java elastic 常用查询

java Elastic 客户端基本使用

引入jar

   compile 'org.elasticsearch:elasticsearch:5.5.0'   compile 'org.elasticsearch.client:transport:5.5.0

client基本使用

得到client

Settings settings = Settings.builder().put("cluster.name", "lw-6-test").build();TransportClient client = new PreBuiltTransportClient(settings);client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("10.10.10.6"), 9300));

关闭资源

client.close();

• 搜索关键字全部要小写。

get得到指定index type id的数据

  public static void prepareGet(TransportClient client) throws Exception {        GetResponse response = client.prepareGet("mytest", "test", "p1").get();        System.out.println(response);    } output:{"_index":"mytest","_type":"test","_id":"p1","_version":1,"found":true,"_source":{"name":"mac Book 笔记本","price":1233,"description":"这是笔记本","cats":["3c","computer"]}}

insert添加数据

  public static void insert(TransportClient client) throws Exception {        Map<String,Object> resource = new HashMap<>();        resource.put("name","mac Note");        resource.put("price",8877);        resource.put("description","mac Note 新款");        IndexRequestBuilder index = client.prepareIndex("mytest", "test");        IndexResponse insertResponse = index.setSource(resource).execute().get();        System.out.println(insertResponse);    }output:    IndexResponse[index=mytest,type=test,id=AV8CZmTgGnilLCUrybiV,version=1,result=created,shards={"total":2,"successful":1,"failed":0}]

delete删除数据

  public static void delete(TransportClient client) throws Exception{        BulkByScrollResponse response = DeleteByQueryAction.INSTANCE.newRequestBuilder(client)                .filter(QueryBuilders.matchQuery("name", "mac")) //搜索                .source("mytest") //index                .get();       long deleted = response.getDeleted();       System.out.println("删除个数: "+deleted);    }

update 修改数据

 public static void update(TransportClient client) throws Exception{        Map<String,Object> data = new HashMap<>();        data.put("name","new mac node");        UpdateRequest updateRequest =  new UpdateRequest();        updateRequest.index("mytest");        updateRequest.type("test");        updateRequest.id("AV8CfcSLGnilLCUryoEl");        updateRequest.doc(data);        UpdateResponse response = client.update(updateRequest).get();        System.out.println(response);    }output:UpdateResponse[index=mytest,type=test,id=AV8CfcSLGnilLCUryoEl,version=2,result=updated,shards=ShardInfo{total=2, successful=1, failures=[]}]

MultiGet查询多个index

    public static void multiIndex(TransportClient client) throws Exception {        MultiGetResponse multiGetItemResponses = client.prepareMultiGet()                .add("mytest","test","AV8CfcSLGnilLCUryoEl") //多个index                .add("instestdb_business_log-2017.09","instestdb_business_log","AV7KHPtGDF9uyeK_lXln") //多个index                .get();        for(MultiGetItemResponse itemResponses : multiGetItemResponses) {            GetResponse response = itemResponses.getResponse();            if(response.isExists()) {                 String json = response.getSourceAsString(); //获取到_source field                System.out.println(json);            }        }    }

Bulk API 一次请求多个添加和删除

 public static void BulkRequest(TransportClient client) throws Exception {        BulkRequestBuilder bulkRequest = client.prepareBulk();        IndexRequestBuilder index1 = client.prepareIndex("mytest", "test");        IndexRequestBuilder index2 = client.prepareIndex("mytest", "test");        Map<String,Object> resource = new HashMap<>();        resource.put("name","华硕");        resource.put("price",5577);        resource.put("description","华硕z460");        index1.setSource(resource);        Map<String,Object> resource1 = new HashMap<>();        resource1.put("name","小米2");        resource1.put("price",4577);        resource1.put("description","新机超薄");        index1.setSource(resource);        index2.setSource(resource1);        bulkRequest.add(index1);        bulkRequest.add(index2);        BulkResponse bulkResponse = bulkRequest.get();        if(bulkResponse.hasFailures()) {            System.out.println(bulkResponse.buildFailureMessage());        }        bulkResponse.forEach(response ->{            System.out.println(response.getId());        });    }

query dsl 使用

import static org.elasticsearch.index.query.QueryBuilders.*; Settings settings = Settings.builder()                .put("cluster.name", "lw-6-test").build();        TransportClient client = new PreBuiltTransportClient(settings);        client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("10.10.10.6"), 9300));        allquery(client); //具体的query dsl查询        client.close();

Match All Query 查询所有的数据

    public static void allquery(TransportClient client) throws Exception{        QueryBuilder qb = matchAllQuery();        SearchResponse response = client.prepareSearch("mytest").setTypes("test").setSize(3).setQuery(qb).get();        System.out.println("length: "+response.getHits().getHits().length );        if(response.getHits().getTotalHits() != 0) {            for (SearchHit hit : response.getHits().getHits()) {                System.out.println(hit.getSourceAsString());            }        }    }

Match Query 查询单一条件的数据

  public static void myMatchQuery(TransportClient client) throws Exception {        QueryBuilder qb = matchQuery("name","mac");        SearchResponse response = client.prepareSearch("mytest").setTypes("test").setQuery(qb).get();        System.out.println("length: "+response.getHits().getHits().length );        if(response.getHits().getTotalHits() != 0) {            for (SearchHit hit : response.getHits().getHits()) {                System.out.println(hit.getScore()+" --> "+hit.getSourceAsString());            }        }    }

MultiMatchQuery 在多个字段中查询一个关键字

  QueryBuilder qb = multiMatchQuery("mac","description","name"); //mac是要搜索的词 description，name 都是字段

Common Terms Query 搜索term

 public static void myCommonTermsQuery(TransportClient client ) throws Exception{        QueryBuilder qb = commonTermsQuery("description","mac");        print(qb,client);    }

Simple Query String Query 简单字符串查询可以使用正则

public static void mySimpleQueryString(TransportClient client ) {        QueryBuilder qb = queryStringQuery("mac*^2").field("name");        print(qb,client);    }

term 搜索关键词一个

 public static void myTermQuery(TransportClient client) {        QueryBuilder qb = termQuery("name","mac2");        print(qb,client);    }

terms 搜索关键词多个

   QueryBuilder qb = termsQuery("name_str","小米","戴尔");   print2(qb,client);

range query 范围搜索

  public static void myRangeQuery(TransportClient client){        QueryBuilder qb = rangeQuery("price").from(3399)                .to(6399)                .includeLower(true)                .includeUpper(false);        print2(qb,client);    }    QueryBuilder qb = rangeQuery("price").gte(3399).lt(6399);

Exists Query 查找字段是否存在 存在则返回所有的数据，不存在返回0

    public static void myExistsQuery(TransportClient client) {        QueryBuilder qb = existsQuery("name_str");        print2(qb,client);    }

Wildcard Query 通配符查询

QueryBuilder qb = wildcardQuery("user", "k?mc*");

​

Regexp Query支持正则表达式的查询

QueryBuilder qb = regexpQuery("name.first", "s.*y");

Fuzzy Query 模糊查询

QueryBuilder qb = fuzzyQuery(    "name",         "kimzhy"    );

ids Query 根据id 查询

QueryBuilder qb = idsQuery("my_type", "type2")    .addIds("1", "4", "100");QueryBuilder qb = idsQuery()     .addIds("AV8HhVC8FiG-4m4G2rYp","AV8HhVB6FiG-4m4G2rYm");

​

复合查询

Contant Score Query 指定score

  QueryBuilder qb = constantScoreQuery(matchQuery("name_str", "联想")).boost(3.0f);

Bool Query must mustNot should 查询

所有的 must 子句必须匹配, 并且所有的 must_not 子句必须不匹配, 但是多少 should 子句应该匹配呢? 默认的,不需要匹配任何 should 子句,一种情况例外:如果没有must子句,就必须至少匹 配一个should子句。

  public static void myBoolQuery(TransportClient client) {        QueryBuilder qb = boolQuery().must(termQuery("name_str","小米"))                .filter(matchQuery("price",3599))                .filter(matchQuery("description","lihao"));        print2(qb, client);    }    QueryBuilder qb = boolQuery()    .must(termQuery("content", "test1"))        .must(termQuery("content", "test4"))        .mustNot(termQuery("content", "test2"))     .should(termQuery("content", "test3"))      .filter(termQuery("content", "test5"));

​

indices query查询多个index

用来查询多个index,对于指定内的index,执行指定的内部query;对于指定外的index,执行no_match_query设定的条件

private static void print(QueryBuilder qb, TransportClient client) {        SearchResponse response = client.prepareSearch("mytest").setTypes("test").setQuery(qb).get();        System.out.println("length: " + response.getHits().getHits().length);        if (response.getHits().getTotalHits() != 0) {            for (SearchHit hit : response.getHits().getHits()) {                System.out.println(hit.getScore() + " --> " + hit.getSourceAsString());            }        }    }    private static void print2(QueryBuilder qb, TransportClient client) {        SearchResponse response = client.prepareSearch("mytest_1").setTypes("test").setQuery(qb).get();        System.out.println("length: " + response.getHits().getHits().length);        if (response.getHits().getTotalHits() != 0) {            for (SearchHit hit : response.getHits().getHits()) {                System.out.println(hit.getScore() + " --> " + hit.getSourceAsString());            }        }    }

scroll分页

 public static void main(String ...arg) throws Exception {        //链接服务器        Settings  settings = Settings.builder()                .put("cluster.name","lw-6-test").build();        TransportClient client = new PreBuiltTransportClient(settings);        client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("10.10.10.6"), 9300));        //设置搜索条件        QueryBuilder qb = termQuery("name_str","笔记本");        // 按照price 降序  每次查询2条  第一次不需要设置sroll  scrollid        SearchResponse scrollResp = client.prepareSearch("mytest_1").setTypes("test")                .addSort("price", SortOrder.DESC)                .setScroll(new TimeValue(30000))                .setQuery(qb)                .setSize(2).get();        System.out.println("length: " + scrollResp.getHits().getHits().length);        int count = 1;        do{            System.out.println("第 " +count+ " 次");            System.out.println();            for (SearchHit hit : scrollResp.getHits().getHits()){                System.out.println(hit.getScore() + " --> " +hit.getSourceAsString());            }            System.out.println("scrollid:  "+scrollResp.getScrollId());            //设置sroll id            scrollResp =client.prepareSearchScroll(scrollResp.getScrollId()).setScroll(new TimeValue(60000)).execute().actionGet();            System.out.println();            count++;        } while (scrollResp.getHits().getHits().length !=0);        client.close();    }

​

prepareMultiSearch多个条件查询

public class MultiSearchDemo {    public static void main(String ...arg) throws Exception{        Settings settings = Settings.builder().put("cluster.name", "lw-6-test").build();        TransportClient client = new PreBuiltTransportClient(settings);        client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("10.10.10.6"), 9300));       QueryBuilder query1 = termQuery("name_str","小米");       QueryBuilder query2 = termQuery("name_str","戴尔");        SearchRequestBuilder srb1 = client.prepareSearch("mytest_1").setTypes("test").setQuery(query1);        SearchRequestBuilder srb2 = client.prepareSearch("mytest_1").setTypes("test").setQuery(query2);        MultiSearchResponse sr = client.prepareMultiSearch().add(srb1).add(srb2).get();        long nbHits =0;        for(MultiSearchResponse.Item item : sr.getResponses()) {            SearchResponse response = item.getResponse();            nbHits += response.getHits().getTotalHits();            if(response.getHits().getHits().length >0) {                for(SearchHit hit : response.getHits().getHits()) {                    System.out.println(hit.getScore()+"  ----->  "+hit.getSourceAsString());                }            }            System.out.println("-------------------------");        }        System.out.println(nbHits);        client.close();    }}

聚合

概述

ES 的聚合框架提供对查询得到的数据进行分组和汇总统计，以提供复杂的统计分析功能。

ES支持在一次聚合查询中，可以同时得到聚合的具体结果并再次进行聚合，也就是聚合是可以嵌套的。

这非常有用,你可以通过一次操作得到多次聚合的结果,从而避免多次请求,减少网络和服务器的负担。

聚合的类型

1：Bucketing（桶）聚合：划分不同的“桶”，将数据分配到不同的“桶” 里，然后再进行聚合，非常类似sql 中的group 语句的含义。

2：Metric(指标)聚合：指标聚合主要针对number类型的数据，在一组文档中，保持对要聚合的指标的跟踪和计算，需要ES做比较多的计算工作。

3：Pipeline(管道)聚合：用来聚合其它聚合输出的结果以及相关指标

聚合的基本语法

"aggregations" : {                      //定义聚合对象,也可用 "aggs"    "<aggregation_name>" : {            //聚合的名称,用户自定义           "<aggregation_type>" : {         //聚合类型,比如 "histogram" "avg"            <aggregation_body>        }        [,"meta" : {  [<meta_data_body>] } ]?        [,"aggregations" : { [<sub_aggregation>]+ } ]?    }    [,"<aggregation_name_2>" : { ... } ]*  ////定义额外的多个平级聚合,只有Bucketing类型才有意义}

​

GET mytest_1/test/_search{  "aggs" : {        "avg_price" : { "avg" : { "field" : "price" } }    }}output{ "aggregations": {    "avg_price": {      "value": 4954.555555555556    }  }

​
​

Metric 使用

求平均值

   public static void main(String ...arg) throws Exception {        Settings settings = Settings.builder().put("cluster.name", "lw-6-test").build();        TransportClient client = new PreBuiltTransportClient(settings);        client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("10.10.10.6"),9300));        avg(client);        client.close();    }     private static void avg(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        SearchResponse sr = search.addAggregation(AggregationBuilders.avg("avg_price").field("price")).execute().actionGet();        Avg result = sr.getAggregations().get("avg_price");        System.out.println(result.getValue());    }    POST mytest_1/test/_search?size=0{  "aggs" : {        "avg_price" : {           "avg" : { "field" : "price" }         }    }}

POST mytest_1/test/_search?size=0{   "aggs" : {        "all_cats" : {          "terms" : { "field" : "tag.keyword" },              "aggs" : {                  "avg_price" : {                      "avg" : { "field" : "price" }                  }              }        }   }}  "aggregations": {    "all_cats": {      "doc_count_error_upper_bound": 0,      "sum_other_doc_count": 0,      "buckets": [        {          "key": "笔记本",          "doc_count": 8,          "avg_price": {            "value": 5124          }        },        {          "key": "联想",          "doc_count": 4,          "avg_price": {            "value": 5649          }        },        {          "key": "小米",          "doc_count": 2,          "avg_price": {            "value": 4399          }        },        {          "key": "惠普",          "doc_count": 1,          "avg_price": {            "value": 2399          }        },        {          "key": "戴尔",          "doc_count": 1,          "avg_price": {            "value": 7199          }        }      ]    }  }

分类求取平均值

PUT mytest_1/_mapping/test{  "properties": {    "tag": {       "type":     "text",      "fielddata": true    }  }}    private static void avg1(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setSize(0).setTypes("test");        TermsAggregationBuilder tag = AggregationBuilders.terms("tags").field("tag.keyword");        AvgAggregationBuilder price = AggregationBuilders.avg("avg_price").field("price");        tag.subAggregation(price);        SearchResponse sr = search.addAggregation(tag).execute().actionGet();        System.out.println(sr);    }

Cardinality 去除重复数据

用来对单个数据进行汇总,计算不重复的值的数量。

    public static void main(String... arg) throws Exception {        Settings settings = Settings.builder().put("cluster.name", "lw-6-test").build();        TransportClient client = new PreBuiltTransportClient(settings);        client.addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName("10.10.10.6"), 9300));        cardinality(client);        client.close();    }  private static void cardinality(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        SearchResponse sr = search.addAggregation(AggregationBuilders.cardinality("type_count").field("price")).execute().actionGet();        Cardinality result = sr.getAggregations().get("type_count");        System.out.println("type_count: "+result.getValue());    }POST mytest_1/test/_search?size=0{    "aggs" : {        "type_count" : {            "cardinality" : {                "field" : "price"            }        }    }}out:{  "took": 7,  "timed_out": false,  "_shards": {    "total": 5,    "successful": 5,    "failed": 0  },  "hits": {    "total": 8,    "max_score": 0,    "hits": []  },  "aggregations": {    "type_count": {      "value": 6    }  }}

​
​

Stats 聚合操作 count min max avg sum

    private static void stats(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        SearchResponse sr =  search.addAggregation(AggregationBuilders.stats("price_stats").field("price")).execute().actionGet();       Stats stats = sr.getAggregations().get("price_stats");        System.out.println(stats.getAvgAsString());        System.out.println(stats.getMaxAsString());        System.out.println(stats.getMinAsString());        System.out.println(stats.getSumAsString());    }POST mytest_1/test/_search?size=0{    "aggs" : {        "price_stats" : { "extended_stats" : { "field" : "price" } }    }}out: "aggregations": {    "price_stats": {      "count": 8,      "min": 2399,      "max": 7199,      "avg": 5124,      "sum": 40992,      "sum_of_squares": 231958008,      "variance": 2739375,      "std_deviation": 1655.1057368035433,      "std_deviation_bounds": {        "upper": 8434.211473607087,        "lower": 1813.7885263929134      }    }  }

​

Percentiles 百分比 查看一个百分比对应的值

这是一个多值的指标聚集,用来计算聚合文档中,在某个百分比或某个区间,所对应的观测值,

1:缺省的percentile的区间是[ 1, 5, 25, 50, 75, 95, 99 ]。

2:观测值通常都是近似的,有很多不同的算法来计算。
​
​ 例如:第九十五个百分值是大于所观察到的值的95%的值。

POST mytest_1/test/_search?size=0{    "aggs" : {        "price_percent" : {            "percentiles" : {                "field" : "price"             }        }    }}POST mytest_1/test/_search?size=0{    "aggs" : {        "price_percent" : {            "percentiles" : {                "field" : "price" ,                "percents" : [0.1,50,95, 99, 100]  //自定义百分比区间            }        }    }}  "aggregations": {    "price_percent": {      "values": {        "1.0": 2468.9999999999995,        "5.0": 2749,        "25.0": 3549,        "50.0": 5799,        "75.0": 6399,        "95.0": 6918.999999999999,        "99.0": 7143      }

 private static void percentile(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        SearchResponse sr =  search.addAggregation(AggregationBuilders.percentiles("price_percent").field("price")).execute().actionGet();       Percentiles percentile = sr.getAggregations().get("price_percent");       System.out.println(percentile.percentileAsString(80));    }    //自定义百分比区间    private static void percentile2(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        SearchResponse sr =  search.addAggregation(AggregationBuilders.percentiles("price_percent").percentiles(0.1,50,95, 99, 100).field("price")).execute().actionGet();        System.out.println(sr);        Percentiles percentile = sr.getAggregations().get("price_percent");        System.out.println(percentile.percentileAsString(80));    }

​

Value Count 计算聚合值的数量

POST mytest_1/test/_search?size=0{"aggs" : {        "types_count" : { "value_count" : { "field" : "price" } }    }}  out:"aggregations": {    "types_count": {      "value": 8    }

 private static void valueCount(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        SearchResponse sr =search.addAggregation(AggregationBuilders.count("value_count").field("price")).execute().actionGet();       ValueCount valueCount = sr.getAggregations().get("value_count");       System.out.println(valueCount.getValue());    }

​

TOP hits

用来取符合条件的前n条数据。 包含的选项有:from、size、sort。

    private static void topHits(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test");        TopHitsAggregationBuilder addtion = AggregationBuilders.topHits("top_price_hits").sort("price", SortOrder.DESC).fieldDataField("price")                .size(5);        SearchResponse sr =search.addAggregation(addtion).execute().actionGet();       TopHits topHits = sr.getAggregations().get("top_price_hits");       System.out.println();        SearchHit[] hits = topHits.getHits().internalHits();        for(SearchHit searchHit : hits) {            System.out.println(searchHit.getSourceAsString());        }    }

​
​

bucket 使用

Histogram

条形图聚合，根据文档中的谋改革字段来分组。一个文档属于某个通，计算过程大致如下：

rem = value % intervalif (rem < 0) {    rem += interval}bucket_key = value - rem

1:可配置的参数:
(1)field:字段,必须为数值类型

(2)interval:分桶间距 (3)min_doc_count:最少文档数,桶过滤,只有不少于这么多文档的桶才会返回

(4)extended_bounds:范围扩展

(5)order:对桶排序,如果 histogram 聚合有一个权值聚合类型的“直接”子聚合,那么排序可以使用 子聚合中的结果

(6)offset:桶边界位移,默认从0开始 (7)keyed:hash结构返回,默认以数组形式返回每一个桶 (8)missing:配置缺省默认值

POST mytest_1/test/_search?size=0{    "aggs" : {        "prices" : {            "histogram" : {                "field" : "price",                "interval" : 2000            }        }    }}out: "aggregations": {    "prices": {      "buckets": [        {          "key": 2000,          "doc_count": 3        },        {          "key": 4000,          "doc_count": 1        },        {          "key": 6000,          "doc_count": 4        }      ]    }

  private static void histogram(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test").setSize(0);        HistogramAggregationBuilder addtion = AggregationBuilders.histogram("prices").interval(2000).field("price");        SearchResponse sr = search.addAggregation(addtion).execute().actionGet();       Histogram histogram = sr.getAggregations().get("prices");       histogram.getBuckets().forEach(bucket->{           System.out.println(bucket.getKeyAsString()+" ---->  "+bucket.getDocCount());       });    }

Range

范围聚合，是对某个字段的值，按照设定的范围进行分组。

POST mytest_1/test/_search?size=0{    "aggs" : {        "price_ranges" : {            "range" : {                "field" : "price",                "ranges" : [                    { "to" : 3000 },                    { "from" : 3000, "to" : 5000 },                    { "from" : 5000 }                ]            }        }    }}out:"aggregations": {    "price_ranges": {      "buckets": [        {          "key": "*-3000.0",          "to": 3000,          "doc_count": 1        },        {          "key": "3000.0-5000.0",          "from": 3000,          "to": 5000,          "doc_count": 2        },        {          "key": "5000.0-*",          "from": 5000,          "doc_count": 5        }      ]    }

   private static void range(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test").setSize(0);        AggregationBuilder addtion = AggregationBuilders.range("price_ranges").field("price")                .addUnboundedTo(3000)                .addRange(3000,5000)                .addUnboundedFrom(5000);        SearchResponse sr = search.addAggregation(addtion).execute().actionGet();       Range histogram = sr.getAggregations().get("price_ranges");       histogram.getBuckets().forEach(bucket->{           String key = bucket.getKeyAsString();           String from = bucket.getFromAsString();           String to = bucket.getToAsString();           long count = bucket.getDocCount();           System.out.println("key : "+key+"\t form: "+from+"\t to:"+to+"\t count:"+count);       });    }

Date Range

Ip Range

Terms

词元聚合，以指定的字段内的每一个不重复的term来分组，并计算每个组内文档的个数。

POST mytest_1/test/_search?size=0{   "aggs" : {        "all_cats" : {          "terms" : { "field" : "tag.keyword" }        }   }}

Filters 过滤

多过滤聚合，用多个过滤条件，来对当前文档进行过滤的聚合，每个过滤都包含所有满足它的文档，

多个bucket中可能重复。

POST mytest_1/test/_search?size=0{  "size": 0,  "aggs" : {    "messages" : {      "filters" : {        "filters" : {          "filter1" :   { "match" : { "tag" : "小米"   }},          "filter2" : { "match" : { "tag" : "戴尔" }}        }      }    }  }}out:"aggregations": {    "messages": {      "buckets": {        "filter1": {          "doc_count": 2        },        "filter2": {          "doc_count": 1        }      }    }

private static  void filters(TransportClient client) {        SearchRequestBuilder search = client.prepareSearch("mytest_1").setTypes("test").setSize(0);        AggregationBuilder aggregation = AggregationBuilders.filters("filters",                new FiltersAggregator.KeyedFilter("xiaomi",matchQuery("tag","小米")),                new FiltersAggregator.KeyedFilter("daier",matchQuery("tag","戴尔"))        );        SearchResponse sr = search.addAggregation(aggregation).execute().actionGet();        System.out.println(sr);        Filters agg =sr.getAggregations().get("filters");        agg.getBuckets().forEach(entry->{            String key = entry.getKeyAsString();            // bucket key            long docCount = entry.getDocCount();            System.out.println("key : "+key+"\t docCount: "+docCount);        });    }

技术点

分词

分词安装

https://github.com/medcl/elasticsearch-analysis-ik/tree/v5.5.0

测试分词

GET _analyze{  "analyzer": "ik_smart",  "text": ["中华人民共和国"]}

参数

boost:权重,缺省1.0

bool

bool 过滤 :可以用来合并多个过滤条件查询结果的布尔逻辑,它包含一下操作符:

must:多个查询条件的完全匹配,相当于 and。

must_not: 多个查询条件的相反匹配,相当于 not。

should: 至少有一个查询条件匹配, 相当于 or。
​

参考文档

https://www.elastic.co/guide/en/elasticsearch/client/java-api/current/_structuring_aggregations.html