总结一些ES不常用的filter

犀牛饲养员

写在前面

ES内置的token filter很多,大部分实际工作中都用不到。这段时间准备ES认证工程师的考试,备考的时候需要熟悉这些不常用的filter。ES官方对一些filter只是一笔带过,我就想着把备考的笔记整理成博客备忘,也希望能帮助到有这方面需求的人。

length filer

官方解释:

A token filter of type length that removes words that are too long or too short for the stream.

这个filter的功能是,去掉过长或者过短的单词。它有两个参数可以设置:

  • min 定义最短长度,默认是0
  • max 定义最长长度,默认是Integer.MAX_VALUE

先来简单测试下它的效果,

GET _analyze
{
  "tokenizer" : "standard",
  "filter": [{"type": "length", "min":1, "max":3 }],  
  "text" : "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone"
}

输出:

{
  "tokens" : [
    {
      "token" : "The",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "the",
      "start_offset" : 36,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 7
    }
  ]
}

可以看到大于3的单词都被过滤掉了。

如果要给某个索引指定length filer,可以参考下面这个示例:

PUT /length_example
{
    "settings" : {
        "analysis" : {
            "analyzer" : {
                "default" : {
                    "tokenizer" : "standard",
                    "filter" : ["my_length"]
                }
            },
            "filter" : {
                "my_length" : {
                    "type" : "length",
                    "min" : 1,
                    "max": 3
                }
            }
        }
    }
}

GET length_example/_analyze
{
  "analyzer": "default", 
  "text" : "The 2 QUICK Brown-Foxes jumped over the lazy dog's bonet"
}

ngram filter

ngram filter的意义可以参考ngram tokenize,后者相当于是keyword tokenizer 加上 ngram filter,效果是一样的。

它的含义是:首先将text文本切分,执行时采用N-gram切割算法。N-grams 算法,像一个穿越单词的滑窗,是一个特定长度的持续的字符序列。

说着挺抽象,来个例子:

GET _analyze
{
  "tokenizer": "ngram",
  "text": "北京大学"
}

GET _analyze
{
  "tokenizer" : "keyword",
  "filter": [{"type": "ngram", "min_gram":1, "max_gram":2 }],  
  "text" : "北京大学"
}

可以看到有两个属性,

  • min_gram 在单词中最小字符长度,且默认为1
  • max_gram 在单词中最大字符长度,且默认为2

max和min的间隔,也就是步长默认最大只能是1,可以通过设置索引的max_ngram_diff修改,示例如下:

PUT /ngram_example
{
    "settings" : {
      "index": {
      "max_ngram_diff": 10
    },
        "analysis" : {
            "analyzer" : {
                "default" : {
                    "tokenizer" : "keyword",
                    "filter" : ["my_ngram"]
                }
            },
            "filter" : {
                "my_ngram" : {
                    "type" : "ngram",
                    "min_gram" : 2,
                    "max_gram": 4
                }
            }
        }
    }
}

使用索引的analyzer测试,

GET ngram_example/_analyze
{
  "analyzer": "default", 
  "text" : "北京大学"
}

输出,

{
  "tokens" : [
    {
      "token" : "北京",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "北京大",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "北京大学",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "京大",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "京大学",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "大学",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    }
  ]
}

你应该已经基本了解ngram filter的用法了,可能会有个疑问,这个过滤器用在什么场景呢?事实上,它适合前缀中缀检索,比如搜索推荐功能,当你只输入了某个句子的一部分时,搜索引擎会显示出以这部分为前缀的一些匹配项,从而实现推荐功能。

trim filter

这个filter从名字也可以看出它的功能,它可以删除前后空格。看个示例:

GET _analyze
{
  "tokenizer" : "keyword",
  "filter": [{"type": "trim"}],  
  "text" : " 北京大学"
}

输出,

{
  "tokens" : [
    {
      "token" : " 北京大学",
      "start_offset" : 0,
      "end_offset" : 5,
      "type" : "word",
      "position" : 0
    }
  ]
}

truncate filter

这个filter有一个length属性,可以截断分词后的term,确保term的长度不会超过length。下面看个示例,

GET _analyze
{
  "tokenizer" : "keyword",
  "filter": [{"type": "truncate", "length": 3}],  
  "text" : "北京大学"
}

输出,

{
  "tokens" : [
    {
      "token" : "北京大",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "word",
      "position" : 0
    }
  ]
}

再来一个示例:

GET _analyze
{
  "tokenizer" : "standard",
  "filter": [{"type": "truncate", "length": 3}],  
  "text" : "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

输出,

{
  "tokens" : [
    {
      "token" : "The",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "QUI",
      "start_offset" : 6,
      "end_offset" : 11,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    ...
    

这个filter在keyword比较长的场景下,可以用来避免出现一些OOM等问题。

unique filter

unique词元过滤器的作用就是保证同样结果的词元只出现一次。看个示例:

GET _analyze
{
    "tokenizer": "standard",
    "filter": ["unique"],
    "text": "this is a test test test"
}

输出,

{
  "tokens" : [
    {
      "token" : "this",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "is",
      "start_offset" : 5,
      "end_offset" : 7,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "a",
      "start_offset" : 8,
      "end_offset" : 9,
      "type" : "<ALPHANUM>",
      "position" : 2
    },
    {
      "token" : "test",
      "start_offset" : 10,
      "end_offset" : 14,
      "type" : "<ALPHANUM>",
      "position" : 3
    }
  ]
}

synonym token filter

同义词过滤器。它的使用场景是这样的,比如有一个文档里面包含番茄这个词,我们希望搜索番茄或者西红柿圣女果都可以找到这个文档。示例如下:

PUT /synonym_example
{
    "settings": {
            "analysis" : {
                "analyzer" : {
                    "synonym" : {
                        "tokenizer" : "whitespace",
                        "filter" : ["my_synonym"]
                    }
                },
                "filter" : {
                    "my_synonym" : {
                        "type" : "synonym",
                        "synonyms_path" : "analysis/synonym.txt"
                    }
                }
            }
    }
}

我们需要在ES实例的config目录下,新建一个analysis/synonym.txt的文件,内容如下:

番茄,西红柿,圣女果

记得要重启。

然后测试下,

GET /synonym_example/_analyze
{
  "analyzer": "synonym",
  "text": "番茄"
}

输出,

{
  "tokens" : [
    {
      "token" : "番茄",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "西红柿",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "SYNONYM",
      "position" : 0
    },
    {
      "token" : "圣女果",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "SYNONYM",
      "position" : 0
    }
  ]
}

如何组合使用多个filter

我们知道一个分析器可以包含多个过滤器,那怎么来实现呢?看下面这个例子:

GET _analyze
{
  "tokenizer" : "standard",
  "filter": [{"type": "length", "min":1, "max":4 },{"type": "truncate", "length": 3}],  
  "text" : "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
}

这个例子中,我们把length filter和truncate filter组合在一起使用,它首先基于标准分词,分词后的term大于4字节的会首先被过滤掉,接着剩下的term会被截断到3个字节。输出结果是,

{
  "tokens" : [
    {
      "token" : "The",
      "start_offset" : 0,
      "end_offset" : 3,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "2",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<NUM>",
      "position" : 1
    },
    {
      "token" : "ove",
      "start_offset" : 31,
      "end_offset" : 35,
      "type" : "<ALPHANUM>",
      "position" : 6
    },
    {
      "token" : "the",
      "start_offset" : 36,
      "end_offset" : 39,
      "type" : "<ALPHANUM>",
      "position" : 7
    },
    {
      "token" : "laz",
      "start_offset" : 40,
      "end_offset" : 44,
      "type" : "<ALPHANUM>",
      "position" : 8
    },
    {
      "token" : "bon",
      "start_offset" : 51,
      "end_offset" : 55,
      "type" : "<ALPHANUM>",
      "position" : 10
    }
  ]
}

如果是在索引中使用的话,参考下面这个例子:

PUT /length_truncate_example
{
    "settings" : {
        "analysis" : {
            "analyzer" : {
                "default" : {
                    "tokenizer" : "standard",
                    "filter" : ["my_length", "my_truncate"]
                }
            },
            "filter" : {
                "my_length" : {
                    "type" : "length",
                    "min" : 1,
                    "max": 4
                },
                "my_truncate" : {
                    "type" : "truncate",
                    "length": 3
                }
            }
        }
    }
}

GET length_truncate_example/_analyze
{
  "analyzer": "default", 
  "text" : "The 2 QUICK Brown-Foxes jumped over the lazy dog's bonet"
}
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