1_ElaticSearch使用term filter来搜索数据

1_ElaticSearch使用term filter来搜索数据

更多干货

1、根据用户ID、是否隐藏、帖子ID、发帖日期来搜索帖子[title3]1）插入一些测试帖子数据[/title3]

POST /forum/article/_bulk
{ "index": { "_id": 1 }}
{ "articleID" : "XHDK-A-1293-#fJ3", "userID" : 1, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 2 }}
{ "articleID" : "KDKE-B-9947-#kL5", "userID" : 1, "hidden": false, "postDate": "2017-01-02" }
{ "index": { "_id": 3 }}
{ "articleID" : "JODL-X-1937-#pV7", "userID" : 2, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 4 }}
{ "articleID" : "QQPX-R-3956-#aD8", "userID" : 2, "hidden": true, "postDate": "2017-01-02" }

初步来说，就先搞4个字段，因为整个es是支持json document格式的，所以说扩展性和灵活性非常之好。如果后续随着业务需求的增加，要在document中增加更多的field，那么我们可以很方便的随时添加field。但是如果是在关系型数据库中，比如mysql，我们建立了一个表，现在要给表中新增一些column，那就很坑爹了，必须用复杂的修改表结构的语法去执行。而且可能对系统代码还有一定的影响。

GET /forum/_mapping/article

{
"forum": {
"mappings": {
"article": {
"properties": {
"articleID": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"hidden": {
"type": "boolean"
},
"postDate": {
"type": "date"
},
"userID": {
"type": "long"
}
}
}
}
}
}

现在es 5.2版本，type=text，默认会设置两个field，一个是field本身，比如articleID，就是分词的；还有一个的话，就是field.keyword，articleID.keyword，默认不分词，会最多保留256个字符[title3]2）根据用户ID搜索帖子[/title3]

GET /forum/article/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"userID" : 1
}
}
}
}
}

term filter/query：对搜索文本不分词，直接拿去倒排索引中匹配，你输入的是什么，就去匹配什么比如说，如果对搜索文本进行分词的话，“helle world” --> “hello”和“world”，两个词分别去倒排索引中匹配term，“hello world” --> “hello world”，直接去倒排索引中匹配“hello world”[title3]3）搜索没有隐藏的帖子[/title3]

GET /forum/article/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"hidden" : false
}
}
}
}
}

[title3]4）根据发帖日期搜索帖子[/title3]

GET /forum/article/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"postDate" : "2017-01-01"
}
}
}
}
}

[title3]5）根据帖子ID搜索帖子[/title3]

GET /forum/article/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"articleID" : "XHDK-A-1293-#fJ3"
}
}
}
}
}

{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 0,
"max_score": null,
"hits": []
}
}

GET /forum/article/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"articleID.keyword" : "XHDK-A-1293-#fJ3"
}
}
}
}
}

{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "1",
"_score": 1,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01"
}
}
]
}
}

articleID.keyword，是es最新版本内置建立的field，就是不分词的。所以一个articleID过来的时候，会建立两次索引，一次是自己本身，是要分词的，分词后放入倒排索引；另外一次是基于articleID.keyword，不分词，保留256个字符最多，直接一个字符串放入倒排索引中。所以term filter，对text过滤，可以考虑使用内置的field.keyword来进行匹配。但是有个问题，默认就保留256个字符。所以尽可能还是自己去手动建立索引，指定not_analyzed吧。在最新版本的es中，不需要指定not_analyzed也可以，将type=keyword即可。[title3]6）查看分词[/title3]

GET /forum/_analyze
{
"field": "articleID",
"text": "XHDK-A-1293-#fJ3"
}

默认是analyzed的text类型的field，建立倒排索引的时候，就会对所有的articleID分词，分词以后，原本的articleID就没有了，只有分词后的各个word存在于倒排索引中。term，是不对搜索文本分词的，XHDK-A-1293-#fJ3 --> XHDK-A-1293-#fJ3；但是articleID建立索引的时候，XHDK-A-1293-#fJ3 --> xhdk，a，1293，fj3[title3]7）重建索引[/title3]

DELETE /forumPUT /forum{"mappings": {"article": {"properties": {"articleID": {"type": "keyword"}}}}}POST /forum/article/_bulk
{ "index": { "_id": 1 }}
{ "articleID" : "XHDK-A-1293-#fJ3", "userID" : 1, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 2 }}
{ "articleID" : "KDKE-B-9947-#kL5", "userID" : 1, "hidden": false, "postDate": "2017-01-02" }
{ "index": { "_id": 3 }}
{ "articleID" : "JODL-X-1937-#pV7", "userID" : 2, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 4 }}
{ "articleID" : "QQPX-R-3956-#aD8", "userID" : 2, "hidden": true, "postDate": "2017-01-02" }

[title3]8）重新根据帖子ID和发帖日期进行搜索[/title3]

GET /forum/article/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"articleID" : "XHDK-A-1293-#fJ3"
}
}
}
}
}

2、知识点

select *
from forum.article
where articleID='XHDK-A-1293-#fJ3'

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