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<div><h1>11 聚合：聚合查询之Metric聚合详解</h1>
<h2>如何理解metric聚合</h2>
<blockquote>
<p>在[bucket聚合]中，我画了一张图辅助你构筑体系，那么metric聚合又如何理解呢？</p>
</blockquote>
<p>如果你直接去看官方文档，大概也有十几种：</p>
<p><img src="assets/es-agg-metric-1.png" alt="img" /></p>
<blockquote>
<p>那么metric聚合又如何理解呢？我认为从两个角度：</p>
</blockquote>
<ul>
<li><strong>从分类看</strong>：Metric聚合分析分为<strong>单值分析</strong>和<strong>多值分析</strong>两类</li>
<li><strong>从功能看</strong>：根据具体的应用场景设计了一些分析api, 比如地理位置，百分数等等</li>
</ul>
<blockquote>
<p>融合上述两个方面，我们可以梳理出大致的一个mind图：</p>
</blockquote>
<ul>
<li>
<p>单值分析</p>
<p>: 只输出一个分析结果</p>
<ul>
<li>标准stat型
<ul>
<li><code>avg</code> 平均值</li>
<li><code>max</code> 最大值</li>
<li><code>min</code> 最小值</li>
<li><code>sum</code> 和</li>
<li><code>value_count</code> 数量</li>
</ul>
</li>
<li>其它类型
<ul>
<li><code>cardinality</code> 基数（distinct去重）</li>
<li><code>weighted_avg</code> 带权重的avg</li>
<li><code>median_absolute_deviation</code> 中位值</li>
</ul>
</li>
</ul>
</li>
<li>
<p>多值分析</p>
<p>: 单值之外的</p>
<ul>
<li>stats型
<ul>
<li><code>stats</code> 包含avg,max,min,sum和count</li>
<li><code>matrix_stats</code> 针对矩阵模型</li>
<li><code>extended_stats</code></li>
<li><code>string_stats</code> 针对字符串</li>
</ul>
</li>
<li>百分数型
<ul>
<li><code>percentiles</code> 百分数范围</li>
<li><code>percentile_ranks</code> 百分数排行</li>
</ul>
</li>
<li>地理位置型
<ul>
<li><code>geo_bounds</code> Geo bounds</li>
<li><code>geo_centroid</code> Geo-centroid</li>
<li><code>geo_line</code> Geo-Line</li>
</ul>
</li>
<li>Top型
<ul>
<li><code>top_hits</code> 分桶后的top hits</li>
<li><code>top_metrics</code></li>
</ul>
</li>
</ul>
</li>
</ul>
<blockquote>
<p><strong>通过上述列表（我就不画图了），我们构筑的体系是基于分类和功能，而不是具体的项（比如avg,percentiles...)；这是不同的认知维度: 具体的项是碎片化，分类和功能这种是你需要构筑的体系</strong>。@pdai</p>
</blockquote>
<h2>单值分析: 标准stat类型</h2>
<h3><code>avg</code> 平均值</h3>
<p>计算班级的平均分</p>
<pre><code class="language-bash">POST /exams/_search?size=0
{
&quot;aggs&quot;: {
&quot;avg_grade&quot;: { &quot;avg&quot;: { &quot;field&quot;: &quot;grade&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;avg_grade&quot;: {
&quot;value&quot;: 75.0
}
}
}
</code></pre>
<h3><code>max</code> 最大值</h3>
<p>计算销售最高价</p>
<pre><code class="language-bash">POST /sales/_search?size=0
{
&quot;aggs&quot;: {
&quot;max_price&quot;: { &quot;max&quot;: { &quot;field&quot;: &quot;price&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;max_price&quot;: {
&quot;value&quot;: 200.0
}
}
}
</code></pre>
<h3><code>min</code> 最小值</h3>
<p>计算销售最低价</p>
<pre><code class="language-bash">POST /sales/_search?size=0
{
&quot;aggs&quot;: {
&quot;min_price&quot;: { &quot;min&quot;: { &quot;field&quot;: &quot;price&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;min_price&quot;: {
&quot;value&quot;: 10.0
}
}
}
</code></pre>
<h3><code>sum</code> 和</h3>
<p>计算销售总价</p>
<pre><code class="language-bash">POST /sales/_search?size=0
{
&quot;query&quot;: {
&quot;constant_score&quot;: {
&quot;filter&quot;: {
&quot;match&quot;: { &quot;type&quot;: &quot;hat&quot; }
}
}
},
&quot;aggs&quot;: {
&quot;hat_prices&quot;: { &quot;sum&quot;: { &quot;field&quot;: &quot;price&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;hat_prices&quot;: {
&quot;value&quot;: 450.0
}
}
}
</code></pre>
<h3><code>value_count</code> 数量</h3>
<p>销售数量统计</p>
<pre><code class="language-bash">POST /sales/_search?size=0
{
&quot;aggs&quot; : {
&quot;types_count&quot; : { &quot;value_count&quot; : { &quot;field&quot; : &quot;type&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;types_count&quot;: {
&quot;value&quot;: 7
}
}
}
</code></pre>
<h2>单值分析: 其它类型</h2>
<h3><code>weighted_avg</code> 带权重的avg</h3>
<pre><code class="language-bash">POST /exams/_search
{
&quot;size&quot;: 0,
&quot;aggs&quot;: {
&quot;weighted_grade&quot;: {
&quot;weighted_avg&quot;: {
&quot;value&quot;: {
&quot;field&quot;: &quot;grade&quot;
},
&quot;weight&quot;: {
&quot;field&quot;: &quot;weight&quot;
}
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;weighted_grade&quot;: {
&quot;value&quot;: 70.0
}
}
}
</code></pre>
<h3><code>cardinality</code> 基数（distinct去重）</h3>
<pre><code class="language-bash">POST /sales/_search?size=0
{
&quot;aggs&quot;: {
&quot;type_count&quot;: {
&quot;cardinality&quot;: {
&quot;field&quot;: &quot;type&quot;
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;type_count&quot;: {
&quot;value&quot;: 3
}
}
}
</code></pre>
<h3><code>median_absolute_deviation</code> 中位值</h3>
<pre><code class="language-bash">GET reviews/_search
{
&quot;size&quot;: 0,
&quot;aggs&quot;: {
&quot;review_average&quot;: {
&quot;avg&quot;: {
&quot;field&quot;: &quot;rating&quot;
}
},
&quot;review_variability&quot;: {
&quot;median_absolute_deviation&quot;: {
&quot;field&quot;: &quot;rating&quot;
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;review_average&quot;: {
&quot;value&quot;: 3.0
},
&quot;review_variability&quot;: {
&quot;value&quot;: 2.0
}
}
}
</code></pre>
<h2>非单值分析：stats型</h2>
<h3><code>stats</code> 包含avg,max,min,sum和count</h3>
<pre><code class="language-bash">POST /exams/_search?size=0
{
&quot;aggs&quot;: {
&quot;grades_stats&quot;: { &quot;stats&quot;: { &quot;field&quot;: &quot;grade&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;grades_stats&quot;: {
&quot;count&quot;: 2,
&quot;min&quot;: 50.0,
&quot;max&quot;: 100.0,
&quot;avg&quot;: 75.0,
&quot;sum&quot;: 150.0
}
}
}
</code></pre>
<h3><code>matrix_stats</code> 针对矩阵模型</h3>
<p>以下示例说明了使用矩阵统计量来描述收入与贫困之间的关系。</p>
<pre><code class="language-bash">GET /_search
{
&quot;aggs&quot;: {
&quot;statistics&quot;: {
&quot;matrix_stats&quot;: {
&quot;fields&quot;: [ &quot;poverty&quot;, &quot;income&quot; ]
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;statistics&quot;: {
&quot;doc_count&quot;: 50,
&quot;fields&quot;: [ {
&quot;name&quot;: &quot;income&quot;,
&quot;count&quot;: 50,
&quot;mean&quot;: 51985.1,
&quot;variance&quot;: 7.383377037755103E7,
&quot;skewness&quot;: 0.5595114003506483,
&quot;kurtosis&quot;: 2.5692365287787124,
&quot;covariance&quot;: {
&quot;income&quot;: 7.383377037755103E7,
&quot;poverty&quot;: -21093.65836734694
},
&quot;correlation&quot;: {
&quot;income&quot;: 1.0,
&quot;poverty&quot;: -0.8352655256272504
}
}, {
&quot;name&quot;: &quot;poverty&quot;,
&quot;count&quot;: 50,
&quot;mean&quot;: 12.732000000000001,
&quot;variance&quot;: 8.637730612244896,
&quot;skewness&quot;: 0.4516049811903419,
&quot;kurtosis&quot;: 2.8615929677997767,
&quot;covariance&quot;: {
&quot;income&quot;: -21093.65836734694,
&quot;poverty&quot;: 8.637730612244896
},
&quot;correlation&quot;: {
&quot;income&quot;: -0.8352655256272504,
&quot;poverty&quot;: 1.0
}
} ]
}
}
}
</code></pre>
<h3><code>extended_stats</code></h3>
<p>根据从汇总文档中提取的数值计算统计信息。</p>
<pre><code class="language-bash">GET /exams/_search
{
&quot;size&quot;: 0,
&quot;aggs&quot;: {
&quot;grades_stats&quot;: { &quot;extended_stats&quot;: { &quot;field&quot;: &quot;grade&quot; } }
}
}
</code></pre>
<p>上面的汇总计算了所有文档的成绩统计信息。聚合类型为extended_stats，并且字段设置定义将在其上计算统计信息的文档的数字字段。</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;grades_stats&quot;: {
&quot;count&quot;: 2,
&quot;min&quot;: 50.0,
&quot;max&quot;: 100.0,
&quot;avg&quot;: 75.0,
&quot;sum&quot;: 150.0,
&quot;sum_of_squares&quot;: 12500.0,
&quot;variance&quot;: 625.0,
&quot;variance_population&quot;: 625.0,
&quot;variance_sampling&quot;: 1250.0,
&quot;std_deviation&quot;: 25.0,
&quot;std_deviation_population&quot;: 25.0,
&quot;std_deviation_sampling&quot;: 35.35533905932738,
&quot;std_deviation_bounds&quot;: {
&quot;upper&quot;: 125.0,
&quot;lower&quot;: 25.0,
&quot;upper_population&quot;: 125.0,
&quot;lower_population&quot;: 25.0,
&quot;upper_sampling&quot;: 145.71067811865476,
&quot;lower_sampling&quot;: 4.289321881345245
}
}
}
}
</code></pre>
<h3><code>string_stats</code> 针对字符串</h3>
<p>用于计算从聚合文档中提取的字符串值的统计信息。这些值可以从特定的关键字字段中检索。</p>
<pre><code class="language-bash">POST /my-index-000001/_search?size=0
{
&quot;aggs&quot;: {
&quot;message_stats&quot;: { &quot;string_stats&quot;: { &quot;field&quot;: &quot;message.keyword&quot; } }
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;message_stats&quot;: {
&quot;count&quot;: 5,
&quot;min_length&quot;: 24,
&quot;max_length&quot;: 30,
&quot;avg_length&quot;: 28.8,
&quot;entropy&quot;: 3.94617750050791
}
}
}
</code></pre>
<h2>非单值分析：百分数型</h2>
<h3><code>percentiles</code> 百分数范围</h3>
<p>针对从聚合文档中提取的数值计算一个或多个百分位数。</p>
<pre><code class="language-bash">GET latency/_search
{
&quot;size&quot;: 0,
&quot;aggs&quot;: {
&quot;load_time_outlier&quot;: {
&quot;percentiles&quot;: {
&quot;field&quot;: &quot;load_time&quot;
}
}
}
}
</code></pre>
<p>默认情况下，百分位度量标准将生成一定范围的百分位：[1，5，25，50，75，95，99]。</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;load_time_outlier&quot;: {
&quot;values&quot;: {
&quot;1.0&quot;: 5.0,
&quot;5.0&quot;: 25.0,
&quot;25.0&quot;: 165.0,
&quot;50.0&quot;: 445.0,
&quot;75.0&quot;: 725.0,
&quot;95.0&quot;: 945.0,
&quot;99.0&quot;: 985.0
}
}
}
}
</code></pre>
<h3><code>percentile_ranks</code> 百分数排行</h3>
<p>根据从汇总文档中提取的数值计算一个或多个百分位等级。</p>
<pre><code class="language-bash">GET latency/_search
{
&quot;size&quot;: 0,
&quot;aggs&quot;: {
&quot;load_time_ranks&quot;: {
&quot;percentile_ranks&quot;: {
&quot;field&quot;: &quot;load_time&quot;,
&quot;values&quot;: [ 500, 600 ]
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-bash">{
...
&quot;aggregations&quot;: {
&quot;load_time_ranks&quot;: {
&quot;values&quot;: {
&quot;500.0&quot;: 90.01,
&quot;600.0&quot;: 100.0
}
}
}
}
</code></pre>
<p>上述结果表示90.01％的页面加载在500ms内完成，而100％的页面加载在600ms内完成。</p>
<h2>非单值分析：地理位置型</h2>
<h3><code>geo_bounds</code> Geo bounds</h3>
<pre><code class="language-bash">PUT /museums
{
&quot;mappings&quot;: {
&quot;properties&quot;: {
&quot;location&quot;: {
&quot;type&quot;: &quot;geo_point&quot;
}
}
}
}
POST /museums/_bulk?refresh
{&quot;index&quot;:{&quot;_id&quot;:1}}
{&quot;location&quot;: &quot;52.374081,4.912350&quot;, &quot;name&quot;: &quot;NEMO Science Museum&quot;}
{&quot;index&quot;:{&quot;_id&quot;:2}}
{&quot;location&quot;: &quot;52.369219,4.901618&quot;, &quot;name&quot;: &quot;Museum Het Rembrandthuis&quot;}
{&quot;index&quot;:{&quot;_id&quot;:3}}
{&quot;location&quot;: &quot;52.371667,4.914722&quot;, &quot;name&quot;: &quot;Nederlands Scheepvaartmuseum&quot;}
{&quot;index&quot;:{&quot;_id&quot;:4}}
{&quot;location&quot;: &quot;51.222900,4.405200&quot;, &quot;name&quot;: &quot;Letterenhuis&quot;}
{&quot;index&quot;:{&quot;_id&quot;:5}}
{&quot;location&quot;: &quot;48.861111,2.336389&quot;, &quot;name&quot;: &quot;Musée du Louvre&quot;}
{&quot;index&quot;:{&quot;_id&quot;:6}}
{&quot;location&quot;: &quot;48.860000,2.327000&quot;, &quot;name&quot;: &quot;Musée d'Orsay&quot;}
POST /museums/_search?size=0
{
&quot;query&quot;: {
&quot;match&quot;: { &quot;name&quot;: &quot;musée&quot; }
},
&quot;aggs&quot;: {
&quot;viewport&quot;: {
&quot;geo_bounds&quot;: {
&quot;field&quot;: &quot;location&quot;,
&quot;wrap_longitude&quot;: true
}
}
}
}
</code></pre>
<p>上面的汇总展示了如何针对具有商店业务类型的所有文档计算位置字段的边界框</p>
<pre><code class="language-json">{
...
&quot;aggregations&quot;: {
&quot;viewport&quot;: {
&quot;bounds&quot;: {
&quot;top_left&quot;: {
&quot;lat&quot;: 48.86111099738628,
&quot;lon&quot;: 2.3269999679178
},
&quot;bottom_right&quot;: {
&quot;lat&quot;: 48.85999997612089,
&quot;lon&quot;: 2.3363889567553997
}
}
}
}
}
</code></pre>
<h3><code>geo_centroid</code> Geo-centroid</h3>
<pre><code class="language-bash">PUT /museums
{
&quot;mappings&quot;: {
&quot;properties&quot;: {
&quot;location&quot;: {
&quot;type&quot;: &quot;geo_point&quot;
}
}
}
}
POST /museums/_bulk?refresh
{&quot;index&quot;:{&quot;_id&quot;:1}}
{&quot;location&quot;: &quot;52.374081,4.912350&quot;, &quot;city&quot;: &quot;Amsterdam&quot;, &quot;name&quot;: &quot;NEMO Science Museum&quot;}
{&quot;index&quot;:{&quot;_id&quot;:2}}
{&quot;location&quot;: &quot;52.369219,4.901618&quot;, &quot;city&quot;: &quot;Amsterdam&quot;, &quot;name&quot;: &quot;Museum Het Rembrandthuis&quot;}
{&quot;index&quot;:{&quot;_id&quot;:3}}
{&quot;location&quot;: &quot;52.371667,4.914722&quot;, &quot;city&quot;: &quot;Amsterdam&quot;, &quot;name&quot;: &quot;Nederlands Scheepvaartmuseum&quot;}
{&quot;index&quot;:{&quot;_id&quot;:4}}
{&quot;location&quot;: &quot;51.222900,4.405200&quot;, &quot;city&quot;: &quot;Antwerp&quot;, &quot;name&quot;: &quot;Letterenhuis&quot;}
{&quot;index&quot;:{&quot;_id&quot;:5}}
{&quot;location&quot;: &quot;48.861111,2.336389&quot;, &quot;city&quot;: &quot;Paris&quot;, &quot;name&quot;: &quot;Musée du Louvre&quot;}
{&quot;index&quot;:{&quot;_id&quot;:6}}
{&quot;location&quot;: &quot;48.860000,2.327000&quot;, &quot;city&quot;: &quot;Paris&quot;, &quot;name&quot;: &quot;Musée d'Orsay&quot;}
POST /museums/_search?size=0
{
&quot;aggs&quot;: {
&quot;centroid&quot;: {
&quot;geo_centroid&quot;: {
&quot;field&quot;: &quot;location&quot;
}
}
}
}
</code></pre>
<p>上面的汇总显示了如何针对所有具有犯罪类型的盗窃文件计算位置字段的质心。</p>
<pre><code class="language-json">{
...
&quot;aggregations&quot;: {
&quot;centroid&quot;: {
&quot;location&quot;: {
&quot;lat&quot;: 51.00982965203002,
&quot;lon&quot;: 3.9662131341174245
},
&quot;count&quot;: 6
}
}
}
</code></pre>
<h3><code>geo_line</code> Geo-Line</h3>
<pre><code class="language-bash">PUT test
{
&quot;mappings&quot;: {
&quot;dynamic&quot;: &quot;strict&quot;,
&quot;_source&quot;: {
&quot;enabled&quot;: false
},
&quot;properties&quot;: {
&quot;my_location&quot;: {
&quot;type&quot;: &quot;geo_point&quot;
},
&quot;group&quot;: {
&quot;type&quot;: &quot;keyword&quot;
},
&quot;@timestamp&quot;: {
&quot;type&quot;: &quot;date&quot;
}
}
}
}
POST /test/_bulk?refresh
{&quot;index&quot;: {}}
{&quot;my_location&quot;: {&quot;lat&quot;:37.3450570, &quot;lon&quot;: -122.0499820}, &quot;@timestamp&quot;: &quot;2013-09-06T16:00:36&quot;}
{&quot;index&quot;: {}}
{&quot;my_location&quot;: {&quot;lat&quot;: 37.3451320, &quot;lon&quot;: -122.0499820}, &quot;@timestamp&quot;: &quot;2013-09-06T16:00:37Z&quot;}
{&quot;index&quot;: {}}
{&quot;my_location&quot;: {&quot;lat&quot;: 37.349283, &quot;lon&quot;: -122.0505010}, &quot;@timestamp&quot;: &quot;2013-09-06T16:00:37Z&quot;}
POST /test/_search?filter_path=aggregations
{
&quot;aggs&quot;: {
&quot;line&quot;: {
&quot;geo_line&quot;: {
&quot;point&quot;: {&quot;field&quot;: &quot;my_location&quot;},
&quot;sort&quot;: {&quot;field&quot;: &quot;@timestamp&quot;}
}
}
}
}
</code></pre>
<p>将存储桶中的所有geo_point值聚合到由所选排序字段排序的LineString中。</p>
<pre><code class="language-json">{
&quot;aggregations&quot;: {
&quot;line&quot;: {
&quot;type&quot; : &quot;Feature&quot;,
&quot;geometry&quot; : {
&quot;type&quot; : &quot;LineString&quot;,
&quot;coordinates&quot; : [
[
-122.049982,
37.345057
],
[
-122.050501,
37.349283
],
[
-122.049982,
37.345132
]
]
},
&quot;properties&quot; : {
&quot;complete&quot; : true
}
}
}
}
</code></pre>
<h2>非单值分析：Top型</h2>
<h3><code>top_hits</code> 分桶后的top hits</h3>
<pre><code class="language-bash">POST /sales/_search?size=0
{
&quot;aggs&quot;: {
&quot;top_tags&quot;: {
&quot;terms&quot;: {
&quot;field&quot;: &quot;type&quot;,
&quot;size&quot;: 3
},
&quot;aggs&quot;: {
&quot;top_sales_hits&quot;: {
&quot;top_hits&quot;: {
&quot;sort&quot;: [
{
&quot;date&quot;: {
&quot;order&quot;: &quot;desc&quot;
}
}
],
&quot;_source&quot;: {
&quot;includes&quot;: [ &quot;date&quot;, &quot;price&quot; ]
},
&quot;size&quot;: 1
}
}
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-json">{
...
&quot;aggregations&quot;: {
&quot;top_tags&quot;: {
&quot;doc_count_error_upper_bound&quot;: 0,
&quot;sum_other_doc_count&quot;: 0,
&quot;buckets&quot;: [
{
&quot;key&quot;: &quot;hat&quot;,
&quot;doc_count&quot;: 3,
&quot;top_sales_hits&quot;: {
&quot;hits&quot;: {
&quot;total&quot; : {
&quot;value&quot;: 3,
&quot;relation&quot;: &quot;eq&quot;
},
&quot;max_score&quot;: null,
&quot;hits&quot;: [
{
&quot;_index&quot;: &quot;sales&quot;,
&quot;_type&quot;: &quot;_doc&quot;,
&quot;_id&quot;: &quot;AVnNBmauCQpcRyxw6ChK&quot;,
&quot;_source&quot;: {
&quot;date&quot;: &quot;2015/03/01 00:00:00&quot;,
&quot;price&quot;: 200
},
&quot;sort&quot;: [
1425168000000
],
&quot;_score&quot;: null
}
]
}
}
},
{
&quot;key&quot;: &quot;t-shirt&quot;,
&quot;doc_count&quot;: 3,
&quot;top_sales_hits&quot;: {
&quot;hits&quot;: {
&quot;total&quot; : {
&quot;value&quot;: 3,
&quot;relation&quot;: &quot;eq&quot;
},
&quot;max_score&quot;: null,
&quot;hits&quot;: [
{
&quot;_index&quot;: &quot;sales&quot;,
&quot;_type&quot;: &quot;_doc&quot;,
&quot;_id&quot;: &quot;AVnNBmauCQpcRyxw6ChL&quot;,
&quot;_source&quot;: {
&quot;date&quot;: &quot;2015/03/01 00:00:00&quot;,
&quot;price&quot;: 175
},
&quot;sort&quot;: [
1425168000000
],
&quot;_score&quot;: null
}
]
}
}
},
{
&quot;key&quot;: &quot;bag&quot;,
&quot;doc_count&quot;: 1,
&quot;top_sales_hits&quot;: {
&quot;hits&quot;: {
&quot;total&quot; : {
&quot;value&quot;: 1,
&quot;relation&quot;: &quot;eq&quot;
},
&quot;max_score&quot;: null,
&quot;hits&quot;: [
{
&quot;_index&quot;: &quot;sales&quot;,
&quot;_type&quot;: &quot;_doc&quot;,
&quot;_id&quot;: &quot;AVnNBmatCQpcRyxw6ChH&quot;,
&quot;_source&quot;: {
&quot;date&quot;: &quot;2015/01/01 00:00:00&quot;,
&quot;price&quot;: 150
},
&quot;sort&quot;: [
1420070400000
],
&quot;_score&quot;: null
}
]
}
}
}
]
}
}
}
</code></pre>
<h3><code>top_metrics</code></h3>
<pre><code class="language-bash">POST /test/_bulk?refresh
{&quot;index&quot;: {}}
{&quot;s&quot;: 1, &quot;m&quot;: 3.1415}
{&quot;index&quot;: {}}
{&quot;s&quot;: 2, &quot;m&quot;: 1.0}
{&quot;index&quot;: {}}
{&quot;s&quot;: 3, &quot;m&quot;: 2.71828}
POST /test/_search?filter_path=aggregations
{
&quot;aggs&quot;: {
&quot;tm&quot;: {
&quot;top_metrics&quot;: {
&quot;metrics&quot;: {&quot;field&quot;: &quot;m&quot;},
&quot;sort&quot;: {&quot;s&quot;: &quot;desc&quot;}
}
}
}
}
</code></pre>
<p>返回</p>
<pre><code class="language-json">{
&quot;aggregations&quot;: {
&quot;tm&quot;: {
&quot;top&quot;: [ {&quot;sort&quot;: [3], &quot;metrics&quot;: {&quot;m&quot;: 2.718280076980591 } } ]
}
}
}
</code></pre>
<h2>参考文章</h2>
<p>https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-metrics.html</p>
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