1. 准备数据
PUT h1/doc/1
{
"name": "rose",
"gender": "female",
"age": 18,
"tags": ["白", "漂亮", "高"]
}
PUT h1/doc/2
{
"name": "lila",
"gender": "female",
"age": 18,
"tags": ["黑", "漂亮", "高"]
}
PUT h1/doc/3
{
"name": "john",
"gender": "male",
"age": 18,
"tags": ["黑", "帅", "高"]
}
运行结果：
{
"_index" : "h1",
"_type" : "doc",
"_id" : "1",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
},
"_seq_no" : 0,
"_primary_term" : 1
}
2. match 查询
2.1 match 按条件查询
# 查询性别是男性的结果
GET h1/doc/_search
{
"query": {
"match": {
"gender": "male"
}
}
}
查询结果：
{
"took" : 59,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "h1", # 索引
"_type" : "doc", # 文档类型
"_id" : "3", # 文档唯一 id
"_score" : 0.2876821, # 打分机制打出来的分数
"_source" : { # 查询结果
"name" : "john",
"gender" : "male",
"age" : 18,
"tags" : [
"黑",
"帅",
"高"
]
}
}
]
}
}
2.2 match_all 查询全部
# 查询 h1 中所有文档
GET h1/doc/_search
{
"query": {
"match_all": {}
}
}
match_all的值为空，表示没有查询条件，那就是查询全部。就像select * from table_name 一样。
查询结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 1.0,
"hits" : [
{
"_index" : "h1",
"_type" : "doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "lila",
"gender" : "female",
"age" : 18,
"tags" : [
"黑",
"漂亮",
"高"
]
}
},
{
"_index" : "h1",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "rose",
"gender" : "female",
"age" : 18,
"tags" : [
"白",
"漂亮",
"高"
]
}
},
{
"_index" : "h1",
"_type" : "doc",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"name" : "john",
"gender" : "male",
"age" : 18,
"tags" : [
"黑",
"帅",
"高"
]
}
}
]
}
}
2.3 match_phrase 短语查询
match 查询时散列映射，包含了我们希望搜索的字段和字符串，即只要文档中有我们希望的那个关键字，但也会带来一些问题。
es 会将文档中的内容进行拆分，对于英文来说可能没有太大的影响，但是中文短语就不太适用，一旦拆分就会失去原有的含义，比如以下：
1、准备数据：
PUT t1/doc/1
{
"title": "中国是世界上人口最多的国家"
}
PUT t1/doc/2
{
"title": "美国是世界上军事实力最强大的国家"
}
PUT t1/doc/3
{
"title": "北京是中国的首都"
}
2、先使用 match 查询含有中国的文档：
GET t1/doc/_search
{
"query": {
"match": {
"title": "中国"
}
}
}
查询结果：
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.68324494,
"hits" : [
{
"_index" : "t1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.68324494,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
},
{
"_index" : "t1",
"_type" : "doc",
"_id" : "3",
"_score" : 0.5753642,
"_source" : {
"title" : "北京是中国的首都"
}
},
{
"_index" : "t1",
"_type" : "doc",
"_id" : "2",
"_score" : 0.39556286,
"_source" : {
"title" : "美国是世界上军事实力最强大的国家"
}
}
]
}
}
发现三篇文档都被返回，与我们的预期有偏差；这是因为 title 中的内容被拆分成一个个单独的字，而 id=2 的文档包含了 国 字也符合，所以也被返回了。es 自带的中文分词处理不太好用，后面可以使用 ik 中文分词器来处理。
3、match_phrase 查询短语
不过可以使用 match_phrase 来匹配短语，将上面的 match 换成 match_phrase 试试：
# 短语查询
GET t1/doc/_search
{
"query": {
"match_phrase": {
"title": "中国"
}
}
}
查询结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.5753642,
"hits" : [
{
"_index" : "t1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.5753642,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
},
{
"_index" : "t1",
"_type" : "doc",
"_id" : "3",
"_score" : 0.5753642,
"_source" : {
"title" : "北京是中国的首都"
}
}
]
}
}
4、slop 间隔查询
当我们要查询的短语，中间有别的词时，可以使用 slop 来跳过。比如上述要查询 中国世界，这个短语中间被 是 隔开了，这时可以使用 slop 来跳过，相当于正则中的中国.*?世界：
# 短语查询，查询中国世界，加 slop
GET t1/doc/_search
{
"query": {
"match_phrase": {
"title": {
"query": "中国世界",
"slop": 1
}
}
}
}
查询结果：
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.7445889,
"hits" : [
{
"_index" : "t1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.7445889,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
}
]
}
}
2.4 match_phrase_prefix 最左前缀查询
场景：当我们要查询的词只能想起前几个字符时
# 最左前缀查询，查询名字为 rose 的文档
GET h1/doc/_search
{
"query": {
"match_phrase_prefix": {
"name": "ro"
}
}
}
查询结果：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "h1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name" : "rose",
"gender" : "female",
"age" : 18,
"tags" : [
"白",
"漂亮",
"高"
]
}
}
]
}
}
限制结果集
最左前缀查询很费性能，返回的是一个很大的集合，一般很少使用，使用的时候最好对结果集进行限制，max_expansions 参数可以设置最大的前缀扩展数量：
# 最左前缀查询
GET h1/doc/_search
{
"query": {
"match_phrase_prefix": {
"gender": {
"query": "fe",
"max_expansions": 1
}
}
}
}
查询结果：
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "h1",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"name" : "lila",
"gender" : "female",
"age" : 18,
"tags" : [
"黑",
"漂亮",
"高"
]
}
},
{
"_index" : "h1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name" : "rose",
"gender" : "female",
"age" : 18,
"tags" : [
"白",
"漂亮",
"高"
]
}
}
]
}
}
2.5 multi_match 多字段查询
1、准备数据：
# 多字段查询
PUT t3/doc/1
{
"title": "maggie is beautiful girl",
"desc": "beautiful girl you are beautiful so"
}
PUT t3/doc/2
{
"title": "beautiful beach",
"desc": "I like basking on the beach,and you? beautiful girl"
}
2、查询包含 beautiful 字段的文档：
GET t3/doc/_search
{
"query": {
"multi_match": {
"query": "beautiful", # 要查询的词
"fields": ["desc", "title"] # 要查询的字段
}
}
}
还可以当做 match_phrase和match_phrase_prefix使用，只需要指定type类型即可：
GET t3/doc/_search
{
"query": {
"multi_match": {
"query": "gi",
"fields": ["title"],
"type": "phrase_prefix"
}
}
}
GET t3/doc/_search
{
"query": {
"multi_match": {
"query": "girl",
"fields": ["title"],
"type": "phrase"
}
}
}
3. term 查询
3.1 初始 es 的分析器
term 查询用于精确查询，但是不适用于 text 类型的字段查询。
在此之前我们先了解 es 的分析机制，默认的标准分析器会对文档进行：
- 删除大多数的标点符号
- 将文档拆分为单个词条，称为
token - 将
token转换为小写
最后保存到倒排序索引上，而倒排序索引用来查询，如 Beautiful girl 经过分析后是这样的：
POST _analyze
{
"analyzer": "standard",
"text": "Beautiful girl"
}
# 结果，转换为小写了
{
"tokens" : [
{
"token" : "beautiful",
"start_offset" : 0,
"end_offset" : 9,
"type" : "<ALPHANUM>",
"position" : 0
},
{
"token" : "girl",
"start_offset" : 10,
"end_offset" : 14,
"type" : "<ALPHANUM>",
"position" : 1
}
]
}
3.2 term 查询
1、准备数据：
# 创建索引，自定义 mapping，后面会讲到
PUT t4
{
"mappings": {
"doc":{
"properties":{
"t1":{
"type": "text" # 定义字段类型为 text
}
}
}
}
}
PUT t4/doc/1
{
"t1": "Beautiful girl!"
}
PUT t4/doc/2
{
"t1": "sexy girl!"
}
2、match 查询：
GET t4/doc/_search
{
"query": {
"match": {
"t1": "Beautiful girl!"
}
}
}
经过分析后，会得到 beautiful、girl 两个 token，然后再去 t4 索引上去查询，会返回两篇文档：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.5753642,
"hits" : [
{
"_index" : "t4",
"_type" : "doc",
"_id" : "1",
"_score" : 0.5753642,
"_source" : {
"title" : "Beautiful girl"
}
},
{
"_index" : "t4",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"title" : "sex girl"
}
}
]
}
}
3、但是我们只想精确查询包含 Beautiful girl 的文档，这时就需要使用 term 来精确查询：
GET t4/doc/_search
{
"query": {
"term": {
"title": "beautiful"
}
}
}
查询结果：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "t4",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"title" : "Beautiful girl"
}
}
]
}
}
注意：
term查询不适用于类型是text的字段，可以使用match查询；另外Beautiful经过分析后变为beautiful，查询时使用Beautiful是查询不到的~
3.3 查询多个
精确查询多个字段：
GET t4/doc/_search
{
"query": {
"terms": {
"title": ["beautiful", "sex"]
}
}
}