Ada*_*ert 6 elasticsearch elasticsearch-dsl elasticsearch-query
我已经使用“AND”默认运算符设置了我的查询字符串搜索。我的查询如下：
{
"query": {
"query_string" : {
"query" : "Adam KT2 7AJ",
"default_operator" : "AND"
}
}
}
我希望这能提供与下面相同的结果......但事实并非如此。
{
"query": {
"query_string" : {
"query" : "Adam AND KT2 AND 7AJ",
"default_operator" : "OR"
}
}
}
虽然我看到 default_operator 对我的搜索结果产生了影响，但它并没有像我希望的那样工作。
例如：
查询 1：
Adam AND KT2 AND 7AJ, default_operator: or查询2：
Adam KT2 7AJ, default_operator: andanddefault_operator 在查询中产生与 AND 不同的结果）查询3：
KT2 7AJ, default_operator: andQUERY4：
KT2 7AJ, default_operator: or编辑
这是我的映射。您会注意到帐单地址中有一个 post_code 字段，但有附加到 shipping_address 和客户的姓名字段。
当我将查询作为 运行时Adam AND KT2 7AJ，我们得到了 where 的结果customer.name:Adam AND billing_address.post_code:KT2 7AJ，这正是我所期望的。
如果我们删除 AND，我们会得到 0 个结果。
{
"t1_orders_1584882055": {
"mappings": {
"properties": {
"__class_name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"billing_address": {
"properties": {
"address1": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"address2": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"address3": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"company": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"country_code": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"created_at": {
"type": "date",
"format": "date_time"
},
"customer_id": {
"type": "long"
},
"email": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "edge_ngram_analyzer"
},
"phone": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"post_code": {
"type": "text",
"fields": {
"text": {
"type": "text"
}
},
"analyzer": "no_space_analyzer"
},
"redacted": {
"type": "boolean"
},
"region": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"town": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"updated_at": {
"type": "date",
"format": "date_time"
}
}
},
"billing_address_id": {
"type": "long"
},
"channel": {
"properties": {
"created_at": {
"type": "date"
},
"id": {
"type": "long"
},
"identifier": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"updated_at": {
"type": "date"
}
}
},
"channel_id": {
"type": "long"
},
"created_at": {
"type": "date",
"format": "date_time"
},
"customer": {
"properties": {
"created_at": {
"type": "date",
"format": "date_time"
},
"email": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "edge_ngram_analyzer"
},
"redacted": {
"type": "boolean"
},
"updated_at": {
"type": "date",
"format": "date_time"
}
}
},
"customer_id": {
"type": "long"
},
"id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "edge_ngram_analyzer"
},
"order_id": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"order_items": {
"properties": {
"created_at": {
"type": "date"
},
"id": {
"type": "long"
},
"order_id": {
"type": "long"
},
"qty": {
"type": "long"
},
"sku": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"updated_at": {
"type": "date"
}
}
},
"received_at": {
"type": "date",
"format": "date_time"
},
"reference_number": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"ship_by": {
"type": "date"
},
"shipping_address": {
"properties": {
"address1": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"address2": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"address3": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"company": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"country_code": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"created_at": {
"type": "date",
"format": "date_time"
},
"customer_id": {
"type": "long"
},
"email": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "edge_ngram_analyzer"
},
"phone": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"post_code": {
"type": "text",
"fields": {
"text": {
"type": "text"
}
},
"analyzer": "no_space_analyzer"
},
"redacted": {
"type": "boolean"
},
"region": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"town": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"updated_at": {
"type": "date",
"format": "date_time"
}
}
},
"shipping_address_id": {
"type": "long"
},
"status": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"updated_at": {
"type": "date",
"format": "date_time"
}
}
}
}
}
示例文档：
{
"_source": {
"id": 6,
"channel_id": 1,
"customer_id": 1,
"shipping_address_id": 11,
"billing_address_id": 12,
"order_id": "1234",
"reference_number": "1234",
"status": "open",
"received_at": "2020-02-24T18:03:21.000000Z",
"ship_by": "2020-02-24T18:03:21.000000Z",
"completed_at": null,
"created_at": "2020-03-18T16:17:28.000000Z",
"updated_at": "2020-03-18T16:17:28.000000Z",
"channel": {
"id": 1,
"name": "Amazon",
"identifier": "The",
"created_at": "2020-03-18T16:17:28.000000Z",
"updated_at": "2020-03-18T16:17:28.000000Z"
},
"customer": {
"id": 1,
"name": "Nam Test",
"email": "Nam@Test.com",
"redacted": false,
"created_at": "2020-03-18T16:17:28.000000Z",
"updated_at": "2020-03-18T16:17:28.000000Z"
},
"shipping_address": {
"id": 11,
"customer_id": 1,
"name": "Nam Test",
"email": "Nam@Test.COm",
"company": "",
"address1": "44",
"address2": "AD CLOSE",
"address3": "",
"town": "KINGSTON UPON THAMES",
"region": "",
"post_code": "KT2 7AJ",
"country_code": null,
"phone": "213223123",
"redacted": false,
"created_at": "2020-03-18T16:17:28.000000Z",
"updated_at": "2020-03-18T16:17:28.000000Z"
},
"billing_address": {
"id": 12,
"customer_id": 1,
"name": "Nam Test",
"email": "Nam@Test.COm",
"company": "",
"address1": "44",
"address2": "AD CLOSE",
"address3": "",
"town": "KINGSTON UPON THAMES",
"region": "",
"post_code": "KT2 7AJ",
"country_code": null,
"phone": "213223123",
"redacted": false,
"created_at": "2020-03-18T16:17:28.000000Z",
"updated_at": "2020-03-18T16:17:28.000000Z"
},
"order_items": [
{
"id": 6,
"order_id": 6,
"sku": "10-2-sk-ue",
"qty": 1,
"created_at": "2020-03-18T16:17:28.000000Z",
"updated_at": "2020-03-18T16:17:28.000000Z"
}
]
}
}
从elasticsearch版本6.x开始，不再使用空格分割。
说明： 采用名称和邮政编码两个字段
1. Adam AND KT2 AND 7AJ，default_operator: or
查询转换为 :-
(Name:Adam OR PostalCode:Adam) OR (Name:KT2 OR PostalCode:KT2) OR (Name:7AJ OR PostalCode:7AJ)
文本按运算符拆分（AND，OR）并且每个分割都传递到各自的字段因此它返回名称为 Adam 且邮政编码包含 7AJ 或 KT2 的所有文档（令牌匹配）
2. Adam AND KT2 AND 7AJ，default_operator：and
查询转换为：-
(姓名：Adam OR 邮政编码：Adam) AND (姓名：KT2 OR 邮政编码：KT2) AND (姓名：7AJ OR 邮政编码：7AJ)
3. Adam KT2 7AJ，default_operator：并且
查询转换为：-（
名称：（Adam AND KT2 AND 7AJ）或邮政编码：（Adam AND KT2 AND 7AJ））
文本不会在空格上分割（这是在版本 6 之前完成的）并且被认为是一个文本块。因此，仅返回在任何字段中具有所有三个标记的文档
4. KT2 7AJ，default_operator：与
第 2 点文档相同，返回在任何字段中都具有两个标记的文档。
5. KT2 7AJ, default_operator: 或
与第 2 点文档相同，返回字段中带有任何 token(OR) 的文档
此链接由 github 上的 Elastic Search 团队提供了类似的说明
如果您想继续使用现有结构，您可以使用括号将相似的单词组合起来。下面的查询将文本拆分为两个标记 Adam 和单个块 KT2 7AJ，这将转换为
(Name:Adam OR PostalCode:Adam) AND (Name:(KT2 AND 7AJ) OR PostalCode:(KT2 AND 7AJ)
{
"query": {
"query_string" : {
"query" : "Adam (KT2 7AJ)",
"default_operator" : "AND"
}
}
}
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