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Elasticsearch 快速入门

Elasticsearch winrains 来源:MacroZheng 5个月前 (04-23) 31次浏览

摘要

记得刚接触Elasticsearch的时候,没找啥资料,直接看了遍Elasticsearch的中文官方文档,中文文档很久没更新了,一直都是2.3的版本。最近又重新看了遍6.0的官方文档,由于官方文档介绍的内容比较多,每次看都很费力,所以这次整理了其中最常用部分,写下了这篇入门教程,希望对大家有所帮助。

简介

Elasticsearch是一个基于Lucene的搜索服务器。它提供了一个分布式的全文搜索引擎,基于restful web接口。Elasticsearch是用Java语言开发的,基于Apache协议的开源项目,是目前最受欢迎的企业搜索引擎。Elasticsearch广泛运用于云计算中,能够达到实时搜索,具有稳定,可靠,快速的特点。

安装

Windows下的安装

Elasticsearch

  • 安装中文分词插件,在elasticsearch-6.2.2\bin目录下执行以下命令;
elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.2.2/elasticsearch-analysis-ik-6.2.2.zip
  • 运行bin目录下的elasticsearch.bat启动Elasticsearch;

Kibana

  • 运行bin目录下的kibana.bat,启动Kibana的用户界面

Linux下的安装

Elasticsearch

  • 下载elasticsearch 6.4.0的docker镜像;
docker pull elasticsearch:6.4.0
  • 修改虚拟内存区域大小,否则会因为过小而无法启动;
sysctl -w vm.max_map_count=262144
  • 使用docker命令启动;
docker run -p 9200:9200 -p 9300:9300 --name elasticsearch \
-e "discovery.type=single-node" \
-e "cluster.name=elasticsearch" \
-v /mydata/elasticsearch/plugins:/usr/share/elasticsearch/plugins \
-v /mydata/elasticsearch/data:/usr/share/elasticsearch/data \
-d elasticsearch:6.4.0
  • 启动时会发现/usr/share/elasticsearch/data目录没有访问权限,只需要修改该目录的权限,再重新启动即可;
chmod 777 /mydata/elasticsearch/data/
  • 安装中文分词器IKAnalyzer,并重新启动;
docker exec -it elasticsearch /bin/bash
#此命令需要在容器中运行
elasticsearch-plugin install https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.4.0/elasticsearch-analysis-ik-6.4.0.zip
docker restart elasticsearch

Kibina

  • 下载kibana 6.4.0的docker镜像;
docker pull kibana:6.4.0
  • 使用docker命令启动;
docker run --name kibana -p 5601:5601 \
--link elasticsearch:es \
-e "elasticsearch.hosts=http://es:9200" \
-d kibana:6.4.0

相关概念

  • Near Realtime(近实时):Elasticsearch是一个近乎实时的搜索平台,这意味着从索引文档到可搜索文档之间只有一个轻微的延迟(通常是一秒钟)。
  • Cluster(集群):群集是一个或多个节点的集合,它们一起保存整个数据,并提供跨所有节点的联合索引和搜索功能。每个群集都有自己的唯一群集名称,节点通过名称加入群集。
  • Node(节点):节点是指属于集群的单个Elasticsearch实例,存储数据并参与集群的索引和搜索功能。可以将节点配置为按集群名称加入特定集群,默认情况下,每个节点都设置为加入一个名为elasticsearch的群集。
  • Index(索引):索引是一些具有相似特征的文档集合,类似于MySql中数据库的概念。
  • Type(类型):类型是索引的逻辑类别分区,通常,为具有一组公共字段的文档类型,类似MySql中表的概念。注意:在Elasticsearch 6.0.0及更高的版本中,一个索引只能包含一个类型。
  • Document(文档):文档是可被索引的基本信息单位,以JSON形式表示,类似于MySql中行记录的概念。
  • Shards(分片):当索引存储大量数据时,可能会超出单个节点的硬件限制,为了解决这个问题,Elasticsearch提供了将索引细分为分片的概念。分片机制赋予了索引水平扩容的能力、并允许跨分片分发和并行化操作,从而提高性能和吞吐量。
  • Replicas(副本):在可能出现故障的网络环境中,需要有一个故障切换机制,Elasticsearch提供了将索引的分片复制为一个或多个副本的功能,副本在某些节点失效的情况下提供高可用性。

集群状态查看

  • 查看集群健康状态;
GET /_cat/health?v
epoch      timestamp cluster       status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent
1585552862 15:21:02  elasticsearch yellow          1         1     27  27    0    0       25             0                  -                 51.9%
  • 查看节点状态;
GET /_cat/nodes?v
ip        heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
127.0.0.1           23          94  28                          mdi       *      KFFjkpV
  • 查看所有索引信息;
GET /_cat/indices?v
health status index    uuid                   pri rep docs.count docs.deleted store.size pri.store.size
green  open   pms      xlU0BjEoTrujDgeL6ENMPw   1   0         41            0     30.5kb         30.5kb
green  open   .kibana  ljKQtJdwT9CnLrxbujdfWg   1   0          2            1     10.7kb         10.7kb

索引操作

  • 创建索引并查看;
PUT /customer
GET /_cat/indices?v
health status index    uuid                   pri rep docs.count docs.deleted store.size pri.store.size
yellow open   customer 9uPjf94gSq-SJS6eOuJrHQ   5   1          0            0       460b           460b
green  open   pms      xlU0BjEoTrujDgeL6ENMPw   1   0         41            0     30.5kb         30.5kb
green  open   .kibana  ljKQtJdwT9CnLrxbujdfWg   1   0          2            1     10.7kb         10.7kb
  • 删除索引并查看;
DELETE /customer
GET /_cat/indices?v
health status index    uuid                   pri rep docs.count docs.deleted store.size pri.store.size
green  open   pms      xlU0BjEoTrujDgeL6ENMPw   1   0         41            0     30.5kb         30.5kb
green  open   .kibana  ljKQtJdwT9CnLrxbujdfWg   1   0          2            1     10.7kb         10.7kb

类型操作

  • 查看文档的类型;
GET /bank/account/_mapping
{
  "bank": {
    "mappings": {
      "account": {
        "properties": {
          "account_number": {
            "type": "long"
          },
          "address": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "age": {
            "type": "long"
          },
          "balance": {
            "type": "long"
          },
          "city": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "email": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "employer": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "firstname": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "gender": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "lastname": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          },
          "state": {
            "type": "text",
            "fields": {
              "keyword": {
                "type": "keyword",
                "ignore_above": 256
              }
            }
          }
        }
      }
    }
  }
}

文档操作

  • 在索引中添加文档;
PUT /customer/doc/1
{
  "name": "John Doe"
}
{
  "_index": "customer",
  "_type": "doc",
  "_id": "1",
  "_version": 1,
  "result": "created",
  "_shards": {
    "total": 2,
    "successful": 1,
    "failed": 0
  },
  "_seq_no": 3,
  "_primary_term": 1
}
  • 查看索引中的文档;
GET /customer/doc/1
{
  "_index": "customer",
  "_type": "doc",
  "_id": "1",
  "_version": 2,
  "found": true,
  "_source": {
    "name": "John Doe"
  }
}
  • 修改索引中的文档:
POST /customer/doc/1/_update
{
  "doc": { "name": "Jane Doe" }
}
{
  "_index": "customer",
  "_type": "doc",
  "_id": "1",
  "_version": 2,
  "result": "updated",
  "_shards": {
    "total": 2,
    "successful": 1,
    "failed": 0
  },
  "_seq_no": 4,
  "_primary_term": 1
}
  • 删除索引中的文档;
DELETE /customer/doc/1
{
  "_index": "customer",
  "_type": "doc",
  "_id": "1",
  "_version": 3,
  "result": "deleted",
  "_shards": {
    "total": 2,
    "successful": 1,
    "failed": 0
  },
  "_seq_no": 2,
  "_primary_term": 1
}
  • 对索引中的文档执行批量操作;
POST /customer/doc/_bulk
{"index":{"_id":"1"}}
{"name": "John Doe" }
{"index":{"_id":"2"}}
{"name": "Jane Doe" }
{
  "took": 45,
  "errors": false,
  "items": [
    {
      "index": {
        "_index": "customer",
        "_type": "doc",
        "_id": "1",
        "_version": 3,
        "result": "updated",
        "_shards": {
          "total": 2,
          "successful": 1,
          "failed": 0
        },
        "_seq_no": 5,
        "_primary_term": 1,
        "status": 200
      }
    },
    {
      "index": {
        "_index": "customer",
        "_type": "doc",
        "_id": "2",
        "_version": 1,
        "result": "created",
        "_shards": {
          "total": 2,
          "successful": 1,
          "failed": 0
        },
        "_seq_no": 0,
        "_primary_term": 1,
        "status": 201
      }
    }
  ]
}

数据搜索

查询表达式(Query DSL)是一种非常灵活又富有表现力的查询语言,Elasticsearch使用它可以以简单的JSON接口来实现丰富的搜索功能,下面的搜索操作都将使用它。

数据准备

  • 首先我们需要导入一定量的数据用于搜索,使用的是银行账户表的例子,数据结构如下:
{
    "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"
}
  • 我们先复制下需要导入的数据,数据地址: github.com/macrozheng/…
  • 然后直接使用批量操作来导入数据,注意本文所有操作都在Kibana的Dev Tools中进行;
POST /bank/account/_bulk
{
  "index": {
    "_id": "1"
  }
}
{
  "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"
}
......省略若干条数据
  • 导入完成后查看索引信息,可以发现bank索引中已经创建了1000条文档。
GET /_cat/indices?v
health status index    uuid                   pri rep docs.count docs.deleted store.size pri.store.size
yellow open   bank     HFjxDLNLRA-NATPKUQgjBw   5   1       1000            0    474.6kb        474.6kb

搜索入门

  • 最简单的搜索,使用match_all来表示,例如搜索全部;
GET /bank/_search
{
  "query": { "match_all": {} }
}
  • 分页搜索,from表示偏移量,从0开始,size表示每页显示的数量;
GET /bank/_search
{
  "query": { "match_all": {} },
  "from": 0,
  "size": 10
}
  • 搜索排序,使用sort表示,例如按balance字段降序排列;
GET /bank/_search
{
  "query": { "match_all": {} },
  "sort": { "balance": { "order": "desc" } }
}
  • 搜索并返回指定字段内容,使用_source表示,例如只返回account_numberbalance两个字段内容:
GET /bank/_search
{
  "query": { "match_all": {} },
  "_source": ["account_number", "balance"]
}

条件搜索

  • 条件搜索,使用match表示匹配条件,例如搜索出account_number20的文档:
GET /bank/_search
{
  "query": {
    "match": {
      "account_number": 20
    }
  }
}
  • 文本类型字段的条件搜索,例如搜索address字段中包含mill的文档,对比上一条搜索可以发现,对于数值类型match操作使用的是精确匹配,对于文本类型使用的是模糊匹配;
GET /bank/_search
{
  "query": {
    "match": {
      "address": "mill"
    }
  },
  "_source": [
    "address",
    "account_number"
  ]
}
  • 短语匹配搜索,使用match_phrase表示,例如搜索address字段中同时包含milllane的文档:
GET /bank/_search
{
  "query": {
    "match_phrase": {
      "address": "mill lane"
    }
  }
}

组合搜索

  • 组合搜索,使用bool来进行组合,must表示同时满足,例如搜索address字段中同时包含milllane的文档;
GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}
  • 组合搜索,should表示满足其中任意一个,搜索address字段中包含mill或者lane的文档;
GET /bank/_search
{
  "query": {
    "bool": {
      "should": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}
  • 组合搜索,must_not表示同时不满足,例如搜索address字段中不包含mill且不包含lane的文档;
GET /bank/_search
{
  "query": {
    "bool": {
      "must_not": [
        { "match": { "address": "mill" } },
        { "match": { "address": "lane" } }
      ]
    }
  }
}
  • 组合搜索,组合mustmust_not,例如搜索age字段等于40state字段不包含ID的文档;
GET /bank/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "age": "40" } }
      ],
      "must_not": [
        { "match": { "state": "ID" } }
      ]
    }
  }
}

过滤搜索

  • 搜索过滤,使用filter来表示,例如过滤出balance字段在20000~30000的文档;
GET /bank/_search
{
  "query": {
    "bool": {
      "must": { "match_all": {} },
      "filter": {
        "range": {
          "balance": {
            "gte": 20000,
            "lte": 30000
          }
        }
      }
    }
  }
}

搜索聚合

  • 对搜索结果进行聚合,使用aggs来表示,类似于MySql中的group by,例如对state字段进行聚合,统计出相同state的文档数量;
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      }
    }
  }
}
  • 嵌套聚合,例如对state字段进行聚合,统计出相同state的文档数量,再统计出balance的平均值;
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword"
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}
  • 对聚合搜索的结果进行排序,例如按balance的平均值降序排列;
GET /bank/_search
{
  "size": 0,
  "aggs": {
    "group_by_state": {
      "terms": {
        "field": "state.keyword",
        "order": {
          "average_balance": "desc"
        }
      },
      "aggs": {
        "average_balance": {
          "avg": {
            "field": "balance"
          }
        }
      }
    }
  }
}
  • 按字段值的范围进行分段聚合,例如分段范围为age字段的[20,30] [30,40] [40,50],之后按gender统计文档个数和balance的平均值;
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"
              }
            }
          }
        }
      }
    }
  }
}

参考资料

http://www.elastic.co/guide/en/el…

作者:MacroZheng

来源:https://juejin.im/post/5e8c7d65518825736512d097


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