文章目录
引言
在大数据时代，高效的数据检索能力是众多应用系统的关键需求。Elasticsearch作为一款强大的开源分布式搜索和分析引擎，提供了两种基本的检索方式，帮助开发者从海量数据中精准获取所需信息。这两种检索方式各有特点，适用于不同的业务场景和查询需求。本文将深入探讨这两种检索方式，并通过丰富的示例和详细的解释，帮助读者全面掌握Elasticsearch的检索技巧。
数据准备：数据JSON
Elasticsearch检索方式概述
两种检索方式介绍
Elasticsearch支持通过REST request uri发送搜索参数和通过REST request body发送搜索参数这两种基本检索方式。理解这两种方式的差异和适用场景，是高效使用Elasticsearch的基础。
方式一：通过REST request uri发送搜索参数
- 原理：将搜索参数以查询字符串的形式直接附加在URI后面，传递给Elasticsearch服务器。这种方式简单直观，适用于简单的搜索场景。
- 示例：
GET bank/_search?q=*&sort=account_number:asc
- 参数解释：
q=*：q代表查询条件，*是通配符，表示查询所有文档。sort=account_number:asc：sort用于指定排序规则，这里表示按照account_number字段进行升序排列，asc表示升序，desc表示降序。
- 返回结果分析：
{
"took" : 235,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "0",
"_score" : null,
"_source" : {
"account_number" : 0,
"balance" : 16623,
"firstname" : "Bradshaw",
"lastname" : "Mckenzie",
"age" : 29,
"gender" : "F",
"address" : "244 Columbus Place",
"employer" : "Euron",
"email" : "bradshawmckenzie@euron.com",
"city" : "Hobucken",
"state" : "CO"
},
"sort" : [
0
]
},
// 此处省略其他文档数据
]
}
}
- `took`：表示Elasticsearch执行查询所花费的时间，单位为毫秒，这里是235毫秒，反映了查询的执行效率。
- `timed_out`：表示搜索请求是否超时，`false`表示未超时，说明查询在规定时间内顺利完成。
- `_shards`：包含搜索的分片信息，`total`表示总分片数，`successful`表示成功搜索的分片数，`skipped`表示跳过的分片数，`failed`表示搜索失败的分片数。这里总分片数为1，且成功搜索了1个分片，说明搜索过程顺利。
- `hits.total.value`：表示找到的匹配文档数量，这里是1000，说明在`bank`索引中共有1000个文档符合查询条件（因为这里是查询所有文档）。
- `max_score`：表示文档的最高相关性得分，由于使用`match_all`查询所有文档，没有相关性得分的概念，所以为`null`。
- `hits.sort`：表示文档的排序位置（当不按相关性得分排序时），这里按照`account_number`升序排列，所以每个文档的`sort`值就是其`account_number`的值。
- `hits._score`：表示文档的相关性得分（使用`match_all`时不适用），这里为`null`。
方式二：通过REST request body发送搜索参数
原理：将搜索参数放在HTTP请求的消息体中发送给Elasticsearch服务器，使用的是一种领域对象语言（DSL），以JSON格式来定义复杂的查询条件、排序规则、分页设置等。这种方式灵活性高，能够满足复杂的搜索需求。
（1）基本语法格式
Elasticsearch提供了一个可以执行查询的Json风格的DSL。这个被称为Query DSL，该查询语言非常全面。
一个查询语句的典型结构
QUERY_NAME:{
ARGUMENT:VALUE,
ARGUMENT:VALUE,...
}
如果针对于某个字段，那么它的结构如下：
{
QUERY_NAME:{
FIELD_NAME:{
ARGUMENT:VALUE,
ARGUMENT:VALUE,...
}
}
}
GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"sort": [
{
"account_number": {
"order": "desc"
}
}
]
}
//match_al查询所有，从第0个数据拿5个数据
query定义如何查询；
- match_all查询类型【代表查询所有的所有】，es中可以在query中组合非常多的查询类型完成复杂查询；
- 除了query参数之外，我们可也传递其他的参数以改变查询结果，如sort，size；
- from+size限定，完成分页功能；
- sort排序，多字段排序，会在前序字段相等时后续字段内部排序，否则以前序为准；
（2）返回部分字段
GET bank/_search
{
"query": {
"match_all": {}
},
"from": 0,
"size": 5,
"sort": [
{
"account_number": {
"order": "desc"
}
}
],
"_source": ["balance","firstname"]
}
查询结果：
{
"took" : 18,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1000,
"relation" : "eq"
},
"max_score" : null,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "999",
"_score" : null,
"_source" : {
"firstname" : "Dorothy",
"balance" : 6087
},
"sort" : [
999
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "998",
"_score" : null,
"_source" : {
"firstname" : "Letha",
"balance" : 16869
},
"sort" : [
998
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "997",
"_score" : null,
"_source" : {
"firstname" : "Combs",
"balance" : 25311
},
"sort" : [
997
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "996",
"_score" : null,
"_source" : {
"firstname" : "Andrews",
"balance" : 17541
},
"sort" : [
996
]
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "995",
"_score" : null,
"_source" : {
"firstname" : "Phelps",
"balance" : 21153
},
"sort" : [
995
]
}
]
}
}
（3）match匹配查询
- 基本类型（非字符串），“account_number”: 20 可加可不加“ ” 不加就是精确匹配
GET bank/_search
{
"query": {
"match": {
"account_number": "20"
}
}
}
match返回account_number=20的数据。
查询结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 1.0,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
}
]
}
}
- 字符串，全文检索“ ” 模糊查询
GET bank/_search
{
"query": {
"match": {
"address": "kings"
}
}
}
全文检索，最终会按照评分进行排序，会对检索条件进行分词匹配。
查询结果：
{
"took" : 30,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 5.990829,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "20",
"_score" : 5.990829,
"_source" : {
"account_number" : 20,
"balance" : 16418,
"firstname" : "Elinor",
"lastname" : "Ratliff",
"age" : 36,
"gender" : "M",
"address" : "282 Kings Place",
"employer" : "Scentric",
"email" : "elinorratliff@scentric.com",
"city" : "Ribera",
"state" : "WA"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "722",
"_score" : 5.990829,
"_source" : {
"account_number" : 722,
"balance" : 27256,
"firstname" : "Roberts",
"lastname" : "Beasley",
"age" : 34,
"gender" : "F",
"address" : "305 Kings Hwy",
"employer" : "Quintity",
"email" : "robertsbeasley@quintity.com",
"city" : "Hayden",
"state" : "PA"
}
}
]
}
}
（4） match_phrase [短句匹配]
将需要匹配的值当成一整个单词（不分词）进行检索
GET bank/_search
{
"query": {
"match_phrase": {
"address": "mill road"
}
}
}
查处address中包含mill_road的所有记录，并给出相关性得分
查看结果：
{
"took" : 32,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 8.926605,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 8.926605,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
match_phrase和match的区别，观察如下实例：
match_phrase是做短语匹配
match是分词匹配，例如990 Mill匹配含有990或者Mill的结果
GET bank/_search
{
"query": {
"match_phrase": {
"address": "990 Mill"
}
}
}
查询结果：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 10.806405,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 10.806405,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
使用match的keyword
GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill"
}
}
}
查询结果，一条也未匹配到
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 0,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
}
}
修改匹配条件为“990 Mill Road”
GET bank/_search
{
"query": {
"match": {
"address.keyword": "990 Mill Road"
}
}
}
查询出一条数据
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 6.5032897,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 6.5032897,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
}
]
}
}
文本字段的匹配，使用keyword，匹配的条件就是要显示字段的全部值，要进行精确匹配的。
match_phrase是做短语匹配，只要文本中包含匹配条件既包含这个短语，就能匹配到。
（5）multi_math【多字段匹配】
GET bank/_search
{
"query": {
"multi_match": {
"query": "mill",
"fields": [
"state",
"address"
]
}
}
}
state或者address中包含mill，并且在查询过程中，会对于查询条件进行分词。
查询结果：
{
"took" : 28,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 4,
"relation" : "eq"
},
"max_score" : 5.4032025,
"hits" : [
{
"_index" : "bank",
"_type" : "account",
"_id" : "970",
"_score" : 5.4032025,
"_source" : {
"account_number" : 970,
"balance" : 19648,
"firstname" : "Forbes",
"lastname" : "Wallace",
"age" : 28,
"gender" : "M",
"address" : "990 Mill Road",
"employer" : "Pheast",
"email" : "forbeswallace@pheast.com",
"city" : "Lopezo",
"state" : "AK"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "136",
"_score" : 5.4032025,
"_source" : {
"account_number" : 136,
"balance" : 45801,
"firstname" : "Winnie",
"lastname" : "Holland",
"age" : 38,
"gender" : "M",
"address" : "198 Mill Lane",
"employer" : "Neteria",
"email" : "winnieholland@neteria.com",
"city" : "Urie",
"state" : "IL"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "345",
"_score" : 5.4032025,
"_source" : {
"account_number" : 345,
"balance" : 9812,
"firstname" : "Parker",
"lastname" : "Hines",
"age" : 38,
"gender" : "M",
"address" : "715 Mill Avenue",
"employer" : "Baluba",
"email" : "parkerhines@baluba.com",
"city" : "Blackgum",
"state" : "KY"
}
},
{
"_index" : "bank",
"_type" : "account",
"_id" : "472",
"_score" : 5.4032025,
"_source" : {
"account_number" : 472,
"balance" : 25571,
"firstname" : "Lee",
"lastname" : "Long",
"age" : 32,
"gender" : "F",
"address" : "288 Mill Street",
"employer" : "Comverges",
"email" : "leelong@comverges.com",
"city" : "Movico",
"state" : "MT"
}
}
]
}
}