Rrr*_*Rrr 6 elasticsearch elasticsearch-percolate
指数：
{
"settings": {
"index.percolator.map_unmapped_fields_as_text": true,
},
"mappings": {
"properties": {
"query": {
"type": "percolator"
}
}
}
}
此测试过滤器查询有效
{
"query": {
"match": {
"message": "blah"
}
}
}
此查询不起作用
{
"query": {
"simple_query_string": {
"query": "bl*"
}
}
}
结果：
{"took":15,"timed_out":false,"_shards":{"total":5,"successful":5,"skipped":0,"failed":0},"hits":{"total":{"value":1,"relation":"eq"},"max_score":0.13076457,"hits":[{"_index":"my-index","_type":"_doc","_id":"1","_score":0.13076457,"_source":{"query":{"match":{"message":"blah"}}},"fields":{"_percolator_document_slot":[0]}}]}}
为什么此simple_query_string查询与文档不匹配？
我也不明白你在问什么。可能你不太了解percolator？这是我现在刚刚尝试的一个例子。
假设您有一个索引（我们称之为索引test），您想要在其中索引一些文档。该索引具有以下映射（只是我的测试设置中的随机测试索引）：
{
"settings": {
"analysis": {
"filter": {
"email": {
"type": "pattern_capture",
"preserve_original": true,
"patterns": [
"([^@]+)",
"(\\p{L}+)",
"(\\d+)",
"@(.+)",
"([^-@]+)"
]
}
},
"analyzer": {
"email": {
"tokenizer": "uax_url_email",
"filter": [
"email",
"lowercase",
"unique"
]
}
}
}
},
"mappings": {
"properties": {
"code": {
"type": "long"
},
"date": {
"type": "date"
},
"part": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"val": {
"type": "long"
},
"email": {
"type": "text",
"analyzer": "email"
}
}
}
}
您注意到它有一个自定义email分析器，可以将类似内容foo@bar.com分成以下标记：foo@bar.com, foo, bar.com, bar, com。
正如文档所述，您可以创建一个单独的渗透器索引，该索引仅保存您的渗透器查询，而不保存文档本身。而且，即使渗透器索引本身不包含文档，它也应该保存保存文档的索引的映射（test在我们的例子中）。
这是渗透器索引（我称之为percolator_index）的映射，它还具有用于分割email字段的特殊分析器：
{
"settings": {
"analysis": {
"filter": {
"email": {
"type": "pattern_capture",
"preserve_original": true,
"patterns": [
"([^@]+)",
"(\\p{L}+)",
"(\\d+)",
"@(.+)",
"([^-@]+)"
]
}
},
"analyzer": {
"email": {
"tokenizer": "uax_url_email",
"filter": [
"email",
"lowercase",
"unique"
]
}
}
}
},
"mappings": {
"properties": {
"query": {
"type": "percolator"
},
"code": {
"type": "long"
},
"date": {
"type": "date"
},
"part": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"val": {
"type": "long"
},
"email": {
"type": "text",
"analyzer": "email"
}
}
}
}
它的映射和设置与我的原始索引几乎相同，唯一的区别是添加到映射query的类型的附加字段。percolator
你感兴趣的查询它——simple_query_string应该放到一个文档里面percolator_index。就像这样：
PUT /percolator_index/_doc/1?refresh
{
"query": {
"simple_query_string" : {
"query" : "month foo@bar.com",
"fields": ["part", "email"]
}
}
}
为了让它更有趣，我添加了email在其中添加了要在查询中专门搜索的字段（默认情况下，会搜索所有字段）。
现在，我们的目标是测试一个文档，该文档最终应根据渗透器索引中的test此查询进入索引。simple_query_string例如：
GET /percolator_index/_search
{
"query": {
"percolate": {
"field": "query",
"document": {
"date":"2004-07-31T11:57:52.000Z","part":"month","code":109,"val":0,"email":"foo@bar.com"
}
}
}
}
显然，下面document是您未来（尚不存在）的文档。这将与上面定义的进行匹配simple_query_string并产生匹配：
{
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 0.39324823,
"hits": [
{
"_index": "percolator_index",
"_type": "_doc",
"_id": "1",
"_score": 0.39324823,
"_source": {
"query": {
"simple_query_string": {
"query": "month foo@bar.com",
"fields": [
"part",
"email"
]
}
}
},
"fields": {
"_percolator_document_slot": [
0
]
}
}
]
}
}
如果我改为渗透此文档会怎样：
{
"query": {
"percolate": {
"field": "query",
"document": {
"date":"2004-07-31T11:57:52.000Z","part":"month","code":109,"val":0,"email":"foo"
}
}
}
}
（注意，电子邮件只是foo）这是结果：
{
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 0.26152915,
"hits": [
{
"_index": "percolator_index",
"_type": "_doc",
"_id": "1",
"_score": 0.26152915,
"_source": {
"query": {
"simple_query_string": {
"query": "month foo@bar.com",
"fields": [
"part",
"email"
]
}
}
},
"fields": {
"_percolator_document_slot": [
0
]
}
}
]
}
}
请注意，分数比第一个渗透文档要低一些。这可能是这样的，因为foo（我的电子邮件）仅匹配我分析的其中一个术语foo@bar.com，而foo@bar.com会匹配所有术语（从而给出更好的分数）
但不确定你在谈论什么分析仪。我认为上面的例子涵盖了唯一的“分析器”问题/未知，我认为可能有点令人困惑。
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