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| |
| <h1 id="approximate-histogram-aggregators">Approximate Histogram aggregators</h1> |
| |
| <p>To use this Apache Druid (incubating) extension, make sure to <a href="../../operations/including-extensions.html">include</a> <code>druid-histogram</code> as an extension.</p> |
| |
| <p>The <code>druid-histogram</code> extension provides an approximate histogram aggregator and a fixed buckets histogram aggregator.</p> |
| |
| <h2 id="approximate-histogram-aggregator-deprecated">Approximate Histogram aggregator (Deprecated)</h2> |
| |
| <div class="note caution"> |
| The Approximate Histogram aggregator is deprecated. Please use <a href="../extensions-core/datasketches-quantiles.html">DataSketches Quantiles</a> instead which provides a superior distribution-independent algorithm with formal error guarantees. |
| </div> |
| |
| <p>This aggregator is based on |
| <a href="http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf">http://jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf</a> |
| 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 <a href="https://metamarkets.com/2013/histograms/">this post</a>.</p> |
| |
| <p>Approximate histogram sketches are still experimental for a reason, and you |
| should understand the limitations of the current implementation before using |
| them. The approximation is heavily data-dependent, which makes it difficult to |
| give good general guidelines, so you should experiment and see what parameters |
| work well for your data.</p> |
| |
| <p>Here are a few things to note before using them:</p> |
| |
| <ul> |
| <li>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.</li> |
| <li>The algorithm is order-dependent, so results can vary for the same query, due |
| to variations in the order in which results are merged.</li> |
| <li>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)</li> |
| <li>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.</li> |
| </ul> |
| |
| <p>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 centroids. 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.</p> |
| |
| <h3 id="creating-approxiate-histogram-sketches-at-ingestion-time">Creating approxiate histogram sketches at ingestion time</h3> |
| |
| <p>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.</p> |
| |
| <p>To query for results, an "approxHistogramFold" aggregator must be included in the |
| query.</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> |
| <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"approxHistogram or approxHistogramFold (at ingestion time), approxHistogramFold (at query time)"</span><span class="p">,</span> |
| <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> |
| <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><metric_name></span><span class="p">,</span> |
| <span class="nt">"resolution"</span> <span class="p">:</span> <span class="err"><integer></span><span class="p">,</span> |
| <span class="nt">"numBuckets"</span> <span class="p">:</span> <span class="err"><integer></span><span class="p">,</span> |
| <span class="nt">"lowerLimit"</span> <span class="p">:</span> <span class="err"><float></span><span class="p">,</span> |
| <span class="nt">"upperLimit"</span> <span class="p">:</span> <span class="err"><float></span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <table><thead> |
| <tr> |
| <th>Property</th> |
| <th>Description</th> |
| <th>Default</th> |
| </tr> |
| </thead><tbody> |
| <tr> |
| <td><code>resolution</code></td> |
| <td>Number of centroids (data points) to store. The higher the resolution, the more accurate results are, but the slower the computation will be.</td> |
| <td>50</td> |
| </tr> |
| <tr> |
| <td><code>numBuckets</code></td> |
| <td>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</td> |
| <td>7</td> |
| </tr> |
| <tr> |
| <td><code>lowerLimit</code>/<code>upperLimit</code></td> |
| <td>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.</td> |
| <td>-INF/+INF</td> |
| </tr> |
| </tbody></table> |
| |
| <h2 id="fixed-buckets-histogram">Fixed Buckets Histogram</h2> |
| |
| <p>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.</p> |
| |
| <p>This histogram supports the min/max/quantiles post-aggregators but does not support the bucketing post-aggregators.</p> |
| |
| <h3 id="when-to-use">When to use</h3> |
| |
| <p>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. </p> |
| |
| <p>For general histogram and quantile use cases, the <a href="../extensions-core/datasketches-quantiles.html">DataSketches Quantiles Sketch</a> extension is recommended.</p> |
| |
| <h3 id="properties">Properties</h3> |
| |
| <table><thead> |
| <tr> |
| <th>Property</th> |
| <th>Description</th> |
| <th>Default</th> |
| </tr> |
| </thead><tbody> |
| <tr> |
| <td><code>type</code></td> |
| <td>Type of the aggregator. Must <code>fixedBucketsHistogram</code>.</td> |
| <td>No default, must be specified</td> |
| </tr> |
| <tr> |
| <td><code>name</code></td> |
| <td>Column name for the aggregator.</td> |
| <td>No default, must be specified</td> |
| </tr> |
| <tr> |
| <td><code>fieldName</code></td> |
| <td>Column name of the input to the aggregator.</td> |
| <td>No default, must be specified</td> |
| </tr> |
| <tr> |
| <td><code>lowerLimit</code></td> |
| <td>Lower limit of the histogram.</td> |
| <td>No default, must be specified</td> |
| </tr> |
| <tr> |
| <td><code>upperLimit</code></td> |
| <td>Upper limit of the histogram.</td> |
| <td>No default, must be specified</td> |
| </tr> |
| <tr> |
| <td><code>numBuckets</code></td> |
| <td>Number of buckets for the histogram. The range [lowerLimit, upperLimit] will be divided into <code>numBuckets</code> intervals of equal size.</td> |
| <td>10</td> |
| </tr> |
| <tr> |
| <td><code>outlierHandlingMode</code></td> |
| <td>Specifies how values outside of [lowerLimit, upperLimit] will be handled. Supported modes are "ignore", "overflow", and "clip". See <a href="#outlier-handling-modes">outlier handling modes</a> for more details.</td> |
| <td>No default, must be specified</td> |
| </tr> |
| </tbody></table> |
| |
| <p>An example aggregator spec is shown below:</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> |
| <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"fixedBucketsHistogram"</span><span class="p">,</span> |
| <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> |
| <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><metric_name></span><span class="p">,</span> |
| <span class="nt">"numBuckets"</span> <span class="p">:</span> <span class="err"><integer></span><span class="p">,</span> |
| <span class="nt">"lowerLimit"</span> <span class="p">:</span> <span class="err"><double></span><span class="p">,</span> |
| <span class="nt">"upperLimit"</span> <span class="p">:</span> <span class="err"><double></span><span class="p">,</span> |
| <span class="nt">"outlierHandlingMode"</span><span class="p">:</span> <span class="err"><mode></span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <h3 id="outlier-handling-modes">Outlier handling modes</h3> |
| |
| <p>The outlier handling mode specifies what should be done with values outside of the histogram's range. There are three supported modes:</p> |
| |
| <ul> |
| <li><code>ignore</code>: Throw away outlier values.</li> |
| <li><code>overflow</code>: A count of outlier values will be tracked by the histogram, available in the <code>lowerOutlierCount</code> and <code>upperOutlierCount</code> fields.</li> |
| <li><code>clip</code>: Outlier values will be clipped to the <code>lowerLimit</code> or the <code>upperLimit</code> and included in the histogram.</li> |
| </ul> |
| |
| <p>If you don't care about outliers, <code>ignore</code> is the cheapest option performance-wise. There is currently no difference in storage size among the modes.</p> |
| |
| <h3 id="output-fields">Output fields</h3> |
| |
| <p>The histogram aggregator's output object has the following fields:</p> |
| |
| <ul> |
| <li><code>lowerLimit</code>: Lower limit of the histogram</li> |
| <li><code>upperLimit</code>: Upper limit of the histogram</li> |
| <li><code>numBuckets</code>: Number of histogram buckets</li> |
| <li><code>outlierHandlingMode</code>: Outlier handling mode</li> |
| <li><code>count</code>: Total number of values contained in the histgram, excluding outliers</li> |
| <li><code>lowerOutlierCount</code>: Count of outlier values below <code>lowerLimit</code>. Only used if the outlier mode is <code>overflow</code>.</li> |
| <li><code>upperOutlierCount</code>: Count of outlier values above <code>upperLimit</code>. Only used if the outlier mode is <code>overflow</code>.</li> |
| <li><code>missingValueCount</code>: Count of null values seen by the histogram.</li> |
| <li><code>max</code>: Max value seen by the histogram. This does not include outlier values.</li> |
| <li><code>min</code>: Min value seen by the histogram. This does not include outlier values.</li> |
| <li><code>histogram</code>: An array of longs with size <code>numBuckets</code>, containing the bucket counts</li> |
| </ul> |
| |
| <h3 id="ingesting-existing-histograms">Ingesting existing histograms</h3> |
| |
| <p>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 <a href="#serialization-formats">Serialization formats</a> below for details.</p> |
| |
| <h3 id="serialization-formats">Serialization formats</h3> |
| |
| <h4 id="full-serialization-format">Full serialization format</h4> |
| |
| <p>This format includes the full histogram bucket count array in the serialization format.</p> |
| <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>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 |
| </code></pre></div> |
| <h4 id="sparse-serialization-format">Sparse serialization format</h4> |
| |
| <p>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 values.</p> |
| <div class="highlight"><pre><code class="language-text" data-lang="text"><span></span>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 |
| </code></pre></div> |
| <h3 id="combining-histograms-with-different-bucketing-schemes">Combining histograms with different bucketing schemes</h3> |
| |
| <p>It is possible to combine two histograms with different bucketing schemes (lowerLimit, upperLimit, numBuckets) together. </p> |
| |
| <p>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). </p> |
| |
| <p>When merging, we assume that values are evenly distributed within the buckets of the "right hand" histogram.</p> |
| |
| <p>When the right-hand histogram contains outliers (when using <code>overflow</code> mode), we assume that all of the outliers counted in the right-hand histogram will be outliers in the left-hand histogram as well.</p> |
| |
| <p>For performance and accuracy reasons, we recommend avoiding aggregation of histograms with different bucketing schemes if possible.</p> |
| |
| <h3 id="null-handling">Null handling</h3> |
| |
| <p>If <code>druid.generic.useDefaultValueForNull</code> is false, null values will be tracked in the <code>missingValueCount</code> field of the histogram.</p> |
| |
| <p>If <code>druid.generic.useDefaultValueForNull</code> is true, null values will be added to the histogram as the default 0.0 value.</p> |
| |
| <h2 id="histogram-post-aggregators">Histogram post-aggregators</h2> |
| |
| <p>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.</p> |
| |
| <h3 id="equal-buckets-post-aggregator">Equal buckets post-aggregator</h3> |
| |
| <p>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.</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> |
| <span class="nt">"type"</span><span class="p">:</span> <span class="s2">"equalBuckets"</span><span class="p">,</span> |
| <span class="nt">"name"</span><span class="p">:</span> <span class="s2">"<output_name>"</span><span class="p">,</span> |
| <span class="nt">"fieldName"</span><span class="p">:</span> <span class="s2">"<aggregator_name>"</span><span class="p">,</span> |
| <span class="nt">"numBuckets"</span><span class="p">:</span> <span class="err"><count></span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <h3 id="buckets-post-aggregator">Buckets post-aggregator</h3> |
| |
| <p>Computes a visual representation given an initial breakpoint, offset, and a bucket size.</p> |
| |
| <p>Bucket size determines the width of the binning interval.</p> |
| |
| <p>Offset determines the value on which those interval bins align.</p> |
| |
| <p>This aggregator is not supported for the fixed buckets histogram.</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> |
| <span class="nt">"type"</span><span class="p">:</span> <span class="s2">"buckets"</span><span class="p">,</span> |
| <span class="nt">"name"</span><span class="p">:</span> <span class="s2">"<output_name>"</span><span class="p">,</span> |
| <span class="nt">"fieldName"</span><span class="p">:</span> <span class="s2">"<aggregator_name>"</span><span class="p">,</span> |
| <span class="nt">"bucketSize"</span><span class="p">:</span> <span class="err"><bucket_size></span><span class="p">,</span> |
| <span class="nt">"offset"</span><span class="p">:</span> <span class="err"><offset></span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <h3 id="custom-buckets-post-aggregator">Custom buckets post-aggregator</h3> |
| |
| <p>Computes a visual representation of the approximate histogram with bins laid out according to the given breaks.</p> |
| |
| <p>This aggregator is not supported for the fixed buckets histogram.</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"customBuckets"</span><span class="p">,</span> <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><aggregator_name></span><span class="p">,</span> |
| <span class="nt">"breaks"</span> <span class="p">:</span> <span class="p">[</span> <span class="err"><value></span><span class="p">,</span> <span class="err"><value></span><span class="p">,</span> <span class="err">...</span> <span class="p">]</span> <span class="p">}</span> |
| </code></pre></div> |
| <h3 id="min-post-aggregator">min post-aggregator</h3> |
| |
| <p>Returns the minimum value of the underlying approximate or fixed buckets histogram aggregator</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"min"</span><span class="p">,</span> <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><aggregator_name></span> <span class="p">}</span> |
| </code></pre></div> |
| <h3 id="max-post-aggregator">max post-aggregator</h3> |
| |
| <p>Returns the maximum value of the underlying approximate or fixed buckets histogram aggregator</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"max"</span><span class="p">,</span> <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><aggregator_name></span> <span class="p">}</span> |
| </code></pre></div> |
| <h4 id="quantile-post-aggregator">quantile post-aggregator</h4> |
| |
| <p>Computes a single quantile based on the underlying approximate or fixed buckets histogram aggregator</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"quantile"</span><span class="p">,</span> <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><aggregator_name></span><span class="p">,</span> |
| <span class="nt">"probability"</span> <span class="p">:</span> <span class="err"><quantile></span> <span class="p">}</span> |
| </code></pre></div> |
| <h4 id="quantiles-post-aggregator">quantiles post-aggregator</h4> |
| |
| <p>Computes an array of quantiles based on the underlying approximate or fixed buckets histogram aggregator</p> |
| <div class="highlight"><pre><code class="language-json" data-lang="json"><span></span><span class="p">{</span> <span class="nt">"type"</span> <span class="p">:</span> <span class="s2">"quantiles"</span><span class="p">,</span> <span class="nt">"name"</span> <span class="p">:</span> <span class="err"><output_name></span><span class="p">,</span> <span class="nt">"fieldName"</span> <span class="p">:</span> <span class="err"><aggregator_name></span><span class="p">,</span> |
| <span class="nt">"probabilities"</span> <span class="p">:</span> <span class="p">[</span> <span class="err"><quantile></span><span class="p">,</span> <span class="err"><quantile></span><span class="p">,</span> <span class="err">...</span> <span class="p">]</span> <span class="p">}</span> |
| </code></pre></div> |
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