代码：
package com.itheima.es;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.search.aggregations.Aggregation;
import org.elasticsearch.search.aggregations.AggregationBuilders;
import org.elasticsearch.search.aggregations.Aggregations;
import org.elasticsearch.search.aggregations.bucket.histogram.*;
import org.elasticsearch.search.aggregations.bucket.terms.Terms;
import org.elasticsearch.search.aggregations.bucket.terms.TermsAggregationBuilder;
import org.elasticsearch.search.aggregations.metrics.*;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit4.SpringRunner;
import java.io.IOException;
import java.util.List;
/**
* creste by itheima.itcast
*/
@SpringBootTest
@RunWith(SpringRunner.class)
public class TestAggs {
@Autowired
RestHighLevelClient client;
//需求一：按照颜色分组，计算每个颜色卖出的个数
@Test
public void testAggs() throws IOException {
// GET /tvs/_search
// {
// "size": 0,
// "query": {"match_all": {}},
// "aggs": {
// "group_by_color": {
// "terms": {
// "field": "color"
// }
// }
// }
// }
//1 构建请求
SearchRequest searchRequest=new SearchRequest("tvs");
//请求体
SearchSourceBuilder searchSourceBuilder=new SearchSourceBuilder();
searchSourceBuilder.size(0);
searchSourceBuilder.query(QueryBuilders.matchAllQuery());
TermsAggregationBuilder termsAggregationBuilder = AggregationBuilders.terms("group_by_color").field("color");
searchSourceBuilder.aggregation(termsAggregationBuilder);
//请求体放入请求头
searchRequest.source(searchSourceBuilder);
//2 执行
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
//3 获取结果
// "aggregations" : {
// "group_by_color" : {
// "doc_count_error_upper_bound" : 0,
// "sum_other_doc_count" : 0,
// "buckets" : [
// {
// "key" : "红色",
// "doc_count" : 4
// },
// {
// "key" : "绿色",
// "doc_count" : 2
// },
// {
// "key" : "蓝色",
// "doc_count" : 2
// }
// ]
// }
Aggregations aggregations = searchResponse.getAggregations();
Terms group_by_color = aggregations.get("group_by_color");
List<? extends Terms.Bucket> buckets = group_by_color.getBuckets();
for (Terms.Bucket bucket : buckets) {
String key = bucket.getKeyAsString();
System.out.println("key:"+key);
long docCount = bucket.getDocCount();
System.out.println("docCount:"+docCount);
System.out.println("=================================");
}
}
// #需求二：按照颜色分组，计算每个颜色卖出的个数，每个颜色卖出的平均价格
@Test
public void testAggsAndAvg() throws IOException {
// GET /tvs/_search
// {
// "size": 0,
// "query": {"match_all": {}},
// "aggs": {
// "group_by_color": {
// "terms": {
// "field": "color"
// },
// "aggs": {
// "avg_price": {
// "avg": {
// "field": "price"
// }
// }
// }
// }
// }
// }
//1 构建请求
SearchRequest searchRequest=new SearchRequest("tvs");
//请求体
SearchSourceBuilder searchSourceBuilder=new SearchSourceBuilder();
searchSourceBuilder.size(0);
searchSourceBuilder.query(QueryBuilders.matchAllQuery());
TermsAggregationBuilder termsAggregationBuilder = AggregationBuilders.terms("group_by_color").field("color");
//terms聚合下填充一个子聚合
AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders.avg("avg_price").field("price");
termsAggregationBuilder.subAggregation(avgAggregationBuilder);
searchSourceBuilder.aggregation(termsAggregationBuilder);
//请求体放入请求头
searchRequest.source(searchSourceBuilder);
//2 执行
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
//3 获取结果
// {
// "key" : "红色",
// "doc_count" : 4,
// "avg_price" : {
// "value" : 3250.0
// }
// }
Aggregations aggregations = searchResponse.getAggregations();
Terms group_by_color = aggregations.get("group_by_color");
List<? extends Terms.Bucket> buckets = group_by_color.getBuckets();
for (Terms.Bucket bucket : buckets) {
String key = bucket.getKeyAsString();
System.out.println("key:"+key);
long docCount = bucket.getDocCount();
System.out.println("docCount:"+docCount);
Aggregations aggregations1 = bucket.getAggregations();
Avg avg_price = aggregations1.get("avg_price");
double value = avg_price.getValue();
System.out.println("value:"+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。
@Test
public void testAggsAndMore() throws IOException {
// GET /tvs/_search
// {
// "size" : 0,
// "aggs": {
// "group_by_color": {
// "terms": {
// "field": "color"
// },
// "aggs": {
// "avg_price": { "avg": { "field": "price" } },
// "min_price" : { "min": { "field": "price"} },
// "max_price" : { "max": { "field": "price"} },
// "sum_price" : { "sum": { "field": "price" } }
// }
// }
// }
// }
//1 构建请求
SearchRequest searchRequest=new SearchRequest("tvs");
//请求体
SearchSourceBuilder searchSourceBuilder=new SearchSourceBuilder();
searchSourceBuilder.size(0);
searchSourceBuilder.query(QueryBuilders.matchAllQuery());
TermsAggregationBuilder termsAggregationBuilder = AggregationBuilders.terms("group_by_color").field("color");
//termsAggregationBuilder里放入多个子聚合
AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders.avg("avg_price").field("price");
MinAggregationBuilder minAggregationBuilder = AggregationBuilders.min("min_price").field("price");
MaxAggregationBuilder maxAggregationBuilder = AggregationBuilders.max("max_price").field("price");
SumAggregationBuilder sumAggregationBuilder = AggregationBuilders.sum("sum_price").field("price");
termsAggregationBuilder.subAggregation(avgAggregationBuilder);
termsAggregationBuilder.subAggregation(minAggregationBuilder);
termsAggregationBuilder.subAggregation(maxAggregationBuilder);
termsAggregationBuilder.subAggregation(sumAggregationBuilder);
searchSourceBuilder.aggregation(termsAggregationBuilder);
//请求体放入请求头
searchRequest.source(searchSourceBuilder);
//2 执行
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
//3 获取结果
// {
// "key" : "红色",
// "doc_count" : 4,
// "max_price" : {
// "value" : 8000.0
// },
// "min_price" : {
// "value" : 1000.0
// },
// "avg_price" : {
// "value" : 3250.0
// },
// "sum_price" : {
// "value" : 13000.0
// }
// }
Aggregations aggregations = searchResponse.getAggregations();
Terms group_by_color = aggregations.get("group_by_color");
List<? extends Terms.Bucket> buckets = group_by_color.getBuckets();
for (Terms.Bucket bucket : buckets) {
String key = bucket.getKeyAsString();
System.out.println("key:"+key);
long docCount = bucket.getDocCount();
System.out.println("docCount:"+docCount);
Aggregations aggregations1 = bucket.getAggregations();
Max max_price = aggregations1.get("max_price");
double maxPriceValue = max_price.getValue();
System.out.println("maxPriceValue:"+maxPriceValue);
Min min_price = aggregations1.get("min_price");
double minPriceValue = min_price.getValue();
System.out.println("minPriceValue:"+minPriceValue);
Avg avg_price = aggregations1.get("avg_price");
double avgPriceValue = avg_price.getValue();
System.out.println("avgPriceValue:"+avgPriceValue);
Sum sum_price = aggregations1.get("sum_price");
double sumPriceValue = sum_price.getValue();
System.out.println("sumPriceValue:"+sumPriceValue);
System.out.println("=================================");
}
}
// #需求四：按照售价每2000价格划分范围，算出每个区间的销售总额 histogram
@Test
public void testAggsAndHistogram() throws IOException {
// GET /tvs/_search
// {
// "size" : 0,
// "aggs":{
// "by_histogram":{
// "histogram":{
// "field": "price",
// "interval": 2000
// },
// "aggs":{
// "income": {
// "sum": {
// "field" : "price"
// }
// }
// }
// }
// }
// }
//1 构建请求
SearchRequest searchRequest=new SearchRequest("tvs");
//请求体
SearchSourceBuilder searchSourceBuilder=new SearchSourceBuilder();
searchSourceBuilder.size(0);
searchSourceBuilder.query(QueryBuilders.matchAllQuery());
HistogramAggregationBuilder histogramAggregationBuilder = AggregationBuilders.histogram("by_histogram").field("price").interval(2000);
SumAggregationBuilder sumAggregationBuilder = AggregationBuilders.sum("income").field("price");
histogramAggregationBuilder.subAggregation(sumAggregationBuilder);
searchSourceBuilder.aggregation(histogramAggregationBuilder);
//请求体放入请求头
searchRequest.source(searchSourceBuilder);
//2 执行
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
//3 获取结果
// {
// "key" : 0.0,
// "doc_count" : 3,
// income" : {
// "value" : 3700.0
// }
// }
Aggregations aggregations = searchResponse.getAggregations();
Histogram group_by_color = aggregations.get("by_histogram");
List<? extends Histogram.Bucket> buckets = group_by_color.getBuckets();
for (Histogram.Bucket bucket : buckets) {
String keyAsString = bucket.getKeyAsString();
System.out.println("keyAsString:"+keyAsString);
long docCount = bucket.getDocCount();
System.out.println("docCount:"+docCount);
Aggregations aggregations1 = bucket.getAggregations();
Sum income = aggregations1.get("income");
double value = income.getValue();
System.out.println("value:"+value);
System.out.println("=================================");
}
}
// #需求五：计算每个季度的销售总额
@Test
public void testAggsAndDateHistogram() throws IOException {
// GET /tvs/_search
// {
// "size" : 0,
// "aggs": {
// "sales": {
// "date_histogram": {
// "field": "sold_date",
// "interval": "quarter",
// "format": "yyyy-MM-dd",
// "min_doc_count" : 0,
// "extended_bounds" : {
// "min" : "2019-01-01",
// "max" : "2020-12-31"
// }
// },
// "aggs": {
// "income": {
// "sum": {
// "field": "price"
// }
// }
// }
// }
// }
// }
//1 构建请求
SearchRequest searchRequest=new SearchRequest("tvs");
//请求体
SearchSourceBuilder searchSourceBuilder=new SearchSourceBuilder();
searchSourceBuilder.size(0);
searchSourceBuilder.query(QueryBuilders.matchAllQuery());
DateHistogramAggregationBuilder dateHistogramAggregationBuilder = AggregationBuilders.dateHistogram("date_histogram").field("sold_date").calendarInterval(DateHistogramInterval.QUARTER)
.format("yyyy-MM-dd").minDocCount(0).extendedBounds(new ExtendedBounds("2019-01-01", "2020-12-31"));
SumAggregationBuilder sumAggregationBuilder = AggregationBuilders.sum("income").field("price");
dateHistogramAggregationBuilder.subAggregation(sumAggregationBuilder);
searchSourceBuilder.aggregation(dateHistogramAggregationBuilder);
//请求体放入请求头
searchRequest.source(searchSourceBuilder);
//2 执行
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
//3 获取结果
// {
// "key_as_string" : "2019-01-01",
// "key" : 1546300800000,
// "doc_count" : 0,
// "income" : {
// "value" : 0.0
// }
// }
Aggregations aggregations = searchResponse.getAggregations();
ParsedDateHistogram date_histogram = aggregations.get("date_histogram");
List<? extends Histogram.Bucket> buckets = date_histogram.getBuckets();
for (Histogram.Bucket bucket : buckets) {
String keyAsString = bucket.getKeyAsString();
System.out.println("keyAsString:"+keyAsString);
long docCount = bucket.getDocCount();
System.out.println("docCount:"+docCount);
Aggregations aggregations1 = bucket.getAggregations();
Sum income = aggregations1.get("income");
double value = income.getValue();
System.out.println("value:"+value);
System.out.println("====================");
}
}
}