@Test public void createIndex(){ /** * 创建索引 * */ client.admin().indices().prepareCreate("player").get(); } /** * 创建映射 */ @Test public void testCreateIndexMapping_boost() throws Exception{ /** * 格式： "mappings": { "player": { "properties": { "name": {"index": "not_analyzed","type": "string"}, "age": {"type": "integer"}, "salary": {"type": "integer"}, "team": {"index": "not_analyzed","type": "string"}, "position": {"index": "not_analyzed","type": "string"} } } } */ //构建json的数据格式，创建映射 XContentBuilder mappingBuilder = XContentFactory.jsonBuilder() .startObject() .startObject("player") .startObject("properties") .startObject("name").field("type","string").field("index", "not_analyzed").endObject() .startObject("age").field("type","integer").endObject() .startObject("salary").field("type","integer").endObject() .startObject("team").field("type","string").field("index", "not_analyzed").endObject() .startObject("position").field("type","string").field("index", "not_analyzed").endObject() .endObject() .endObject() .endObject(); PutMappingRequest request = Requests.putMappingRequest("player") .type("player") .source(mappingBuilder); client.admin().indices().putMapping(request).get(); } @Test public void BulkInsertDocument() throws IOException { BulkRequestBuilder bulkRequest = client.prepareBulk();// either use client#prepare, or use Requests# to directly build index/delete requests bulkRequest.add(client.prepareIndex("player", "player", "1") .setSource(jsonBuilder() .startObject() .field("name", "郭德纲") .field("age", 33) .field("salary",3000) .field("team" , "cav") .field("position" , "sf") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "2") .setSource(jsonBuilder() .startObject() .field("name", "于谦") .field("age", 25) .field("salary",2000) .field("team" , "cav") .field("position" , "pg") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "3") .setSource(jsonBuilder() .startObject() .field("name", "岳云鹏") .field("age", 29) .field("salary",1000) .field("team" , "war") .field("position" , "pg") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "4") .setSource(jsonBuilder() .startObject() .field("name", "孙越") .field("age", 26) .field("salary",2000) .field("team" , "war") .field("position" , "sg") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "5") .setSource(jsonBuilder() .startObject() .field("name", "张云雷") .field("age", 26) .field("salary",2000) .field("team" , "war") .field("position" , "pf") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "6") .setSource(jsonBuilder() .startObject() .field("name", "爱迪生") .field("age", 40) .field("salary",1000) .field("team" , "tim") .field("position" , "pf") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "7") .setSource(jsonBuilder() .startObject() .field("name", "牛顿") .field("age", 21) .field("salary",500) .field("team" , "tim") .field("position" , "c") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "4") .setSource(jsonBuilder() .startObject() .field("name", "爱因斯坦") .field("age", 21) .field("salary",300) .field("team" , "tim") .field("position" , "sg") .endObject() ) ); bulkRequest.add(client.prepareIndex("player", "player", "8") .setSource(jsonBuilder() .startObject() .field("name", "特斯拉") .field("age", 20) .field("salary",500) .field("team" , "tim") .field("position" , "sf") .endObject() ) ); BulkResponse bulkResponse = bulkRequest.get(); if (bulkResponse.hasFailures()) { // process failures by iterating through each bulk response item } }
1：分组求count
计算每个球队的球员数量：
select team, count(*) as player_count from player group by team;
@Testpublic void groupAndCount() { //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team"); //3:将聚合条件放入查询条件中 builder.addAggregation(team); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); System.out.println(teamTerms); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); System.out.println("每个球队 :" + bucket.getKey() + "有 【"+bucket.getDocCount()+"】 个人"); }}
2：Group by 多个字段
计算每个球队每个位置的球员数 select team, position, count(*) as pos_count from player group by team,position;
/** * group by 多个字段 * 计算每个球队每个位置的球员数 * select team, position, count(*) as pos_count from player group by team, position; * */@Testpublic void groupMutilFields() { //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team"); TermsAggregationBuilder potition = AggregationBuilders.terms("position_count").field("position"); //注意父子关系team.subAggregation(potition); //3:将聚合条件放入查询条件中 builder.addAggregation(team).addAggregation(potition); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); //8:获取所有子聚合 Map<String, Aggregation> position_map = bucket.getAggregations().asMap(); StringTerms potitionTerms = (StringTerms)position_map.get("position_count"); //9：对子集合下面的内容迭代 队名--位置--球员 Iterator<StringTerms.Bucket> sub_iterator = potitionTerms.getBuckets().iterator(); while (sub_iterator.hasNext()){ StringTerms.Bucket sub_bucket = sub_iterator.next(); System.out.println("球队 :" + bucket.getKey() + "下面的 "+sub_bucket.getKey()+"的位置 有"+sub_bucket.getDocCount()); } }}
3：分组求最大
计算每个球队年龄最大/最小/总/平均的球员年龄
select team, max(age) as max_age from player group by team;
/** * 分组求：最大值、最小值、平均值 * 计算每个球队年龄最大/最小/总/平均的球员年龄 select team, max(age) as max_age from player group by team; * * */@Testpublic void groupMax(){ //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team"); //3: 指定要查哪一个字段的最大值 MaxAggregationBuilder ageFiled = AggregationBuilders.max("max_age").field("age"); //: 找到聚合关系：父子关系 team.subAggregation(ageFiled); //3:将聚合条件放入查询条件中 builder.addAggregation(team); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); //8:获取所有子聚合 Map age_map = bucket.getAggregations().asMap(); int age = (int)((InternalMax) age_map.get("max_age")).getValue(); System.out.println("球队 :" + bucket.getKey() + " 最大年龄： "+age); }}
4：分组求最小
计算每个球队年龄最大/最小/总/平均的球员年龄 select team, min(age) as max_age from player group by team;
/** * 分组求：最大值、最小值、平均值 * 计算每个球队年龄最大/最小/总/平均的球员年龄 select team, min(age) as max_age from player group by team; * * */@Testpublic void groupMin(){ //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team"); //3: 指定要查哪一个字段的最大值 MinAggregationBuilder ageFiled = AggregationBuilders.min("min_age").field("age"); //: 找到聚合关系：父子关系 team.subAggregation(ageFiled); //3:将聚合条件放入查询条件中 builder.addAggregation(team); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); //8:获取所有子聚合 Map age_map = bucket.getAggregations().asMap(); int age = (int)((InternalMin) age_map.get("max_age")).getValue(); System.out.println("球队 :" + bucket.getKey() + " 最小年龄： "+age); }}
5：分组求平均值
计算每个球队年龄最大/最小/总/平均的球员年龄 select team, min(age) as max_age from player group by team;
/** * 分组求：最大值、最小值、平均值 * 计算每个球队年龄最大/最小/总/平均的球员年龄 select team, min(age) as max_age from player group by team; * * */@Testpublic void groupAvg(){ //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team"); //3: 指定要查哪一个字段的最大值 AvgAggregationBuilder ageFiled = AggregationBuilders.avg("avg_age").field("age"); //: 找到聚合关系：父子关系 team.subAggregation(ageFiled); //3:将聚合条件放入查询条件中 builder.addAggregation(team); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); //8:获取所有子聚合 Map age_map = bucket.getAggregations().asMap(); Double age = ((InternalAvg) age_map.get("avg_age")).getValue(); System.out.println("球队 :" + bucket.getKey() + " 平均年龄： "+age); }}
6：分组求和
计算每个球队球员的平均年龄，同时又要计算总年薪 select team, avg(age)as avg_age, sum(salary) as total_salary from player group by team;
/** * 分组求：最大值、最小值、平均值 * 计算每个球队球员的平均年龄，同时又要计算总年薪 select team, avg(age)as avg_age, sum(salary) as total_salary from player group by team; * * */@Testpublic void groupsum(){ //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team"); //3: 指定要查哪一个字段 AvgAggregationBuilder ageFiled = AggregationBuilders.avg("avg_age").field("age"); SumAggregationBuilder salaryField= AggregationBuilders.sum("sum_salary").field("salary"); //: 找到聚合关系：父子关系 team.subAggregation(ageFiled).subAggregation(salaryField); //3:将聚合条件放入查询条件中 builder.addAggregation(team); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); //8:获取所有子聚合 Map age_map = bucket.getAggregations().asMap(); Double age = ((InternalAvg) age_map.get("avg_age")).getValue(); double sum_salary = ((InternalSum) age_map.get("sum_salary")).getValue(); System.out.println("球队 :" + bucket.getKey() + " 平均年龄： "+age + " 球队总salary：" + sum_salary); }}
7：聚合排序
计算每个球队总年薪，并按照总年薪倒序排列 select team, sum(salary) as total_salary from player group by team order by total_salary desc;
/** * 排序 计算每个球队总年薪，并按照总年薪倒序排列 select team, sum(salary) as total_salary from player group by team order by total_salary desc; * */@Testpublic void groupOrder(){ /** * * TermsBuilder teamAgg= AggregationBuilders.terms("team").order(Order.aggregation("total_salary ", false); SumBuilder salaryAgg= AggregationBuilders.avg("total_salary ").field("salary"); sbuilder.addAggregation(teamAgg.subAggregation(salaryAgg)); * */ //1：构建查询提交 SearchRequestBuilder builder = client.prepareSearch("player").setTypes("player"); //2：指定聚合条件 TermsAggregationBuilder team = AggregationBuilders.terms("player_count").field("team") .order(Terms.Order.aggregation("total_salary", false));//false是降序，true是升序 //3: 指定要查哪一个字段 SumAggregationBuilder salaryField= AggregationBuilders.sum("total_salary").field("salary"); //: 找到聚合关系：父子关系 team.subAggregation(salaryField); //3:将聚合条件放入查询条件中 builder.addAggregation(team); //4:执行action，返回searchResponse SearchResponse searchResponse = builder.get(); //5:将查询返回的searchResponse转换成map Map<String, Aggregation> aggregationMap = searchResponse.getAggregations().asMap(); //6:取出聚合的字段 StringTerms teamTerms = (StringTerms)aggregationMap.get("player_count"); //7:对聚合的字段进行迭代 Iterator<StringTerms.Bucket> iterator = teamTerms.getBuckets().iterator(); while (iterator.hasNext()){ StringTerms.Bucket bucket = iterator.next(); //8:获取所有子聚合 Map age_map = bucket.getAggregations().asMap(); double sum_salary = ((InternalSum) age_map.get("total_salary")).getValue(); System.out.println("球队 :" + bucket.getKey() + " 球队总salary：" + sum_salary); }}
来源：https://www.cnblogs.com/niutao/p/10909310.html