更好的阅读体验，打开【阅读原文】，在PC上浏览
Lucene用了很久，其版本更新也很快。在ES出来之后，直接使用Lucene的时候就比较少了，更多的就在ES框架下一站式完成，ES目前在项目中几乎占据了半壁江山。
ES的功能很强大，使用过程中，有一个问题是绕不过的：就是中文分词。这是至关重要的一个问题，直接影响搜索结果的准确和召回。
一般来讲，分词的问题本身目前解决的已经相当不错了，大家用的比较多的是jieba分词，还有清华、斯坦福、复旦等开源的中文分词。如果要在ES中使用jieba分词，就需要定制一个ES的分词插件，将jieba分词load到ES中。
几年之前，因为项目需要，我撸过一个简单的ES插件，在github上开源: jieba分词ES插件，也有一些用户在使用，期间也在断断续续的更新。
其中的关键，通过阅读代码就会发现，在处理token的过程中，有以下属性需要处理：
分别代表了分词的结果的最小单元：term，分词的offset：startOffset和endOffset，以及词性，例如word、或者数字、字母等等。
最后一个属性PositionIncrementAttribute比较难以理解，在特定的场合下才需要特殊的处理，大部分情况下默认的结果就可以，但在特定的场合下，会丢掉部分的文档。下文我们就详细解释这个属性，通过例子来说明这个是如何产生影响的，以及该如何解决。
我们先解释一下分词的结果，使用到的ES，以及插件版本如下：
安装好插件，启动ES：
./bin/elasticsearch
有如下输出，则说明插件加载成功：
...
[2018-10-26T23:04:12,572][INFO ][o.e.p.PluginsService ] [z7z-6dR] loaded plugin [analysis-jieba]
...
准备好示例文档：
现在 高级产品经理\n2003。4-2003。11 产品副经理\n向产品群经理汇报工作\ 负责产品为：得普利麻\n2002。5-2003。3 产品副经理\n向产品群经理汇报工作\n负责推广产品为：精分（思瑞康），麻醉（得普利麻）
jieba包括两种分词模式：
我们验证一下分词插件，以及两种模式的影响，通过如下命令，我们先看看search模式的分词效果：
curl -X GET "localhost:9200/_analyze" -H 'Content-Type: application/json' -d' { "tokenizer" : "jieba_search", "text" : "现在 高级产品经理\n2003。4-2003。11 产品副经理\n向产品群经理汇报工作\ 负责产品为：得普利麻\n2002。5-2003。3 产品副经理\n向产品群经理汇报工作\n负责推广产品为：精分（思瑞康），麻醉（得普利麻）" }‘
查看输出：
{
"tokens": [
{
"token": "现在",
"start_offset": 0,
"end_offset": 2,
"type": "word",
"position": 0
},
{
"token": " ",
"start_offset": 2,
"end_offset": 3,
"type": "word",
"position": 1
},
{
"token": "高级",
"start_offset": 3,
"end_offset": 5,
"type": "word",
"position": 2
},
{
"token": "产品",
"start_offset": 5,
"end_offset": 7,
"type": "word",
"position": 3
},
{
"token": "经理",
"start_offset": 7,
"end_offset": 9,
"type": "word",
"position": 4
},
{
"token": "\n",
"start_offset": 9,
"end_offset": 10,
"type": "word",
"position": 5
},
{
"token": "2003",
"start_offset": 10,
"end_offset": 14,
"type": "word",
"position": 6
},
{
"token": "。",
"start_offset": 14,
"end_offset": 15,
"type": "word",
"position": 7
},
{
"token": "4",
"start_offset": 15,
"end_offset": 16,
"type": "word",
"position": 8
},
{
"token": "-",
"start_offset": 16,
"end_offset": 17,
"type": "word",
"position": 9
},
{
"token": "2003"
,
"start_offset": 17,
"end_offset": 21,
"type": "word",
"position": 10
},
{
"token": "。",
"start_offset": 21,
"end_offset": 22,
"type": "word",
"position": 11
},
{
"token": "11",
"start_offset": 22,
"end_offset": 24,
"type": "word",
"position": 12
},
{
"token": " ",
"start_offset": 24,
"end_offset": 25,
"type": "word",
"position": 13
},
{
"token": "产品",
"start_offset": 25,
"end_offset": 27,
"type": "word",
"position": 14
},
{
"token": "副经理",
"start_offset": 27,
"end_offset": 30,
"type": "word",
"position": 15
},
{
"token": "\n",
"start_offset": 30,
"end_offset": 31,
"type": "word",
"position": 16
},
{
"token": "向",
"start_offset": 31,
"end_offset": 32,
"type": "word",
"position": 17
},
{
"token": "产品",
"start_offset": 32,
"end_offset": 34,
"type": "word",
"position": 18
},
{
"token": "群",
"start_offset": 34,
"end_offset": 35,
"type": "word",
"position": 19
},
{
"token": "经理",
"start_offset": 35,
"end_offset":
37,
"type": "word",
"position": 20
},
{
"token": "汇报工作",
"start_offset": 37,
"end_offset": 41,
"type": "word",
"position": 21
},
{
"token": "\n",
"start_offset": 41,
"end_offset": 42,
"type": "word",
"position": 22
},
{
"token": "负责",
"start_offset": 42,
"end_offset": 44,
"type": "word",
"position": 23
},
{
"token": "产品",
"start_offset": 44,
"end_offset": 46,
"type": "word",
"position": 24
},
{
"token": "为",
"start_offset": 46,
"end_offset": 47,
"type": "word",
"position": 25
},
{
"token": "：",
"start_offset": 47,
"end_offset": 48,
"type": "word",
"position": 26
},
{
"token": "得",
"start_offset": 48,
"end_offset": 49,
"type": "word",
"position": 27
},
{
"token": "普利",
"start_offset": 49,
"end_offset": 51,
"type": "word",
"position": 28
},
{
"token": "麻",
"start_offset": 51,
"end_offset": 52,
"type": "word",
"position": 29
},
{
"token": "\n",
"start_offset": 52,
"end_offset": 53,
"type": "word",
"position":
30
},
{
"token": "2002",
"start_offset": 53,
"end_offset": 57,
"type": "word",
"position": 31
},
{
"token": "。",
"start_offset": 57,
"end_offset": 58,
"type": "word",
"position": 32
},
{
"token": "5",
"start_offset": 58,
"end_offset": 59,
"type": "word",
"position": 33
},
{
"token": "-",
"start_offset": 59,
"end_offset": 60,
"type": "word",
"position": 34
},
{
"token": "2003",
"start_offset": 60,
"end_offset": 64,
"type": "word",
"position": 35
},
{
"token": "。",
"start_offset": 64,
"end_offset": 65,
"type": "word",
"position": 36
},
{
"token": "3",
"start_offset": 65,
"end_offset": 66,
"type": "word",
"position": 37
},
{
"token": " ",
"start_offset": 66,
"end_offset": 67,
"type": "word",
"position": 38
},
{
"token": "产品",
"start_offset": 67,
"end_offset": 69,
"type": "word",
"position": 39
},
{
"token": "副经理",
"start_offset": 69,
"end_offset": 72,
"type": "word",
"position": 40
},
{
"token": "\n"
,
"start_offset": 72,
"end_offset": 73,
"type": "word",
"position": 41
},
{
"token": "向",
"start_offset": 73,
"end_offset": 74,
"type": "word",
"position": 42
},
{
"token": "产品",
"start_offset": 74,
"end_offset": 76,
"type": "word",
"position": 43
},
{
"token": "群",
"start_offset": 76,
"end_offset": 77,
"type": "word",
"position": 44
},
{
"token": "经理",
"start_offset": 77,
"end_offset": 79,
"type": "word",
"position": 45
},
{
"token": "汇报工作",
"start_offset": 79,
"end_offset": 83,
"type": "word",
"position": 46
},
{
"token": "\n",
"start_offset": 83,
"end_offset": 84,
"type": "word",
"position": 47
},
{
"token": "负责",
"start_offset": 84,
"end_offset": 86,
"type": "word",
"position": 48
},
{
"token": "推广",
"start_offset": 86,
"end_offset": 88,
"type": "word",
"position": 49
},
{
"token": "产品",
"start_offset": 88,
"end_offset": 90,
"type": "word",
"position": 50
},
{
"token": "为",
"start_offset": 90,
"end_offset": 91
,
"type": "word",
"position": 51
},
{
"token": "：",
"start_offset": 91,
"end_offset": 92,
"type": "word",
"position": 52
},
{
"token": "精分",
"start_offset": 92,
"end_offset": 94,
"type": "word",
"position": 53
},
{
"token": "（",
"start_offset": 94,
"end_offset": 95,
"type": "word",
"position": 54
},
{
"token": "思",
"start_offset": 95,
"end_offset": 96,
"type": "word",
"position": 55
},
{
"token": "瑞康",
"start_offset": 96,
"end_offset": 98,
"type": "word",
"position": 56
},
{
"token": "）",
"start_offset": 98,
"end_offset": 99,
"type": "word",
"position": 57
},
{
"token": "，",
"start_offset": 99,
"end_offset": 100,
"type": "word",
"position": 58
},
{
"token": "麻醉",
"start_offset": 100,
"end_offset": 102,
"type": "word",
"position": 59
},
{
"token": "（",
"start_offset": 102,
"end_offset": 103,
"type": "word",
"position": 60
},
{
"token": "得",
"start_offset": 103,
"end_offset": 104,
"type": "word",
"position":
61
},
{
"token": "普利",
"start_offset": 104,
"end_offset": 106,
"type": "word",
"position": 62
},
{
"token": "麻",
"start_offset": 106,
"end_offset": 107,
"type": "word",
"position": 63
},
{
"token": "）",
"start_offset": 107,
"end_offset": 108,
"type": "word",
"position": 64
}
]}分词结果中，token对应的就是term属性，start_offset和end_offset对应的就是Offset属性，type类似于词性。这几个都是比较好理解的，那么position是什么含义呢？通过观察：
position是分词之后term/token的先对位置，代表了顺序和距离。
这个例子中产品和副经理是紧挨着的，中间没有间隔。也就意味着如下的查询
{
"query": {
"match_phrase":{
"field1": {
"query": "产品经理",
"slop": 0
} } }}
能够搜到我们的示例文档。这里要注意，slop默认是0，可以不写。当slop要求为0的时候，就要求搜索词组产品经理在文档中连起来的，这个时候命中的是产品经理，而不是产品|群|经理，|表示token分割。如果设置slop为1，则产品|群|经理也会命中。slop的大小，就是position的大小差异。
看下index模式，要更加复杂，PositionIncrement的作用也是在这里体现。同样是上面的文本：
curl -X GET "localhost:9200/_analyze" -H 'Content-Type: application/json' -d' { "tokenizer" : "jieba_index", "text" : "现在 高级产品经理\n2003。4-2003。11 产品副经理\n向产品群经理汇报工作\ 负责产品为：得普利麻\n2002。5-2003。3 产品副经理\n向产品群经理汇报工作\n负责推广产品为：精分（思瑞康），麻醉（得普利麻）" }‘
结果如下，需要仔细对比和search的差异。
{
"tokens": [
{
"token": "现在",
"start_offset": 0,
"end_offset": 2,
"type": "word",
"position": 0
},
{
"token": " ",
"start_offset": 2,
"end_offset": 3,
"type": "word",
"position": 1
},
{
"token": "高级",
"start_offset": 3,
"end_offset": 5,
"type": "word",
"position": 2
},
{
"token": "产品",
"start_offset": 5,
"end_offset": 7,
"type": "word",
"position": 3
},
{
"token": "经理",
"start_offset": 7,
"end_offset": 9,
"type": "word",
"position": 4
},
{
"token": "\n",
"start_offset": 9,
"end_offset": 10,
"type": "word",
"position": 5
},
{
"token": "2003",
"start_offset": 10,
"end_offset": 14,
"type": "word",
"position": 6
},
{
"token": "。",
"start_offset": 14,
"end_offset": 15,
"type": "word",
"position": 7
},
{
"token": "4",
"start_offset": 15,
"end_offset": 16,
"type": "word"
,
"position": 8
},
{
"token": "-",
"start_offset": 16,
"end_offset": 17,
"type": "word",
"position": 9
},
{
"token": "2003",
"start_offset": 17,
"end_offset": 21,
"type": "word",
"position": 10
},
{
"token": "。",
"start_offset": 21,
"end_offset": 22,
"type": "word",
"position": 11
},
{
"token": "11",
"start_offset": 22,
"end_offset": 24,
"type": "word",
"position": 12
},
{
"token": " ",
"start_offset": 24,
"end_offset": 25,
"type": "word",
"position": 13
},
{
"token": "产品",
"start_offset": 25,
"end_offset": 27,
"type": "word",
"position": 14
},
{
"token": "副经理",
"start_offset": 27,
"end_offset": 30,
"type": "word",
"position": 15
},
{
"token": "经理",
"start_offset": 28,
"end_offset": 30,
"type": "word",
"position": 16
},
{
"token": "\n",
"start_offset": 30,
"end_offset": 31,
"type": "word",
"position": 17
},
{
"token": "向",
"start_offset": 31,
"end_offset": 32,
"type": "word",
"position": 18
},
{
"token": "产品"
,
"start_offset": 32,
"end_offset": 34,
"type": "word",
"position": 19
},
{
"token": "群",
"start_offset": 34,
"end_offset": 35,
"type": "word",
"position": 20
},
{
"token": "经理",
"start_offset": 35,
"end_offset": 37,
"type": "word",
"position": 21
},
{
"token": "汇报",
"start_offset": 37,
"end_offset": 39,
"type": "word",
"position": 22
},
{
"token": "汇报工作",
"start_offset": 37,
"end_offset": 41,
"type": "word",
"position": 22
},
{
"token": "工作",
"start_offset": 39,
"end_offset": 41,
"type": "word",
"position": 23
},
{
"token": "\n",
"start_offset": 41,
"end_offset": 42,
"type": "word",
"position": 24
},
{
"token": "负责",
"start_offset": 42,
"end_offset": 44,
"type": "word",
"position": 25
},
{
"token": "产品",
"start_offset": 44,
"end_offset": 46,
"type": "word",
"position": 26
},
{
"token": "为",
"start_offset": 46,
"end_offset": 47,
"type": "word",
"position": 27
},
{
"token": "：",
"start_offset": 47,
"end_offset":
48,
"type": "word",
"position": 28
},
{
"token": "得",
"start_offset": 48,
"end_offset": 49,
"type": "word",
"position": 29
},
{
"token": "普利",
"start_offset": 49,
"end_offset": 51,
"type": "word",
"position": 30
},
{
"token": "麻",
"start_offset": 51,
"end_offset": 52,
"type": "word",
"position": 31
},
{
"token": "\n",
"start_offset": 52,
"end_offset": 53,
"type": "word",
"position": 32
},
{
"token": "2002",
"start_offset": 53,
"end_offset": 57,
"type": "word",
"position": 33
},
{
"token": "。",
"start_offset": 57,
"end_offset": 58,
"type": "word",
"position": 34
},
{
"token": "5",
"start_offset": 58,
"end_offset": 59,
"type": "word",
"position": 35
},
{
"token": "-",
"start_offset": 59,
"end_offset": 60,
"type": "word",
"position": 36
},
{
"token": "2003",
"start_offset": 60,
"end_offset": 64,
"type": "word",
"position": 37
},
{
"token": "。",
"start_offset": 64,
"end_offset": 65,
"type": "word",
"position
": 38
},
{
"token": "3",
"start_offset": 65,
"end_offset": 66,
"type": "word",
"position": 39
},
{
"token": " ",
"start_offset": 66,
"end_offset": 67,
"type": "word",
"position": 40
},
{
"token": "产品",
"start_offset": 67,
"end_offset": 69,
"type": "word",
"position": 41
},
{
"token": "副经理",
"start_offset": 69,
"end_offset": 72,
"type": "word",
"position": 42
},
{
"token": "经理",
"start_offset": 70,
"end_offset": 72,
"type": "word",
"position": 43
},
{
"token": "\n",
"start_offset": 72,
"end_offset": 73,
"type": "word",
"position": 44
},
{
"token": "向",
"start_offset": 73,
"end_offset": 74,
"type": "word",
"position": 45
},
{
"token": "产品",
"start_offset": 74,
"end_offset": 76,
"type": "word",
"position": 46
},
{
"token": "群",
"start_offset": 76,
"end_offset": 77,
"type": "word",
"position": 47
},
{
"token": "经理",
"start_offset": 77,
"end_offset": 79,
"type": "word",
"position": 48
},
{
"token": "汇报"
,
"start_offset": 79,
"end_offset": 81,
"type": "word",
"position": 49
},
{
"token": "汇报工作",
"start_offset": 79,
"end_offset": 83,
"type": "word",
"position": 49
},
{
"token": "工作",
"start_offset": 81,
"end_offset": 83,
"type": "word",
"position": 50
},
{
"token": "\n",
"start_offset": 83,
"end_offset": 84,
"type": "word",
"position": 51
},
{
"token": "负责",
"start_offset": 84,
"end_offset": 86,
"type": "word",
"position": 52
},
{
"token": "推广",
"start_offset": 86,
"end_offset": 88,
"type": "word",
"position": 53
},
{
"token": "产品",
"start_offset": 88,
"end_offset": 90,
"type": "word",
"position": 54
},
{
"token": "为",
"start_offset": 90,
"end_offset": 91,
"type": "word",
"position": 55
},
{
"token": "：",
"start_offset": 91,
"end_offset": 92,
"type": "word",
"position": 56
},
{
"token": "精分",
"start_offset": 92,
"end_offset": 94,
"type": "word",
"position": 57
},
{
"token": "（",
"start_offset": 94,
"end_offset":
95,
"type": "word",
"position": 58
},
{
"token": "思",
"start_offset": 95,
"end_offset": 96,
"type": "word",
"position": 59
},
{
"token": "瑞康",
"start_offset": 96,
"end_offset": 98,
"type": "word",
"position": 60
},
{
"token": "）",
"start_offset": 98,
"end_offset": 99,
"type": "word",
"position": 61
},
{
"token": "，",
"start_offset": 99,
"end_offset": 100,
"type": "word",
"position": 62
},
{
"token": "麻醉",
"start_offset": 100,
"end_offset": 102,
"type": "word",
"position": 63
},
{
"token": "（",
"start_offset": 102,
"end_offset": 103,
"type": "word",
"position": 64
},
{
"token": "得",
"start_offset": 103,
"end_offset": 104,
"type": "word",
"position": 65
},
{
"token": "普利",
"start_offset": 104,
"end_offset": 106,
"type": "word",
"position": 66
},
{
"token": "麻",
"start_offset": 106,
"end_offset": 107,
"type": "word",
"position": 67
},
{
"token": "）",
"start_offset": 107,
"end_offset": 108,
"type": "word",
"position":
68
}
]}
因为index模式的原因，产品副经理分为了产品|副经理|经理。这个时候，合理的position就十分重要了。通过我最新的插件的实现，这里的position分别是14,15,16。这是正确的，因为要正确处理下面的结果。
当我们执行如下搜索：
{
"query": {
"match_phrase":{
"field1": {
"query": "产品经理"
} } },
"highlight" : {
"fields" : {
"field1" : {} } }}
命中我们的示例文本，无间隔的产品经理可以命中，并且可以高亮，但是产品副经理没有命中，也没有高亮。
再看这个例子：
{
"query": {
"match_phrase":{
"field1": {
"query": "产品经理",
"slop": 2
} } },
"highlight" : {
"fields" : {
"field1" : {} } }}
则，无间隔的产品经理可以命中，并且可以高亮；同时，产品副经理有命中，产品和经理分别高亮。这两个例子的差别，大家要细细体会。
那么如何正确的处理position呢，关键就在于PositionIncrementAttribute属性的处理，通常我们使用search模式类似的分词是不会遇到问题的，即使使用默认的PositionIncrementAttribute的实现：根据分词得到的token，每次+1，从而得到position。
但默认的实现，遇到如下的情况，就会出现问题：
示例文本：
中国人民解放军胜利了。
如果采用默认的实现，则输出：
{
"tokens": [
{
"token": "中国",
"start_offset": 0,
"end_offset": 2,
"type": "word",
"position": 0
},
{
"token": "中国人",
"start_offset": 0,
"end_offset": 3,
"type": "word",
"position": 1
},
{
"token": "中国人民解放军",
"start_offset": 0,
"end_offset": 7,
"type": "word",
"position": 2
},
{
"token": "国人",
"start_offset": 1,
"end_offset": 3,
"type": "word",
"position": 4
},
{
"token": "人民",
"start_offset": 2,
"end_offset": 4,
"type": "word",
"position": 5
},
{
"token": "解放",
"start_offset": 4,
"end_offset": 6,
"type": "word",
"position": 6
},
{
"token": "解放军",
"start_offset": 4,
"end_offset": 7,
"type": "word",
"position": 7
},
{
"token": "胜利",
"start_offset": 7,
"end_offset": 9,
"type": "word",
"position": 8
},
{
"token": "了",
"start_offset": 9,
"end_offset": 10,
"type": "word",
"position": 9
}
]}
根据这样的position，我们如下的查询，就找不到这个示例文档，从而产生丢数据的现象。
{
"query": {
"match_phrase":{
"field1": {
"query": "中国人民"
} } },
"highlight" : {
"fields" : {
"field1" : {} } }}本来中国人民在示例中是无间隔紧邻的，但是由于position解析的问题，直接导致slop已经变成了4，所以必须制定查询中的slop比较大，才能够返回正确的文档，但这里Rank也会受到影响。
看一下正确position的结果。
{
"tokens": [
{
"token": "中国",
"start_offset": 0,
"end_offset": 2,
"type": "word",
"position": 0
},
{
"token": "中国人",
"start_offset": 0,
"end_offset": 3,
"type": "word",
"position": 0
},
{
"token": "中国人民解放军",
"start_offset": 0,
"end_offset": 7,
"type": "word",
"position": 0
},
{
"token": "国人",
"start_offset": 1,
"end_offset": 3,
"type": "word",
"position": 0
},
{
"token": "人民",
"start_offset": 2,
"end_offset": 4,
"type": "word",
"position": 1
},
{
"token": "解放",
"start_offset": 4,
"end_offset": 6,
"type": "word",
"position": 2
},
{
"token": "解放军",
"start_offset": 4,
"end_offset": 7,
"type": "word",
"position": 2
},
{
"token": "胜利",
"start_offset": 7,
"end_offset": 9,
"type": "word",
"position": 3
},
{
"token": "了",
"start_offset": 9,
"end_offset": 10,
"type": "word",
"position": 4
}
]}
其中，中国是0，人民是1，就可以命中了。
基本上，在处理token的时候，要判断``是1，还是0。这里的Lucene实现机制不好，对于分词的实现约束比较多，并且只考虑了英文。现在的实现，优先考虑了召回。极个别情况，还是会有些准确率的问题。
另外一个层面，要从词的切分的角度处理，分词的结果应该提供一个最细粒度的、无交叉的切分，这个方式用来做索引，会比较好一些。那这样，默认的PositionIncrement也是能够满足需求的。接下来看看，jieba是否可以改造一下，支持第三种分词的模式：最细粒度的、无交叉的切分。
