query context & filter context
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- 高级搜索的功能：支持多想文本输入，针对多个字段进行搜索
- 搜索引擎一般也提供基于时间，价格等条件的guolv
- elasticsearch中，有query和filter两种不同的context
- query contest：相关性算分
- filter context：不需要算分，可以利用cache获得更好的性能
条件组合
- 假设要搜索一本电影，包含了以下一些条件
- 评论中包含了guitar，用户打分高于3分，同时上映日期要在1993到2000年之间
- 这个搜索其实包含了3段逻辑，针对不同的字段
- 评论字段中要包含guitar/用户评分大于3/上映日期需要在给定的范围
- 同时包含这三个逻辑，并且有比较好的性能？
- 符合查询：bool query
bool 查询
- 一个bool查询，是一个或者多个查询子句的组合
- 总共包括4种子句，其中2种会影响算分，2种不影响算分
- 相关性并不只是全文本检索的专利，也适用于yes | no的子句，匹配的子句越多，相关性评分越高。如果多条查询子句被合并为一条符合查询语句，比如bool查询，则每个查询子句计算得出的评分会被合并到总的相关性评分中
| 英文 | 描述 |
|---|---|
| must | 必须匹配，贡献算分 |
| should | 选择性匹配，贡献算分 |
| must_not | filter context，查询子句，必须不能匹配 |
| filter | filter context，必须匹配，但是不贡献算分 |
bool查询语法
- 子查询可以以任意顺序出席那
- 可以嵌套多个查询
- 如果你的bool查询中 ，没有must条件，should中必须至少满足一条查询
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如何解决结构化查询-“包含而不是相等”的问题
-
解决方案：增加一个genre count字段进行计数
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-
从业务角度，按需修改elasticsearch的数据模型
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filter context - 不影响算分
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bool嵌套
-
实现了should not的逻辑
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查询语句的结构，会对相关度算分产生影响
- 同一层级下的竞争字段，具有相同的权重
-
通过嵌套bool查询，可以改变对算分的影响
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控制字段的boosting
- boosting是控制相关度的一种手段
- 索引，字段或者查询子条件
- 参数boost的含义
- 当boost>1时候，打分的相关度相对性提升
- 当0<boost<1时候，打分的相关度相对性降低
-
当boost<0时候，贡献负分
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not quite not
-
要求苹果公司的产品信息优先
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回顾
- query context vs filter context
- bool query - 更多的条件组合
- 查询结构与相关性算分
- 如何控制查询的精确度
- boosting & boosting query
示例
POST /products/_bulk
{ "index": { "_id": 1 }}
{ "price" : 10,"avaliable":true,"date":"2018-01-01", "productID" : "XHDK-A-1293-#fJ3" }
{ "index": { "_id": 2 }}
{ "price" : 20,"avaliable":true,"date":"2019-01-01", "productID" : "KDKE-B-9947-#kL5" }
{ "index": { "_id": 3 }}
{ "price" : 30,"avaliable":true, "productID" : "JODL-X-1937-#pV7" }
{ "index": { "_id": 4 }}
{ "price" : 30,"avaliable":false, "productID" : "QQPX-R-3956-#aD8" }
#基本语法
POST /products/_search
{
"query": {
"bool" : {
"must" : {
"term" : { "price" : "30" }
},
"filter": {
"term" : { "avaliable" : "true" }
},
"must_not" : {
"range" : {
"price" : { "lte" : 10 }
}
},
"should" : [
{ "term" : { "productID.keyword" : "JODL-X-1937-#pV7" } },
{ "term" : { "productID.keyword" : "XHDK-A-1293-#fJ3" } }
],
"minimum_should_match" :1
}
}
}
#改变数据模型，增加字段。解决数组包含而不是精确匹配的问题
POST /newmovies/_bulk
{ "index": { "_id": 1 }}
{ "title" : "Father of the Bridge Part II","year":1995, "genre":"Comedy","genre_count":1 }
{ "index": { "_id": 2 }}
{ "title" : "Dave","year":1993,"genre":["Comedy","Romance"],"genre_count":2 }
#must，有算分
POST /newmovies/_search
{
"query": {
"bool": {
"must": [
{"term": {"genre.keyword": {"value": "Comedy"}}},
{"term": {"genre_count": {"value": 1}}}
]
}
}
}
#Filter。不参与算分，结果的score是0
POST /newmovies/_search
{
"query": {
"bool": {
"filter": [
{"term": {"genre.keyword": {"value": "Comedy"}}},
{"term": {"genre_count": {"value": 1}}}
]
}
}
}
#Filtering Context
POST _search
{
"query": {
"bool" : {
"filter": {
"term" : { "avaliable" : "true" }
},
"must_not" : {
"range" : {
"price" : { "lte" : 10 }
}
}
}
}
}
#Query Context
POST /products/_bulk
{ "index": { "_id": 1 }}
{ "price" : 10,"avaliable":true,"date":"2018-01-01", "productID" : "XHDK-A-1293-#fJ3" }
{ "index": { "_id": 2 }}
{ "price" : 20,"avaliable":true,"date":"2019-01-01", "productID" : "KDKE-B-9947-#kL5" }
{ "index": { "_id": 3 }}
{ "price" : 30,"avaliable":true, "productID" : "JODL-X-1937-#pV7" }
{ "index": { "_id": 4 }}
{ "price" : 30,"avaliable":false, "productID" : "QQPX-R-3956-#aD8" }
POST /products/_search
{
"query": {
"bool": {
"should": [
{
"term": {
"productID.keyword": {
"value": "JODL-X-1937-#pV7"}}
},
{"term": {"avaliable": {"value": true}}
}
]
}
}
}
#嵌套，实现了 should not 逻辑
POST /products/_search
{
"query": {
"bool": {
"must": {
"term": {
"price": "30"
}
},
"should": [
{
"bool": {
"must_not": {
"term": {
"avaliable": "false"
}
}
}
}
],
"minimum_should_match": 1
}
}
}
#Controll the Precision
POST _search
{
"query": {
"bool" : {
"must" : {
"term" : { "price" : "30" }
},
"filter": {
"term" : { "avaliable" : "true" }
},
"must_not" : {
"range" : {
"price" : { "lte" : 10 }
}
},
"should" : [
{ "term" : { "productID.keyword" : "JODL-X-1937-#pV7" } },
{ "term" : { "productID.keyword" : "XHDK-A-1293-#fJ3" } }
],
"minimum_should_match" :2
}
}
}
POST /animals/_search
{
"query": {
"bool": {
"should": [
{ "term": { "text": "brown" }},
{ "term": { "text": "red" }},
{ "term": { "text": "quick" }},
{ "term": { "text": "dog" }}
]
}
}
}
POST /animals/_search
{
"query": {
"bool": {
"should": [
{ "term": { "text": "quick" }},
{ "term": { "text": "dog" }},
{
"bool":{
"should":[
{ "term": { "text": "brown" }},
{ "term": { "text": "brown" }},
]
}
}
]
}
}
}
DELETE blogs
POST /blogs/_bulk
{ "index": { "_id": 1 }}
{"title":"Apple iPad", "content":"Apple iPad,Apple iPad" }
{ "index": { "_id": 2 }}
{"title":"Apple iPad,Apple iPad", "content":"Apple iPad" }
POST blogs/_search
{
"query": {
"bool": {
"should": [
{"match": {
"title": {
"query": "apple,ipad",
"boost": 1.1
}
}},
{"match": {
"content": {
"query": "apple,ipad",
"boost":
}
}}
]
}
}
}
DELETE news
POST /news/_bulk
{ "index": { "_id": 1 }}
{ "content":"Apple Mac" }
{ "index": { "_id": 2 }}
{ "content":"Apple iPad" }
{ "index": { "_id": 3 }}
{ "content":"Apple employee like Apple Pie and Apple Juice" }
POST news/_search
{
"query": {
"bool": {
"must": {
"match":{"content":"apple"}
}
}
}
}
POST news/_search
{
"query": {
"bool": {
"must": {
"match":{"content":"apple"}
},
"must_not": {
"match":{"content":"pie"}
}
}
}
}
POST news/_search
{
"query": {
"boosting": {
"positive": {
"match": {
"content": "apple"
}
},
"negative": {
"match": {
"content": "pie"
}
},
"negative_boost": 0.5
}
}
}
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