ElasticSearch多種查詢操作
- 前言
- 1 詞條查詢
- 1.1 等值查詢-term
- 1.2 多值查詢-terms
- 1.3 范圍查詢-range
- 1.4 前綴查詢-prefix
- 1.5 通配符查詢-wildcard
- 2 復合查詢
- 2.1 布爾查詢
- 2.2 Filter查詢
- 3 聚合查詢
- 3.1 最值、平均值、求和
- 3.2 去重查詢
- 3.3 分組聚合
- 3.3.1 單條件分組
- 3.3.2 多條件分組
- 3.4 過濾聚合
前言
- ElasticSearch第一篇：ElasticSearch基礎：從倒排索引說起，快速認知ES
這篇博文的主題是ES的查詢，因此我整理了盡可能齊全的ES查詢場景，形成下面的圖：
本文基于elasticsearch 7.13.2版本，es從7.0以后，發生了很大的更新，7.3以后，已經不推薦使用TransportClient這個client，取而代之的是Java High Level REST Client，
Mysql中的部分測驗資料：
| id | name | age | sex | address | sect | skill | power | create_time | modify_time |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 張無忌 | 18 | 男 | 光明頂 | 明教 | 九陽神功 | 99 | 2021-05-14 16:50:33 | 2021-06-29 16:48:56 |
| 2 | 周芷若 | 17 | 女 | 峨眉山 | 峨嵋派 | 九陰真經 | 88 | 2021-05-14 11:37:07 | 2021-06-29 16:56:40 |
| 3 | 趙敏 | 14 | 女 | 大都 | 朝廷 | 無 | 40 | 2021-05-14 11:37:07 | 2021-06-29 15:22:24 |
ES中的一個檔案：
{
"_index" : "person",
"_type" : "_doc",
"_id" : "4",
"_score" : 1.0,
"_source" : {
"address" : "峨眉山",
"modifyTime" : "2021-06-29 19:46:25",
"createTime" : "2021-05-14 11:37:07",
"sect" : "峨嵋派",
"sex" : "男",
"skill" : "降龍十八掌",
"name" : "宋青書",
"id" : 4,
"power" : 50,
"age" : 21
}
}
簡單梳理了一下ES JavaAPI的相關體系，感興趣的可以自己研讀一下原始碼，
1 詞條查詢
所謂詞條查詢，也就是ES不會對查詢條件進行分詞處理，只有當詞條和查詢字串完全匹配時，才會被查詢到，
1.1 等值查詢-term
等值查詢，即篩選出一個欄位等于特定值的所有記錄，
SQL：
select * from person where name = '張無忌';
而使用ES查詢陳述句卻很不一樣（注意查詢欄位帶上keyword）：
GET /person/_search
{
"query": {
"term": {
"name.keyword": {
"value": "張無忌",
"boost": 1.0
}
}
}
}
ElasticSearch 5.0以后，string型別有重大變更，移除了string型別，string欄位被拆分成兩種新的資料型別: text用于全文搜索的，而keyword用于關鍵詞搜索，
查詢結果：
{
"took" : 0,
"timed_out" : false,
"_shards" : { // 分片資訊
"total" : 1, // 總計分片數
"successful" : 1, // 查詢成功的分片數
"skipped" : 0, // 跳過查詢的分片數
"failed" : 0 // 查詢失敗的分片數
},
"hits" : { // 命中結果
"total" : {
"value" : 1, // 數量
"relation" : "eq" // 關系：等于
},
"max_score" : 2.8526313, // 最高分數
"hits" : [
{
"_index" : "person", // 索引
"_type" : "_doc", // 型別
"_id" : "1",
"_score" : 2.8526313,
"_source" : {
"address" : "光明頂",
"modifyTime" : "2021-06-29 16:48:56",
"createTime" : "2021-05-14 16:50:33",
"sect" : "明教",
"sex" : "男",
"skill" : "九陽神功",
"name" : "張無忌",
"id" : 1,
"power" : 99,
"age" : 18
}
}
]
}
}
Java中構造ES請求的方式：（后續例子中只保留SearchSourceBuilder的構建陳述句）
/**
* term精確查詢
*
* @throws IOException
*/
@Autowired
private RestHighLevelClient client;
@Test
public void queryTerm() throws IOException {
// 根據索引創建查詢請求
SearchRequest searchRequest = new SearchRequest("person");
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.termQuery("name.keyword", "張無忌"));
System.out.println("searchSourceBuilder=====================" + searchSourceBuilder);
searchRequest.source(searchSourceBuilder);
SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
System.out.println(JSONObject.toJSON(response));
}
仔細觀察查詢結果，會發現ES查詢結果中會帶有_score這一項，ES會根據結果匹配程度進行評分，打分是會耗費性能的，如果確認自己的查詢不需要評分，就設定查詢陳述句關閉評分：
GET /person/_search
{
"query": {
"constant_score": {
"filter": {
"term": {
"sect.keyword": {
"value": "張無忌",
"boost": 1.0
}
}
},
"boost": 1.0
}
}
}
Java構建查詢陳述句：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 這樣構造的查詢條件，將不進行score計算，從而提高查詢效率
searchSourceBuilder.query(QueryBuilders.constantScoreQuery(QueryBuilders.termQuery("sect.keyword", "明教")));
1.2 多值查詢-terms
多條件查詢類似Mysql里的IN查詢，例如：
select * from persons where sect in('明教','武當派');
ES查詢陳述句：
GET /person/_search
{
"query": {
"terms": {
"sect.keyword": [
"明教",
"武當派"
],
"boost": 1.0
}
}
}
Java實作：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.termsQuery("sect.keyword", Arrays.asList("明教", "武當派")));
}
1.3 范圍查詢-range
范圍查詢，即查詢某欄位在特定區間的記錄，例如：
SQL:
select * from pesons where age between 18 and 22;
ES查詢陳述句：
GET /person/_search
{
"query": {
"range": {
"age": {
"from": 10,
"to": 20,
"include_lower": true,
"include_upper": true,
"boost": 1.0
}
}
}
}
Java構建查詢條件：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.rangeQuery("age").gte(10).lte(30));
}
1.4 前綴查詢-prefix
前綴查詢類似于SQL中的模糊查詢，例如：
SQL：
select * from persons where sect like '武當%';
ES查詢陳述句：
{
"query": {
"prefix": {
"sect.keyword": {
"value": "武當",
"boost": 1.0
}
}
}
}
Java構建查詢條件：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.prefixQuery("sect.keyword","武當"));
1.5 通配符查詢-wildcard
通配符查詢，與前綴查詢類似，都屬于模糊查詢的范疇，但通配符顯然功能更強，例如：
SQL：
select * from persons where name like '張%忌';
ES查詢陳述句：
{
"query": {
"wildcard": {
"sect.keyword": {
"wildcard": "張*忌",
"boost": 1.0
}
}
}
}
Java構建查詢條件：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.wildcardQuery("sect.keyword","張*忌"));
2 復合查詢
前面的例子都是單個條件查詢，在實際應用中，我們很有可能會過濾多個值或欄位，先看一個簡單的例子：
select * from persons where sex = '女' and sect = '明教';
這樣的多條件等值查詢，就要借用到組合過濾器了，其查詢陳述句是：
{
"query": {
"bool": {
"must": [
{
"term": {
"sex": {
"value": "女",
"boost": 1.0
}
}
},
{
"term": {
"sect.keywords": {
"value": "明教",
"boost": 1.0
}
}
}
],
"adjust_pure_negative": true,
"boost": 1.0
}
}
}
Java構造查詢陳述句：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.boolQuery()
.must(QueryBuilders.termQuery("sex", "女"))
.must(QueryBuilders.termQuery("sect.keyword", "明教"))
);
2.1 布爾查詢
布爾過濾器（bool filter）屬于復合過濾器（compound filter）的一種 ，可以接受多個其他過濾器作為引數，并將這些過濾器結合成各式各樣的布爾（邏輯）組合，
bool 過濾器下可以有4種子條件，可以任選其中任意一個或多個，filter是比較特殊的，這里先不說，
{
"bool" : {
"must" : [],
"should" : [],
"must_not" : [],
}
}
must：所有的陳述句都必須匹配，與 ‘=’ 等價，must_not：所有的陳述句都不能匹配，與 ‘!=’ 或 not in 等價，should：至少有n個陳述句要匹配，n由引數控制，
精度控制：
所有 must 陳述句必須匹配，所有 must_not 陳述句都必須不匹配，但有多少 should 陳述句應該匹配呢？默認情況下，沒有 should 陳述句是必須匹配的，只有一個例外：那就是當沒有 must 陳述句的時候，至少有一個 should 陳述句必須匹配，
我們可以通過 minimum_should_match 引數控制需要匹配的 should 陳述句的數量，它既可以是一個絕對的數字，又可以是個百分比：
GET /person/_search
{
"query": {
"bool": {
"must": [
{
"term": {
"sex": {
"value": "女",
"boost": 1.0
}
}
}
],
"should": [
{
"term": {
"address.keyword": {
"value": "峨眉山",
"boost": 1.0
}
}
},
{
"term": {
"sect.keyword": {
"value": "明教",
"boost": 1.0
}
}
}
],
"adjust_pure_negative": true,
"minimum_should_match": "1",
"boost": 1.0
}
}
}
Java構建查詢陳述句：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.boolQuery()
.must(QueryBuilders.termQuery("sex", "女"))
.should(QueryBuilders.termQuery("address.word", "峨眉山"))
.should(QueryBuilders.termQuery("sect.keyword", "明教"))
.minimumShouldMatch(1)
);
最后，看一個復雜些的例子，將bool的各子句聯合使用：
select
*
from
persons
where
sex = '女'
and
age between 30 and 40
and
sect != '明教'
and
(address = '峨眉山' OR skill = '暗器')
用 Elasticsearch 來表示上面的 SQL 例子：
GET /person/_search
{
"query": {
"bool": {
"must": [
{
"term": {
"sex": {
"value": "女",
"boost": 1.0
}
}
},
{
"range": {
"age": {
"from": 30,
"to": 40,
"include_lower": true,
"include_upper": true,
"boost": 1.0
}
}
}
],
"must_not": [
{
"term": {
"sect.keyword": {
"value": "明教",
"boost": 1.0
}
}
}
],
"should": [
{
"term": {
"address.keyword": {
"value": "峨眉山",
"boost": 1.0
}
}
},
{
"term": {
"skill.keyword": {
"value": "暗器",
"boost": 1.0
}
}
}
],
"adjust_pure_negative": true,
"minimum_should_match": "1",
"boost": 1.0
}
}
}
用Java構建這個查詢條件：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery()
.must(QueryBuilders.termQuery("sex", "女"))
.must(QueryBuilders.rangeQuery("age").gte(30).lte(40))
.mustNot(QueryBuilders.termQuery("sect.keyword", "明教"))
.should(QueryBuilders.termQuery("address.keyword", "峨眉山"))
.should(QueryBuilders.rangeQuery("power.keyword").gte(50).lte(80))
.minimumShouldMatch(1); // 設定should至少需要滿足幾個條件
// 將BoolQueryBuilder構建到SearchSourceBuilder中
searchSourceBuilder.query(boolQueryBuilder);
2.2 Filter查詢
query和filter的區別：query查詢的時候，會先比較查詢條件，然后計算分值，最后回傳檔案結果；而filter是先判斷是否滿足查詢條件，如果不滿足會快取查詢結果（記錄該檔案不滿足結果），滿足的話，就直接快取結果，filter不會對結果進行評分，能夠提高查詢效率，
filter的使用方式比較多樣，下面用幾個例子演示一下，
方式一，單獨使用：
{
"query": {
"bool": {
"filter": [
{
"term": {
"sex": {
"value": "男",
"boost": 1.0
}
}
}
],
"adjust_pure_negative": true,
"boost": 1.0
}
}
}
單獨使用時，filter與must基本一樣，不同的是filter不計算評分，效率更高，
Java構建查詢陳述句：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.boolQuery()
.filter(QueryBuilders.termQuery("sex", "男"))
);
方式二，和must、must_not同級，相當于子查詢：
select * from (select * from persons where sect = '明教')) a where sex = '女';
ES查詢陳述句：
{
"query": {
"bool": {
"must": [
{
"term": {
"sect.keyword": {
"value": "明教",
"boost": 1.0
}
}
}
],
"filter": [
{
"term": {
"sex": {
"value": "女",
"boost": 1.0
}
}
}
],
"adjust_pure_negative": true,
"boost": 1.0
}
}
}
Java：
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.boolQuery()
.must(QueryBuilders.termQuery("sect.keyword", "明教"))
.filter(QueryBuilders.termQuery("sex", "女"))
);
方式三，將must、must_not置于filter下，這種方式是最常用的：
{
"query": {
"bool": {
"filter": [
{
"bool": {
"must": [
{
"term": {
"sect.keyword": {
"value": "明教",
"boost": 1.0
}
}
},
{
"range": {
"age": {
"from": 20,
"to": 35,
"include_lower": true,
"include_upper": true,
"boost": 1.0
}
}
}
],
"must_not": [
{
"term": {
"sex.keyword": {
"value": "女",
"boost": 1.0
}
}
}
],
"adjust_pure_negative": true,
"boost": 1.0
}
}
],
"adjust_pure_negative": true,
"boost": 1.0
}
}
}
Java:
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 構建查詢陳述句
searchSourceBuilder.query(QueryBuilders.boolQuery()
.filter(QueryBuilders.boolQuery()
.must(QueryBuilders.termQuery("sect.keyword", "明教"))
.must(QueryBuilders.rangeQuery("age").gte(20).lte(35))
.mustNot(QueryBuilders.termQuery("sex.keyword", "女")))
);
3 聚合查詢
接下來，我們將用一些案例演示ES聚合查詢，
3.1 最值、平均值、求和
案例：查詢最大年齡、最小年齡、平均年齡，
SQL：
select max(age) from persons;
ES：
GET /person/_search
{
"aggregations": {
"max_age": {
"max": {
"field": "age"
}
}
}
}
Java：
@Autowired
private RestHighLevelClient client;
@Test
public void maxQueryTest() throws IOException {
// 聚合查詢條件
AggregationBuilder aggBuilder = AggregationBuilders.max("max_age").field("age");
SearchRequest searchRequest = new SearchRequest("person");
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 將聚合查詢條件構建到SearchSourceBuilder中
searchSourceBuilder.aggregation(aggBuilder);
System.out.println("searchSourceBuilder----->" + searchSourceBuilder);
searchRequest.source(searchSourceBuilder);
// 執行查詢，獲取SearchResponse
SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
System.out.println(JSONObject.toJSON(response));
}
使用聚合查詢，結果中默認只會回傳10條檔案資料（當然我們關心的是聚合的結果，而非檔案），回傳多少條資料可以自主控制：
GET /person/_search
{
"size": 20,
"aggregations": {
"max_age": {
"max": {
"field": "age"
}
}
}
}
而Java中只需增加下面一條陳述句即可：
searchSourceBuilder.size(20);
與max類似，其他統計查詢也很簡單：
AggregationBuilder minBuilder = AggregationBuilders.min("min_age").field("age");
AggregationBuilder avgBuilder = AggregationBuilders.avg("min_age").field("age");
AggregationBuilder sumBuilder = AggregationBuilders.sum("min_age").field("age");
AggregationBuilder countBuilder = AggregationBuilders.count("min_age").field("age");
3.2 去重查詢
案例：查詢一共有多少個門派，
SQL：
select count(distinct sect) from persons;
ES：
{
"aggregations": {
"sect_count": {
"cardinality": {
"field": "sect.keyword"
}
}
}
}
Java：
@Test
public void cardinalityQueryTest() throws IOException {
// 創建某個索引的request
SearchRequest searchRequest = new SearchRequest("person");
// 查詢條件
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 聚合查詢
AggregationBuilder aggBuilder = AggregationBuilders.cardinality("sect_count").field("sect.keyword");
searchSourceBuilder.size(0);
// 將聚合查詢構建到查詢條件中
searchSourceBuilder.aggregation(aggBuilder);
System.out.println("searchSourceBuilder----->" + searchSourceBuilder);
searchRequest.source(searchSourceBuilder);
// 執行查詢，獲取結果
SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
System.out.println(JSONObject.toJSON(response));
}
3.3 分組聚合
3.3.1 單條件分組
案例：查詢每個門派的人數
SQL：
select sect,count(id) from mytest.persons group by sect;
ES：
{
"size": 0,
"aggregations": {
"sect_count": {
"terms": {
"field": "sect.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
"_count": "desc"
},
{
"_key": "asc"
}
]
}
}
}
}
Java：
SearchRequest searchRequest = new SearchRequest("person");
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
searchSourceBuilder.size(0);
// 按sect分組
AggregationBuilder aggBuilder = AggregationBuilders.terms("sect_count").field("sect.keyword");
searchSourceBuilder.aggregation(aggBuilder);
3.3.2 多條件分組
案例：查詢每個門派各有多少個男性和女性
SQL：
select sect,sex,count(id) from mytest.persons group by sect,sex;
ES：
{
"aggregations": {
"sect_count": {
"terms": {
"field": "sect.keyword",
"size": 10
},
"aggregations": {
"sex_count": {
"terms": {
"field": "sex.keyword",
"size": 10
}
}
}
}
}
}
3.4 過濾聚合
前面所有聚合的例子請求都省略了 query ，整個請求只不過是一個聚合，這意味著我們對全部資料進行了聚合，但現實應用中，我們常常對特定范圍的資料進行聚合，例如下例，
案例：查詢明教中的最大年齡， 這涉及到聚合與條件查詢一起使用，
SQL：
select max(age) from mytest.persons where sect = '明教';
ES：
GET /person/_search
{
"query": {
"term": {
"sect.keyword": {
"value": "明教",
"boost": 1.0
}
}
},
"aggregations": {
"max_age": {
"max": {
"field": "age"
}
}
}
}
Java：
SearchRequest searchRequest = new SearchRequest("person");
SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
// 聚合查詢條件
AggregationBuilder maxBuilder = AggregationBuilders.max("max_age").field("age");
// 等值查詢
searchSourceBuilder.query(QueryBuilders.termQuery("sect.keyword", "明教"));
searchSourceBuilder.aggregation(maxBuilder);
另外還有一些更復雜的查詢例子，
案例：查詢0-20，21-40，41-60，61以上的各有多少人，
SQL：
select
sum(case when age<=20 then 1 else 0 end) ageGroup1,
sum(case when age >20 and age <=40 then 1 else 0 end) ageGroup2,
sum(case when age >40 and age <=60 then 1 else 0 end) ageGroup3,
sum(case when age >60 and age <=200 then 1 else 0 end) ageGroup4
from
mytest.persons;
ES：
{
"size": 0,
"aggregations": {
"age_avg": {
"range": {
"field": "age",
"ranges": [
{
"from": 0.0,
"to": 20.0
},
{
"from": 21.0,
"to": 40.0
},
{
"from": 41.0,
"to": 60.0
},
{
"from": 61.0,
"to": 200.0
}
],
"keyed": false
}
}
}
}
Java：
查詢結果：
"aggregations" : {
"age_avg" : {
"buckets" : [
{
"key" : "0.0-20.0",
"from" : 0.0,
"to" : 20.0,
"doc_count" : 3
},
{
"key" : "21.0-40.0",
"from" : 21.0,
"to" : 40.0,
"doc_count" : 13
},
{
"key" : "41.0-60.0",
"from" : 41.0,
"to" : 60.0,
"doc_count" : 4
},
{
"key" : "61.0-200.0",
"from" : 61.0,
"to" : 200.0,
"doc_count" : 1
}
]
}
}
以上是ElasticSearch查詢的全部內容，豐富詳實，堪比操作手冊，強烈建議保存！
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標籤：其他