##使用多字段聚合SearchResponse agg2 = client.prepareSearch("t_mdc_drg") .setTypes("cz", "cx") .addAggregation( AggregationBuilders.terms("id_mdc").field("id_mdc")//设置聚合条件 group by id_mdc,id_drg .subAggregation( AggregationBuilders.terms("name_mdc").field("id_drg") .subAggregation(AggregationBuilders.avg("avg").field("date"))// 聚合结果 avg(date) .subAggregation(AggregationBuilders.max("max").field("date"))//聚合结果 max(date) ) ) .execute().actionGet(); Map maps = agg2.getAggregations().asMap(); StringTerms id_mdc=(StringTerms)maps.get("id_mdc"); for (StringTerms.Bucket bucket:id_mdc.getBuckets() ) { StringTerms id_drg=(StringTerms)bucket.getAggregations().asMap().get("name_mdc"); for (StringTerms.Bucket bucket2: id_drg.getBuckets() ) { InternalAvg avg = (InternalAvg)bucket2.getAggregations().asMap().get("avg"); InternalMax max = (InternalMax)bucket2.getAggregations().asMap().get("max"); System.out.println(bucket.getKeyAsString()+"|"+bucket2.getKeyAsString()+"="+avg+"|"+max); } } QueryBuilders：
QueryBuilders: boolQuery: must:相当于sql的and must not:相当于sql的not should:相当于sql的or mathcquery:单个匹配 mathcAllQuery:匹配所有 termQuery:termQuery("key", obj) 完全匹配 ;termsQuery("key", obj1, obj2..) 一次匹配多个值 multiMatchQuery:multiMatchQuery("text", "field1", "field2"..); 匹配多个字段, field有通配符忒行 idsQuery:构造一个只会匹配的特定数据 id 的查询 constantScoreQuery:看了一下这个类的构造函数ConstantScoreQuery(Filter filter) ，我的理解就是通过构造filter来完成文档的过滤，并且返回一个复合当前过滤条件的文档的常量分数，这个分数等于为查询条件设置的boost fuzzyQuery:模糊查询 moreLikeThisQuery:文档中的文本查询 prefixQuery:前缀查询 rangeQuery:在一个范围内查询相匹配的文档 termQuery:一个查询相匹配的文件包含一个术语 termsQuery:一个查询相匹配的多个value---minimumMatch(1); // 设置最小数量的匹配提供了条件。默认为1。 wildcardQuery:通配符查询 nestedQuery:嵌套查询---scoreMode("total");// max, total, avg or none disMaxQuery:对子查询的结果做union, score沿用子查询score的最大值, spanFirstQuery:跨度查询,还包括(spanNearQuery,spanNotQuery,spanOrQuery,spanTermQuery) 其他字段解释：
percent_terms_to_match：匹配项（term）的百分比，默认是0.3 min_term_freq：一篇文档中一个词语至少出现次数，小于这个值的词将被忽略，默认是2 max_query_terms：一条查询语句中允许最多查询词语的个数，默认是25 stop_words：设置停止词，匹配时会忽略停止词 min_doc_freq：一个词语最少在多少篇文档中出现，小于这个值的词会将被忽略，默认是无限制 max_doc_freq：一个词语最多在多少篇文档中出现，大于这个值的词会将被忽略，默认是无限制 min_word_len：最小的词语长度，默认是0 max_word_len：最多的词语长度，默认无限制 boost_terms：设置词语权重，默认是1 boost：设置查询权重，默认是1 analyzer：设置使用的分词器，默认是使用该字段指定的分词器
操作例子（es版本还是1.7.2）：
//查询某个医生的明细-- boolean query and 条件组合查询(时间+护士名称)BoolQueryBuilder must = QueryBuilders.boolQuery().must( QueryBuilders.matchQuery("name_doc", "681_护士206")) .must(QueryBuilders.rangeQuery("yke123").gte(startTime + START_DATE_YUE) .lte(endTime + END_DATE_YUE));SearchResponse searchResponse = mSRB.setQuery(must).execute().actionGet();/** * 在es中所有的查询结果都会保存在SearchResponse中，在从SearchResponse中读取数据的时候，有两种方式：第一种是对Query的结果进行读取， * 使用的是hit，每一条查询到的doc都是一个hit，可以将每个hit转换为map形式的数据，map的具体形式为<"field"，"value">的形式 */for(SearchHit hit:searchResponse.getHits()){ Map source = hit.getSource();//每条数据 if (!source.isEmpty()) { String name_depa=(String) source.get("name_depa"); String name_doc=(String)source.get("name_doc"); System.out.println(name_depa+"=="+name_doc); }}//查询该人的工时SearchResponse searchResponse1 = mSRB.setQuery(must).addAggregation(AggregationBuilders.sum("hushi").field("hushi")).get();InternalSum hushi = searchResponse1.getAggregations().get("hushi");System.out.println("总工的工时"+hushi.getValue());/** * 第二种方式是针对查询中的聚合问题（aggregation），聚合完成后的每条doc都是一个bucket（桶），他的访问只能通过bucket来进行，而不能使用hit *///设置聚合查询条件---使用SearchResponse封装结果SearchResponse searchResponse3 = mSRB.addAggregation(AggregationBuilders.terms("name_doc").field("name_doc").size(0))//返回所有数据用0 .execute().actionGet();System.out.println(searchResponse);Terms depa_count = searchResponse.getAggregations().get("name_doc");for (Terms.Bucket bucket : depa_count.getBuckets()) { String name_depa = bucket.getKey();//科室名称 long docCount = bucket.getDocCount();//科室出现的次数 System.out.println(name_depa + "==" + docCount);}//该医生在该月的总条数long l = searchResponse.getHits().totalHits();System.out.println(l+"==============="); 使用游标sroll进行分页查询遍历
client = (Client) Pools.getPool().borrowObject();//从链接池中获取客户端//构建查询器SearchRequestBuilder mSRB = client .prepareSearch(index1)//索引--查询工时表 .setSearchType(SearchType.SCAN)//设置查询类型--DFS_QUERY_THEN_FETCH .setSize(100).setScroll(TimeValue.timeValueMinutes(8));String startTime = "2013-08";String endTime = "2013-09";String timeType = "0";BoolQueryBuilder must = QueryBuilders.boolQuery() //.must(QueryBuilders.matchQuery("name_doc", "681_护士206")) .must(QueryBuilders.rangeQuery("yke123").gte(startTime + START_DATE_YUE) .lte(endTime + END_DATE_YUE));SearchResponse searchResponse = mSRB.setQuery(must).execute().actionGet();//使用scroll遍历查询的数据Date begin = new Date();long count = searchResponse.getHits().getTotalHits();//第一次不返回数据for(int i=0,sum=0; sum source = hit.getSource(); String name_depa=(String) source.get("name_depa"); String name_doc=(String)source.get("name_doc"); String yke123=(String)source.get("yke123"); System.out.println("科室:"+name_depa+"医生:"+name_doc+"开单时间:"+yke123); }; System.out.println("总量"+count+" 已经查到"+sum);}Date end = new Date();System.out.println("耗时: "+(end.getTime()-begin.getTime())); 引用他人的http://blog.csdn.net/xr568897472/article/details/73826255：
- （1）统计某个字段的数量
- ValueCountBuilder vcb= AggregationBuilders.count("count_uid").field("uid");
- （2）去重统计某个字段的数量（有少量误差）
- CardinalityBuilder cb= AggregationBuilders.cardinality("distinct_count_uid").field("uid");
- （3）聚合过滤
- FilterAggregationBuilder fab= AggregationBuilders.filter("uid_filter").filter(QueryBuilders.queryStringQuery("uid:001"));
- （4）按某个字段分组
- TermsBuilder tb= AggregationBuilders.terms("group_name").field("name");
- （5）求和
- SumBuilder sumBuilder= AggregationBuilders.sum("sum_price").field("price");
- （6）求平均
- AvgBuilder ab= AggregationBuilders.avg("avg_price").field("price");
- （7）求最大值
- MaxBuilder mb= AggregationBuilders.max("max_price").field("price");
- （8）求最小值
- MinBuilder min= AggregationBuilders.min("min_price").field("price");
- （9）按日期间隔分组
- DateHistogramBuilder dhb= AggregationBuilders.dateHistogram("dh").field("date");
- （10）获取聚合里面的结果
- TopHitsBuilder thb= AggregationBuilders.topHits("top_result");
- （11）嵌套的聚合
- NestedBuilder nb= AggregationBuilders.nested("negsted_path").path("quests");
- （12）反转嵌套
- AggregationBuilders.reverseNested("res_negsted").path("kps ");
Elasticsearch的补充：
//创建索引 client.admin().indices().prepareCreate("").execute().actionGet(); //删除索引 client.admin().indices().prepareDelete("").execute().actionGet(); //获取查询的数据 client.prepareGet("index","type","id").execute().actionGet(); //更新数据 UpdateRequest up = new UpdateRequest(); up.index("index").type("type").id("id").doc("data"); //更新后获取响应信息 client.update(up).actionGet().status().getStatus(); //添加数据 HashMap map = new HashMap<>(); map.put("aa","bb"); client.prepareIndex("index","type").setSource(map).get(); ArrayList fields = new ArrayList<>(); //创建索引+mapping CreateIndexRequestBuilder index = client.admin().indices().prepareCreate("index"); XContentBuilder mapping = XContentFactory.jsonBuilder().startObject().startObject("properties"); //都统一映射成一种类型，需要特殊映射可以做判断 for (String field: fields ) { mapping.startObject(field) .field("type","text") .field("index","not_analyzed") .endObject(); } mapping.endObject() .endObject(); index.addMapping("type",mapping); index.execute().actionGet(); //查询 //1.构建查询条件 //构建查询器 SearchRequestBuilder srg = client .prepareSearch("index") .setSearchType(SearchType.DFS_QUERY_THEN_FETCH); //封装查询条件 BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery(); boolQueryBuilder.must(QueryBuilders.termQuery("field","value"));//指定字段条件 boolQueryBuilder.mustNot(QueryBuilders.termQuery("field","value"));//过滤 boolQueryBuilder.must(QueryBuilders.rangeQuery("field").gte("").lte(""));//时间范围条件 srg.setQuery(boolQueryBuilder); //对查询结果进聚合 srg.addAggregation( AggregationBuilders.terms("alias").field("field") .subAggregation(AggregationBuilders.stats("alias2").field("field2")) ).execute().actionGet(); //排序构建 SortBuilder sortBuilder= SortBuilders.fieldSort("age"); //单个查询条件构建 QueryBuilder queryBuilder = QueryBuilders.termQuery("field", "value"); //通配符查询 QueryBuilders.wildcardQuery("user", "k*hy17*"); //指定取哪些字段---setFetchSource client.prepareSearch("index") .setSearchType("") .setFetchSource("age","")// setFetchSource有两个参数 第一个参数是包含哪些参数 第二个参数是排除哪些参数 .setFrom(0).setSize(10); //聚合函数的统计api AggregationBuilders.count("ageCount").field("age"); AggregationBuilders.max("max").field("age"); AggregationBuilders.sum("sum").field("age"); AggregationBuilders.avg("avg").field("age"); //统计样本基本指标 AggregationBuilders.stats("stats").field("age");//包含了max，min， avg,count,sum SearchResponse response=srg.get(); Stats stats= response.getAggregations().get("stats"); stats.getAvgAsString(); stats.getMaxAsString(); stats.getMinAsString(); stats.getSumAsString(); stats.getCount(); //数据的遍历：非聚合+聚合，非聚合使用的是hits遍历，聚合则使用的是buckets Terms terms = response.getAggregations().get(""); for (Terms.Bucket bucket:terms.getBuckets() ) { bucket.getKey();//聚合字段的名称---一个字段一个桶 bucket.getDocCount();//该聚合字段出现的次数 } //解析例子： select count(age),sum(age) from t_user group by age AggregationBuilder by_age=AggregationBuilders.terms("by_age").field("age"); AggregationBuilder ageSum=AggregationBuilders.sum("ageSum").field("age"); SearchResponse sr=srg.addAggregation(by_age.subAggregation(ageSum)).get(); Aggregations aggregations = sr.getAggregations(); for (Aggregation agg: aggregations ) { Terms term = (Terms)agg; for(Terms.Bucket bucket:term.getBuckets()){ String age = bucket.getKeyAsString();//by_age字段-----若只有一个分组的话可以直接这样获取，多个的话 需要使用 //Aggregation alias = bucket.getAggregations().asMap().get("alias");//多个聚合的话需要这样获取每个聚合分组的桶 long count = bucket.getDocCount();//count(age)---age出现的次数 //获取分组后，再通过聚合函数取得的值 Aggregation by_age1 = bucket.getAggregations().getAsMap().get("by_age"); Terms terms_by_age = (Terms)by_age1; for (Terms.Bucket bucket_by_age: terms_by_age.getBuckets() ) { String filed_ageSum2= bucket_by_age.getKeyAsString();//聚合字段 double value_ageSum=((Sum)bucket_by_age.getAggregations().asMap().get("ageSum")).getValue();//聚合的值 } } } } 操作实例：
//求其他统计值 SearchResponse sr = srb.setQuery(QueryBuilders.boolQuery() .must(QueryBuilders.termQuery(FieldConstant.HOSPITAL_GRADE_FIELD, hospitalDegree))//医院等级 .must(QueryBuilders.matchQuery(FieldConstant.ID_MDC, mdcID)) .must(QueryBuilders.matchQuery(FieldConstant.ID_DRG, drgID)) .must(QueryBuilders.rangeQuery(FieldConstant.HOSPITAL_LEAVE_TIME_FIELD).gte(startTime + FieldConstant.START_DATE).lte(endTime + FieldConstant.END_DATE)) //出院时间 .mustNot(QueryBuilders.termQuery(FieldConstant.DIAGNOSE_TYPE_FIELD, "0202")) //过滤门诊 ) .addAggregation(AggregationBuilders.terms("doctor") .field(FieldConstant.DOCTOR_ID_FIELD)//医生ID .size((int) DOCTOR_NUM) .subAggregation(AggregationBuilders.terms("addrg") .field(FieldConstant.NAME_AD_DRG) .size((int) ADDRG_NUM) .subAggregation(AggregationBuilders.sum("numWeight") .field(FieldConstant.WEIGHT)//权重 ) .subAggregation(AggregationBuilders.sum("moneySumWeight") .field(FieldConstant.MONEY_SUM_WEIGHT)//医保总费用*权重 ) .subAggregation(AggregationBuilders.sum("zyscWeight") .field(FieldConstant.ZYSC_WEIGHT)//住院时长*权重 ) .subAggregation(AggregationBuilders.extendedStats("zyscStd") .field(FieldConstant.ZHUYUAN_TIME)//住院时长标准差 ) .subAggregation(AggregationBuilders.sum("zzyWeight") .field(FieldConstant.ZZY_WEIGHT)//再住院标识*权重 ) .subAggregation(AggregationBuilders.sum("tskssWeight") .field(FieldConstant.TSKSS_FLAG_WEIGHT)//特殊抗生素使用标识*权重 ) ) ).execute().actionGet(); Terms termsDoc = sr.getAggregations().get("doctor"); for (Bucket bk : termsDoc.getBuckets()) { //医生基本信息 String doctorId = bk.getKey();//医生ID //可信度等级判断 DoctorModel dm = new DoctorModel(); if(doctorLevelMap.containsKey(doctorId)){ dm = doctorLevelMap.get(doctorId); } ArrayList adDrgDocList = new ArrayList (); Terms termsAdDrg = bk.getAggregations().get("addrg"); for (Bucket bkAdDrg : termsAdDrg.getBuckets()) { //该医生该AD_DRG的就诊次数 long treatmentNum = bkAdDrg.getDocCount(); if(treatmentNum >= 3){ String adDrgName = bkAdDrg.getKey();//AD_DRG名称 //就诊人次*权重 Sum num_agg = bkAdDrg.getAggregations().get("numWeight"); double num = num_agg.getValue(); //医保总费用*权重 Sum money_sum_agg = bkAdDrg.getAggregations().get("moneySumWeight"); double moneySum = money_sum_agg.getValue(); //住院时长*权重 Sum zysc_agg = bkAdDrg.getAggregations().get("zyscWeight"); double zysc = zysc_agg.getValue(); //住院时长标准差 ExtendedStats zysc_std_agg = bkAdDrg.getAggregations().get("zyscStd"); double zyscStd = zysc_std_agg.getStdDeviation(); if(Double.isNaN(zyscStd)){ zyscStd = 0.0; } //再住院标识*权重 Sum zzy_agg = bkAdDrg.getAggregations().get("zzyWeight"); double zzy = zzy_agg.getValue(); //特殊抗生素使用标识*权重 Sum tskss_agg = bkAdDrg.getAggregations().get("tskssWeight"); double tskss = tskss_agg.getValue(); AdDrgDocModel ad = new AdDrgDocModel(num, moneySum, zysc, zyscStd, zzy, tskss, drgID, adDrgName); //设置ADDRG的可信度 if(dm.getAdDrgDocList() != null){ for(AdDrgDocModel addm:dm.getAdDrgDocList()){ if(addm.getAdDrgName().equals(adDrgName)){ ad.setLevel(addm.getLevel()); break; } } } ad.setTreatmentNum(treatmentNum); adDrgDocList.add(ad); } } if(adDrgDocList.size() > 0){ DoctorModel doctorModel = new DoctorModel(); doctorModel.setDoctorId(doctorId);//医生ID doctorModel.setDrgID(drgID);//DRGID doctorModel.setAdDrgDocList(adDrgDocList);//ADDRG doctorModel.setLevel(dm.getLevel());//可信度等级 doctorList.add(doctorModel); } }