示例#1
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  @VisibleForTesting
  QuantileDigest(double maxError, double alpha, Ticker ticker, boolean compressAutomatically) {
    checkArgument(maxError >= 0 && maxError <= 1, "maxError must be in range [0, 1]");
    checkArgument(alpha >= 0 && alpha < 1, "alpha must be in range [0, 1)");

    this.maxError = maxError;
    this.alpha = alpha;
    this.ticker = ticker;
    this.compressAutomatically = compressAutomatically;

    landmarkInSeconds = TimeUnit.NANOSECONDS.toSeconds(ticker.read());
  }
示例#2
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  /*
   * Get the exponentially-decayed approximate counts of values in multiple buckets. The elements in
   * the provided list denote the upper bound each of the buckets and must be sorted in ascending
   * order.
   *
   * The approximate count in each bucket is guaranteed to be within 2 * totalCount * maxError of
   * the real count.
   */
  public List<Bucket> getHistogram(List<Long> bucketUpperBounds) {
    checkArgument(
        Ordering.natural().isOrdered(bucketUpperBounds),
        "buckets must be sorted in increasing order");

    final ImmutableList.Builder<Bucket> builder = ImmutableList.builder();
    final PeekingIterator<Long> iterator = Iterators.peekingIterator(bucketUpperBounds.iterator());

    final AtomicDouble sum = new AtomicDouble();
    final AtomicDouble lastSum = new AtomicDouble();

    // for computing weighed average of values in bucket
    final AtomicDouble bucketWeightedSum = new AtomicDouble();

    final double normalizationFactor = weight(TimeUnit.NANOSECONDS.toSeconds(ticker.read()));

    postOrderTraversal(
        root,
        new Callback() {
          @Override
          public boolean process(Node node) {

            while (iterator.hasNext() && iterator.peek() <= node.getUpperBound()) {
              double bucketCount = sum.get() - lastSum.get();

              Bucket bucket =
                  new Bucket(
                      bucketCount / normalizationFactor, bucketWeightedSum.get() / bucketCount);

              builder.add(bucket);
              lastSum.set(sum.get());
              bucketWeightedSum.set(0);
              iterator.next();
            }

            bucketWeightedSum.addAndGet(node.getMiddle() * node.weightedCount);
            sum.addAndGet(node.weightedCount);
            return iterator.hasNext();
          }
        });

    while (iterator.hasNext()) {
      double bucketCount = sum.get() - lastSum.get();
      Bucket bucket =
          new Bucket(bucketCount / normalizationFactor, bucketWeightedSum.get() / bucketCount);

      builder.add(bucket);

      iterator.next();
    }

    return builder.build();
  }
示例#3
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  /** Adds a value to this digest. The value must be {@code >= 0} */
  public void add(long value, long count) {
    checkArgument(count > 0, "count must be > 0");

    long nowInSeconds = TimeUnit.NANOSECONDS.toSeconds(ticker.read());

    int maxExpectedNodeCount = 3 * calculateCompressionFactor();
    if (nowInSeconds - landmarkInSeconds >= RESCALE_THRESHOLD_SECONDS) {
      rescale(nowInSeconds);
      compress(); // need to compress to get rid of nodes that may have decayed to ~ 0
    } else if (nonZeroNodeCount > MAX_SIZE_FACTOR * maxExpectedNodeCount && compressAutomatically) {
      // The size (number of non-zero nodes) of the digest is at most 3 * compression factor
      // If we're over MAX_SIZE_FACTOR of the expected size, compress
      // Note: we don't compress as soon as we go over expectedNodeCount to avoid unnecessarily
      // running a compression for every new added element when we're close to boundary
      compress();
    }

    double weight = weight(TimeUnit.NANOSECONDS.toSeconds(ticker.read())) * count;

    max = Math.max(max, value);
    min = Math.min(min, value);

    insert(longToBits(value), weight);
  }
示例#4
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  private void rescaleToCommonLandmark(QuantileDigest one, QuantileDigest two) {
    long nowInSeconds = TimeUnit.NANOSECONDS.toSeconds(ticker.read());

    // 1. rescale this and other to common landmark
    long targetLandmark = Math.max(one.landmarkInSeconds, two.landmarkInSeconds);

    if (nowInSeconds - targetLandmark >= RESCALE_THRESHOLD_SECONDS) {
      targetLandmark = nowInSeconds;
    }

    if (targetLandmark != one.landmarkInSeconds) {
      one.rescale(targetLandmark);
    }

    if (targetLandmark != two.landmarkInSeconds) {
      two.rescale(targetLandmark);
    }
  }
示例#5
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 /**
  * Create a QuantileDigest with a maximum error guarantee of "maxError" and exponential decay with
  * factor "alpha".
  *
  * @param maxError the max error tolerance
  * @param alpha the exponential decay factor
  */
 public QuantileDigest(double maxError, double alpha) {
   this(maxError, alpha, Ticker.systemTicker(), true);
 }
示例#6
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 /** Number (decayed) of elements added to this quantile digest */
 public double getCount() {
   return weightedCount / weight(TimeUnit.NANOSECONDS.toSeconds(ticker.read()));
 }