初识elasticsearch_2(查询和整合springboot)

初始化

首先将官网所下载的json文件,放入到es中,采用如下命令:

curl -H "Content-Type: application/json" -XPOST ‘localhost:9200/bank/account/_bulk?pretty&refresh‘ --data-binary "@accounts.json"
curl ‘localhost:9200/_cat/indices?v‘

search API

接下来可以开始查询啦.可以通过2种方式进行查询,分别为将其放在RESTAPI中或者将其放在RESTAPI的请求体中.显然请求体的形式更加具有代表性并且也更加易读/
先看放在RESTAPI中的,下面的语句查询出了bank索引的所有的文档.

GET /bank/_search?q=*&sort=account_number:asc&pretty

参数列表代表q=*查询所有,sort=account_number:asc,代表结果按照account_number升序排列,pretty代表将返回结果以格式化JSON的形式输出.
可以看看返回值,返回值说明写在注释里面:

{
  "took" : 63,
  // 是否延迟
  "timed_out" : false,
 // 当前搜索的有多少个shards 
  "_shards" : {
    "total" : 5,
    "successful" : 5,
    "skipped" : 0,
    "failed" : 0
  },
  // 搜索结果
  "hits" : {
    // 符合搜索结果的条数
    "total" : 1000,
    "max_score" : null,
    // 结果的数组,默认显示前10条
    "hits" : [ {
      "_index" : "bank",
      "_type" : "account",
      "_id" : "0",
    // 排序字段
      "sort": [0],
      "_score" : null,
      "_source" : {"account_number":0,"balance":16623,"firstname":"Bradshaw","lastname":"Mckenzie","age":29,"gender":"F","address":"244 Columbus Place","employer":"Euron","email":"bradshawmckenzie@euron.com","city":"Hobucken","state":"CO"}
    }, {
      "_index" : "bank",
      "_type" : "account",
      "_id" : "1",
      "sort": [1],
      "_score" : null,
      "_source" : {"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"amberduke@pyrami.com","city":"Brogan","state":"IL"}
    }, ...
    ]
  }
}

可以采用请求体的方式去请求:

GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": [
    { "account_number": "asc" }
  ]
}

返回的结果是一样的.
通过增加参数,可以控制返回的结果条数:

// 展示一条
GET /bank/_search
{
  "query": { "match_all": {} },
  "size": 1
}

// 第10条~第20条
GET /bank/_search
{
  "query": { "match_all": {} },
  "from": 10,
  "size": 10
}

下面的是根据balance进行倒序排列

GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": { "balance": { "order": "desc" } }
}

默认情况下,返回的source是包含所有的数据结构的,如果我们不想返回document的所有的数据结构,可以采用下面的语句:

GET /bank/_search
{
  "query": { "match_all": {} },
  "_source": ["account_number", "balance"]
}

可以看看返回值:

{
    "took": 11,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
    },
    "hits": {
        "total": 999,
        "max_score": 1,
        "hits": [
            {
                "_index": "bank",
                "_type": "account",
                "_id": "25",
                "_score": 1,
                "_source": {
                    "account_number": 25,
                    "balance": 40540
                }
            }
        ]
    }
}

接下来可以看看根据字段过滤的,下面的筛选了account_number为20的订单

GET /bank/_search
{
  "query": { "match": { "account_number": 20 } }
}

下面筛选出了地址值包含mill,lane的结果

GET /bank/_search
{
  "query": { "match": { "address": "mill lane" } }
}

如果要筛选包含短语mill lane的呢:

GET /bank/_search
{
  "query": { "match_phrase": { "address": "mill lane" } }
}

紧接着来看看bool查询.
以下bool查询和上面的查询是一样的,查询出包含短语包含短语mill lane的:

GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}

Must代表所有的查询都必须返回true.再看看下面的语句:

GET /bank/_search
{
  "query": {
    "bool": {
      "should": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}

should代表这些查询中,当中的一个,必须返回true.
下面的语句,代表地址中既不能包含mill也不能包含lane:

GET /bank/_search
{
  "query": {
    "bool": {
      "must_not": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}

must_not要求查询结果对于所有的query都不满足
各个条件之间是可以相互组合的,如下:

GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "age": "40" } }
      ],
      "must_not": [
        { "match": { "state": "ID" } }
      ]
    }
  }
}

我们可以通过过滤器(filter)搜索banalance在20000到30000之间的东西

GET /bank/_search
{
  "query": {
    "bool": {
      "must": { "match_all": {} },
      "filter": {
        "range": {
          "balance": {
            "gte": 20000,
            "lte": 30000
          }
        }
      }
    }
  }
}

注意,must中”match”是不支持gte和lte的.
分组,注意,es可以在额外返回一个aggressions的数组,可以通过参数说明对返回的数组进行分组.如下所示:

GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      }
    }
  }
}

上面的语句大概等同于如下SQL:

SELECT state, COUNT(*) FROM bank GROUP BY state ORDER BY COUNT(*) DESC

下面的语句计算了按照state分类后,balance的平均值

GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}

注意,我们使用了两次aggs,注意,当我们需要对结果进行操作的时候,我们可以使用aggs嵌套的方式去从返回值中提取需要的数据.
下面是一个演示aggs嵌套的例子:

GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_age": {
      "range": {
        "field": "age",
        "ranges": [
          {
            "from": 20,
            "to": 30
          },
          {
            "from": 30,
            "to": 40
          },
          {
            "from": 40,
            "to": 50
          }
        ]
      },
      "aggs": {
        "group_by_gender": {
          "terms": {
            "field": "gender.keyword"
          },
          "aggs": {
            "average_balance": {
              "avg": {
                "field": "balance"
              }
            }
          }
        }
      }
    }
  }
}

这行语句的目的主要是先按照年龄段进行分组,在按照性别进行分组,最后取balance的平均值.返回值如下:

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 999,
    "max_score": 0,
    "hits": []
  },
  "aggregations": {
    "group_by_age": {
      "buckets": [
        {
          "key": "20.0-30.0",
          "from": 20,
          "to": 30,
          "doc_count": 450,
          "group_by_gender": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "M",
                "doc_count": 231,
                "average_balance": {
                  "value": 27400.982683982686
                }
              },
              {
                "key": "F",
                "doc_count": 219,
                "average_balance": {
                  "value": 25341.260273972603
                }
              }
            ]
          }
        },
        {
          "key": "30.0-40.0",
          "from": 30,
          "to": 40,
          "doc_count": 504,
          "group_by_gender": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "F",
                "doc_count": 253,
                "average_balance": {
                  "value": 25670.869565217392
                }
              },
              {
                "key": "M",
                "doc_count": 251,
                "average_balance": {
                  "value": 24288.239043824702
                }
              }
            ]
          }
        },
        {
          "key": "40.0-50.0",
          "from": 40,
          "to": 50,
          "doc_count": 45,
          "group_by_gender": {
            "doc_count_error_upper_bound": 0,
            "sum_other_doc_count": 0,
            "buckets": [
              {
                "key": "M",
                "doc_count": 24,
                "average_balance": {
                  "value": 26474.958333333332
                }
              },
              {
                "key": "F",
                "doc_count": 21,
                "average_balance": {
                  "value": 27992.571428571428
                }
              }
            ]
          }
        }
      ]
    }
  }
}

springboot整合elasticsearch

由于springboot使用的是spring-data-elasticsearch,但是目前这个最高版本对应的es版本没有到5,因此我们使用较低的es版本进行测试.采用的es版本是2.3.2,对应的spring-data-elasticsearch版本为2.1.0,spring-boot版本采用1.5.1,springboot-starter-elasticsearch版本为1.5.1.RELEASE

• pom.xml

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
            <version>1.5.1.RELEASE</version>
        </dependency>

• application.properties

# ES
spring.data.elasticsearch.repositories.enabled = true
spring.data.elasticsearch.cluster-nodes = 127.0.0.1:9300

• 实体类(Account)

需要注意的是,indexName,type都不能有大写.否则会报错

@Document(indexName = "bank",type = "account")
public class Account implements Serializable{

    @Id
    private Long id;

    private Integer account_number;

    private Long balance;

    private String firstname;

    private String lastname;

    private Integer age;

    private String gender;

    private String address;

    private String employer;

    private String email;

    private String city;

    private String state;

        //  get&set
}

• 操作es的repository

非常简单只需要继承即可.

public interface AccountRepository extends ElasticsearchRepository<Account,Long> {

}

• service

需要注意的是,在保存的时候,当文档对应的索引没有的时候,es会为我们手动创建,在保存文档的时候需要手动指定id,否则es会将null作为文档的id.

@Service
public class AccountServiceEsImpl {

    @Autowired AccountRepository accountRepository;

    /**
     * 保存账号
     */
    public Long save(Account account) {
        Account acountSaved = accountRepository.save(account);
        return acountSaved.getId();
    }

    /**
     * 根据地址值过滤
     * @return
     */
    public List<Account> queryByAddress() {
        // 根据地址值过滤
        Pageable page = new PageRequest(0,10);
        BoolQueryBuilder queryBuilder = QueryBuilders.boolQuery();
        queryBuilder.must(QueryBuilders.matchQuery("address","Beijing"));
        SearchQuery query =
                new NativeSearchQueryBuilder().withQuery(queryBuilder).withPageable(page).build();
        Page<Account> pages = accountRepository.search(query);
        return pages.getContent();
    }
}