I have a class whose fields can't help but lazily initialized.
class Some {
public Some getPrevious() {
{
final Some result = previous;
if (result != null) {
return result;
}
}
synchornized (this) {
if (previous == null) {
previous = computePrevious();
}
return previous;
}
}
// all fields are final initialized in constructor
private final String name;
// this is a lazy initialized self-type value.
private volatile Some previous;
}
Now sonarcloud keep complaining with java:S3077.
Use a thread-safe type; adding "volatile" is not enough to make this field thread-safe.
Is there anything wrong with the code?
Can(Should) I ignore it?
What about using AtomicReference? Isn't it an overkill?
A 'thread safe type' means one which can be used by several threads without issues.
So if Other is immutable, it is a 'thread-safe type' for the purposes of S3077.
If it is a class which is designed to be used by several threads, e.g. a ConcurrentHashMap, then it is also a 'thread-safe type'.
If you google S3077 you can find useful discussions which answer your question, e.g. https://community.sonarsource.com/t/java-rule-s3077-should-not-apply-to-references-to-immutable-objects/15200
In the book "Java Concurrency in Practice" is mentioned that the following code is not threadsafe:
#NotThreadSafe
public class DoubleCheckedLocking {
private static Resource resource;
public static Resource getInstance(){
if(resource == null){
synchronized (DoubleCheckedLocking.class){
if(resource == null)
resource = new Resource();
}
}
return resource;
}
}
It is not thread safe because because:
- one thread can create new instance of Resource
- another thread at the same time in the "if" condition can get not empty reference but the object of Resource will not be completly initialized
In this question is similar code. Resources are stored in concurentHashMap and people say that it is threadSafe. Something like this:
public class DoubleCheckedLocking2 {
private static ConcurrentHashMap<String, ComplexObject> cache = new ConcurrentHashMap<String, ComplexObject>();
public static ComplexObject getInstance(String key) {
ComplexObject result = cache.get(key);
if (result == null) {
synchronized (DoubleCheckedLocking2.class) {
ComplexObject currentValue = cache.get(key);
if (currentValue == null) {
result = new ComplexObject();
cache.put(key, result);
} else {
result = currentValue;
}
}
}
return result;
}
}
Why does storing the values in ConcurrentHashMap make the code threadSafe? I think that it is still possible that the ComplexObject won't be completely initialized and this "partial object" will be saved in the map. And other threads will be reading partial not fully initialized objects.
I think I know what is "happens-before", I've analyzed code in JDK 8.0_31 and I still don't know the answer.
I am aware of the functions like computeIfAbsent, putIfAbsent. I know that this code can be written differently. I just wan't know details which make this code threadsafe.
Happens before actually is the key here. There's a happens before edge extending from map.put(key, object) to a subsequent map.get(key), therefore the object you retrieve is at least as up to date as it was at the time it was stored in the map.
The question actually refers to a different question, which was closed as duplicate because it was probably not well formulated.
What would be an effective alternative lazy initialization idiom instead of double-checked locking for this code sample (in a multithreaded environment):
public class LazyEvaluator {
private final Object state;
private volatile LazyValue lazyValue;
public LazyEvaluator(Object state) {
this.state = state;
}
public LazyValue getLazyValue() {
if (lazyValue == null) {
synchronized (this) {
if (lazyValue == null) {
lazyValue = new LazyValue(someSlowOperationWith(state), 42);
}
}
}
return lazyValue;
}
public static class LazyValue {
private String name;
private int value;
private LazyValue(String name, int value) {
this.name = name;
this.value = value;
}
private String getName() {
return name;
}
private int getValue() {
return value;
}
}
}
EDIT Updated to include a slow operation and added explicit mention about multithreaded environment
If I understand you, then you could change this
public LazyValue getLazyValue() {
if (lazyValue == null) {
synchronized (this) {
if (lazyValue == null) {
lazyValue = new LazyValue(state.toString());
}
}
}
return lazyValue;
}
to this
public synchronized LazyValue getLazyValue() {
if (lazyValue == null) {
lazyValue = new LazyValue(state.toString());
}
return lazyValue;
}
But it's only necessary pre-Java 5 (which doesn't support acquire/release semantics for volatile) and if mulitple threads might access the same instance of your LazyEvaluator. If each thread has a thread-local instance then you don't need to synchronize.
The simplest solution would be
public LazyValue getLazyValue() {
return new LazyValue(state.toString(), 42);
}
as LazyValue is a trivial object which is not worth to be remembered at all.
If an expensive computation is involved you can turn the LazyValue into a true immutable object by declaring its fields final:
public static class LazyValue {
private final String name;
private final int value;
// …
this way you could publish the instance even through a data race:
// with lazyValue even not being volatile
public LazyValue getLazyValue() {
return lazyValue!=null? lazyValue:
(lazyValue=new LazyValue(state.toString(), 42));
}
In this case the value might be calculated multiple times in the unlikely case that multiple threads access it concurrently but once a thread sees a non-null value, it will be a correctly initialized value due to the final field initialization guaranty.
If the calculation is so expensive that even an unlikely concurrent calculation must be avoided, then simply declare getLazyValue() synchronized as its overhead will be negligible compared to the calculation that will be saved.
Finally, if you really encounter a scenario were the computation is so heavy, that overlapping concurrent computation must be avoided at all cost but profiling shows that later-on synchronization is a bottleneck, you might have encountered one of the very rare cases were double-checked locking could be an option (really rare).
In this case, there’s still an alternative to your question’s code. Combine the DCL with my suggestion above of declaring all LazyValue’s fields as final and make the lazyValue holder field non-volatile. That way you can even save the volatile read after the lazy value has been constructed. However, I still say, it should be really rarely needed.
Maybe that’s the non-technical reason why DCL has so much negative reputation: it’s appearance in discussions (or on StackOverflow) is way out of all proportion to its real need.
Well, "effective alternative lazy initialization idiom" leaves a lot of flexibility, so I'll put my two cents in the ring by noting that this might be a good place to apply a library. In particular, Guava. https://code.google.com/p/guava-libraries/
// You have some long method call you want to make lazy
MyValue someLongMethod(int input) { ... }
// So you wrap it in a supplier so it's lazy
Supplier<MyValue> lazy = new Supplier<MyValue>() {
public MyValue get() {
return someLongMethod(2);
}
}
// and you want it only to be called once ...
Supplier<MyValue> cached = Suppliers.memoize(lazy);
// ... and someLongMethod won't actually be called until
cached.get();
Double-checked-locking is is used (properly) by the Suppliers class. AS far as idioms go, Supplier is certainly effective and quite popular --java.util.function.Supplier came in Java 8.
Good luck.
I was wondering if this approach was correct :
public ITask getState()
{
statePredicate[Some predicate definition];
ITask nextRunnable = null;
try {
nextRunnable = Iterables.find((Iterable)queue, statePredicate);
}
catch (NoSuchElementException e)
{}
return nextRunnable;
}
The points on which I am wondering are :
should the predicate be cached as a member of the class ?
I do nothing with the catch, I do not even log it because it is
normal for my app to not find anything.
t return null because I do a final return.
Thank you for your input !
-
1) If the predicate is always the same, I would make it a static final class member.
2) There is also a version of Iterables.find that you can specify a default value to (assuming you're using Google Guava). Then you don't need to deal with the NoSuchElementException at all.
3) Is there a reason to cast queue to Iterable? If this is not necessary, then don't cast.
class MyClass {
private static final Predicate STATE_PREDICATE = new Predicate<ITask>() {
#Override
public boolean apply(ITask input) {
// ... your code here
}
};
public ITask getState() {
return Iterables.find(queue, STATE_PREDICATE, null);
}
}
If the exception is really the usual case in your approach than you should put at least a comment into the catch area to make clear for everyone who reads the code that it was intentional and not a mistake. In my opinion returning Null is something different, but it some circumstanced not avoidable.
let's say we have a CountryList object in our application that should return the list of countries. The loading of countries is a heavy operation, so the list should be cached.
Additional requirements:
CountryList should be thread-safe
CountryList should load lazy (only on demand)
CountryList should support the invalidation of the cache
CountryList should be optimized considering that the cache will be invalidated very rarely
I came up with the following solution:
public class CountryList {
private static final Object ONE = new Integer(1);
// MapMaker is from Google Collections Library
private Map<Object, List<String>> cache = new MapMaker()
.initialCapacity(1)
.makeComputingMap(
new Function<Object, List<String>>() {
#Override
public List<String> apply(Object from) {
return loadCountryList();
}
});
private List<String> loadCountryList() {
// HEAVY OPERATION TO LOAD DATA
}
public List<String> list() {
return cache.get(ONE);
}
public void invalidateCache() {
cache.remove(ONE);
}
}
What do you think about it? Do you see something bad about it? Is there other way to do it? How can i make it better? Should i look for totally another solution in this cases?
Thanks.
google collections actually supplies just the thing for just this sort of thing: Supplier
Your code would be something like:
private Supplier<List<String>> supplier = new Supplier<List<String>>(){
public List<String> get(){
return loadCountryList();
}
};
// volatile reference so that changes are published correctly see invalidate()
private volatile Supplier<List<String>> memorized = Suppliers.memoize(supplier);
public List<String> list(){
return memorized.get();
}
public void invalidate(){
memorized = Suppliers.memoize(supplier);
}
Thanks you all guys, especially to user "gid" who gave the idea.
My target was to optimize the performance for the get() operation considering the invalidate() operation will be called very rare.
I wrote a testing class that starts 16 threads, each calling get()-Operation one million times. With this class I profiled some implementation on my 2-core maschine.
Testing results
Implementation Time
no synchronisation 0,6 sec
normal synchronisation 7,5 sec
with MapMaker 26,3 sec
with Suppliers.memoize 8,2 sec
with optimized memoize 1,5 sec
1) "No synchronisation" is not thread-safe, but gives us the best performance that we can compare to.
#Override
public List<String> list() {
if (cache == null) {
cache = loadCountryList();
}
return cache;
}
#Override
public void invalidateCache() {
cache = null;
}
2) "Normal synchronisation" - pretty good performace, standard no-brainer implementation
#Override
public synchronized List<String> list() {
if (cache == null) {
cache = loadCountryList();
}
return cache;
}
#Override
public synchronized void invalidateCache() {
cache = null;
}
3) "with MapMaker" - very poor performance.
See my question at the top for the code.
4) "with Suppliers.memoize" - good performance. But as the performance the same "Normal synchronisation" we need to optimize it or just use the "Normal synchronisation".
See the answer of the user "gid" for code.
5) "with optimized memoize" - the performnce comparable to "no sync"-implementation, but thread-safe one. This is the one we need.
The cache-class itself:
(The Supplier interfaces used here is from Google Collections Library and it has just one method get(). see http://google-collections.googlecode.com/svn/trunk/javadoc/com/google/common/base/Supplier.html)
public class LazyCache<T> implements Supplier<T> {
private final Supplier<T> supplier;
private volatile Supplier<T> cache;
public LazyCache(Supplier<T> supplier) {
this.supplier = supplier;
reset();
}
private void reset() {
cache = new MemoizingSupplier<T>(supplier);
}
#Override
public T get() {
return cache.get();
}
public void invalidate() {
reset();
}
private static class MemoizingSupplier<T> implements Supplier<T> {
final Supplier<T> delegate;
volatile T value;
MemoizingSupplier(Supplier<T> delegate) {
this.delegate = delegate;
}
#Override
public T get() {
if (value == null) {
synchronized (this) {
if (value == null) {
value = delegate.get();
}
}
}
return value;
}
}
}
Example use:
public class BetterMemoizeCountryList implements ICountryList {
LazyCache<List<String>> cache = new LazyCache<List<String>>(new Supplier<List<String>>(){
#Override
public List<String> get() {
return loadCountryList();
}
});
#Override
public List<String> list(){
return cache.get();
}
#Override
public void invalidateCache(){
cache.invalidate();
}
private List<String> loadCountryList() {
// this should normally load a full list from the database,
// but just for this instance we mock it with:
return Arrays.asList("Germany", "Russia", "China");
}
}
Whenever I need to cache something, I like to use the Proxy pattern.
Doing it with this pattern offers separation of concerns. Your original
object can be concerned with lazy loading. Your proxy (or guardian) object
can be responsible for validation of the cache.
In detail:
Define an object CountryList class which is thread-safe, preferably using synchronization blocks or other semaphore locks.
Extract this class's interface into a CountryQueryable interface.
Define another object, CountryListProxy, that implements the CountryQueryable.
Only allow the CountryListProxy to be instantiated, and only allow it to be referenced
through its interface.
From here, you can insert your cache invalidation strategy into the proxy object. Save the time of the last load, and upon the next request to see the data, compare the current time to the cache time. Define a tolerance level, where, if too much time has passed, the data is reloaded.
As far as Lazy Load, refer here.
Now for some good down-home sample code:
public interface CountryQueryable {
public void operationA();
public String operationB();
}
public class CountryList implements CountryQueryable {
private boolean loaded;
public CountryList() {
loaded = false;
}
//This particular operation might be able to function without
//the extra loading.
#Override
public void operationA() {
//Do whatever.
}
//This operation may need to load the extra stuff.
#Override
public String operationB() {
if (!loaded) {
load();
loaded = true;
}
//Do whatever.
return whatever;
}
private void load() {
//Do the loading of the Lazy load here.
}
}
public class CountryListProxy implements CountryQueryable {
//In accordance with the Proxy pattern, we hide the target
//instance inside of our Proxy instance.
private CountryQueryable actualList;
//Keep track of the lazy time we cached.
private long lastCached;
//Define a tolerance time, 2000 milliseconds, before refreshing
//the cache.
private static final long TOLERANCE = 2000L;
public CountryListProxy() {
//You might even retrieve this object from a Registry.
actualList = new CountryList();
//Initialize it to something stupid.
lastCached = Long.MIN_VALUE;
}
#Override
public synchronized void operationA() {
if ((System.getCurrentTimeMillis() - lastCached) > TOLERANCE) {
//Refresh the cache.
lastCached = System.getCurrentTimeMillis();
} else {
//Cache is okay.
}
}
#Override
public synchronized String operationB() {
if ((System.getCurrentTimeMillis() - lastCached) > TOLERANCE) {
//Refresh the cache.
lastCached = System.getCurrentTimeMillis();
} else {
//Cache is okay.
}
return whatever;
}
}
public class Client {
public static void main(String[] args) {
CountryQueryable queryable = new CountryListProxy();
//Do your thing.
}
}
Your needs seem pretty simple here. The use of MapMaker makes the implementation more complicated than it has to be. The whole double-checked locking idiom is tricky to get right, and only works on 1.5+. And to be honest, it's breaking one of the most important rules of programming:
Premature optimization is the root of
all evil.
The double-checked locking idiom tries to avoid the cost of synchronization in the case where the cache is already loaded. But is that overhead really causing problems? Is it worth the cost of more complex code? I say assume it is not until profiling tells you otherwise.
Here's a very simple solution that requires no 3rd party code (ignoring the JCIP annotation). It does make the assumption that an empty list means the cache hasn't been loaded yet. It also prevents the contents of the country list from escaping to client code that could potentially modify the returned list. If this is not a concern for you, you could remove the call to Collections.unmodifiedList().
public class CountryList {
#GuardedBy("cache")
private final List<String> cache = new ArrayList<String>();
private List<String> loadCountryList() {
// HEAVY OPERATION TO LOAD DATA
}
public List<String> list() {
synchronized (cache) {
if( cache.isEmpty() ) {
cache.addAll(loadCountryList());
}
return Collections.unmodifiableList(cache);
}
}
public void invalidateCache() {
synchronized (cache) {
cache.clear();
}
}
}
I'm not sure what the map is for. When I need a lazy, cached object, I usually do it like this:
public class CountryList
{
private static List<Country> countryList;
public static synchronized List<Country> get()
{
if (countryList==null)
countryList=load();
return countryList;
}
private static List<Country> load()
{
... whatever ...
}
public static synchronized void forget()
{
countryList=null;
}
}
I think this is similar to what you're doing but a little simpler. If you have a need for the map and the ONE that you've simplified away for the question, okay.
If you want it thread-safe, you should synchronize the get and the forget.
What do you think about it? Do you see something bad about it?
Bleah - you are using a complex data structure, MapMaker, with several features (map access, concurrency-friendly access, deferred construction of values, etc) because of a single feature you are after (deferred creation of a single construction-expensive object).
While reusing code is a good goal, this approach adds additional overhead and complexity. In addition, it misleads future maintainers when they see a map data structure there into thinking that there's a map of keys/values in there when there is really only 1 thing (list of countries). Simplicity, readability, and clarity are key to future maintainability.
Is there other way to do it? How can i make it better? Should i look for totally another solution in this cases?
Seems like you are after lazy-loading. Look at solutions to other SO lazy-loading questions. For example, this one covers the classic double-check approach (make sure you are using Java 1.5 or later):
How to solve the "Double-Checked Locking is Broken" Declaration in Java?
Rather than just simply repeat the solution code here, I think it is useful to read the discussion about lazy loading via double-check there to grow your knowledge base. (sorry if that comes off as pompous - just trying teach to fish rather than feed blah blah blah ...)
There is a library out there (from atlassian) - one of the util classes called LazyReference. LazyReference is a reference to an object that can be lazily created (on first get). it is guarenteed thread safe, and the init is also guarenteed to only occur once - if two threads calls get() at the same time, one thread will compute, the other thread will block wait.
see a sample code:
final LazyReference<MyObject> ref = new LazyReference() {
protected MyObject create() throws Exception {
// Do some useful object construction here
return new MyObject();
}
};
//thread1
MyObject myObject = ref.get();
//thread2
MyObject myObject = ref.get();
This looks ok to me (I assume MapMaker is from google collections?) Ideally you wouldn't need to use a Map because you don't really have keys but as the implementation is hidden from any callers I don't see this as a big deal.
This is way to simple to use the ComputingMap stuff. You only need a dead simple implementation where all methods are synchronized, and you should be fine. This will obviously block the first thread hitting it (getting it), and any other thread hitting it while the first thread loads the cache (and the same again if anyone calls the invalidateCache thing - where you also should decide whether the invalidateCache should load the cache anew, or just null it out, letting the first attempt at getting it again block), but then all threads should go through nicely.
Use the Initialization on demand holder idiom
public class CountryList {
private CountryList() {}
private static class CountryListHolder {
static final List<Country> INSTANCE = new List<Country>();
}
public static List<Country> getInstance() {
return CountryListHolder.INSTANCE;
}
...
}
Follow up to Mike's solution above. My comment didn't format as expected... :(
Watch out for synchronization issues in operationB, especially since load() is slow:
public String operationB() {
if (!loaded) {
load();
loaded = true;
}
//Do whatever.
return whatever;
}
You could fix it this way:
public String operationB() {
synchronized(loaded) {
if (!loaded) {
load();
loaded = true;
}
}
//Do whatever.
return whatever;
}
Make sure you ALWAYS synchronize on every access to the loaded variable.