如何通过Elasticsearch模糊匹配电子邮件或电话？

我想通过 Elasticsearch 对电子邮件或电话进行模糊匹配。例如：

匹配所有以以下结尾的电子邮件@gmail.com

or

匹配所有电话开头136.

我知道我可以使用通配符，

{
 "query": {
    "wildcard" : {
      "email": "*gmail.com"
    }
  }
}

但性能很差。我尝试使用正则表达式：

{"query": {"regexp": {"email": {"value": "*163\.com*"} } } }

但不起作用。

有更好的制作方法吗？

卷曲 -XGET 本地主机：9200/user_data

{
    "user_data": {
        "aliases": {},
        "mappings": {
            "user_data": {
                "properties": {
                    "address": {
                        "type": "string"
                    },
                    "age": {
                        "type": "long"
                    },
                    "comment": {
                        "type": "string"
                    },
                    "created_on": {
                        "type": "date",
                        "format": "dateOptionalTime"
                    },
                    "custom": {
                        "properties": {
                            "key": {
                                "type": "string"
                            },
                            "value": {
                                "type": "string"
                            }
                        }
                    },
                    "gender": {
                        "type": "string"
                    },
                    "name": {
                        "type": "string"
                    },
                    "qq": {
                        "type": "string"
                    },
                    "tel": {
                        "type": "string"
                    },
                    "updated_on": {
                        "type": "date",
                        "format": "dateOptionalTime"
                    },
                }
            }
        },
        "settings": {
            "index": {
                "creation_date": "1458832279465",
                "uuid": "Fbmthc3lR0ya51zCnWidYg",
                "number_of_replicas": "1",
                "number_of_shards": "5",
                "version": {
                    "created": "1070299"
                }
            }
        },
        "warmers": {}
    }
}

映射：

{
  "settings": {
    "analysis": {
      "analyzer": {
        "index_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "digit_edge_ngram_tokenizer",
          "filter": [ "trim" ]
        },
        "search_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "keyword",
          "filter": [ "trim" ]
        },
        "index_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "name_ngram_filter", "trim" ]
        },
        "search_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "trim" ]
        }
      },
      "char_filter": {
        "digit_only": {
          "type": "pattern_replace",
          "pattern": "\\D+",
          "replacement": ""
        }
      },
      "tokenizer": {
        "digit_edge_ngram_tokenizer": {
          "type": "edgeNGram",
          "min_gram": "3",
          "max_gram": "15",
          "token_chars": [ "digit" ]
        }
      },
      "filter": {
        "name_ngram_filter": {
          "type": "ngram",
          "min_gram": "3",
          "max_gram": "20"
        }
      }
    }
  },
  "mappings" : {
    "user_data" : {
      "properties" : {
        "name" : {
          "type" : "string",
          "analyzer" : "ik"
        },
        "age" : {
          "type" : "integer"
        },
        "gender": {
          "type" : "string"
        },
        "qq" : {
          "type" : "string"
        },
        "email" : {
          "type" : "string",
          "analyzer": "index_email_analyzer",
          "search_analyzer": "search_email_analyzer"
        },
        "tel" : {
          "type" : "string",
          "analyzer": "index_phone_analyzer",
          "search_analyzer": "search_phone_analyzer"
        },
        "address" : {
          "type": "string",
          "analyzer" : "ik"
        },
        "comment" : {
          "type" : "string",
          "analyzer" : "ik"
        },
        "created_on" : {
          "type" : "date",
          "format" : "dateOptionalTime"
        },
        "updated_on" : {
          "type" : "date",
          "format" : "dateOptionalTime"
        },
        "custom": {
          "type" : "nested",
          "properties" : {
            "key" : {
              "type" : "string"
            },
            "value" : {
              "type" : "string"
            }
          }
        }
      }
    }
  }
}

执行此操作的一个简单方法是创建一个自定义分析器，该分析器使用n 元词法过滤器 https://www.elastic.co/guide/en/elasticsearch/reference/2.2/analysis-ngram-tokenfilter.html对于电子邮件（=> 见下文index_email_analyzer and search_email_analyzer + email_url_analyzer用于精确的电子邮件匹配）和边缘-ngram 标记过滤器 https://www.elastic.co/guide/en/elasticsearch/reference/2.2/analysis-edgengram-tokenfilter.html对于手机（=> 见下文index_phone_analyzer and search_phone_analyzer).

完整的索引定义如下。

PUT myindex
{
  "settings": {
    "analysis": {
      "analyzer": {
        "email_url_analyzer": {
          "type": "custom",
          "tokenizer": "uax_url_email",
          "filter": [ "trim" ]
        },
        "index_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "digit_edge_ngram_tokenizer",
          "filter": [ "trim" ]
        },
        "search_phone_analyzer": {
          "type": "custom",
          "char_filter": [ "digit_only" ],
          "tokenizer": "keyword",
          "filter": [ "trim" ]
        },
        "index_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "name_ngram_filter", "trim" ]
        },
        "search_email_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [ "lowercase", "trim" ]
        }
      },
      "char_filter": {
        "digit_only": {
          "type": "pattern_replace",
          "pattern": "\\D+",
          "replacement": ""
        }
      },
      "tokenizer": {
        "digit_edge_ngram_tokenizer": {
          "type": "edgeNGram",
          "min_gram": "1",
          "max_gram": "15",
          "token_chars": [ "digit" ]
        }
      },
      "filter": {
        "name_ngram_filter": {
          "type": "ngram",
          "min_gram": "1",
          "max_gram": "20"
        }
      }
    }
  },
  "mappings": {
    "your_type": {
      "properties": {
        "email": {
          "type": "string",
          "analyzer": "index_email_analyzer",
          "search_analyzer": "search_email_analyzer"
        },
        "phone": {
          "type": "string",
          "analyzer": "index_phone_analyzer",
          "search_analyzer": "search_phone_analyzer"
        }
      }
    }
  }
}

现在，让我们一点一点地剖析它。

For the phone字段，其想法是将电话值索引为index_phone_analyzer，它使用 Edge-ngram 分词器来索引电话号码的所有前缀。所以如果您的电话号码是1362435647，将产生以下代币：1, 13, 136, 1362, 13624, 136243, 1362435, 13624356, 13624356, 136243564, 1362435647.

然后在搜索时我们使用另一个分析器search_phone_analyzer它将简单地获取输入数字（例如136）并将其与phone字段使用一个简单的match or term query:

POST myindex
{ 
    "query": {
        "term": 
            { "phone": "136" }
    }
}

For the email字段，我们以类似的方式进行，我们用index_email_analyzer，它使用 ngram 令牌过滤器，它将生成可从电子邮件值中获取的不同长度（1 到 20 个字符之间）的所有可能令牌。例如：[email protected] /cdn-cgi/l/email-protection将被标记为j, jo, joh, ..., gmail.com, ..., [email protected] /cdn-cgi/l/email-protection.

然后在搜索时，我们将使用另一个名为search_email_analyzer它将获取输入并尝试将其与索引标记进行匹配。

POST myindex
{ 
    "query": {
        "term": 
            { "email": "@gmail.com" }
    }
}

The email_url_analyzer本示例中未使用分析器，但我已将其包含在内，以防您需要匹配确切的电子邮件值。