as the key is unreferenced, it will be garbage collected. And if it is strongly referenced, you could use it directly.
Using synchronized on an Integer sounds really wrong by design.
If you need to synchronize each item individually only during retrieve/store you can create a Set and store there the currently locked items. In another words,
// this contains only those IDs that are currently locked, that is, this
// will contain only very few IDs most of the time
Set<Integer> activeIds = ...
Object retrieve(Integer id) {
// acquire "lock" on item #id
synchronized(activeIds) {
while(activeIds.contains(id)) {
try {
activeIds.wait();
} catch(InterruptedExcption e){...}
}
activeIds.add(id);
}
try {
// do the retrieve here...
return value;
} finally {
// release lock on item #id
synchronized(activeIds) {
activeIds.remove(id);
activeIds.notifyAll();
}
}
}
The same goes to the store.
The bottom line is: there is no single line of code that solves this problem exactly the way you need.
How about a ConcurrentHashMap with the Integer objects as keys?
You could have a look at this code for creating a mutex from an ID. The code was written for String IDs, but could easily be edited for Integer objects.
As you can see from the variety of answers, there are various ways to skin this cat:
Goetz et al's approach of keeping a cache of FutureTasks works quite well in situations like this where you're "caching something anyway" so don't mind building up a map of FutureTask objects (and if you did mind the map growing, at least it's easy to make pruning it concurrent)
As a general answer to "how to lock on ID", the approach outlined by Antonio has the advantage that it's obvious when the map of locks is added to/removed from.
You may need to watch out for a potential issue with Antonio's implementation, namely that the notifyAll() will wake up threads waiting on all IDs when one of them becomes available, which may not scale very well under high contention. In principle, I think you can fix that by having a Condition object for each currently locked ID, which is then the thing that you await/signal. Of course, if in practice there's rarely more than one ID being waited on at any given time, then this isn't an issue.
Steve,
your proposed code has a bunch of problems with synchronization. (Antonio's does as well).
To summarize:
You need to cache an expensive
object.
You need to make sure that while one thread is doing the retrieval, another thread does not also attempt to retrieve the same object.
That for n-threads all attempting to get the object only 1 object is ever retrieved and returned.
That for threads requesting different objects that they do not contend with each other.
pseudo code to make this happen (using a ConcurrentHashMap as the cache):
ConcurrentMap<Integer, java.util.concurrent.Future<Page>> cache = new ConcurrentHashMap<Integer, java.util.concurrent.Future<Page>>;
public Page getPage(Integer id) {
Future<Page> myFuture = new Future<Page>();
cache.putIfAbsent(id, myFuture);
Future<Page> actualFuture = cache.get(id);
if ( actualFuture == myFuture ) {
// I am the first w00t!
Page page = getFromDataBase(id);
myFuture.set(page);
}
return actualFuture.get();
}
Note:
java.util.concurrent.Future is an interface
java.util.concurrent.Future does not actually have a set() but look at the existing classes that implement Future to understand how to implement your own Future (Or use FutureTask)
Pushing the actual retrieval to a worker thread will almost certainly be a good idea.
See section 5.6 in Java Concurrency in Practice: "Building an efficient, scalable, result cache". It deals with the exact issue you are trying to solve. In particular, check out the memoizer pattern.
(source: umd.edu)