Project: Druid (App Definition and Development / Database)
File: approximate-histograms.md

id: approximate-histograms title: "Approximate Histogram aggregators" <!-- ~ 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 ~ ~ http://www.apache.org/licenses/LICENSE-2.0 ~ ~ 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. --> To use this Apache Druid extension, `druid-histogram` in the extensions load list. The `druid-histogram` extension provides an approximate histogram aggregator and a fixed buckets histogram aggregator. <a name="approximate-histogram-aggregator"></a> :::info The Approximate Histogram aggregator is deprecated. Please use instead which provides a superior distribution-independent algorithm with formal error guarantees. ::: This aggregator is based on to compute approximate histograms, with the following modifications: some tradeoffs in accuracy were made in the interest of speed (see below) the sketch maintains the exact original data as long as the number of distinct data points is fewer than the resolutions (number of centroids), increasing accuracy when there are few data points, or when dealing with discrete data points. You can find some of the details in . Here are a few things to note before using approximate histograms: As indicated in the original paper, there are no formal error bounds on the approximation. In practice, the approximation gets worse if the distribution is skewed. The algorithm is order-dependent, so results can vary for the same query, due to variations in the order in which results are merged. In general, the algorithm only works well if the data that comes is randomly distributed (i.e. if data points end up sorted in a column, approximation will be horrible) We traded accuracy for aggregation speed, taking some shortcuts when adding histograms together, which can lead to pathological cases if your data is ordered in some way, or if your distribution has long tails. It should be cheaper to increase the resolution of the sketch to get the accuracy you need. That being said, those sketches can be useful to get a first order approximation when averages are not good enough. Assuming most rows in your segment store fewer data points than the resolution of histogram, you should be able to use them for monitoring purposes and detect meaningful variations with a few hundred

To get good accuracy readings on 95th percentiles with millions of rows of data, you may want to use several thousand centroids, especially with long tails, since that's where the approximation will be worse. To use this feature, an "approxHistogram" or "approxHistogramFold" aggregator must be included at indexing time. The ingestion aggregator can only apply to numeric values. If you use "approxHistogram" then any input rows missing the value will be considered to have a value of 0, while with "approxHistogramFold" such rows will be ignored. To query for results, an "approxHistogramFold" aggregator must be included in the query. ```json { "type" : "approxHistogram or approxHistogramFold (at ingestion time), approxHistogramFold (at query time)", "name" : <output_name>, "fieldName" : <metric_name>, "resolution" : <integer>, "numBuckets" : <integer>, "lowerLimit" : <float>, "upperLimit" : <float> } ``` |Property |Description |Default | |-||-| |`resolution` |Number of centroids (data points) to store. The higher the resolution, the more accurate results are, but the slower the computation will be.|50| |`numBuckets` |Number of output buckets for the resulting histogram. Bucket intervals are dynamic, based on the range of the underlying data. Use a post-aggregator to have finer control over the bucketing scheme|7| |`lowerLimit`/`upperLimit`|Restrict the approximation to the given range. The values outside this range will be aggregated into two centroids. Counts of values outside this range are still maintained. |-INF/+INF| |`finalizeAsBase64Binary` |If true, the finalized aggregator value will be a Base64-encoded byte array containing the serialized form of the histogram. If false, the finalized aggregator value will be a JSON representation of the histogram.|false| The fixed buckets histogram aggregator builds a histogram on a numeric column, with evenly-sized buckets across a specified value range. Values outside of the range are handled based on a user-specified outlier handling mode. This histogram supports the min/max/quantiles post-aggregators but does not support the bucketing post-aggregators. The accuracy/usefulness of the fixed buckets histogram is extremely data-dependent; it is provided to support special use cases where the user has a great deal of prior information about the data being aggregated and knows that a fixed buckets implementation is suitable. For general histogram and quantile use cases, the extension is recommended. |Property |Description |Default | |-||-| |`type`|Type of the aggregator. Must `fixedBucketsHistogram`.|No default, must be specified| |`name`|Column name for the aggregator.|No default, must be specified| |`fieldName`|Column name of the input to the aggregator.|No default, must be specified| |`lowerLimit`|Lower limit of the histogram. |No default, must be specified| |`upperLimit`|Upper limit of the histogram. |No default, must be specified| |`numBuckets`|Number of buckets for the histogram. The range [lowerLimit, upperLimit] will be divided into `numBuckets` intervals of equal size.|10| |`outlierHandlingMode`|Specifies how values outside of for more

default, must be specified| |`finalizeAsBase64Binary`|If true, the finalized aggregator value will be a Base64-encoded byte array containing the of the histogram. If false, the finalized aggregator value will be a JSON representation of the histogram.|false| An example aggregator spec is shown below: ```json { "type" : "fixedBucketsHistogram", "name" : <output_name>, "fieldName" : <metric_name>, "numBuckets" : <integer>, "lowerLimit" : <double>, "upperLimit" : <double>, "outlierHandlingMode": <mode> } ``` The outlier handling mode specifies what should be done with values outside of the histogram's range. There are three supported modes: `ignore`: Throw away outlier values. `overflow`: A count of outlier values will be tracked by the histogram, available in the `lowerOutlierCount` and `upperOutlierCount` fields. `clip`: Outlier values will be clipped to the `lowerLimit` or the `upperLimit` and included in the histogram. If you don't care about outliers, `ignore` is the cheapest option performance-wise. There is currently no difference in storage size among the modes. The histogram aggregator's output object has the following fields: `lowerLimit`: Lower limit of the histogram `upperLimit`: Upper limit of the histogram `numBuckets`: Number of histogram buckets `outlierHandlingMode`: Outlier handling mode `count`: Total number of values contained in the histogram, excluding outliers `lowerOutlierCount`: Count of outlier values below `lowerLimit`. Only used if the outlier mode is `overflow`. `upperOutlierCount`: Count of outlier values above `upperLimit`. Only used if the outlier mode is `overflow`. `missingValueCount`: Count of null values seen by the histogram. `max`: Max value seen by the histogram. This does not include outlier values. `min`: Min value seen by the histogram. This does not include outlier values. `histogram`: An array of longs with size `numBuckets`, containing the bucket counts It is also possible to ingest existing fixed buckets histograms. The input must be a Base64 string encoding a byte array that contains a serialized histogram object. Both "full" and "sparse" formats can be used. Please see below for details. This format includes the full histogram bucket count array in the serialization format. ``` byte: serialization version, must be 0x01 byte: encoding mode, 0x01 for full double: lowerLimit double: upperLimit int: numBuckets byte: outlier handling mode (0x00 for `ignore`, 0x01 for `overflow`, and 0x02 for `clip`) long: count, total number of values contained in the histogram, excluding outliers long: lowerOutlierCount long: upperOutlierCount long: missingValueCount double: max double: min array of longs: bucket counts for the histogram ``` This format represents the histogram bucket counts as (bucketNum, count) pairs. This serialization format is used when less than half of the histogram's buckets have

``` byte: serialization version, must be 0x01 byte: encoding mode, 0x02 for sparse double: lowerLimit double: upperLimit int: numBuckets byte: outlier handling mode (0x00 for `ignore`, 0x01 for `overflow`, and 0x02 for `clip`) long: count, total number of values contained in the histogram, excluding outliers long: lowerOutlierCount long: upperOutlierCount long: missingValueCount double: max double: min int: number of following (bucketNum, count) pairs sequence of (int, long) pairs: int: bucket number count: bucket count ``` It is possible to combine two histograms with different bucketing schemes (lowerLimit, upperLimit, numBuckets) together. The bucketing scheme of the "left hand" histogram will be preserved (i.e., when running a query, the bucketing schemes specified in the query's histogram aggregators will be preserved). When merging, we assume that values are evenly distributed within the buckets of the "right hand" histogram. When the right-hand histogram contains outliers (when using `overflow` mode), we assume that all of the outliers counted in the right-hand histogram will be outliers in the left-hand histogram as well. For performance and accuracy reasons, we recommend avoiding aggregation of histograms with different bucketing schemes if possible. If `druid.generic.useDefaultValueForNull` is false, null values will be tracked in the `missingValueCount` field of the histogram. If `druid.generic.useDefaultValueForNull` is true, null values will be added to the histogram as the default 0.0 value. Post-aggregators are used to transform opaque approximate histogram sketches into bucketed histogram representations, as well as to compute various distribution metrics such as quantiles, min, and max. Computes a visual representation of the approximate histogram with a given number of equal-sized bins. Bucket intervals are based on the range of the underlying data. This aggregator is not supported for the fixed buckets histogram. ```json { "type": "equalBuckets", "name": "<output_name>", "fieldName": "<aggregator_name>", "numBuckets": <count> } ``` Computes a visual representation given an initial breakpoint, offset, and a bucket size. Bucket size determines the width of the binning interval. Offset determines the value on which those interval bins align. This aggregator is not supported for the fixed buckets histogram. ```json { "type": "buckets", "name": "<output_name>", "fieldName": "<aggregator_name>", "bucketSize": <bucket_size>, "offset": <offset> } ``` Computes a visual representation of the approximate histogram with bins laid out according to the given breaks. This aggregator is not supported for the fixed buckets histogram. ```json { "type" : "customBuckets", "name" : <outputname>, "fieldName" : <aggregatorname>, "breaks" : [ <value>, <value>, ... ] } ``` Returns the minimum value of the underlying approximate or fixed buckets histogram aggregator ```json { "type" : "min", "name" : <outputname>, "fieldName" : <aggregatorname> } ``` Returns the maximum value of the underlying approximate or fixed buckets histogram aggregator ```json { "type" : "max", "name" : <outputname>, "fieldName" : <aggregatorname> } ``` Computes a single quantile based on the underlying approximate or fixed buckets histogram aggregator ```json { "type" : "quantile", "name" : <outputname>, "fieldName" : <aggregatorname>, "probability" : <quantile> } ``` Computes an array of quantiles based on the underlying approximate or fixed buckets histogram aggregator ```json { "type" : "quantiles", "name" : <outputname>, "fieldName" : <aggregatorname>, "probabilities" : [ <quantile>, <quantile>, ... ] } ```