es java api 子查询_elasticsearch elk最全java api 搜索 聚合、嵌套查询

目录

matchAllQuery()方法用来匹配全部文档

public static void matchAllQuery(Client client ) {

SearchResponse res = null;

QueryBuilder qb = QueryBuilders.matchAllQuery();

res = client.prepareSearch("search_test")

.setTypes("article")

.setSearchType(SearchType.DFS_QUERY_THEN_FETCH)

.setQuery(qb)

.setFrom(0)

.setSize(10)

.execute().actionGet();

for (SearchHit hit: res.getHits().getHits()){

System.out.println(hit.getSourceAsString());

}

for有选择的打印

1.  for (SearchHit searchHit : searchHits) {

2.              String name = (String) searchHit.getSource().get("name");

3.              String birth = (String) searchHit.getSource().get("birth");

4.              String interest = (String) searchHit.getSource().get("interest");

5.              System.out.println("-------------" + (++i) + "------------");

6.              System.out.println(name);

7.              System.out.println(birth);

8.              System.out.println(interest);

9.          }

不能写为matchQuery("name", "to*")

matchQuery("filedname","value")匹配单个字段，匹配字段名为filedname,值为value的文档

QueryBuilder qb = QueryBuilders.matchQuery("title", "article");

多个字段匹配某一个值

1.  QueryBuilder queryBuilder = QueryBuilders.multiMatchQuery("music",

2.             "name", "interest");//搜索name中或interest中包含有music的文档(必须与music一致)

模糊查询，?匹配单个字符，*匹配多个字符

1.  WildcardQueryBuilder queryBuilder = QueryBuilders.wildcardQuery("name",

2.             "*jack*");//搜索名字中含有jack文档(name中只要包含jack即可)

一种略高级的查询，充分考虑了stop-word的低优先级，提高了查询精确性。

将terms分为了两种：more-importent(low-frequency) and less important(high-frequency)。less-important比如stop-words，eg：the and。

QueryBuilder qb = QueryBuilders

.commonTermsQuery("title","article");

* termQuery("key", obj) 完全匹配

* termsQuery("key", obj1, obj2..)   一次匹配多个值

QueryBuilder qb =QueryBuilders

.termQuery("title","article");

//      QueryBuilder qb = QueryBuilders

//              .termsQuery("title","article","relevence");

参考网址：https://www.cnblogs.com/wenbronk/p/6432990.html

/**

* 前缀查询

*/

@Test

public void testPrefixQuery() {

QueryBuilder queryBuilder = QueryBuilders.matchQuery("user", "kimchy");

searchFunction(queryBuilder);

}

// 闭区间 QueryBuilderquery = QueryBuilders.rangeQuery("age").from(10).to(20); // 开区间 QueryBuilder query = QueryBuilders.rangeQuery("age").gt(10).lt(20);

QueryBuilder qb = QueryBuilders

.rangeQuery("like")

.gte(5)

.lt(7);

//               QueryBuilderqb = QueryBuilders

//                                 .rangeQuery("like")

//                                 .from(5)

//                                  .to(7)

//                                  .includeLower(true)// 包含上届

//                                  .includeUpper(false);// 包含下届

在关系查询中，存在一对多和多对一的关系。因为就会出现两种查询情况。

在解释查询关系之前，需要理解一下Relationship Name，如文档中contact和account的关系  ，一个Account会有多个contact，一个Contact也会有多个Account，但是最终归结的关系为Account对contact的关系为一对多。也就是说 在contact上保存有对account'的引用，这个引用的名称就是RelationshipName(区别于field name)，类似于外键的名称。

下面介绍两种查询

1、多对一的查询。

salesforce 中特有的__r模式，直接关联到parent上，如contact上存有对account的引用，那么我可以直接关联出account上的相关字段。

1. select id,name ,account.name,account.id from contact

2、一对多的查询

嵌入式查询(nestedquery),这种方式适合在父的一端查询相关子的记录。如：我想查找到负责这个account的全部contact。

1. select id,name,(select id,name from contacts)

2.  from account

查询结果如图：

这样就会关联出所以的contact数据，contact部分的展示形式json串。注意contacts不是对象名称，是Relationshipname

QueryBuilder qb =QueryBuilders.existsQuery("str");

//QueryBuilder qb =QueryBuilders.prefixQuery("name", "prefix");

//QueryBuilder qb =QueryBuilders.regexpQuery("user", "k.*y");

正则表达式

/**

* 模糊查询

* 不能用通配符, 不知道干啥用

*/

//QueryBuilder qb = QueryBuilders.fuzzyQuery("name", "kimzhy");

//QueryBuilder qb =QueryBuilders.typeQuery("my_type");

/**

* 只查询一个id的

* QueryBuilders.idsQuery(String...type).ids(Collection ids)

*/

//QueryBuilder qb = QueryBuilders.idsQuery("my_type","type2").addIds("1","2","5");

publicstatic void avgQuery(Client client ) {

SearchResponseres = null;

AvgBuilderagg = AggregationBuilders

.avg("avg_num")

.field("like");

res= client.prepareSearch("search_test")

.setTypes("article")

.setSearchType(SearchType.DFS_QUERY_THEN_FETCH)

.addAggregation(agg)

.setFrom(0)

.setSize(10)

.execute().actionGet();

System.out.println(res);

//on shutdown

client.close();

}

MinBuilderagg = AggregationBuilders

.min("min_num")

.field("like");

MaxBuilderagg = AggregationBuilders

.max("max_num")

.field("like");

SearchResponseres = null;

ExtendedStatsBuilderagg = AggregationBuilders

.extendedStats("extended_stats_num")

.field("like");

返回聚合分析后所有指标，比Stats多三个统计结果：平方和、方差、标准差

1

2

3

4

5

{

"aggs" : {

"grades_stats" : { "extended_stats" : { "field" : "grade" } }

}

}

ExtendedStatsBuilder agg =AggregationBuilders.extendedStats("extended_stats_num").field("like");

PercentilesBuilderagg = AggregationBuilders

.percentiles("percentile_num")

.field("like")

.percentiles(95,99,99.9);

PercentileRanksBuilderagg = AggregationBuilders

.percentileRanks("percentile_rank_num")

.field("like")

.percentiles(3,5);

AggregationBuilder agg =

AggregationBuilders

.range("agg")

.field("like")

.addUnboundedTo(3)

.addRange(3, 5)

.addUnboundedFrom(5);

TopHitsBuilder thb=  AggregationBuilders.topHits("top_result");

NestedBuilder nb= AggregationBuilders.nested("negsted_path").path("quests");

AggregationBuilders.reverseNested("res_negsted").path("kps ");

上面这些基本就是常用的聚合查询了，在嵌套(nested)下面的子聚合查询就是嵌套查询了，除了嵌套查询，其他的聚合查询也可以无限级添加子查询

举个例子

SearchRequestBuildersearch= client.prepareSearch("index").setTypes("type");

TermsBuilderone=  AggregationBuilders.terms("group_name").field("name");

TermsBuildertwo=  AggregationBuilders.terms("group_age").field("age");

one.subAggregation(two)

search.addAggregation(one);

Terms terms=search.get().getAggregations().get("group_name");

for(Terms.Bucket name_buk:terms.getBuckets()){

//一级分组的内容

Terms terms_age= name_buk.getAggregations().get("group_age");

for(Terms.Bucket age_buk:terms_age.getBuckets()){

//二级分组的内容

System.out.println(name_buk.getKey()+"  "+age_buk.getKey()+"  "+age_buk.getDocCount());

}

}

/**

* 包裹查询, 高于设定分数, 不计算相关性

*/

@Test

public void testConstantScoreQuery() {

QueryBuilder queryBuilder = QueryBuilders.constantScoreQuery(QueryBuilders.termQuery("name", "kimchy")).boost(2.0f);

searchFunction(queryBuilder);

/**

* 组合查询

* must(QueryBuilders) :   AND

* mustNot(QueryBuilders): NOT

* should:                  : OR

*/

publicstaticvoid booQuery(Client client) {//最有用的嵌套查询

SearchResponse res = null;

QueryBuilder qb =QueryBuilders.boolQuery()

.should(QueryBuilders.termQuery("title", "02"))

//              .mustNot(QueryBuilders.termQuery("title","article"))

.should(QueryBuilders.termQuery("title", "relevance"));

//              .filter(QueryBuilders.termQuery("title","article"));

res = client.prepareSearch("search_test").setTypes("article").setSearchType(SearchType.DFS_QUERY_THEN_FETCH)

.setQuery(qb).setFrom(0).setSize(10).execute().actionGet();

for (SearchHit hit : res.getHits().getHits()) {

System.out.println(hit.getSourceAsString());

}

如果需要查询(addr = Beijing) && (sex = false) && (10 < age< 20)的doc：

public static QueryBuilder createQuery() {

BoolQueryBuilder query =QueryBuilders.boolQuery();

// addr = Beijing

query.must(new QueryStringQueryBuilder("Beijing").field("addr"));

// sex = falese

query.must(new QueryStringQueryBuilder("false").field("sex"));

// age ∈ (10,20)

query.must(new RangeQueryBuilder("age").gt(10).lt(20));

return query;

}

返回结果：

{"pid":168,"age":16,"sex":false,"name":"Tom","addr":"Beijing"}

{"pid":276,"age":19,"sex":false,"name":"Bill","addr":"Beijing"}

{"pid":565,"age":16,"sex":false,"name":"Brown","addr":"Beijing"}

{"pid":73,"age":13,"sex":false,"name":"David","addr":"Beijing"}

作者：唐影若凡

链接：https://www.jianshu.com/p/a3694b13bf89

來源：简书

著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。

/**

* disMax查询

* 对子查询的结果做union, score沿用子查询score的最大值,

* 广泛用于muti-field查询

*/

@Test

public void testDisMaxQuery() {

QueryBuilder queryBuilder = QueryBuilders.disMaxQuery()

.add(QueryBuilders.termQuery("user", "kimch"))  // 查询条件

.add(QueryBuilders.termQuery("message", "hello"))

.boost(1.3f)

.tieBreaker(0.7f);

searchFunction(queryBuilder);

}

curl -XPUT'http://169.254.135.217:9200/search_test/' -d '{

"settings" : {

"index" : {

"number_of_shards" : 3,

"number_of_replicas" : 1

}

},

"mappings" : {

"article" : {

"properties" : {

"title" : { "type" : "string"},

"body" : { "type" : "string"},

"like" : { "type" : "long"},

"publish_date" : { "type" : "date"}

}

}

}

}'

curl -XGET'http://169.254.135.217:9200/search_test/_mapping?pretty'

curl -XGET'http://169.254.135.217:9200/search_test/_mapping/article?pretty'

curl -XHEAD -i'http://169.254.135.217:9200/search_test/article'

/search_test/article/1

{

"title": "What's relevance?",

"body": "atticle body of relevence:Term frequency/inversedocument frequency",

"like": "1",

"publish_date": "2016-03-24"

}

/search_test/article/2

{

"title": "article 02",

"body": "article 02 atticlebody of relevence:Term frequency/inverse document frequency",

"like": "2",

"publish_date":"2016-05-24"

}

/search_test/article/3

{

"title": "article 03",

"body": "article 03 atticlebody of relevence:Term frequency/inverse document frequency",

"like": "3",

"publish_date":"2016-07-24"

}

/search_test/article/4

{

"title": "article 04",

"body": "article 04 atticlebody of relevence:Term frequency/inverse document frequency",

"like": "4",

"publish_date":"2016-09-24"

}

/search_test/article/5

{

"title": "article 05",

"body": "article 04 atticlebody of relevence:Term frequency/inverse document frequency",

"like": "5",

"publish_date":"2016-11-24"

}

/search_test/article/6

{

"title": "Quick brownrabbits",

"body": "Brown rabbits arecommonly seen.",

"like": "6",

"publish_date":"2016-12-24"

}

/search_test/article/7

{

"title": "Keeping petshealthy",

"body": "My quick brown foxeats rabbits on a regular basis.",

"like": "7",

"publish_date":"2017-11-24"

}