Examples of org.jdk8.backport.ConcurrentHashMap8

org.jdk8.backport.LongAdder
A hash table supporting full concurrency of retrievals and high expected concurrency for updates. This class obeys the same functional specification as {@link java.util.Hashtable}, and includes versions of methods corresponding to each method of {@code Hashtable}. However, even though all operations are thread-safe, retrieval operations do not entail locking, and there is not any support for locking the entire table in a way that prevents all access. This class is fully interoperable with {@code Hashtable} in programs that rely on itsthread safety but not on its synchronization details.
Retrieval operations (including {@code get}) generally do not block, so may overlap with update operations (including {@code put}and {@code remove}). Retrievals reflect the results of the most recently completed update operations holding upon their onset. (More formally, an update operation for a given key bears a happens-before relation with any (non-null) retrieval for that key reporting the updated value.) For aggregate operations such as {@code putAll} and {@code clear}, concurrent retrievals may reflect insertion or removal of only some entries. 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. They do not throw {@link ConcurrentModificationException}. However, iterators are designed to be used by only one thread at a time. Bear in mind that the results of aggregate status methods including {@code size}, {@code isEmpty}, and {@code containsValue} are typically useful only whena map is not undergoing concurrent updates in other threads. Otherwise the results of these methods reflect transient states that may be adequate for monitoring or estimation purposes, but not for program control.
The table is dynamically expanded when there are too many collisions (i.e., keys that have distinct hash codes but fall into the same slot modulo the table size), with the expected average effect of maintaining roughly two bins per mapping (corresponding to a 0.75 load factor threshold for resizing). There may be much variance around this average as mappings are added and removed, but overall, this maintains a commonly accepted time/space tradeoff for hash tables. However, resizing this or any other kind of hash table may be a relatively slow operation. When possible, it is a good idea to provide a size estimate as an optional {@code initialCapacity} constructor argument. An additional optional{@code loadFactor} constructor argument provides a further means ofcustomizing initial table capacity by specifying the table density to be used in calculating the amount of space to allocate for the given number of elements. Also, for compatibility with previous versions of this class, constructors may optionally specify an expected {@code concurrencyLevel} as an additional hint forinternal sizing. Note that using many keys with exactly the same {@code hashCode()} is a sure way to slow down performance of anyhash table.
A {@link Set} projection of a ConcurrentHashMapV8 may be created(using {@link #newKeySet()} or {@link #newKeySet(int)}), or viewed (using {@link #keySet(Object)} when only keys are of interest, and themapped values are (perhaps transiently) not used or all take the same mapping value.
A ConcurrentHashMapV8 can be used as scalable frequency map (a form of histogram or multiset) by using {@link LongAdder} valuesand initializing via {@link #computeIfAbsent}. For example, to add a count to a {@code ConcurrentHashMapV8 freqs}, you can use {@code freqs.computeIfAbsent(k -> newLongAdder()).increment();}
This class and its views and iterators implement all of the optional methods of the {@link Map} and {@link Iterator}interfaces.
Like {@link Hashtable} but unlike {@link HashMap}, this class does not allow {@code null} to be used as a key or value.
- forEach: Perform a given action on each element. A variant form applies a given transformation on each element before performing the action.
- search: Return the first available non-null result of applying a given function on each element; skipping further search when a result is found.
- reduce: Accumulate each element. The supplied reduction function cannot rely on ordering (more formally, it should be both associative and commutative). There are five variants:
  - Plain reductions. (There is not a form of this method for (key, value) function arguments since there is no corresponding return type.)
  - Mapped reductions that accumulate the results of a given function applied to each element.
  - Reductions to scalar doubles, longs, and ints, using a given basis value.
The concurrency properties of bulk operations follow from those of ConcurrentHashMapV8: Any non-null result returned from {@code get(key)} and related access methods bears ahappens-before relation with the associated insertion or update. The result of any bulk operation reflects the composition of these per-element relations (but is not necessarily atomic with respect to the map as a whole unless it is somehow known to be quiescent). Conversely, because keys and values in the map are never null, null serves as a reliable atomic indicator of the current lack of any result. To maintain this property, null serves as an implicit basis for all non-scalar reduction operations. For the double, long, and int versions, the basis should be one that, when combined with any other value, returns that other value (more formally, it should be the identity element for the reduction). Most common reductions have these properties; for example, computing a sum with basis 0 or a minimum with basis MAX_VALUE.
Search and transformation functions provided as arguments should similarly return null to indicate the lack of any result (in which case it is not used). In the case of mapped reductions, this also enables transformations to serve as filters, returning null (or, in the case of primitive specializations, the identity basis) if the element should not be combined. You can create compound transformations and filterings by composing them yourself under this "null means there is nothing there now" rule before using them in search or reduce operations.
Methods accepting and/or returning Entry arguments maintain key-value associations. They may be useful for example when finding the key for the greatest value. Note that "plain" Entry arguments can be supplied using {@code newAbstractMap.SimpleEntry(k,v)}.
Bulk operations may complete abruptly, throwing an exception encountered in the application of a supplied function. Bear in mind when handling such exceptions that other concurrently executing functions could also have thrown exceptions, or would have done so if the first exception had not occurred.
Parallel speedups for bulk operations compared to sequential processing are common but not guaranteed. Operations involving brief functions on small maps may execute more slowly than sequential loops if the underlying work to parallelize the computation is more expensive than the computation itself. Similarly, parallelization may not lead to much actual parallelism if all processors are busy performing unrelated tasks.
All arguments to all task methods must be non-null.
jsr166e note: During transition, this class uses nested functional interfaces with different names but the same forms as those expected for JDK8. @since 1.5 @author Doug Lea @param < K> the type of keys maintained by this map @param < V> the type of mapped values


    /**
     * @param args Arguments.
     */
    public static void main(String[] args) throws Exception {
        QueuePolicy qPlc = args.length > 0 ? QueuePolicy.valueOf(args[0]) : SINGLE_Q;
        int threadCnt = args.length > 1 ? Integer.valueOf(args[1]) : Runtime.getRuntime().availableProcessors();


        X.println("Queue policy: " + qPlc);
        X.println("Threads: " + threadCnt);

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    /**
     * @param args Arguments.
     */
    public static void main(String[] args) throws Exception {
        QueuePolicy qPlc = args.length > 0 ? QueuePolicy.valueOf(args[0]) : SINGLE_Q;
        int threadCnt = args.length > 1 ? Integer.valueOf(args[1]) : Runtime.getRuntime().availableProcessors();


        X.println("Queue policy: " + qPlc);
        X.println("Threads: " + threadCnt);

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        for (int i = 0; i < 5; i++) {
            final int loops = 5000;
            int threads = 17;


            final LongAdder sum = new LongAdder();


            multithreaded(new Callable<Object>() {
                @Override public Object call() throws Exception {
                    for (int i = 0; i < loops; i++) {
                        exec.submit(new Callable<Void>() {
                            @Override
                            public Void call() throws Exception {
                                sum.increment();


                                return null;
                            }
                        });
                    }


                    return null;
                }
            }, threads);


            while (exec.active() != 0) {
                X.println("__ active: " + exec.active());


                Thread.sleep(200);
            }


            assertEquals(threads * loops, sum.sum());


            X.println("_ ok");
        }


        assertTrue(exec.shutdown(0));

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     */
    public void testShutdown() throws Exception {
        for (int i = 0; i < 5; i++) {
            final GridHadoopExecutorService exec = new GridHadoopExecutorService(log, "_GRID_NAME_", 10, 5);


            final LongAdder sum = new LongAdder();


            final AtomicBoolean finish = new AtomicBoolean();


            GridFuture<?> fut = multithreadedAsync(new Callable<Object>() {
                @Override public Object call() throws Exception {
                    while (!finish.get()) {
                        exec.submit(new Callable<Void>() {
                            @Override public Void call() throws Exception {
                                sum.increment();


                                return null;
                            }
                        });
                    }


                    return null;
                }
            }, 19);


            Thread.sleep(200);


            assertTrue(exec.shutdown(50));


            long res = sum.sum();


            assertTrue(res > 0);


            finish.set(true);


            fut.get();


            assertEquals(res, sum.sum()); // Nothing was executed after shutdown.


            X.println("_ ok");
        }
    }

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