DELETE us
POST /_bulk
{ "create": { "_index": "us", "_type": "tweet", "_id": "1" }}
{ "email" : "[email protected]", "name" : "John Smith", "username" : "@john" }
{ "create": { "_index": "us", "_type": "tweet", "_id": "2" }}
{ "email" : "[email protected]", "name" : "Mary Jones", "username" : "@mary" }
{ "create": { "_index": "us", "_type": "tweet", "_id": "3" }}
{ "date" : "2014-09-13", "name" : "Mary Jones", "tweet" : "Elasticsearch means full text search has never been so easy", "user_id" : 2 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "4" }}
{ "date" : "2014-09-14", "name" : "John Smith", "tweet" : "@mary it is not just text, it does everything", "user_id" : 1 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "5" }}
{ "date" : "2014-09-15", "name" : "Mary Jones", "tweet" : "However did I manage before Elasticsearch?", "user_id" : 2 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "6" }}
{ "date" : "2014-09-16", "name" : "John Smith", "tweet" : "The Elasticsearch API is really easy to use", "user_id" : 1 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "7" }}
{ "date" : "2014-09-17", "name" : "Mary Jones", "tweet" : "The Query DSL is really powerful and flexible", "user_id" : 2 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "8" }}
{ "date" : "2014-09-18", "name" : "John Smith", "user_id" : 1 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "9" }}
{ "date" : "2014-09-19", "name" : "Mary Jones", "tweet" : "Geo-location aggregations are really cool", "user_id" : 2 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "10" }}
{ "date" : "2014-09-20", "name" : "John Smith", "tweet" : "Elasticsearch surely is one of the hottest new NoSQL products", "user_id" : 1 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "11" }}
{ "date" : "2014-09-21", "name" : "Mary Jones", "tweet" : "Elasticsearch is built for the cloud, easy to scale", "user_id" : 2 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "12" }}
{ "date" : "2014-09-22", "name" : "John Smith", "tweet" : "Elasticsearch and I have left the honeymoon stage, and I still love her.", "user_id" : 1 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "13" }}
{ "date" : "2014-09-23", "name" : "Mary Jones", "tweet" : "So yes, I am an Elasticsearch fanboy", "user_id" : 2 }
{ "create": { "_index": "us", "_type": "tweet", "_id": "14" }}
{ "date" : "2014-09-24", "name" : "John Smith", "tweet" : "How many more cheesy tweets do I have to write?", "user_id" : 1 }
按照一般的查询流程来说,如果我想查询前10条数据:
from
和size
两个关键字, from定义了目标数据的偏移量(每页的其实索引), size定义每页显示条数GET /us/_search?pretty
{
"from" : 0 , "size" : 10
}
GET /us/_search?pretty
{
"from" : 10 , "size" : 10
}
对于上面介绍的浅分页,当Elasticsearch响应请求时,它必须确定docs的顺序,排列响应结果。
如果请求的页数较少(假设每页20个docs), Elasticsearch不会有什么问题,但是如果页数较大时,比如请求第20页,Elasticsearch不得不取出第1页到第20页的所有docs,再去除第1页到第19页的docs,得到第20页的docs。
解决的方式就是使用scroll,scroll就是维护了当前索引段的一份快照信息–缓存(这个快照信息是你执行这个scroll查询时的快照)。
可以把 scroll 分为初始化和遍历两步:
1、初始化时将所有符合搜索条件的搜索结果缓存起来,可以想象成快照;
2、遍历时,从这个快照里取数据;
# 初始化的时候就像是普通的search一样
# 其中的scroll=3m代表当前查询的数据缓存3分钟
# Size:3 代表当前查询3条数据
GET us/_search?scroll=3m
{
"query": {"match_all": {}},
"size": 3
}
GET /_search/scroll
{
"scroll" : "1m",
"scroll_id" : "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAAAPXFk0xN1BmSnlVUldhYThEdWVzZ19xbkEAAAAAAAAAIxZuQWVJU0VSZ1JzcVZtMGVYZ3RDaFlBAAAAAAAAA9oWTVZOdHJ2cXBSOU9wN3c1dk5vcWd4QQAAAAAAAAPYFk0xN1BmSnlVUldhYThEdWVzZ19xbkEAAAAAAAAAIhZuQWVJU0VSZ1JzcVZtMGVYZ3RDaFlB"
}
【注意】:每次都要传参数scroll,刷新搜索结果的缓存时间,另外不需要指定index和type(不要把缓存的时时间设置太长,占用内存)
浅分页,每次查询都会去索引库(本地文件夹)中查询pageNum*page条数据,然后截取掉前面的数据,留下最后的数据。 这样的操作在每个分片上都会执行,最后会将多个分片的数据合并到一起,再次排序,截取需要的。
深分页,可以一次性将所有满足查询条件的数据,都放到内存中。分页的时候,在内存中查询。相对浅分页,就可以避免多次读取磁盘。