es 过滤查询字段 elasticsearch过滤

转载

mob64ca14193248 2024-06-06 10:23:34

文章标签 es 过滤查询字段大数据 json 数据库 bc 文章分类 架构后端开发

阅读此文请先阅读上文：[大数据]-Elasticsearch5.3.1 IK分词，同义词/联想搜索设置，前面介绍了ES，Kibana5.3.1的安装配置，以及IK分词的安装和同义词设置，这里主要记录Logstash导入mysql数据到Elasticsearch5.3.1并设置IK分词和同义词。由于logstash配置好JDBC，ES连接之后运行脚本一站式创建index，mapping，导入数据。但是如果我们要配置IK分词器就需要修改创建index,mapping的配置，下面详细介绍。

一、Logstash-5.3.1下载安装:

下载：https://www.elastic.co/cn/downloads/logstash
解压：tar -zxf logstash-5.3.1.tar.gz
启动：bin/logstash -e 'input { stdin { } } output { stdout {} }'

Sending Logstash's logs to /home/rzxes/logstash-5.3.1/logs which is now configured via log4j2.properties
[2017-05-16T10:27:36,957][INFO ][logstash.setting.writabledirectory] Creating directory {:setting=>"path.queue", :path=>"/home/rzxes/logstash-5.3.1/data/queue"}
[2017-05-16T10:27:37,041][INFO ][logstash.agent           ] No persistent UUID file found. Generating new UUID {:uuid=>"c987803c-9b18-4395-bbee-a83a90e6ea60", :path=>"/home/rzxes/logstash-5.3.1/data/uuid"}
[2017-05-16T10:27:37,581][INFO ][logstash.pipeline        ] Starting pipeline {"id"=>"main", "pipeline.workers"=>1, "pipeline.batch.size"=>125, "pipeline.batch.delay"=>5, "pipeline.max_inflight"=>125}
[2017-05-16T10:27:37,682][INFO ][logstash.pipeline        ] Pipeline main started
The stdin plugin is now waiting for input:
[2017-05-16T10:27:37,886][INFO ][logstash.agent           ] Successfully started Logstash API endpoint {:port=>9600}

二、Logstash-5.3.1连接mysql作为数据源，ES作为数据输出端：

由于此版本的logstash已经集成了jdbc插件，我们只需要添加一个配置文件xxx.conf。内容如下test.conf：

input {
    stdin {
    }
    jdbc {
      # 数据库地址  端口  数据库名
      jdbc_connection_string => "jdbc:mysql://IP:3306/dbname"
      # 数据库用户名
      jdbc_user => "user"
      # 数据库密码
      jdbc_password => "pass"
      # mysql java驱动地址
      jdbc_driver_library => "/home/rzxes/logstash-5.3.1/mysql-connector-java-5.1.17.jar"
      jdbc_driver_class => "com.mysql.jdbc.Driver"
      jdbc_paging_enabled => "true"
      jdbc_page_size => "100000"
      # sql 语句文件，也可以直接写SQL，如statement => "select * from table1"
      statement_filepath => "/home/rzxes/logstash-5.3.1/test.sql"
      schedule => "* * * * *"
      type => "jdbc"
    }
}
output {
    stdout {
        codec => json_lines
    }
    elasticsearch {
        hosts  => "192.168.230.150:9200"
        index => "test-1" #索引名称
        document_type => "form" #type名称
        document_id => "%{id}" #id必须是待查询的数据表的序列字段

} }

创建一个SQL文件：如上配置test.sql内容: select * from table1
test.conf,test.sql文件都在logstash的根目录下。
运行logstash脚本导入数据： bin/logstash -f test.conf
等待数据导入完成。开启Es-head,访问9100端口如下：
可以看到已经导入了11597条数据。
更多详细的配置参考官方文档：plugins-inputs-jdbc-jdbc_driver_library

三、logstash是如何创建index，mapping，并导入数据？

ES导入数据必须先创建index，mapping，但是在logstash中并没有直接创建，我们只传入了index，type等参数，logstash是通过es的mapping template来创建的，这个模板文件不需要指定字段，就可以根据输入自动生成。在logstash启动的时候这个模板已经输出了如下log:

[2017-05-23T15:58:45,801][WARN ][logstash.outputs.elasticsearch] Restored connection to ES instance {:url=>#<URI::HTTP:0x68f0d43b URL:http://192.168.230.150:9200/>}
[2017-05-23T15:58:45,805][INFO ][logstash.outputs.elasticsearch] Using mapping template from {:path=>nil}
[2017-05-23T15:58:45,979][INFO ][logstash.outputs.elasticsearch] Attempting to install template {:manage_template=>{"template"=>"logstash-*", "version"=>50001, "settings"=>{"index.refresh_interval"=>"5s"}, "mappings"=>{"_default_"=>{"_all"=>{"enabled"=>true, "norms"=>false}, "dynamic_templates"=>[{"message_field"=>{"path_match"=>"message", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false}}}, {"string_fields"=>{"match"=>"*", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false, "fields"=>{"keyword"=>{"type"=>"keyword"}}}}}], "properties"=>{"@timestamp"=>{"type"=>"date", "include_in_all"=>false}, "@version"=>{"type"=>"keyword", "include_in_all"=>false}, "geoip"=>{"dynamic"=>true, "properties"=>{"ip"=>{"type"=>"ip"}, "location"=>{"type"=>"geo_point"}, "latitude"=>{"type"=>"half_float"}, "longitude"=>{"type"=>"half_float"}}}}}}}}

添加IK分词，只需要创建一个json文件： vim /home/rzxes/logstash-5.3.1/template/logstash.json

{
    "template": "*",
    "version": 50001,
    "settings": {
        "index.refresh_interval": "5s"
    },
    "mappings": {
        "_default_": {
            "_all": {
                "enabled": true,
                "norms": false
            },
            "dynamic_templates": [
                {
                    "message_field": {
                        "path_match": "message",
                        "match_mapping_type": "string",
                        "mapping": {
                            "type": "text",
                            "norms": false
                        }
                    }
                },
                {
                    "string_fields": {
                        "match": "*",
                        "match_mapping_type": "string",
                        "mapping": {
                            "type": "text",
                            "norms": false,
                            "analyzer": "ik_max_word",#只需要添加这一行即可设置分词器为ik_max_word
                            "fields": {
                                "keyword": {
                                    "type": "keyword"
                                }
                            }
                        }
                    }
                }
            ],
            "properties": {
                "@timestamp": {
                    "type": "date",
                    "include_in_all": false
                },
                "@version": {
                    "type": "keyword",
                    "include_in_all": false
                }
            }
        }
    }
}

如需配置同义词，需自定义分词器，配置同义词过滤<IK分词同义词详见上一篇文章>。修改模板logstash.json如下:

{
    "template" : "*",
    "version" : 50001,
    "settings" : {
        "index.refresh_interval" : "5s",
        #分词，同义词配置：自定义分词器，过滤器，如不配同义词则没有index这一部分
        "index": {
          "analysis": {
            "analyzer": {
              "by_smart": {
                "type": "custom",
                "tokenizer": "ik_smart",
                "filter": ["by_tfr","by_sfr"],
                "char_filter": ["by_cfr"]
              },
              "by_max_word": {
                "type": "custom",
                "tokenizer": "ik_max_word",
                "filter": ["by_tfr","by_sfr"],
                "char_filter": ["by_cfr"]
              }
            },
            "filter": {
              "by_tfr": {
                "type": "stop",
                "stopwords": [" "]
              },
              "by_sfr": {
                "type": "synonym",
                "synonyms_path": "analysis/synonyms.txt" #同义词路径
              }
            },
            "char_filter": {
              "by_cfr": {
                "type": "mapping",
                "mappings": ["| => |"]
              }
            }
          }
        } # index --end--
      },
    "mappings" : {
        "_default_" : {
            "_all" : {
                "enabled" : true,
                "norms" : false
            },
            "dynamic_templates" : [
                {
                    "message_field" : {
                        "path_match" : "message",
                        "match_mapping_type" : "string",
                        "mapping" : {
                            "type" : "text",
                            "norms" : false
                        }}
                    },
                {
                    "string_fields" : {
                        "match" : "*",
                        "match_mapping_type" : "string",
                        "mapping" : {
                            "type" : "text",
                            "norms" : false,
                            #选择分词器：自定义分词器，或者ik_mmax_word
                            "analyzer" : "by_max_word",
                            "fields" : {
                                "keyword" : {
                                    "type" : "keyword"
                                }
                            }
                        }
                    }
                 }
            ],
            "properties" : {
                "@timestamp" : {
                    "type" : "date",
                    "include_in_all" : false
                },
                "@version" : {
                    "type" : "keyword",
                    "include_in_all" : false
                }
            }
        }
    }
}

有了自定义模板文件，test.conf中配置模板覆盖使模板生效。test.conf最终配置如下：

input {
            stdin {
            }
            jdbc {
              # 数据库地址  端口  数据库名
              jdbc_connection_string => "jdbc:mysql://IP:3306/dbname"
              # 数据库用户名
              jdbc_user => "user"
              # 数据库密码
              jdbc_password => "pass"
              # mysql java驱动地址
              jdbc_driver_library => "/home/rzxes/logstash-5.3.1/mysql-connector-java-5.1.17.jar"
              jdbc_driver_class => "com.mysql.jdbc.Driver"
              jdbc_paging_enabled => "true"
              jdbc_page_size => "100000"
              # sql 语句文件
              statement_filepath => "/home/rzxes/logstash-5.3.1/mytest.sql"
              schedule => "* * * * *"
              type => "jdbc"
            }
        }
        output {
            stdout {
                codec => json_lines
            }
            elasticsearch {
                hosts  => "192.168.230.150:9200"
                index => "test-1"
                document_type => "form"
                document_id => "%{id}" #id必须是待查询的数据表的序列字段
                template_overwrite => true
                template => "/home/rzxes/logstash-5.3.1/template/logstash.json"
                }
        }

删除上次创建的index（由于数据导入时会根据原有数据的index，mapping进行索引创建），重新启动logstash。
最终在Kibana中检索关键词番茄，就会发现西红柿也会被检索到。如下图：
致此logstash数据导入的template重写就完成了。
另一种方式配置IK分词:全局配置，不需要自定义模板。

curl -XPUT "http://192.168.230.150:9200/_template/rtf" -H 'Content-Type: application/json' -d'
{
            "template" : "*",
            "version" : 50001,
            "settings" : {
                "index.refresh_interval" : "5s",
                "index": {
                  "analysis": {
                    "analyzer": {
                      "by_smart": {
                        "type": "custom",
                        "tokenizer": "ik_smart",
                        "filter": ["by_tfr","by_sfr"],
                        "char_filter": ["by_cfr"]
                      },
                      "by_max_word": {
                        "type": "custom",
                        "tokenizer": "ik_max_word",
                        "filter": ["by_tfr","by_sfr"],
                        "char_filter": ["by_cfr"]
                      }
                    },
                    "filter": {
                      "by_tfr": {
                        "type": "stop",
                        "stopwords": [" "]
                      },
                      "by_sfr": {
                        "type": "synonym",
                        "synonyms_path": "analysis/synonyms.txt"
                      }
                    },
                    "char_filter": {
                      "by_cfr": {
                        "type": "mapping",
                        "mappings": ["| => |"]
                      }
                    }
                  }
                }
              },
            "mappings" : {
                "_default_" : {
                    "_all" : {
                        "enabled" : true,
                        "norms" : false
                    },
                    "dynamic_templates" : [
                        {
                            "message_field" : {
                                "path_match" : "message",
                                "match_mapping_type" : "string",
                                "mapping" : {
                                    "type" : "text",
                                    "norms" : false
                                }}
                            },
                        {
                            "string_fields" : { 
                                "match" : "*",   
                                "match_mapping_type" : "string", 
                                "mapping" : {
                                    "type" : "text",
                                    "norms" : false,
                                    "analyzer" : "by_max_word", 
                                    "fields" : {  
                                        "keyword" : {
                                            "type" : "keyword"
                                        }
                                    }
                                }
                            }
                         }
                    ],
                    "properties" : {
                        "@timestamp" : {
                            "type" : "date",
                            "include_in_all" : false
                        },
                        "@version" : {
                            "type" : "keyword",
                            "include_in_all" : false
                        }
                    }
                }
            }
        }'