elasticsearch索引名称（ElasticSearch--聚合查询）
elasticsearch索引名称（ElasticSearch--聚合查询）我们来看最简单的词条桶，brand_aggs就是自定义桶的名字 terms表示词条桶，field:brand表示按照字段brand来划分桶 size为0表示不想返回查询结果，从这里可以看出分页不影响聚合的结果，也就是说可以实现分页查询和聚合结果一起返回。Avg Aggregation：求平均值Max Aggregation：求最大值Min Aggregation：求最小值Percentiles Aggregation：求百分比Stats Aggregation：同时返回avg、max、min、sum、count等Sum Aggregation：求和Top hits Aggregation：求前几Value Count Aggregation：求总数可以看出ES的分组方式相当强大，mysql的group by只能实现类似Terms Aggregation的分组效果，而ES还可以根据阶梯和范围
聚合(aggs)聚合一般用于数据的统计分析 类似于mysql的group by。
聚合里面有两个基本概念，一个叫桶，一个叫度量。
桶的作用，是按照某种方式对数据进行分组，每一组数据成为一个桶。比如对手机品牌分组，可以得到小米桶，华为桶。
桶的分组方式Date Histogram Aggregation：根据日期阶梯分组，例如给定阶梯为周，会自动每周分为一组
Histogram Aggregation：根据数值阶梯分组，与日期类似
Terms Aggregation：根据词条内容分组，词条内容完全匹配的为一组
Range Aggregation：数值和日期的范围分组，指定开始和结束，然后按段分组
可以看出ES的分组方式相当强大，mysql的group by只能实现类似Terms Aggregation的分组效果，而ES还可以根据阶梯和范围来分组。
度量度量类似mysql的avg max等函数，用来求分组内平均值，最大值等。
比较常用的一些度量聚合方式：
Avg Aggregation：求平均值
Max Aggregation：求最大值
Min Aggregation：求最小值
Percentiles Aggregation：求百分比
Stats Aggregation：同时返回avg、max、min、sum、count等
Sum Aggregation：求和
Top hits Aggregation：求前几
Value Count Aggregation：求总数
我们来看最简单的词条桶，brand_aggs就是自定义桶的名字 terms表示词条桶，field:brand表示按照字段brand来划分桶 size为0表示不想返回查询结果，从这里可以看出分页不影响聚合的结果，也就是说可以实现分页查询和聚合结果一起返回。
下面的查询是通过品牌名来分组统计
GET /goods/_search
{
"size" : 0
"aggs" : {
"brand_aggs" : {
"terms" : {
"field" : "brand"
}
}
}
}
查询结果:
{
"took" : 3
"timed_out" : false
"_shards" : {
"total" : 3
"successful" : 3
"skipped" : 0
"failed" : 0
}
"hits" : {
"total" : {
"value" : 5
"relation" : "eq"
}
"max_score" : null
"hits" : [ ]
}
"aggregations" : {
"brand_aggs" : { //桶的名字
"doc_count_error_upper_bound" : 0
"sum_other_doc_count" : 0
"buckets" : [ //查询结果
{
"key" : "华为" //品牌名，因为是按照品牌分组
"doc_count" : 3 //统计的数量
}
{
"key" : "小米"
"doc_count" : 2
}
]
}
}
}
可以看到不需要加度量默认就把总数求出来了，如果要求品牌下平均手机价格，就需要加度量了
度量平均值GET /goods/_search
{
"size" : 0
"aggs" : {
"brand_aggs" : {
"terms" : {
"field" : "brand"
}
"aggs":{
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
返回结果:
{
"took" : 1
"timed_out" : false
"_shards" : {
"total" : 3
"successful" : 3
"skipped" : 0
"failed" : 0
}
"hits" : {
"total" : {
"value" : 5
"relation" : "eq"
}
"max_score" : null
"hits" : [ ]
}
"aggregations" : {
"brand_aggs" : {
"doc_count_error_upper_bound" : 0
"sum_other_doc_count" : 0
"buckets" : [
{
"key" : "华为"
"doc_count" : 3
"avg_price" : {
"value" : 4500.0
}
}
{
"key" : "小米"
"doc_count" : 2
"avg_price" : {
"value" : 5000.0
}
}
]
}
}
}
代码实现
public void testAggs() {
AbstractAggregationBuilder aggregationBuilder = AggregationBuilders.terms("brand_aggs").field("brand");//通过品牌分组
aggregationBuilder.subAggregation(AggregationBuilders.avg("avg_price").field("price")); //平均值度量，计算price平均值
NativeSearchQuery nativeSearchQuery = new NativeSearchQueryBuilder()
.withPageable(PageRequest.of(0 1)) //size只能大于0
.addAggregation(aggregationBuilder)
.build();
SearchHits<GoodsInfo> goodsInfos = elasticsearchRestTemplate.search(nativeSearchQuery GoodsInfo.class);
Terms brandTerms = goodsInfos.getAggregations().get("brand_aggs");
brandTerms.getBuckets().stream().forEach(bucket -> {
System.out.println(bucket.getKey()); //获取品牌名
System.out.println(bucket.getDocCount()); //获取总数
ParsedAvg avgPrice = bucket.getAggregations().get("avg_price"); //获取平均价格
System.out.println(avgPrice.getValue());
});
}
下面的例子是按照500为一个阶梯统计不同价位手机数量
GET /goods/_search
{
"size":0
"aggs":{
"price_histogram":{
"histogram": {
"field": "price"
"interval": 500
}
}
}
}
结果:
{
"took" : 103
"timed_out" : false
"_shards" : {
"total" : 3
"successful" : 3
"skipped" : 0
"failed" : 0
}
"hits" : {
"total" : {
"value" : 5
"relation" : "eq"
}
"max_score" : null
"hits" : [ ]
}
"aggregations" : {
"price_histogram" : {
"buckets" : [
{
"key" : 3500.0
"doc_count" : 1
}
{
"key" : 4000.0
"doc_count" : 0
}
{
"key" : 4500.0
"doc_count" : 2
}
{
"key" : 5000.0
"doc_count" : 0
}
{
"key" : 5500.0
"doc_count" : 2
}
]
}
}
}
代码:
public void testHistogram() {
AbstractAggregationBuilder aggregationBuilder = AggregationBuilders.histogram("price_histogram").field("price").interval(500);//500一个阶梯统计
NativeSearchQuery nativeSearchQuery = new NativeSearchQueryBuilder()
.withPageable(PageRequest.of(0 1)) //size只能大于0
.addAggregation(aggregationBuilder)
.build();
SearchHits<GoodsInfo> goodsInfos = elasticsearchRestTemplate.search(nativeSearchQuery GoodsInfo.class);
ParsedHistogram priceHistogram = goodsInfos.getAggregations().get("price_histogram");
priceHistogram.getBuckets().stream().forEach(bucket -> {
System.out.println(bucket.getKey()); //阶梯值
System.out.println(bucket.getDocCount()); //获取总数
});
}
统计价格在4000-6000手机的数量
GET /goods/_search
{
"size": 0
"aggs": {
"price_range": {
"range": {
"field": "price"
"ranges": [
{
"from": 4000
"to": 6000
}
]
}
}
}
}
结果:
{
"took" : 1
"timed_out" : false
"_shards" : {
"total" : 3
"successful" : 3
"skipped" : 0
"failed" : 0
}
"hits" : {
"total" : {
"value" : 5
"relation" : "eq"
}
"max_score" : null
"hits" : [ ]
}
"aggregations" : {
"price_range" : {
"buckets" : [
{
"key" : "4000.0-6000.0"
"from" : 4000.0
"to" : 6000.0
"doc_count" : 4
}
]
}
}
}
代码:
public void testRangeAggrs() {
AbstractAggregationBuilder aggregationBuilder = AggregationBuilders.range("price_range").field("price").addRange(4000 6000);//500一个阶梯统计
NativeSearchQuery nativeSearchQuery = new NativeSearchQueryBuilder()
.withPageable(PageRequest.of(0 1)) //size只能大于0
.addAggregation(aggregationBuilder)
.build();
SearchHits<GoodsInfo> goodsInfos = elasticsearchRestTemplate.search(nativeSearchQuery GoodsInfo.class);
ParsedRange priceHistogram = goodsInfos.getAggregations().get("price_range");
priceHistogram.getBuckets().stream().forEach(bucket -> {
System.out.println(bucket.getKey()); //key值
System.out.println(bucket.getDocCount()); //获取总数
});
}
GET /cars/_search
{
"size":0
"aggs" : {
"date" : {
"date_histogram" : {
"field" : "sold"
"interval" : "1M"
"format" : "yyyy-MM"
"time_zone": "-01:00"
"min_doc_count": 1
}
}
}
}
结果:
"aggregations" : {
"date" : {
"buckets" : [
{
"key_as_string" : "2013-12"
"key" : 1385859600000
"doc_count" : 1
}
{
"key_as_string" : "2014-02"
"key" : 1391216400000
"doc_count" : 1
}
{
"key_as_string" : "2014-05"
"key" : 1398906000000
"doc_count" : 1
}
{
"key_as_string" : "2014-07"
"key" : 1404176400000
"doc_count" : 1
}
{
"key_as_string" : "2014-08"
"key" : 1406854800000
"doc_count" : 1
}
{
"key_as_string" : "2014-10"
"key" : 1412125200000
"doc_count" : 1
}
{
"key_as_string" : "2014-11"
"key" : 1414803600000
"doc_count" : 2
}
]
}
}
