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 fieldSystem.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。

GET mttest-2017.10.41/web1/_search
{"query": {"bool": {"must": [{"match": {"request": "choosehospital/index"}},{"match": {"response": "500"}}]}}
}

  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  scrollidSearchResponse 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 idscrollResp =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 % interval
if (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 keylong 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

https://www.jianshu.com/p/a890e27b80e7

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