部分匹配
邮政编码和结构化数据
PUT /my_index
{
"mappings": {
"address": {
"properties": {
"postcode": {
"type": "string",
"index": "not_analyzed"
}
}
}
}
}
插入数据
PUT /my_index/address/_bulk
{"index":{"_id":1}}
{"postcode":"W1V 3DG"}
{"index":{"_id":2}}
{"postcode":"W2F 8HW"}
{"index":{"_id":3}}
{"postcode":"W1F 7HW"}
{"index":{"_id":4}}
{"postcode":"WC1N 1LZ"}
{"index":{"_id":5}}
{"postcode":"SW5 0BE"}
查询条件
GET /my_index/address/_search
{
"query": {
"prefix": {
"postcode": "W1"
}
}
}
查询的结果
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "my_index",
"_type": "address",
"_id": "1",
"_score": 1,
"_source": {
"postcode": "W1V 3DG"
}
},
{
"_index": "my_index",
"_type": "address",
"_id": "3",
"_score": 1,
"_source": {
"postcode": "W1F 7HW"
}
}
]
}
prefix 查询是低级别的查询，它在查询之前不会对query string分词。
Tip prefix查询和filter很相似，默认情况下不会计分，即给所有返回的结果_score 都是1，filter和prefix的唯一区别是prefix不会缓存。 为了支持动态前缀匹配，查询将执行以下操作：
- 略过条件列表找到w1开始的位置
- 收集有关联文档的ID
- 移到下一个条件
- 如果条件仍然是从w1开始，查询重复第二步直到结束。
注意prefix查询在查询条件少时能够自由的使用，但是在大规模使用时会对集群造成很大的压力。尝试使用长的前缀，以减少它们对集群的影响。这会减少对集群访问的项数。
通配和正则查询
GET /my_index/address/_search
{
"query": {
"wildcard": {
"postcode": "W?F*HW"
}
}
}
结果为
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "my_index",
"_type": "address",
"_id": "2",
"_score": 1,
"_source": {
"postcode": "W2F 8HW"
}
},
{
"_index": "my_index",
"_type": "address",
"_id": "3",
"_score": 1,
"_source": {
"postcode": "W1F 7HW"
}
}
]
}
正则表达式查询
GET /my_index/address/_search
{
"query": {
"regexp": {
"postcode": "W[0-9].+"
}
}
}
查询的结果
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "my_index",
"_type": "address",
"_id": "2",
"_score": 1,
"_source": {
"postcode": "W2F 8HW"
}
},
{
"_index": "my_index",
"_type": "address",
"_id": "1",
"_score": 1,
"_source": {
"postcode": "W1V 3DG"
}
},
{
"_index": "my_index",
"_type": "address",
"_id": "3",
"_score": 1,
"_source": {
"postcode": "W1F 7HW"
}
}
]
}
注意 prefix，wildcard，regexp是条件操作，如果使用它们来查询已分词的字段，它们将检查该字段的每个条件，而不是把它们看做一个整体。
Ngrams 部分匹配
如查询单词quick
- Length 1 (unigram): [ q, u, i, c, k ]
- Length 2 (bigram): [ qu, ui, ic, ck ]
- Length 3 (trigram): [ qui, uic, ick ]
- Length 4 (four-gram): [ quic, uick ]
- Length 5 (five-gram): [ quick ]
例如
PUT /my_index
{
"settings": {
"number_of_shards": 1,
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
},
"analyzer": {
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter"
]
}
}
}
}
}
edge_ngram是一种type，我们自己定义了一个filter方法autocomplete_filter定义了最长和最短的模式。其本身是有分词的。
GET /my_index/_analyze
{
"analyzer": "autocomplete",
"text": "quick brown"
}
结果
{
"tokens": [
{
"token": "q",
"start_offset": 0,
"end_offset": 5,
"type": "word",
"position": 0
},
{
"token": "qu",
"start_offset": 0,
"end_offset": 5,
"type": "word",
"position": 0
},
{
"token": "qui",
"start_offset": 0,
"end_offset": 5,
"type": "word",
"position": 0
},
{
"token": "quic",
"start_offset": 0,
"end_offset": 5,
"type": "word",
"position": 0
},
{
"token": "quick",
"start_offset": 0,
"end_offset": 5,
"type": "word",
"position": 0
},
{
"token": "b",
"start_offset": 6,
"end_offset": 11,
"type": "word",
"position": 1
},
{
"token": "br",
"start_offset": 6,
"end_offset": 11,
"type": "word",
"position": 1
},
{
"token": "bro",
"start_offset": 6,
"end_offset": 11,
"type": "word",
"position": 1
},
{
"token": "brow",
"start_offset": 6,
"end_offset": 11,
"type": "word",
"position": 1
},
{
"token": "brown",
"start_offset": 6,
"end_offset": 11,
"type": "word",
"position": 1
}
]
}
这是一种比正则更高效的查询方式。 看下面的例子
PUT /my_index/_mapping/my_type
{
"my_type": {
"properties": {
"name": {
"type": "string",
"analyzer": "autocomplete"
}
}
}
}
POST /my_index/my_type/_bulk
{ "index": { "_id": 1 }}
{ "name": "Brown foxes" }
{ "index": { "_id": 2 }}
{ "name": "Yellow furballs" }
查询字段
GET /my_index/my_type/_search
{
"query": {
"match": {
"name": "brown fo"
}
}
}
结果
"hits": [
{
"_id": "1",
"_score": 1.5753809,
"_source": {
"name": "Brown foxes"
}
},
{
"_id": "2",
"_score": 0.012520773,
"_source": {
"name": "Yellow furballs"
}
}
]