Example usage for com.google.common.util.concurrent AtomicLongMap create

Introduction

In this page you can find the example usage for com.google.common.util.concurrent AtomicLongMap create.

Prototype

public static <K> AtomicLongMap<K> create() 

Source Link

Document

Creates an AtomicLongMap .

Usage

From source file:core.MultiConcurrentLinkedPriorityQueueDRR.java

public MultiConcurrentLinkedPriorityQueueDRR(List<Integer> weights) {
    shutdown = false;// w w  w  . j  ava2 s .c  o  m
    qcount = weights.size();
    for (Integer x : weights)
        weightSum += x;
    this.weights = weights;
    r = new Random();
    queues = new ArrayList<ConcurrentLinkedQueue<E>>();
    DC = AtomicLongMap.create();
    for (int i = 0; i < qcount; i++) {
        queues.add(new ConcurrentLinkedQueue<E>());
        DC.put(i, 0);
    }

    Runnable runnable = new Runnable() {
        public void run() {
            while (!shutdown) {
                schedule();
            }
        }
    };
    scheduler = new Thread(runnable, "REQUEST-SCHEDULER");
    scheduler.start();
}

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From source file:org.zenoss.app.consumer.metric.impl.MetricsQueue.java

MetricsQueue() {
    this.queue = new LinkedBlockingQueue<>();
    this.perClientBacklog = AtomicLongMap.create();
    this.totalErrorsMetric = Metrics.newCounter(errorsMetricName());
    this.totalInFlightMetric = Metrics.newCounter(inFlightMetricName());
    this.totalClientCountMetric = Metrics.newGauge(clientCountMetricName(), new Gauge<Long>() {
        @Override//w  ww . ja v  a 2  s .c  o  m
        public Long value() {
            return clientCount();
        }
    });
    this.totalIncomingMetric = registerIncoming();
    this.totalOutGoingMetric = registerOutgoing();
    this.totalReceivedMetric = registerReceived();
    this.totalRejectedMetric = registerRejected();
    this.totalLostMetric = registerLost();
    this.totalHighCollisionMetric = registerHighCollision();
    this.totalLowCollisionMetric = registerLowCollision();
    this.totalClientCollisionMetric = registerClientCollision();
    this.totalBroadcastHighCollisionMetric = registerBroadcastHighCollision();
    this.totalBroadcastLowCollisionMetric = registerBroadcastLowCollision();
    this.totalSentClientCollisionMetric = registerSentClientCollision();
    this.recentClientIds = CacheBuilder.newBuilder().expireAfterAccess(60, TimeUnit.SECONDS)
            .build(CacheLoader.from(YEPYEP));
}

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From source file:ch.ethz.bsse.cf.utils.Utils.java

public static void parseBAM(String location) {
    File bam = new File(location);
    int size = 0;
    int length = 0;
    try (SAMFileReader sfr = new SAMFileReader(bam)) {
        AbstractBAMFileIndex index = (AbstractBAMFileIndex) sfr.getIndex();
        int nRefs = index.getNumberOfReferences();
        for (int i = 0; i < nRefs; i++) {
            BAMIndexMetaData meta = index.getMetaData(i);
            size += meta.getAlignedRecordCount();
            if (length < sfr.getFileHeader().getSequence(i).getSequenceLength()) {
                length = sfr.getFileHeader().getSequence(i).getSequenceLength();
            }//from  ww w  .ja  va 2  s . c  o m
        }
    }
    for (int i = 0; i < length; i++) {
        Globals.DELETION_MAP.put(i, AtomicLongMap.create());
    }
    StatusUpdate.getINSTANCE().printForce("Read count\t\t" + size);
    try (SAMFileReader sfr = new SAMFileReader(bam)) {
        if (Globals.SINGLE_CORE) {
            StatusUpdate.getINSTANCE().println("Computing\t\t");
            int i = 0;
            for (SAMRecord r : sfr) {
                if (i++ % 1000 == 0) {
                    StatusUpdate.getINSTANCE()
                            .print("Computing\t\t" + Math.round(((double) i * 100) / size) + "%");
                }
                SFRComputing.single(r);
            }
            StatusUpdate.getINSTANCE().printForce("Computing\t\t100%");
        } else {
            StatusUpdate.getINSTANCE().println("Loading BAM");

            List<List<SAMRecord>> records = new LinkedList();
            List<SAMRecord> tmp = new LinkedList<>();
            SAMRecordIterator it = sfr.iterator();
            for (int x = 0, y = 0; x < size; x += STEP_SIZE) {
                records.clear();
                tmp.clear();
                y = x + STEP_SIZE < size ? x + STEP_SIZE : size - 1;
                StatusUpdate.getINSTANCE()
                        .print("Loading BAM\t\t" + Math.round(((double) y * 100) / size) + "%");
                final int max = y - x < 100 ? 100
                        : (int) Math.ceil((y - x) / Runtime.getRuntime().availableProcessors());
                int c = 0;
                do {
                    if (++c % max == 0) {
                        records.add(tmp);
                        tmp = new LinkedList<>();
                    }
                    if (it.hasNext()) {
                        tmp.add(it.next());
                    } else {
                        records.add(tmp);
                        break;
                    }
                } while (c < y - x);
                records.add(tmp);
                Parallel.ForEach(records, new LoopBody<List<SAMRecord>>() {
                    @Override
                    public void run(List<SAMRecord> l) {

                        for (SAMRecord r : l) {
                            SFRComputing.single(r);
                        }
                    }
                });
            }
            StatusUpdate.getINSTANCE().printForce("Loading BAM\t\t100%\n");
        }
    }
}

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From source file:org.elasticsearch.action.termvectors.TermVectorsFilter.java

public TermVectorsFilter(Fields termVectorsByField, Fields topLevelFields, Set<String> selectedFields,
        @Nullable AggregatedDfs dfs) {//from  ww  w .  j av  a 2s. c om
    this.fields = termVectorsByField;
    this.topLevelFields = topLevelFields;
    this.selectedFields = selectedFields;

    this.dfs = dfs;
    this.scoreTerms = new HashMap<>();
    this.sizes = AtomicLongMap.create();
    this.similarity = new DefaultSimilarity();
}

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From source file:org.apache.hadoop.hbase.replication.regionserver.DumpReplicationQueues.java

public DumpReplicationQueues() {
    deadRegionServers = new ArrayList<String>();
    deletedQueues = new ArrayList<String>();
    peersQueueSize = AtomicLongMap.create();
    totalSizeOfWALs = 0;/* w  ww  .  j ava2s .c  om*/
    numWalsNotFound = 0;
}

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From source file:ch.ethz.bsse.cf.utils.SFRComputing.java

public static void add(int position, byte base, Map<Integer, AtomicLongMap> alignmentMap) {
    try {//from w  ww.  java2  s  .c  o m
        if (!alignmentMap.containsKey(position)) {
            alignmentMap.put(position, AtomicLongMap.create());
        }
        alignmentMap.get(position).incrementAndGet(base);
    } catch (IllegalArgumentException e) {
        System.out.println(e);
    }
}

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From source file:com.cloudera.oryx.app.mllib.rdf.RDFUpdate.java

/**
 * @param trainPointData data to run down trees
 * @param model random decision forest model to count on
 * @return map of predictor index to the number of training examples that reached a
 *  node whose decision is based on that feature. The index is among predictors, not all
 *  features, since there are fewer predictors than features. That is, the index will
 *  match the one used in the {@link RandomForestModel}.
 *//*from  w w w .j  a  va 2 s.c om*/
private static Map<Integer, Long> predictorExampleCounts(JavaRDD<LabeledPoint> trainPointData,
        final RandomForestModel model) {
    return trainPointData.mapPartitions(new FlatMapFunction<Iterator<LabeledPoint>, AtomicLongMap<Integer>>() {
        @Override
        public Iterable<AtomicLongMap<Integer>> call(Iterator<LabeledPoint> data) {
            AtomicLongMap<Integer> featureIndexCount = AtomicLongMap.create();
            while (data.hasNext()) {
                LabeledPoint datum = data.next();
                double[] featureVector = datum.features().toArray();
                for (DecisionTreeModel tree : model.trees()) {
                    org.apache.spark.mllib.tree.model.Node node = tree.topNode();
                    // This logic cloned from Node.predict:
                    while (!node.isLeaf()) {
                        Split split = node.split().get();
                        int featureIndex = split.feature();
                        // Count feature
                        featureIndexCount.incrementAndGet(featureIndex);
                        node = nextNode(featureVector, node, split, featureIndex);
                    }
                }
            }
            return Collections.singleton(featureIndexCount);
        }
    }).reduce(new Function2<AtomicLongMap<Integer>, AtomicLongMap<Integer>, AtomicLongMap<Integer>>() {
        @Override
        public AtomicLongMap<Integer> call(AtomicLongMap<Integer> a, AtomicLongMap<Integer> b) {
            return merge(a, b);
        }
    }).asMap();
}

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From source file:org.mondemand.Client.java

/**
 * Given context&Stats map, increment according to context and key/value
 * @param context context// ww  w  .  java 2s .  c  o  m
 * @param keyType key
 * @param value value
 * @throws MondemandException
 */
public void increment(ContextList context, String keyType, long value) throws MondemandException {
    if (!isKeyValid(keyType)) {
        throw new MondemandException("key is invalid: " + keyType);
    }

    // Note: add could be lost due to a race condition but no
    //       synchronization is required.
    AtomicLongMap<String> stats = contextStats.get(context);
    if (stats == null) {
        AtomicLongMap<String> newStats = AtomicLongMap.create();
        stats = contextStats.putIfAbsent(context, newStats);
        if (stats == null) {
            stats = newStats;
        }
    }
    stats.addAndGet(keyType, value);
}

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