Elasticsearch分组--- java api 实现-Toy模板网

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这篇文章将已实战的方式带你实现es 的各种分组操作

1.：按照颜色分组，计算每个颜色卖出的个数

elasticsearch 分组,elasticsearch,elasticsearch,java,搜索引擎

 @Autowired
    RestHighLevelClient restHighLevelClient ;

    private String tvIndex = "tvs" ;

    @Test
    public void testAgg() throws IOException {
        //1. 构建请求
        SearchRequest request = new SearchRequest(tvIndex);
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

          // 构建查询
            // size(0) :不需要 查询的结果 ， 只需要分组的结果
        searchSourceBuilder.size(0) ;
        searchSourceBuilder.query(QueryBuilders.matchAllQuery()) ;

          // 构建Agg
        TermsAggregationBuilder colorAgg = AggregationBuilders.terms("group_by_color").field("color");

         // agg结合进 searchSourceBuilder
        searchSourceBuilder.aggregation(colorAgg);


        //结合
        request.source(searchSourceBuilder);

        //2. 执行

        SearchResponse searchResponse = restHighLevelClient.search(request, RequestOptions.DEFAULT);

        //3.处理返回结果
        Aggregations aggregations = searchResponse.getAggregations();

          //他是按照Terms 进行聚合的 所以取出来也需要 Terms 获取运行结果
        Terms colorAggRes = aggregations.get("group_by_color");
        List<? extends Terms.Bucket> colorBuckets = colorAggRes.getBuckets();

        // 遍历结果
        for (Terms.Bucket colorBucket : colorBuckets) {
            System.out.println("key: "+ colorBucket.getKeyAsString());
            System.out.println("docCount: "+colorBucket.getDocCount());
            System.out.println("=================");
        }

    }

elasticsearch 分组,elasticsearch,elasticsearch,java,搜索引擎

2.统计每种颜色电视平均价格

GET /tvs/_search
{
   "size" : 0,
   "aggs": {
      "colors": {
         "terms": {
            "field": "color"
         },
         "aggs": { 
            "avg_price": { 
               "avg": {
                  "field": "price" 
               }
            }
         }
      }
   }
}



 @Test
    public void testAgg02() throws IOException {
        //1. 构建请求
        SearchRequest request = new SearchRequest(tvIndex);
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        // 构建查询
        // size(0) :不需要 查询的结果 ， 只需要分组的结果
        searchSourceBuilder.size(0) ;
        searchSourceBuilder.query(QueryBuilders.matchAllQuery()) ;

        // 构建Agg
        TermsAggregationBuilder colorAgg = AggregationBuilders.terms("group_by_color").field("color");
        colorAgg.subAggregation(AggregationBuilders.avg("avgPrice").field("price"));


        // agg结合进 searchSourceBuilder
        searchSourceBuilder.aggregation(colorAgg);


        //结合
        request.source(searchSourceBuilder);

        //2. 执行

        SearchResponse searchResponse = restHighLevelClient.search(request, RequestOptions.DEFAULT);

        //3.处理返回结果
        Aggregations aggregations = searchResponse.getAggregations();
        //他是按照Terms 进行聚合的 所以取出来也需要 Terms 获取运行结果
        Terms colorAggRes = aggregations.get("group_by_color");
        List<? extends Terms.Bucket> colorBuckets = colorAggRes.getBuckets();

        // 遍历结果
        for (Terms.Bucket colorBucket : colorBuckets) {
            System.out.println("key: "+ colorBucket.getKeyAsString());
            System.out.println("docCount: "+colorBucket.getDocCount());

            //获得子聚合
            Aggregations subAgg = colorBucket.getAggregations();
              // 他是一个AVG 的聚合
            Avg avgPrice = subAgg.get("avgPrice");
            double value = avgPrice.getValue();
            System.out.println("已颜色区分的话平均价格是: "+value);
            System.out.println("=================");
        }

    }

需求三：按照颜色分组，计算每个颜色卖出的个数，以及每个颜色卖出的平均值、最大值、最小值、总和。

GET /tvs/_search
{
   "size" : 0,
   "aggs": {
      "colors": {
         "terms": {
            "field": "color"
         },
         "aggs": { 
            "avg_price": { 
               "avg": {
                  "field": "price" 
               }
            }
         }
      }
   }
}


 // 需求三：按照颜色分组，计算每个颜色卖出的个数，以及每个颜色卖出的平均值、最大值、最小值、总和。
    @Test
    public void testAgg03() throws IOException {
        //1. 构建请求
        SearchRequest request = new SearchRequest(tvIndex);
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();

        // 构建查询
        // size(0) :不需要 查询的结果 ， 只需要分组的结果
        searchSourceBuilder.size(0) ;
        searchSourceBuilder.query(QueryBuilders.matchAllQuery()) ;

        // 构建Agg
        TermsAggregationBuilder colorAgg = AggregationBuilders.terms("group_by_color").field("color");
        colorAgg.subAggregation(AggregationBuilders.avg("avgPrice").field("price"));
        colorAgg.subAggregation(AggregationBuilders.max("maxPrice").field("price"));
        colorAgg.subAggregation(AggregationBuilders.min("minPrice").field("price"));

        // agg结合进 searchSourceBuilder
        searchSourceBuilder.aggregation(colorAgg);


        //结合
        request.source(searchSourceBuilder);

        //2. 执行
        SearchResponse searchResponse = restHighLevelClient.search(request, RequestOptions.DEFAULT);

        //3.处理返回结果
        Aggregations aggregations = searchResponse.getAggregations();
        //他是按照Terms 进行聚合的 所以取出来也需要 Terms 获取运行结果
        Terms colorAggRes = aggregations.get("group_by_color");
        List<? extends Terms.Bucket> colorBuckets = colorAggRes.getBuckets();

        // 遍历结果
        for (Terms.Bucket colorBucket : colorBuckets) {
            System.out.println("key: "+ colorBucket.getKeyAsString());
            System.out.println("docCount: "+colorBucket.getDocCount());

            //获得子聚合
            Aggregations subAgg = colorBucket.getAggregations();
            // 他是一个AVG 的聚合
            Avg avgPrice = subAgg.get("avgPrice");
            Max maxPrice = subAgg.get("maxPrice");
            Min minPrice = subAgg.get("minPrice");

            double value = avgPrice.getValue();
            System.out.println("已颜色区分的话平均价格是: "+avgPrice.getValue());
            System.out.println("已颜色区分的话最大价格是: "+maxPrice.getValue());
            System.out.println("已颜色区分的话最小价格是: "+minPrice.getValue());
            System.out.println("=================");
        }

    }

划分范围 histogram 按照2000进行分组

GET /tvs/_search
{
   "size" : 0,
   "aggs":{
      "price":{
         "histogram":{ 
            "field": "price",
            "interval": 2000
         },
         "aggs":{
            "income": {
               "sum": { 
                 "field" : "price"
               }
             }
         }
      }
   }
}

 @Test
    public void testAgg05() throws IOException {

        SearchRequest request = new SearchRequest(tvIndex);

        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery()) ;
        searchSourceBuilder.size(0) ;

        // price agg
        HistogramAggregationBuilder priceAgg =
                AggregationBuilders.histogram("priceAggIn").field("price").interval(2000);

           // price agg sum
        priceAgg.subAggregation(AggregationBuilders.sum("sumPrice").field("price"));

        //综合
        searchSourceBuilder.aggregation(priceAgg) ;
        request.source(searchSourceBuilder) ;

        //执行查询
        SearchResponse searchResponse = restHighLevelClient.search(request, RequestOptions.DEFAULT);

        // 获得 Agg
        Aggregations aggregations = searchResponse.getAggregations();
        Histogram priceAggIn = aggregations.get("priceAggIn");
        for (Histogram.Bucket bucket : priceAggIn.getBuckets()) {
            System.out.println("priceAggIn的总数是: "+bucket.getDocCount());
            System.out.println("priceAggIn的key 是: "+bucket.getKeyAsString());
            Aggregations subAgg = bucket.getAggregations();
            Sum sumPrice = subAgg.get("sumPrice");
            System.out.println("价格区间中的总价是: "+sumPrice.getValue());
        }
    }

elasticsearch 分组,elasticsearch,elasticsearch,java,搜索引擎文章来源地址https://www.toymoban.com/news/detail-529537.html

按照季度进行间隔

GET /tvs/_search
{
   "size" : 0,
   "aggs": {
      "sales": {
         "date_histogram": {
            "field": "sold_date",
            "interval": "month", 
            "format": "yyyy-MM-dd",
            "min_doc_count" : 0, 
            "extended_bounds" : { 
                "min" : "2019-01-01",
                "max" : "2020-12-31"
            }
         }
      }
   }
}

 // 按照季度进行间隔
    @Test
    public void t3() throws IOException {

    SearchRequest searchRequest = new SearchRequest(tvIndex);
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery()) ;
        searchSourceBuilder.size(0) ;

        // make Agg
        DateHistogramAggregationBuilder dataHis = AggregationBuilders.dateHistogram("dataHistogram")
                .field("sold_date")
                //按照季度进行分组
                .calendarInterval(DateHistogramInterval.QUARTER)
                .format("yyyy-MM-dd")
                // 以上的min_doc_count参数会强制返回空桶
                .minDocCount(0)
                .extendedBounds(new ExtendedBounds("2019-01-01","2019-01-01")) ;

        //总结
        searchSourceBuilder.aggregation(dataHis);
        searchRequest.source(searchSourceBuilder);

        //查询
        SearchResponse searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);

        Aggregations aggregations = searchResponse.getAggregations();
        Histogram  dataHistogram = aggregations.get("dataHistogram");
        for (Histogram.Bucket bucket : dataHistogram.getBuckets()) {
            System.out.println("key :"+bucket.getKeyAsString());
            System.out.println("count :"+bucket.getDocCount());
        }


    }