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Elasticsearch(二)

程序员文章站 2022-07-05 07:50:31
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Elasticsearch(二)

一. analysis与analyzer

​ analysis，文本分析是将全文本转换为一系列单词的过程，也叫分词。analysis是通过analyzer(分词器)来实现的，可以使用Elasticsearch内置的分词器，也可以自己去定制一些分词器。除了在数据写入的是词条进行转换，那么在查询的时候也需要使用相同的分析器对语句进行分析。

​ anaylzer是由三部分组成，例如有

Hello a World, the world is beautifu

： 
1. Character Filter: 将文本中html标签剔除掉。
2. Tokenizer: 按照规则进行分词，在英文中按照空格分词。
3. Token Filter: 去掉stop world(停顿词，a, an, the, is)，然后转换小写。

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Qo4tX983-1580444315063)(images2/analysis.png)]

1.1 内置的分词器

分词器名称 处理过程
Standard Analyzer 默认的分词器，按词切分，小写处理
Simple Analyzer 按照非字母切分(符号被过滤)，小写处理
Stop Analyzer 小写处理，停用词过滤(the, a, this)
Whitespace Analyzer 按照空格切分，不转小写
Keyword Analyzer 不分词，直接将输入当做输出
Pattern Analyzer 正则表达式，默认是\W+(非字符串分隔)

1.2 内置分词器示例

A. Standard Analyzer

GET _analyze
{
  "analyzer": "standard",
  "text": "2 Running quick brown-foxes leap over lazy dog in the summer evening"
}

B. Simple Analyzer

GET _analyze
{
  "analyzer": "simple",
  "text": "2 Running quick brown-foxes leap over lazy dog in the summer evening"
}

C. Stop Analyzer

GET _analyze
{
  "analyzer": "stop",
  "text": "2 Running quick brown-foxes leap over lazy dog in the summer evening"
}

D. Whitespace Analyzer

GET _analyze
{
  "analyzer": "whitespace",
  "text": "2 Running quick brown-foxes leap over lazy dog in the summer evening"
}

E. Keyword Analyzer

GET _analyze
{
  "analyzer": "keyword",
  "text": "2 Running quick brown-foxes leap over lazy dog in the summer evening"
}

F. Pattern Analyzer

GET _analyze
{
  "analyzer": "pattern",
  "text": "2 Running quick brown-foxes leap over lazy dog in the summer evening"
}

1.3 中文分词

​ 中文分词在所有的搜索引擎中都是一个很大的难点，中文的句子应该是切分成一个个的词，一句中文，在不同的上下文中，其实是有不同的理解，例如下面这句话：

这个苹果，不大好吃/这个苹果，不大，好吃
1.3.1 IK分词器

IK分词器支持自定义词库，支持热更新分词字典，地址为 https://github.com/medcl/elasticsearch-analysis-ik

elasticsearch-plugin.bat install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.3.0/elasticsearch-analysis-ik-6.3.0.zip

安装步骤：

  1. 下载zip包，下载路径为：https://github.com/medcl/elasticsearch-analysis-ik/releases
  2. 在Elasticsearch的plugins目录下创建名为 analysis-ik 的目录，将下载好的zip包解压在该目录下
  3. 在dos命令行进入Elasticsearch的bin目录下，执行 elasticsearch-plugin.bat list 即可查看到该插件

IK分词插件对应的分词器有以下几种：

  • ik_smart
  • ik_max_word
1.3.2 HanLP

安装步骤如下：

  1. 下载ZIP包，下载路径为：https://pan.baidu.com/s/1mFPNJXgiTPzZeqEjH_zifw#list/path=%2F，密码i0o7
  2. 在Elasticsearch的plugins目录下创建名为 analysis-hanlp 的目录，将下载好的zip包解压在该目录下.
  3. 下载词库，地址为：https://github.com/hankcs/HanLP/releases
  4. 将analyzer-hanlp目录下的data目录删掉，然后将词库解压到anayler-hanlp目录下

HanLP对应的分词器如下：

  • hanlp，默认的分词
  • hanlp_standard，标准分词
  • hanlp_index，索引分词
  • hanlp_nlp，nlp分词
  • hanlp_n_short，N-最短路分词
  • hanlp_dijkstra，最短路分词
  • hanlp_speed，极速词典分词
1.3.3 pinyin分词器

安装步骤：

  1. 下载ZIP包，下载路径为：https://github.com/medcl/elasticsearch-analysis-pinyin/releases
  2. 在Elasticsearch的plugins目录下创建名为 analyzer-pinyin 的目录，将下载好的zip包解压在该目录下.

1.4 中文分词演示

ik_smart

GET _analyze
{
  "analyzer": "ik_smart",
  "text": ["剑桥分析公司多位高管对卧底记者说，他们确保了唐纳德·特朗普在总统大选中获胜"]
}

hanlp

GET _analyze
{
  "analyzer": "hanlp",
  "text": ["剑桥分析公司多位高管对卧底记者说，他们确保了唐纳德·特朗普在总统大选中获胜"]
}

hanlp_standard

GET _analyze
{
  "analyzer": "hanlp_standard",
  "text": ["剑桥分析公司多位高管对卧底记者说，他们确保了唐纳德·特朗普在总统大选中获胜"]
}

hanlp_speed

GET _analyze
{
  "analyzer": "hanlp_speed",
  "text": ["剑桥分析公司多位高管对卧底记者说，他们确保了唐纳德·特朗普在总统大选中获胜"]
}

1.5 分词的实际应用

​ 在如上列举了很多的分词器，那么在实际中该如何应用？

1.5.1 设置mapping

​ 要想使用分词器，先要指定我们想要对那个字段使用何种分词，如下所示：

PUT customers
{
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "hanlp_standard"
      }
    }
  }
}
1.5.2 插入数据
POST customers/_bulk
{"index":{}}
{"content":"如不能登录，请在百端登录百度首页，点击【登录遇到问题】，进行找回密码操作"}
{"index":{}}
{"content":"网盘客户端访问隐藏空间需要输入密码方可进入。"}
{"index":{}}
{"content":"剑桥的网盘不好用"}
1.5.3 查询
GET customers/_search
{
  "query": {
    "match": {
      "content": "密码"
    }
  }
}

1.6 拼音分词器

​ 在查询的过程中我们可能需要使用拼音来进行查询，在中文分词器中我们介绍过 pinyin 分词器，那么在实际的工作中该如何使用呢？

1.6.1 设置settings
PUT /medcl 
{
    "settings" : {
        "analysis" : {
            "analyzer" : {
                "pinyin_analyzer" : {
                    "tokenizer" : "my_pinyin"
                 }
            },
            "tokenizer" : {
                "my_pinyin" : {
                    "type" : "pinyin",
                    "keep_separate_first_letter" : false,
                    "keep_full_pinyin" : true,
                    "keep_original" : true,
                    "limit_first_letter_length" : 16,
                    "lowercase" : true,
                    "remove_duplicated_term" : true
                }
            }
        }
    }
}

如上所示，我们基于现有的拼音分词器定制了一个名为 pinyin_analyzer 这样一个分词器。可用的参数可以参照：https://github.com/medcl/elasticsearch-analysis-pinyin

1.6.2 设置mapping
PUT medcl/_mapping
{
        "properties": {
            "name": {
                "type": "keyword",
                "fields": {
                    "pinyin": {
                        "type": "text",
                        "analyzer": "pinyin_analyzer",
                        "boost": 10
                    }
                }
            }
        }
}
1.6.3 数据的插入
POST medcl/_bulk
{"index":{}}
{"name": "刘德华"}
{"index":{}}
{"name": "张学友"}
{"index":{}}
{"name": "四大天王"}
{"index":{}}
{"name": "柳岩"}
{"index":{}}
{"name": "angel baby"}
1.6.4 查询
GET medcl/_search
{
  "query": {
    "match": {
      "name.pinyin": "ldh"
    }
  }
}

1.7 中文、拼音混合查找

1.7.1 设置settings
PUT goods
{
  "settings": {
    "analysis": {
      "analyzer": {
        "hanlp_standard_pinyin":{
          "type": "custom",
          "tokenizer": "hanlp_standard",
          "filter": ["my_pinyin"]
        }
      },
      "filter": {
        "my_pinyin": {
          "type" : "pinyin",
          "keep_separate_first_letter" : false,
          "keep_full_pinyin" : true,
          "keep_original" : true,
          "limit_first_letter_length" : 16,
          "lowercase" : true,
          "remove_duplicated_term" : true
        }
      }
    }
  }
}
1.7.2 mappings设置
PUT goods/_mapping
{"properties": {
    "content": {
      "type": "text",
      "analyzer": "hanlp_standard_pinyin"
    }
  }
}
1.7.3 添加数据
POST goods/_bulk
{"index":{}}
{"content":"如不能登录，请在百端登录百度首页，点击【登录遇到问题】，进行找回密码操作"}
{"index":{}}
{"content":"网盘客户端访问隐藏空间需要输入密码方可进入。"}
{"index":{}}
{"content":"剑桥的网盘不好用"}
1.7.4 查询
GET goods/_search
{
  "query": {
    "match": {
      "content": "caozuo"
    }
  },
  "highlight": {
    "pre_tags": "<em>",
    "post_tags": "</em>",
    "fields": {
      "content": {}
    }
  }
}

二. spring boot与Elasticsearch的整合

2.1 添加依赖

<dependency>
	<groupId>org.springframework.boot</groupId>
	<artifactId>spring-boot-starter-data-elasticsearch</artifactId>
</dependency>

2.2 配置

spring:
  elasticsearch:
    rest:
      uris: http://localhost:9200

2.3 获取ElasticsearchTemplate

@Configuration
public class ElasticsearchConfig extends ElasticsearchConfigurationSupport {

    @Bean
    public Client elasticsearchClient() throws UnknownHostException {
        Settings settings = Settings.builder().put("cluster.name", "my-application").build();
        TransportClient client = new PreBuiltTransportClient(settings);
        client.addTransportAddress(new TransportAddress(InetAddress.getByName("127.0.0.1"), 9300));
        return client;
    }

    @Bean(name = {"elasticsearchOperations", "elasticsearchTemplate"})
    public ElasticsearchTemplate elasticsearchTemplate() throws UnknownHostException {
        return new ElasticsearchTemplate(elasticsearchClient(), entityMapper());
    }

    // use the ElasticsearchEntityMapper
    @Bean
    @Override
    public EntityMapper entityMapper() {
        ElasticsearchEntityMapper entityMapper = new ElasticsearchEntityMapper(elasticsearchMappingContext(),
                new DefaultConversionService());
        entityMapper.setConversions(elasticsearchCustomConversions());
        return entityMapper;
    }
}

2.4 POJO类的定义

@Document(indexName = "movies", type = "_doc")
public class Movie {
    private String id;
    private String title;
    private Integer year;
    private List<String> genre;
    // setters and getters
}

2.5 查询

A. 分页查询

// 分页查询
@RequestMapping("/page")
public Object pageQuery(
    @RequestParam(required = false, defaultValue = "10") Integer size,
    @RequestParam(required = false, defaultValue = "1") Integer page) {
    SearchQuery searchQuery = new NativeSearchQueryBuilder()
        .withPageable(PageRequest.of(page, size))
        .build();

    List<Movie> movies = elasticsearchTemplate
        .queryForList(searchQuery, Movie.class);

    return movies;
}

B. range查询

// 单条件范围查询, 查询电影的上映日期在2016年到2018年间的所有电影
@RequestMapping("/range")
public Object rangeQuery() {
	SearchQuery searchQuery = new NativeSearchQueryBuilder()
				.withQuery(new RangeQueryBuilder("year").from(2016).to(2018))
				.build();

	List<Movie> movies = elasticsearchTemplate
				.queryForList(searchQuery, Movie.class);

	return movies;
}

C. match查询

// 单条件查询只要包含其中一个字段
@RequestMapping("/match")
public Object singleCriteriaQuery(String searchText) {
	SearchQuery searchQuery = new NativeSearchQueryBuilder()
			.withQuery(new MatchQueryBuilder("title", searchText))
			.build();

	List<Movie> movies = elasticsearchTemplate
			.queryForList(searchQuery, Movie.class);

	return movies;
}

D. 多条件分页查询

@RequestMapping("/match/multiple")
    public Object multiplePageQuery(
            @RequestParam(required = true) String searchText,
            @RequestParam(required = false, defaultValue = "10") Integer size,
            @RequestParam(required = false, defaultValue = "1") Integer page) {
        SearchQuery searchQuery = new NativeSearchQueryBuilder()
            .withQuery(
                  new BoolQueryBuilder()
                        .must(new MatchQueryBuilder("title", searchText))
                        .must(new RangeQueryBuilder("year").from(2016).to(2018))
                ).withPageable(PageRequest.of(page, size))
                .build();

        List<Movie> movies = elasticsearchTemplate
            .queryForList(searchQuery, Movie.class);

        return movies;
    }

E. 多条件或者查询

// 多条件并且分页查询
    @RequestMapping("/match/or/multiple")
    public Object multipleOrQuery(@RequestParam(required = true) String searchText) {
        SearchQuery searchQuery = new NativeSearchQueryBuilder()
            .withQuery(
                  new BoolQueryBuilder()
                        .should(new MatchQueryBuilder("title", searchText))
                        .should(new RangeQueryBuilder("year").from(2016).to(2018))
                ).build();

        List<Movie> movies = elasticsearchTemplate
            	.queryForList(searchQuery, Movie.class);

        return movies;
    }

F. 精准匹配一个单词，且查询就一个单词

//其中包含有某个给定单词，必须是一个词
@RequestMapping("/term")
public Object termQuery(@RequestParam(required = true) String searchText) {
    SearchQuery searchQuery = new NativeSearchQueryBuilder()
        .withQuery(new TermQueryBuilder("title", searchText)).build();

    List<Movie> movies = elasticsearchTemplate.queryForList(searchQuery, Movie.class);

    return movies;
}

精准匹配多个单词

//其中包含有某个几个单词
@RequestMapping("/terms")
public Object termsQuery(@RequestParam(required = true) String searchText) {
    SearchQuery searchQuery = new NativeSearchQueryBuilder()
        .withQuery(new TermsQueryBuilder("title", searchText.split("\\s+"))).build();

    List<Movie> movies = elasticsearchTemplate.queryForList(searchQuery, Movie.class);

    return movies;
}

G. 短语匹配

@RequestMapping("/phrase")
public Object phraseQuery(@RequestParam(required = true) String searchText) {
	SearchQuery searchQuery = new NativeSearchQueryBuilder()
			.withQuery(new MatchPhraseQueryBuilder("title", searchText))
			.build();

	List<Movie> movies = elasticsearchTemplate
			.queryForList(searchQuery, Movie.class);

	return movies;
}

H. 只查询部分列

@RequestMapping("/source")
public Object sourceQuery(@RequestParam(required = true) String searchText) {
	SearchQuery searchQuery = new NativeSearchQueryBuilder()
		.withSourceFilter(new FetchSourceFilter(
               new String[]{"title", "year", "id"}, new String[]{}))
		.withQuery(new MatchPhraseQueryBuilder("title", searchText))
		.build();

	List<Movie> movies = elasticsearchTemplate.queryForList(searchQuery, Movie.class);

	return movies;
}

I. 多字段匹配

@RequestMapping("/multiple/field")
public Object allTermsQuery(@RequestParam(required = true) String searchText) {
	SearchQuery searchQuery = new NativeSearchQueryBuilder()
		.withQuery(new MultiMatchQueryBuilder(searchText, "title", "genre")
                   .type(MultiMatchQueryBuilder.Type.MOST_FIELDS))
		.build();

	List<Movie> movies = elasticsearchTemplate.queryForList(searchQuery, Movie.class);

	return movies;
}

J. 多单词同时包含

// 多单词同时包含
@RequestMapping("/also/include")
public Object alsoInclude(@RequestParam(required = true) String searchText) {
    SearchQuery searchQuery = new NativeSearchQueryBuilder()
        .withQuery(new QueryStringQueryBuilder(searchText)
                   .field("title").defaultOperator(Operator.AND))
        .build();

    List<Movie> movies = elasticsearchTemplate.queryForList(searchQuery, Movie.class);

    return movies;
}

三. logstash导入mysql数据

input {
  jdbc {
    jdbc_driver_class => "com.mysql.jdbc.Driver"
    jdbc_connection_string => "jdbc:mysql://localhost:3306/es?useSSL=false&serverTimezone=UTC"
    jdbc_user => es
    jdbc_password => "123456"
    #启用追踪，如果为true，则需要指定tracking_column
    use_column_value => false
    #指定追踪的字段，
    tracking_column => "id"
    #追踪字段的类型，目前只有数字(numeric)和时间类型(timestamp)，默认是数字类型
    tracking_column_type => "numeric"
    #记录最后一次运行的结果
    record_last_run => true
    #上面运行结果的保存位置
    last_run_metadata_path => "mysql-position.txt"
    statement => "SELECT * FROM news where tags is not null"
    #表示每天的 17:57分执行
    schedule => " 0 57 17 * * *"
  }
}

filter {
  mutate {
    split => { "tags" => ","}
  }
}
output {
  elasticsearch {
    document_id => "%{id}"
    document_type => "_doc"
    index => "news"
    hosts => ["http://localhost:9200"]
  }
  stdout{
    codec => rubydebug
  }
}

四. 搜索案例

4.1 自定义analyzer

PUT news
{
  "settings": {
    "analysis": {
      "analyzer": {
        "hanlp_standard_pinyin":{
          "type": "custom",
          "tokenizer": "hanlp_standard",
          "filter": ["my_pinyin"]
        }
      },
      "filter": {
        "my_pinyin": {
          "type" : "pinyin",
          "keep_separate_first_letter" : false,
          "keep_full_pinyin" : true,
          "keep_original" : true,
          "limit_first_letter_length" : 16,
          "lowercase" : true,
          "remove_duplicated_term" : true
        }
      }
    }
  }
}

4.2 定义mappings

PUT news/_mapping
{
  "dynamic": false,
  "properties": {
    "id": {
      "type": "long"
    },
    "title": {
      "type": "text",
      "analyzer": "hanlp_standard"
    },
    "content": {
      "type": "text",
      "analyzer": "hanlp_standard"
    },
    "tags": {
      "type": "completion",
      "analyzer": "hanlp_standard",
      "fields": {
        "tag_pinyin": {
          "type": "completion",
          "analyzer": "hanlp_standard_pinyin"
        }
      }
    }
  }
}

4.3 导入mysql的数据集

D:\logstash-datas\bin>logstash.bat -f ../config/logstash-mysql.conf

脚本参照第三章，数据库的脚本为news.sql

附录：

  1. 设置mappings的时候，可以指定 “dynamic”: false，意思是如果mappings中有些字段并没有指定，那么在数据导入的时候，该字段的数据会存入到es中，但是不会进行分词。
  2. 在使用suggestion的时候，“skip_duplicates”: true，表示的意思是如果出现相同的建议，那么只会保留一个。
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