ElasticSearch之Completion Suggester

写在前面

通过completion suggester可以实现如下的效果：

其实就是做的like xxx%这种。通过FST这种数据结构来存储，实现快速的前缀匹配，并且可以将es所有的数据加载到内存中所以速度completion的查询速度非常快。

需要注意，如果是某个字段想要使用completion suggester的功能，需要将其类型设置为completion，也就是我们需要显示的设置mapping来指定。

1：例子

首先来创建索引并指定mapping：

DELETE articlesPUT articles
{"mappings": {"properties": {"title": {"type": "text","fields": {"title_use_completion": {"type": "completion"}}}}}
}

接着插入数据：

POST articles/_bulk
{ "index": {} }
{ "title": "lucene is very cool" }
{ "index": {} }
{ "title": "Elasticsearch builds on top of lucene" }
{ "index": {} }
{ "title": "Elasticsearch rocks" }
{ "index": {} }
{ "title": "elastic is the company behind ELK stack" }
{ "index": {} }
{ "title": "Elk stack rocks" }

查询：

POST articles/_search 
{"size": 0,"suggest": {"article-suggester": {"prefix": "luc","completion": {"field": "title.title_use_completion"}}}
}

另外，es还支持一种基于上下文的suggestion，Context Suggerter，如下：

context分为两类，category和geo，如下：

以context为里来看下。

• 首先来定义mapping
  在mapping中指定context的信息:

# 删除
DELETE comments
# 创建
PUT comments
# 指定mapping
PUT comments/_mapping
{"properties": {"comment_autocomplete": {"type": "completion","contexts": [{"type": "category","name": "comment_category"}]}}
}

数据：

# 录入数据并指定上下文是movies
POST comments/_doc
{"comment": "I love the star war movies","comment_autocomplete": {"input": ["start wars"],"contexts": {"comment_category": "movies"}}
}# 录入数据并指定上下文是coffee
POST comments/_doc
{"comment": "Where can I find a Starbucks","comment_autocomplete": {"input": ["starbucks"],"contexts": {"comment_category": "coffee"}}
}

movies上下文查询：

# 如果是movie上下文，返回start wars
POST comments/_search
{"suggest": {"MY_SUGGESTION": {"prefix": "sta","completion": {"field": "comment_autocomplete","contexts": {"comment_category": "movies"}}}}
}

coffee上下文查询：

# 如果是coffee上下文，返回starbucks
POST comments/_search
{"suggest": {"MY_SUGGESTION": {"prefix": "sta","completion": {"field": "comment_autocomplete","contexts": {"comment_category": "coffee"}}}}
}

最后看下term，phrase，completion三者的对比：

写在后面

参考文章列表

倒排索引：ES倒排索引底层原理及FST算法的实现过程 。