Elasticsearch-高级搜索（拼音|首字母|简繁|二级搜索）-创新互联

需求：

1. 中文搜索、英文搜索、中英混搜
2. 全拼搜索、首字母搜索、中文+全拼、中文+首字母混搜
3. 简繁搜索
4. 二级搜索（对第一次搜索结果，再进行搜索）

IK分词：

GitHub - medcl/elasticsearch-analysis-ik: The IK Analysis plugin integrates Lucene IK analyzer into elasticsearch, support customized dictionary.

拼音：

https://github.com/medcl/elasticsearch-analysis-pinyin

简繁体：

ehttps://github.com/medcl/elasticsearch-analysis-stconvert

analysis分析是 Elasticsearch 在文档发送之前对文档正文执行的过程，以添加到反向索引中（inverted index）。 在将文档添加到索引之前，Elasticsearch 会为每个分析的字段执行许多步骤：

• Character filtering (字符过滤器): 使用字符过滤器转换字符
• Breaking text into tokens (把文字转化为标记): 将文本分成一组一个或多个标记
• Token filtering：使用标记过滤器转换每个标记
• Token indexing：把这些标记存于索引中

详细介绍：Elasticsearch: analyzer_Elastic 中国社区官方博客的博客-博客_elasticsearch analyzer如果大家之前看过我写的文章“开始使用Elasticsearch （3）”，在文章的最后部分写了有关于analyzer的有关介绍。在今天的文章中，我们来进一步了解analyzer。 analyzer执行将输入字符流分解为token的过程，它一般发生在两个场合：在indexing的时候，也即在建立索引的时候在searching的时候，也即在搜索时，分析需要搜索的词语什么是analysis...https://blog.csdn.net/UbuntuTouch/article/details/100392478

PUT /_template/test_template
{
  "index_patterns": [
    "test-*"
  ],
  "aliases": {
    "test_read": {}
  },
  "settings": {
    "index": {
      "max_result_window": "100000",
      "refresh_interval": "5s",
      "number_of_shards": "5",
      "translog": {
        "flush_threshold_size": "1024mb",
        "sync_interval": "30s",
        "durability": "async"
      },
      "number_of_replicas": "1"
    },
    "analysis": {
      "char_filter": {
        "tsconvert": {
          "type": "stconvert",
          "convert_type": "t2s"
        }
      },
      "analyzer": {
        "ik_t2s_pinyin_analyzer": {
          "type": "custom",
          "char_filter": [
            "tsconvert"
          ],
          "tokenizer": "ik_max_word",
          "filter": [
            "pinyin_filter",
            "lowercase"
          ]
        },
        "stand_t2s_pinyin_analyzer": {
          "type": "custom",
          "char_filter": [
            "tsconvert"
          ],
          "tokenizer": "standard",
          "filter": [
            "pinyin_filter",
            "lowercase"
          ]
        },
        "ik_t2s_analyzer": {
          "type": "custom",
          "char_filter": [
            "tsconvert"
          ],
          "tokenizer": "ik_max_word",
          "filter": [
            "lowercase"
          ]
        },
        "stand_t2s_analyzer": {
          "type": "custom",
          "char_filter": [
            "tsconvert"
          ],
          "tokenizer": "standard",
          "filter": [
            "lowercase"
          ]
        },
        "ik_pinyin_analyzer": {
          "type": "custom",
          "tokenizer": "ik_max_word",
          "filter": [
            "pinyin_filter",
            "lowercase"
          ]
        },
        "stand_pinyin_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "pinyin_filter",
            "lowercase"
          ]
        }
      },
      "filter": {
        "pinyin_first_letter_and_full_pinyin_filter": {
          "type": "pinyin",
          "keep_first_letter": true,
          "keep_separate_first_letter": false,
          "keep_full_pinyin": false,
          "keep_joined_full_pinyin": true,
          "keep_none_chinese": true,
          "none_chinese_pinyin_tokenize": false,
          "keep_none_chinese_in_joined_full_pinyin": true,
          "keep_original": false,
          "limit_first_letter_length": 1000,
          "lowercase": true,
          "trim_whitespace": true,
          "remove_duplicated_term": true
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "name": {
        "index_phrases": true,
        "analyzer": "ik_max_word",
        "index": true,
        "type": "text",
        "fields": {
          "keyword": {
            "ignore_above": 256,
            "type": "keyword"
          },
          "stand": {
            "analyzer": "standard",
            "type": "text"
          },
          "STPA": {
            "type": "text",
            "analyzer": "stand_t2s_pinyin_analyzer"
          },
          "ITPA": {
            "type": "text",
            "analyzer": "ik_t2s_pinyin_analyzer"
          }
        }
      },
      "desc": {
        "index_phrases": true,
        "analyzer": "ik_max_word",
        "index": true,
        "type": "text",
        "fields": {
          "keyword": {
            "ignore_above": 256,
            "type": "keyword"
          },
          "stand": {
            "analyzer": "standard",
            "type": "text"
          },
          "STPA": {
            "type": "text",
            "analyzer": "stand_t2s_pinyin_analyzer"
          },
          "ITPA": {
            "type": "text",
            "analyzer": "ik_t2s_pinyin_analyzer"
          }
        }
      },
      "abstr": {
        "index_phrases": true,
        "analyzer": "ik_max_word",
        "index": true,
        "type": "text",
        "fields": {
          "keyword": {
            "ignore_above": 256,
            "type": "keyword"
          },
          "stand": {
            "analyzer": "standard",
            "type": "text"
          },
          "STPA": {
            "type": "text",
            "analyzer": "stand_t2s_pinyin_analyzer"
          },
          "ITPA": {
            "type": "text",
            "analyzer": "ik_t2s_pinyin_analyzer"
          }
        }
      }
    }
  }
}

GET /test_read/_search
{
  "from": 0,
  "size": 10,
  "terminate_after": 100000,
  "query": {
    "bool": {
      "must": [
        {
          "query_string": {
            "query": "bj天安门 OR 测试",
            "fields": [
              "name.ITPA"
            ],
            "type": "phrase",
            "default_operator": "and"
          }
        }
      ],
      "adjust_pure_negative": true,
      "boost": 1
    }
  },
  "post_filter": {
    "bool": {
      "must": [
        {
          "match": {
            "name": "天安门"
          }
        }
      ]
    }
  },
  "highlight": {
    "fragment_size": 1000,
    "pre_tags": [
      ""
    ],
    "post_tags": [
      ""
    ],
    "fields": {
      "name.stand": {},
      "desc.stand": {},
      "abstr.stand": {},
      "name.IPA": {},
      "desc.IPA": {},
      "abstr.IPA": {},
      "name.ITPA": {},
      "desc.ITPA": {},
      "abstr.ITPA": {}
    }
  }
}

post_filter：后过滤器 | Elasticsearch: 权威指南 | Elastic

PS：post_filter实现二次搜索功能，post_filter无法使用es高亮功能，需要自己通过代码进行手动标记高亮；根据上面的DSL语句，可写出对应的代码啦~

拼音插件配置：

• keep_first_letter:这个参数会将词的第一个字母全部拼起来.例如:刘德华->ldh.默认为:true
• keep_separate_first_letter:这个会将第一个字母一个个分开.例如:刘德华->l,d,h.默认为:flase.如果开启,可能导致查询结果太过于模糊,准确率太低.
• limit_first_letter_length:设置大keep_first_letter结果的长度,默认为:16
• keep_full_pinyin:如果打开,它将保存词的全拼,并按字分开保存.例如:刘德华>[liu,de,hua],默认为:true
• keep_joined_full_pinyin:如果打开将保存词的全拼.例如:刘德华>[liudehua],默认为:false
• keep_none_chinese:将非中文字母或数字保留在结果中.默认为:true
• keep_none_chinese_together:保证非中文在一起.默认为: true, 例如: DJ音乐家 ->DJ,yin,yue,jia, 如果设置为:false, 例如: DJ音乐家 ->D,J,yin,yue,jia, 注意: keep_none_chinese应该先开启.
• keep_none_chinese_in_first_letter:将非中文字母保留在首字母中.例如: 刘德华AT2016->ldhat2016, 默认为:true
• keep_none_chinese_in_joined_full_pinyin:将非中文字母保留为完整拼音. 例如: 刘德华2016->liudehua2016, 默认为: false
• none_chinese_pinyin_tokenize:如果他们是拼音,切分非中文成单独的拼音项. 默认为:true,例如: liudehuaalibaba13zhuanghan ->liu,de,hua,a,li,ba,ba,13,zhuang,han, 注意: keep_none_chinese和keep_none_chinese_together需要先开启.
• keep_original:是否保持原词.默认为:false
• lowercase:小写非中文字母.默认为:true
• trim_whitespace:去掉空格.默认为:true
• remove_duplicated_term:保存索引时删除重复的词语.例如: de的>de, 默认为: false, 注意:开启可能会影响位置相关的查询.
• ignore_pinyin_offset:在6.0之后,严格限制偏移量,不允许使用重叠的标记.使用此参数时,忽略偏移量将允许使用重叠的标记.请注意,所有与位置相关的查询或突出显示都将变为错误,您应使用多个字段并为不同的字段指定不同的设置查询目的.如果需要偏移量,请将其设置为false。默认值:true

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