| /** |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * <p> |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * <p> |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.hadoop.hdfs.server.datanode.metrics; |
| |
| import com.google.common.annotations.VisibleForTesting; |
| import com.google.common.collect.ImmutableMap; |
| import org.apache.hadoop.classification.InterfaceAudience; |
| import org.apache.hadoop.classification.InterfaceStability; |
| import org.slf4j.Logger; |
| import org.slf4j.LoggerFactory; |
| |
| import java.util.ArrayList; |
| import java.util.Collections; |
| import java.util.HashMap; |
| import java.util.List; |
| import java.util.Map; |
| |
| |
| /** |
| * A utility class to help detect resources (nodes/ disks) whose aggregate |
| * latency is an outlier within a given set. |
| * |
| * We use the median absolute deviation for outlier detection as |
| * described in the following publication: |
| * |
| * Leys, C., et al., Detecting outliers: Do not use standard deviation |
| * around the mean, use absolute deviation around the median. |
| * http://dx.doi.org/10.1016/j.jesp.2013.03.013 |
| * |
| * We augment the above scheme with the following heuristics to be even |
| * more conservative: |
| * |
| * 1. Skip outlier detection if the sample size is too small. |
| * 2. Never flag resources whose aggregate latency is below a low threshold. |
| * 3. Never flag resources whose aggregate latency is less than a small |
| * multiple of the median. |
| */ |
| @InterfaceAudience.Private |
| @InterfaceStability.Unstable |
| public class OutlierDetector { |
| public static final Logger LOG = |
| LoggerFactory.getLogger(OutlierDetector.class); |
| |
| /** |
| * Minimum number of resources to run outlier detection. |
| */ |
| private final long minNumResources; |
| |
| /** |
| * The multiplier is from Leys, C. et al. |
| */ |
| private static final double MAD_MULTIPLIER = (double) 1.4826; |
| |
| /** |
| * Threshold in milliseconds below which a node/ disk is definitely not slow. |
| */ |
| private final long lowThresholdMs; |
| |
| /** |
| * Deviation multiplier. A sample is considered to be an outlier if it |
| * exceeds the median by (multiplier * median abs. deviation). 3 is a |
| * conservative choice. |
| */ |
| private static final int DEVIATION_MULTIPLIER = 3; |
| |
| /** |
| * If most of the samples are clustered together, the MAD can be |
| * low. The median multiplier introduces another safeguard to avoid |
| * overaggressive outlier detection. |
| */ |
| @VisibleForTesting |
| static final int MEDIAN_MULTIPLIER = 3; |
| |
| public OutlierDetector(long minNumResources, long lowThresholdMs) { |
| this.minNumResources = minNumResources; |
| this.lowThresholdMs = lowThresholdMs; |
| } |
| |
| /** |
| * Return a set of nodes/ disks whose latency is much higher than |
| * their counterparts. The input is a map of (resource -> aggregate latency) |
| * entries. |
| * |
| * The aggregate may be an arithmetic mean or a percentile e.g. |
| * 90th percentile. Percentiles are a better choice than median |
| * since latency is usually not a normal distribution. |
| * |
| * This method allocates temporary memory O(n) and |
| * has run time O(n.log(n)), where n = stats.size(). |
| * |
| * @return |
| */ |
| public Map<String, Double> getOutliers(Map<String, Double> stats) { |
| if (stats.size() < minNumResources) { |
| LOG.debug("Skipping statistical outlier detection as we don't have " + |
| "latency data for enough resources. Have {}, need at least {}", |
| stats.size(), minNumResources); |
| return ImmutableMap.of(); |
| } |
| // Compute the median absolute deviation of the aggregates. |
| final List<Double> sorted = new ArrayList<>(stats.values()); |
| Collections.sort(sorted); |
| final Double median = computeMedian(sorted); |
| final Double mad = computeMad(sorted); |
| Double upperLimitLatency = Math.max( |
| lowThresholdMs, median * MEDIAN_MULTIPLIER); |
| upperLimitLatency = Math.max( |
| upperLimitLatency, median + (DEVIATION_MULTIPLIER * mad)); |
| |
| final Map<String, Double> slowResources = new HashMap<>(); |
| |
| LOG.trace("getOutliers: List={}, MedianLatency={}, " + |
| "MedianAbsoluteDeviation={}, upperLimitLatency={}", |
| sorted, median, mad, upperLimitLatency); |
| |
| // Find resources whose latency exceeds the threshold. |
| for (Map.Entry<String, Double> entry : stats.entrySet()) { |
| if (entry.getValue() > upperLimitLatency) { |
| slowResources.put(entry.getKey(), entry.getValue()); |
| } |
| } |
| |
| return slowResources; |
| } |
| |
| /** |
| * Compute the Median Absolute Deviation of a sorted list. |
| */ |
| public static Double computeMad(List<Double> sortedValues) { |
| if (sortedValues.size() == 0) { |
| throw new IllegalArgumentException( |
| "Cannot compute the Median Absolute Deviation " + |
| "of an empty list."); |
| } |
| |
| // First get the median of the values. |
| Double median = computeMedian(sortedValues); |
| List<Double> deviations = new ArrayList<>(sortedValues); |
| |
| // Then update the list to store deviation from the median. |
| for (int i = 0; i < sortedValues.size(); ++i) { |
| deviations.set(i, Math.abs(sortedValues.get(i) - median)); |
| } |
| |
| // Finally get the median absolute deviation. |
| Collections.sort(deviations); |
| return computeMedian(deviations) * MAD_MULTIPLIER; |
| } |
| |
| /** |
| * Compute the median of a sorted list. |
| */ |
| public static Double computeMedian(List<Double> sortedValues) { |
| if (sortedValues.size() == 0) { |
| throw new IllegalArgumentException( |
| "Cannot compute the median of an empty list."); |
| } |
| |
| Double median = sortedValues.get(sortedValues.size() / 2); |
| if (sortedValues.size() % 2 == 0) { |
| median += sortedValues.get((sortedValues.size() / 2) - 1); |
| median /= 2; |
| } |
| return median; |
| } |
| } |