使用ES对中文文章进行分词，并进行词频统计排序

前言：首先有这样一个需求，需要统计一篇10000字的文章，需要统计里面哪些词出现的频率比较高，这里面比较重要的是如何对文章中的一段话进行分词，例如“北京是中华人民共和国的首都”，“北京”，“中华人民共和国”，“中华”，“华人”，“人民”，“共和国”，“首都”这些是一个词，需要切分出来，而“京是”“民共”这些就不是有意义的词，所以不能分出来。这些分词的规则如果自己去写，是一件很麻烦的事，利用开源的IK分词，就可以很容易的做到。并且可以根据分词的模式来决定分词的颗粒度。

ik_max_word: 会将文本做最细粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,中华人民,中华,华人,人民共和国,人民,人,民,共和国,共和,和,国国,国歌”，会穷尽各种可能的组合；

ik_smart: 会做最粗粒度的拆分，比如会将“中华人民共和国国歌”拆分为“中华人民共和国,国歌”。

一：首先要准备环境

如果有ES环境可以跳过前两步，这里我假设你只有一台刚装好的CentOS6.X系统，方便你跑通这个流程。
（1）安装jdk。

$ wget http://download.oracle.com/otn-pub/java/jdk/8u111-b14/jdk-8u111-linux-x64.rpm $ rpm -ivh jdk-8u111-linux-x64.rpm

（2）安装ES

$ wget  https://download.elastic.co/elasticsearch/release/org/elasticsearch/distribution/rpm/elasticsearch/2.4.2/elasticsearch-2.4.2.rpm $ rpm -iv elasticsearch-2.4.2.rpm

（3）安装IK分词器
在github上面下载1.10.2版本的ik分词，注意：es版本为2.4.2，兼容的版本为1.10.2。

$ mkdir /usr/share/elasticsearch/plugins/ik
$ wget https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v1.10.2/elasticsearch-analysis-ik-1.10.2.zip $ unzip elasticsearch-analysis-ik-1.10.2.zip -d /usr/share/elasticsearch/plugins/ik

（4）配置ES

$ vim /etc/elasticsearch/elasticsearch.yml
###### Cluster ######
cluster.name: test
###### Node ######
node.name: test-10.10.10.10
node.master: true
node.data: true
###### Index ######
index.number_of_shards: 5
index.number_of_replicas: 0
###### Path ######
path.data: /data/elk/es
path.logs: /var/log/elasticsearch
path.plugins: /usr/share/elasticsearch/plugins
###### Refresh ######
refresh_interval: 5s
###### Memory ######
bootstrap.mlockall: true
###### Network ######
network.publish_host: 10.10.10.10
network.bind_host: 0.0.0.0
transport.tcp.port: 9300
###### Http ######
http.enabled: true
http.port : 9200
###### IK ########
index.analysis.analyzer.ik.alias: [ik_analyzer]
index.analysis.analyzer.ik.type: ik
index.analysis.analyzer.ik_max_word.type: ik
index.analysis.analyzer.ik_max_word.use_smart: false
index.analysis.analyzer.ik_smart.type: ik
index.analysis.analyzer.ik_smart.use_smart: true
index.analysis.analyzer.default.type: ik

（5）启动ES

$ /etc/init.d/elasticsearch start

（6）检查es节点状态

$ curl localhost:9200/_cat/nodes?v    #看到一个节点正常
host         ip           heap.percent ram.percent load node.role master name
10.10.10.10 10.10.10.10           16          52 0.00 d         *      test-10.10.10.10

$ curl localhost:9200/_cat/health?v   #集群状态为green
epoch      timestamp cluster            status node.total node.data shards pri relo init
1483672233 11:10:33  test               green           1         1     0   0    0    0

二：检测分词功能
（1）创建测试索引

$ curl -XPUT http://localhost:9200/test[/code] 
（2）创建mapping

$ curl -XPOST http://localhost:9200/test/fulltext/_mapping -d'
{
"fulltext": {
"_all": {
"analyzer": "ik"
},
"properties": {
"content": {
"type" : "string",
"boost" : 8.0,
"term_vector" : "with_positions_offsets",
"analyzer" : "ik",
"include_in_all" : true
}
}
}
}'


（3）测试数据
$ curl 'http://localhost:9200/index/_analyze?analyzer=ik&pretty=true' -d '{ "text":"美国留给伊拉克的是个烂摊子吗" }'
返回内容：
{
"tokens" : [ {
"token" : "美国",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 0
}, {
"token" : "留给",
"start_offset" : 2,
"end_offset" : 4,
"type" : "CN_WORD",
"position" : 1
}, {
"token" : "伊拉克",
"start_offset" : 4,
"end_offset" : 7,
"type" : "CN_WORD",
"position" : 2
}, {
"token" : "伊",
"start_offset" : 4,
"end_offset" : 5,
"type" : "CN_WORD",
"position" : 3
}, {
"token" : "拉",
"start_offset" : 5,
"end_offset" : 6,
"type" : "CN_CHAR",
"position" : 4
}, {
"token" : "克",
"start_offset" : 6,
"end_offset" : 7,
"type" : "CN_WORD",
"position" : 5
}, {
"token" : "个",
"start_offset" : 9,
"end_offset" : 10,
"type" : "CN_CHAR",
"position" : 6
}, {
"token" : "烂摊子",
"start_offset" : 10,
"end_offset" : 13,
"type" : "CN_WORD",
"position" : 7
}, {
"token" : "摊子",
"start_offset" : 11,
"end_offset" : 13,
"type" : "CN_WORD",
"position" : 8
}, {
"token" : "摊",
"start_offset" : 11,
"end_offset" : 12,
"type" : "CN_WORD",
"position" : 9
}, {
"token" : "子",
"start_offset" : 12,
"end_offset" : 13,
"type" : "CN_CHAR",
"position" : 10
}, {
"token" : "吗",
"start_offset" : 13,
"end_offset" : 14,
"type" : "CN_CHAR",
"position" : 11
} ]
}


三：开始导入真正的数据
（1）将中文的文本文件上传到linux上面。
$ cat /tmp/zhongwen.txt
京津冀重污染天气持续 督查发现有企业恶意生产
《孤芳不自赏》被指“抠像演戏” 制片人：特效不到位
奥巴马不顾特朗普反对坚持外迁关塔那摩监狱囚犯
.
.
.
.
韩媒：日本叫停韩日货币互换磋商 韩财政部表遗憾
中国百万年薪须交40多万个税 精英无奈出国发展
注意：确保文本文件编码为utf-8，否则后面传到es会乱码。
$ vim /tmp/zhongwen.txt
命令模式下输入：set fineencoding,即可看到fileencoding=utf-8。
如果是 fileencoding=utf-16le，则输入：set fineencoding=utf-8

（2）创建索引和mapping
创建索引
$ curl -XPUT http://localhost:9200/index[/code]创建mapping  #对要分词的字段message进行分词器设置和fielddata设置。
$ curl -XPOST http://localhost:9200/index/logs/_mapping -d '
{
"logs": {
"_all": {
"analyzer": "ik"
},
"properties": {
"path": {
"type": "string"
},
"@timestamp": {
"format": "strict_date_optional_time||epoch_millis",
"type": "date"
},
"@version": {
"type": "string"
},
"host": {
"type": "string"
},
"message": {
"include_in_all": true,
"analyzer": "ik",
"term_vector": "with_positions_offsets",
"boost": 8,
"type": "string",
"fielddata" : { "format" : "true" }
},
"tags": {
"type": "string"
}
}
}
}'


（3）使用logstash 将文本文件写入到es中
安装logstash
$ wget https://download.elasticsearch.org/elasticsearch/release/org/elasticsearch/distribution/rpm/elasticsearch/2.1.1/elasticsearch-2.1.1.rpm $ rpm -ivh  logstash-2.1.1.rpm
配置logstash
$ vim /etc/logstash/conf.d/logstash.conf
input {
file {
codec => 'json'
path => "/tmp/zhongwen.txt"
start_position => "beginning"
}
}
output {
elasticsearch {
hosts => "10.10.10.10:9200"
index => "index"
flush_size => 3000
idle_flush_time => 2
workers => 4
}
stdout { codec => rubydebug }
}
启动
$ /etc/init.d/logstash start
查看stdout输出，就能判断是否写入es中。
$ tail -f /var/log/logstash.stdout


（4）检查索引中是否有数据
$ curl 'localhost:9200/_cat/indices/index?v'  #可以看到有6007条数据。
health status index pri rep docs.count docs.deleted store.size pri.store.size
green  open   index   5   0       6007            0      2.5mb          2.5mb
$ curl -XPOST  "http://localhost:9200/index/_search?pretty"
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 5227,
"max_score" : 1.0,
"hits" : [ {
"_index" : "index",
"_type" : "logs",
"_id" : "AVluC7Dpbw7ZlXPmUTSG",
"_score" : 1.0,
"_source" : {
"message" : "中国百万年薪须交40多万个税 精英无奈出国发展",
"tags" : [ "_jsonparsefailure" ],
"@version" : "1",
"@timestamp" : "2017-01-05T09:52:56.150Z",
"host" : "0.0.0.0",
"path" : "/tmp/333.log"
}
}, {
"_index" : "index",
"_type" : "logs",
"_id" : "AVluC7Dpbw7ZlXPmUTSN",
"_score" : 1.0,
"_source" : {
"message" : "奥巴马不顾特朗普反对坚持外迁关塔那摩监狱囚犯",
"tags" : [ "_jsonparsefailure" ],
"@version" : "1",
"@timestamp" : "2017-01-05T09:52:56.222Z",
"host" : "0.0.0.0",
"path" : "/tmp/333.log"
}
}


四：开始计算分词的词频，排序
（1）查询所有词出现频率最高的top10
$ curl -XGET "http://localhost:9200/index/_search?pretty" -d'
{
"size" : 0,
"aggs" : {
"messages" : {
"terms" : {
"size" : 10,
"field" : "message"
}
}
}
}'
返回结果
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 6007,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"messages" : {
"doc_count_error_upper_bound" : 154,
"sum_other_doc_count" : 94992,
"buckets" : [ {
"key" : "一",
"doc_count" : 1582
}, {
"key" : "后",
"doc_count" : 560
}, {
"key" : "人",
"doc_count" : 541
}, {
"key" : "家",
"doc_count" : 538
}, {
"key" : "出",
"doc_count" : 489
}, {
"key" : "发",
"doc_count" : 451
}, {
"key" : "个",
"doc_count" : 440
}, {
"key" : "州",
"doc_count" : 421
}, {
"key" : "岁",
"doc_count" : 405
}, {
"key" : "子",
"doc_count" : 402
} ]
}
}
}


（2）查询所有两字词出现频率最高的top10
$ curl -XGET "http://localhost:9200/index/_search?pretty" -d'
{
"size" : 0,
"aggs" : {
"messages" : {
"terms" : {
"size" : 10,
"field" : "message",
"include" : "[\u4E00-\u9FA5][\u4E00-\u9FA5]"
}
}
},
"highlight": {
"fields": {
"message": {}
}
}
}'
返回
{
"took" : 22,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 6007,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"messages" : {
"doc_count_error_upper_bound" : 73,
"sum_other_doc_count" : 42415,
"buckets" : [ {
"key" : "女子",
"doc_count" : 291
}, {
"key" : "男子",
"doc_count" : 264
}, {
"key" : "竟然",
"doc_count" : 257
}, {
"key" : "上海",
"doc_count" : 255
}, {
"key" : "这个",
"doc_count" : 238
}, {
"key" : "女孩",
"doc_count" : 174
}, {
"key" : "这些",
"doc_count" : 167
}, {
"key" : "一个",
"doc_count" : 159
}, {
"key" : "注意",
"doc_count" : 143
}, {
"key" : "这样",
"doc_count" : 142
} ]
}
}
}


（3）查询所有两字词且不包含“女”字，出现频率最高的top10
curl -XGET "http://localhost:9200/index/_search?pretty" -d'
{
"size" : 0,
"aggs" : {
"messages" : {
"terms" : {
"size" : 10,
"field" : "message",
"include" : "[\u4E00-\u9FA5][\u4E00-\u9FA5]",
"exclude" : "女.*"
}
}
},
"highlight": {
"fields": {
"message": {}
}
}
}'
返回
{
"took" : 19,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 5227,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"messages" : {
"doc_count_error_upper_bound" : 71,
"sum_other_doc_count" : 41773,
"buckets" : [ {
"key" : "男子",
"doc_count" : 264
}, {
"key" : "竟然",
"doc_count" : 257
}, {
"key" : "上海",
"doc_count" : 255
}, {
"key" : "这个",
"doc_count" : 238
}, {
"key" : "这些",
"doc_count" : 167
}, {
"key" : "一个",
"doc_count" : 159
}, {
"key" : "注意",
"doc_count" : 143
}, {
"key" : "这样",
"doc_count" : 142
}, {
"key" : "重庆",
"doc_count" : 142
}, {
"key" : "结果",
"doc_count" : 137
} ]
}
}
}


还有更多的分词策略，例如设置近义词（设置“番茄”和“西红柿”为同义词，搜索“番茄”，“西红柿”也会出来），设置拼音分词（搜索“zhonghua”，“中华”也可以搜索出来）等等。