Suggest As You Type，即在用户输入搜索的过程中，进行自动补全或者纠错。
参考地址：
Elasticsearch Suggester详解 https://elasticsearch.cn/article/142
为Elasticsearch添加中文分词 http://keenwon.com/1404.html
Suggesters API
- Term Suggester
- Phrase Suggester
- Completion Suggester
- Context Suggester
Term Suggester
创建索引
PUT /blogs/
{
"mappings": {
"tech": {
"properties": {
"body": {
"type": "text"
}
}
}
}
}
bulk api 导入数据
POST _bulk/?refresh=true
{ "index" : { "_index" : "blogs", "_type" : "tech" } }
{ "body": "Lucene is cool"}
{ "index" : { "_index" : "blogs", "_type" : "tech" } }
{ "body": "Elasticsearch builds on top of lucene"}
{ "index" : { "_index" : "blogs", "_type" : "tech" } }
{ "body": "Elasticsearch rocks"}
{ "index" : { "_index" : "blogs", "_type" : "tech" } }
{ "body": "Elastic is the company behind ELK stack"}
{ "index" : { "_index" : "blogs", "_type" : "tech" } }
{ "body": "elk rocks"}
{ "index" : { "_index" : "blogs", "_type" : "tech" } }
{ "body": "elasticsearch is rock solid"}
这里需要注意的是，需要在完成一次操作之后，添加一行空格
https://stackoverflow.com/questions/35840740/bulk-indexing-using-elastic-search
每一个操作都有2行数据组成，末尾要回车换行。第一行用来说明操作命令和原数据、第二行是自定义的选项。
使用suggester搜索
POST /blogs/_search
{
"suggest": {
"my-suggestion": {
"text": "lucne rock",
"term": {
"suggest_mode": "missing",
"field": "body"
}
}
}
}
suggest就是一种特殊类型的搜索，DSL内部的”text”指的是api调用方提供的文本，也就是通常用户界面上用户输入的内容。这里的lucne是错误的拼写，模拟用户输入错误。 “term”表示这是一个term suggester。 “field”指定suggester针对的字段，另外有一个可选的”suggest_mode”。 范例里的”missing”实际上就是缺省值
返回结果
{
"took": 9,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": 0,
"hits": []
},
"suggest": {
"my-suggestion": [
{
"text": "lucne",
"offset": 0,
"length": 5,
"options": [
{
"text": "lucene",
"score": 0.8,
"freq": 2
}
]
},
{
"text": "rock",
"offset": 6,
"length": 4,
"options": [
{
"text": "rocks",
"score": 0.75,
"freq": 2
}
]
}
]
}
}
my-suggestion中的 text的内容是用户输入，options text中的内容是建议项。
suggest_mode 还有popular，always
Term suggester正如其名，只基于analyze过的单个term去提供建议，并不会考虑多个term之间的关系
Phrase suggester
Phrase suggester在Term suggester的基础上，会考量多个term之间的关系，比如是否同时出现在索引的原文里，相邻程度，以及词频等等
POST /blogs/_search
{
"suggest": {
"my-suggestion": {
"text": "lucne and elasticsear rock",
"phrase": {
"field": "body",
"highlight": {
"pre_tag": "<em>",
"post_tag": "</em>"
}
}
}
}
}
返回结果：
{
"took": 11,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": 0,
"hits": []
},
"suggest": {
"my-suggestion": [
{
"text": "lucne and elasticsear rock",
"offset": 0,
"length": 26,
"options": [
{
"text": "lucne and elasticsearch rocks",
"highlighted": "lucne and <em>elasticsearch rocks</em>",
"score": 0.12709484
},
{
"text": "lucne and elasticsearch rock",
"highlighted": "lucne and <em>elasticsearch</em> rock",
"score": 0.10422645
},
{
"text": "lucne and elasticsear rocks",
"highlighted": "lucne and elasticsear <em>rocks</em>",
"score": 0.10036137
}
]
}
]
}
}
Completion Suggester
Completion Suggester，它主要针对的应用场景就是”Auto Completion”。 此场景下用户每输入一个字符的时候，就需要即时发送一次查询请求到后端查找匹配项，在用户输入速度较高的情况下对后端响应速度要求比较苛刻。因此实现上它和前面两个Suggester采用了不同的数据结构，索引并非通过倒排来完成，而是将analyze过的数据编码成FST和索引一起存放。对于一个open状态的索引，FST会被ES整个装载到内存里的，进行前缀查找速度极快。但是FST只能用于前缀查找，这也是Completion Suggester的局限所在。
创建索引
PUT /blogs_completion/
{
"mappings": {
"tech": {
"properties": {
"body": {
"type": "completion"
}
}
}
}
}
导入数据
POST _bulk/?refresh=true
{ "index" : { "_index" : "blogs_completion", "_type" : "tech" } }
{ "body": "Lucene is cool"}
{ "index" : { "_index" : "blogs_completion", "_type" : "tech" } }
{ "body": "Elasticsearch builds on top of lucene"}
{ "index" : { "_index" : "blogs_completion", "_type" : "tech" } }
{ "body": "Elasticsearch rocks"}
{ "index" : { "_index" : "blogs_completion", "_type" : "tech" } }
{ "body": "Elastic is the company behind ELK stack"}
{ "index" : { "_index" : "blogs_completion", "_type" : "tech" } }
{ "body": "the elk stack rocks"}
{ "index" : { "_index" : "blogs_completion", "_type" : "tech" } }
{ "body": "elasticsearch is rock solid"}
注意点同理
查找
POST blogs_completion/_search?pretty
{ "size": 0,
"suggest": {
"blog-suggest": {
"prefix": "elastic i",
"completion": {
"field": "body"
}
}
}
}
结果
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 0,
"max_score": 0,
"hits": []
},
"suggest": {
"blog-suggest": [
{
"text": "elastic i",
"offset": 0,
"length": 9,
"options": [
{
"text": "Elastic is the company behind ELK stack",
"_index": "blogs_completion",
"_type": "tech",
"_id": "AfuEGWIBTnJNJ3DYfBHg",
"_score": 1,
"_source": {
"body": "Elastic is the company behind ELK stack"
}
}
]
}
]
}
}