Example usage for com.google.common.collect EvictingQueue create

Introduction

In this page you can find the example usage for com.google.common.collect EvictingQueue create.

Prototype

public static <E> EvictingQueue<E> create(int maxSize) 

Source Link

Document

Creates and returns a new evicting queue that will hold up to maxSize elements.

Usage

From source file:com.arpnetworking.tsdcore.sinks.HttpSinkActor.java

/**
 * Public constructor./*from   w w  w . ja  v a  2 s .com*/
 *
 * @param client Http client to create requests from.
 * @param sink Sink that controls request creation and data serialization.
 * @param maximumConcurrency Maximum number of concurrent requests.
 * @param maximumQueueSize Maximum number of pending requests.
 * @param spreadPeriod Maximum time to delay sending new aggregates to spread load.
 */
public HttpSinkActor(final AsyncHttpClient client, final HttpPostSink sink, final int maximumConcurrency,
        final int maximumQueueSize, final Period spreadPeriod) {
    _client = client;
    _sink = sink;
    _maximumConcurrency = maximumConcurrency;
    _pendingRequests = EvictingQueue.create(maximumQueueSize);
    if (Period.ZERO.equals(spreadPeriod)) {
        _spreadingDelayMillis = 0;
    } else {
        _spreadingDelayMillis = new Random().nextInt((int) spreadPeriod.toStandardDuration().getMillis());
    }
}

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From source file:org.hawkular.datamining.forecast.models.DoubleExponentialSmoothing.java

private DoubleExponentialSmoothing(double levelSmoothing, double trendSmoothing, MetricContext metricContext) {
    super(metricContext);

    if (levelSmoothing < MIN_LEVEL_TREND_SMOOTHING || levelSmoothing > MAX_LEVEL_TREND_SMOOTHING) {
        throw new IllegalArgumentException("Level parameter should be in interval 0-1");
    }/*www . ja  v  a 2s . c o  m*/
    if (trendSmoothing < MIN_LEVEL_TREND_SMOOTHING || trendSmoothing > MAX_LEVEL_TREND_SMOOTHING) {
        throw new IllegalArgumentException("Trend parameter should be in 0-1");
    }

    this.levelSmoothing = levelSmoothing;
    this.trendSmoothing = trendSmoothing;
    this.residuals = EvictingQueue.create(50);
}

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From source file:org.elasticsearch.search.aggregations.pipeline.movavg.models.MovAvgModel.java

/**
 * Predicts the next `n` values in the series, using the smoothing model to generate new values.
 * Default prediction mode is to simply continuing calling <code>next()</code> and adding the
 * predicted value back into the windowed buffer.
 *
 * @param values            Collection of numerics to movingAvg, usually windowed
 * @param numPredictions    Number of newly generated predictions to return
 * @param <T>               Type of numeric
 * @return                  Returns an array of doubles, since most smoothing methods operate on floating points
 *//*from w  w w  .  j  av  a  2  s.c o  m*/
public <T extends Number> double[] predict(Collection<T> values, int numPredictions) {
    double[] predictions = new double[numPredictions];

    // If there are no values, we can't do anything.  Return an array of NaNs.
    if (values.size() == 0) {
        return emptyPredictions(numPredictions);
    }

    // special case for one prediction, avoids allocation
    if (numPredictions < 1) {
        throw new IllegalArgumentException("numPredictions may not be less than 1.");
    } else if (numPredictions == 1) {
        predictions[0] = next(values);
        return predictions;
    }

    Collection<Number> predictionBuffer = EvictingQueue.create(values.size());
    predictionBuffer.addAll(values);

    for (int i = 0; i < numPredictions; i++) {
        predictions[i] = next(predictionBuffer);

        // Add the last value to the buffer, so we can keep predicting
        predictionBuffer.add(predictions[i]);
    }

    return predictions;
}

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From source file:org.elasticsearch.search.aggregations.pipeline.serialdiff.SerialDiffPipelineAggregator.java

@Override
public InternalAggregation reduce(InternalAggregation aggregation, ReduceContext reduceContext) {
    InternalHistogram histo = (InternalHistogram) aggregation;
    List<? extends InternalHistogram.Bucket> buckets = histo.getBuckets();
    InternalHistogram.Factory<? extends InternalHistogram.Bucket> factory = histo.getFactory();

    List newBuckets = new ArrayList<>();
    EvictingQueue<Double> lagWindow = EvictingQueue.create(lag);
    int counter = 0;

    for (InternalHistogram.Bucket bucket : buckets) {
        Double thisBucketValue = resolveBucketValue(histo, bucket, bucketsPaths()[0], gapPolicy);
        InternalHistogram.Bucket newBucket = bucket;

        counter += 1;//from w  ww.  j  a v a 2  s  .c o m

        // Still under the initial lag period, add nothing and move on
        Double lagValue;
        if (counter <= lag) {
            lagValue = Double.NaN;
        } else {
            lagValue = lagWindow.peek(); // Peek here, because we rely on add'ing to always move the window
        }

        // Normalize null's to NaN
        if (thisBucketValue == null) {
            thisBucketValue = Double.NaN;
        }

        // Both have values, calculate diff and replace the "empty" bucket
        if (!Double.isNaN(thisBucketValue) && !Double.isNaN(lagValue)) {
            double diff = thisBucketValue - lagValue;

            List<InternalAggregation> aggs = new ArrayList<>(
                    Lists.transform(bucket.getAggregations().asList(), AGGREGATION_TRANFORM_FUNCTION));
            aggs.add(new InternalSimpleValue(name(), diff, formatter, new ArrayList<PipelineAggregator>(),
                    metaData()));
            newBucket = factory.createBucket(bucket.getKey(), bucket.getDocCount(),
                    new InternalAggregations(aggs), bucket.getKeyed(), bucket.getFormatter());
        }

        newBuckets.add(newBucket);
        lagWindow.add(thisBucketValue);

    }
    return factory.create(newBuckets, histo);
}

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From source file:com.android.build.gradle.integration.common.fixture.Logcat.java

private synchronized void start(@NonNull IDevice device, @Nullable final LogCatFilter filter) {
    mLogCatMessages = EvictingQueue.create(400);
    mFilteredLogCatMessages = Lists.newArrayList();
    mLogCatReceiverTask = new LogCatReceiverTask(device);
    mLogCatReceiverTask.addLogCatListener(new LogCatListener() {
        @Override/*from  ww w . ja  va2s.c  om*/
        public void log(List<LogCatMessage> msgList) {
            for (LogCatMessage message : msgList) {
                mLogCatMessages.add(message);
                if (filter == null || filter.matches(message)) {
                    mFilteredLogCatMessages.add(message);
                }
            }
        }
    });
    mThread = new Thread(mLogCatReceiverTask);
    mThread.setDaemon(true);
    mThread.start();
}

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From source file:org.apache.jackrabbit.oak.plugins.document.secondary.SecondaryStoreCache.java

public SecondaryStoreCache(NodeStore nodeStore, NodeStateDiffer differ, PathFilter pathFilter,
        StatisticsProvider statisticsProvider) {
    this.differ = differ;
    this.store = nodeStore;
    this.pathFilter = pathFilter;
    this.unknownPaths = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_UNKNOWN", StatsOptions.DEFAULT);
    this.knownMissed = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_KNOWN_MISSED", StatsOptions.DEFAULT);
    this.knownMissedOld = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_KNOWN_MISSED_OLD",
            StatsOptions.DEFAULT);/* w  ww.  j  av  a2  s. c o m*/
    this.knownMissedNew = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_KNOWN_MISSED_NEW",
            StatsOptions.DEFAULT);
    this.knownMissedInRange = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_KNOWN_MISSED_IN_RANGE",
            StatsOptions.DEFAULT);
    this.headRevMatched = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_HEAD", StatsOptions.DEFAULT);
    this.prevRevMatched = statisticsProvider.getMeter("DOCUMENT_CACHE_SEC_OLD", StatsOptions.DEFAULT);
    this.queue = EvictingQueue.create(maxSize);
}

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From source file:org.owasp.webgoat.plugin.StoredXssComments.java

@RequestMapping(method = RequestMethod.POST)
@ResponseBody//w ww.  j a v a  2 s  .  c  om
public AttackResult createNewComment(@RequestBody String commentStr) throws IOException {

    Comment comment = parseJson(commentStr);

    EvictingQueue<Comment> comments = userComments.getOrDefault(webSession.getUserName(),
            EvictingQueue.create(100));
    comment.setDateTime(DateTime.now().toString(fmt));
    comment.setUser(webSession.getUserName());

    comments.add(comment);
    userComments.put(webSession.getUserName(), comments);

    if (comment.getText().contains(phoneHomeString)) {
        return (success().feedback("xss-stored-comment-success").build());
    } else {
        return (failed().feedback("xss-stored-comment-failure").build());
    }
}

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From source file:org.lizardirc.beancounter.commands.sed.SedListener.java

public SedListener(ExecutorService executorService, int windowSize) {
    windows = CacheBuilder.newBuilder().build(CacheLoader.from(() -> EvictingQueue.create(windowSize)));
    this.executorService = executorService;
}

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From source file:org.apache.aurora.common.stats.TimeSeriesRepositoryImpl.java

@Inject
public TimeSeriesRepositoryImpl(StatRegistry statRegistry,
        @Named(SAMPLE_PERIOD) Amount<Long, Time> samplePeriod,
        @Named(SAMPLE_RETENTION_PERIOD) final Amount<Long, Time> retentionPeriod, BuildInfo buildInfo) {
    this.statRegistry = checkNotNull(statRegistry);
    this.samplePeriod = checkNotNull(samplePeriod);
    this.buildInfo = checkNotNull(buildInfo);
    Preconditions.checkArgument(samplePeriod.getValue() > 0, "Sample period must be positive.");
    checkNotNull(retentionPeriod);/*from   w w w  .  ja va 2  s .  co m*/
    Preconditions.checkArgument(retentionPeriod.getValue() > 0, "Sample retention period must be positive.");

    retainedSampleLimit = (int) (retentionPeriod.as(Time.SECONDS) / samplePeriod.as(Time.SECONDS));
    Preconditions.checkArgument(retainedSampleLimit > 0,
            "Sample retention period must be greater than sample period.");

    timeSeries = CacheBuilder.newBuilder().build(new CacheLoader<String, TimeSeriesImpl>() {
        @Override
        public TimeSeriesImpl load(final String name) {
            TimeSeriesImpl timeSeries = new TimeSeriesImpl(name);

            // Backfill so we have data for pre-accumulated timestamps.
            int numTimestamps = timestamps.size();
            if (numTimestamps != 0) {
                for (int i = 1; i < numTimestamps; i++) {
                    timeSeries.addSample(0L);
                }
            }

            return timeSeries;
        }
    });

    timestamps = EvictingQueue.create(retainedSampleLimit);
}

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From source file:org.hawkular.datamining.forecast.models.TripleExponentialSmoothing.java

private TripleExponentialSmoothing(int periods, double levelSmoothing, double trendSmoothing,
        double seasonalSmoothing, MetricContext metricContext) {
    super(metricContext);

    if (levelSmoothing < MIN_LEVEL_SMOOTHING || levelSmoothing > MAX_LEVEL_SMOOTHING) {
        throw new IllegalArgumentException("Level smoothing should be in interval 0-1");
    }/* w ww .  j a v a  2  s .c o  m*/
    if (trendSmoothing < MIN_TREND_SMOOTHING || trendSmoothing > MAX_TREND_SMOOTHING) {
        throw new IllegalArgumentException("Trend smoothing should be in 0-1");
    }
    if (seasonalSmoothing < MIN_SEASONAL_SMOOTHING || seasonalSmoothing > MAX_SEASONAL_SMOOTHING) {
        throw new IllegalArgumentException("Seasonal smoothing should be in 0-1");
    }

    if (periods < 2) {
        throw new IllegalArgumentException("Periods < 2, use non seasonal model.");
    }

    this.periods = periods;
    this.levelSmoothing = levelSmoothing;
    this.trendSmoothing = trendSmoothing;
    this.seasonalSmoothing = seasonalSmoothing;
    this.residuals = new EvictingQueue[this.periods];
    for (int i = 0; i < residuals.length; i++) {
        this.residuals[i] = EvictingQueue.create(50);
    }
}

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