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使用DataX实现mysql与hive数据互相导入导出

一、概论

1.1 什么是DataX

     DataX 是**阿里巴巴开源**的一个异构数据源离线同步工具，致力于实现包括关系型数据库(MySQL、Oracle 等)、HDFS、Hive、ODPS、HBase、FTP 等**各种异构数据源之间稳定高效的数据同步**功能。

1.2 DataX 的设计

     为了解决异构数据源同步问题，DataX 将复杂的**网状**的同步链路变成了**星型**数据链路，DataX 作为中间传输载体负责连接各种数据源。当需要接入一个新的数据源的时候，只需要将此数据源对接到 DataX，便能跟已有的数据源做到**无缝数据同步**。

在这里插入图片描述

1.3 框架设计

在这里插入图片描述

  • Reader：数据采集模块，负责采集数据源的数据，将数据发给Framework。
  • Wiriter: 数据写入模块，负责不断向Framwork取数据，并将数据写入到目的端。
  • Framework:用于连接read和writer,作为两者的数据传输通道，并处理缓冲，流控，并发，数据转换等核心技术问题。 运行原理在这里插入图片描述
  • Job：单个作业的管理节点，负责数据清理、子任务划分、TaskGroup监控管理。
  • Task：由Job切分而来，是DataX作业的最小单元，每个Task负责一部分数据的同步工作。
  • Schedule：将Task组成TaskGroup，单个TaskGroup的并发数量为5。
  • TaskGroup：负责启动Task。

1.4 Datax所支持的渠道

类型数据源读者作家（写）文件RDBMS关系型数据库MySQL√√读，写 甲骨文 √ √ 读，写SQL服务器√√读，写PostgreSQL的√√读，写DRDS√√读，写通用RDBMS（支持所有关系型数据库）√√读，写阿里云数仓数据存储ODPS√√读，写美国存托凭证√写开源软件√√读，写OCS√√读，写NoSQL数据存储OTS√√读，写Hbase0.94√√读，写Hbase1.1√√读，写凤凰4.x√√读，写凤凰5.x√√读，写MongoDB√√读，写蜂巢√√读，写卡桑德拉√√读，写无结构化数据存储文本文件√√读，写的FTP√√读，写HDFS√√读，写弹性搜索√写时间序列数据库OpenTSDB√读技术开发局√√读，写

二、快速入门

2.1 环境搭建

下载地址： http://datax-opensource.oss-cn-hangzhou.aliyuncs.com/datax.tar.gz
源码地址： https://github.com/alibaba/DataX

配置要求：

  • Linux
  • JDK(1.8以上 建议1.8) 下载
  • Python(推荐 Python2.6.X)下载安装：

1） 将下载好的datax.tar.gz上传到服务器的任意节点,我这里上传到node01上的/exprot/soft
2）解压到/export/servers/

[root@node01 soft]# tar -zxvf datax.tar.gz  -C ../servers/

3）运行自检脚本

出现以下结果说明你得环境没有问题

[/opt/module/datax/plugin/reader/._hbase094xreader/plugin.json]不存在. 请检查您的配置文件.
在这里插入图片描述

2.2搭建环境注意事项

[/opt/module/datax/plugin/reader/._hbase094xreader/plugin.json]不存在. 请检查您的配置文件.

参考：

find ./* -type f -name ".*er"  | xargs rm -rf
find: paths must precede expression: ｜
Usage: find [-H] [-L] [-P] [-Olevel] [-D help|tree|search|stat|rates|opt|exec] [path...] [expression]

find /datax/plugin/reader/ -type f -name "._*er" | xargs rm -rf
find /datax/plugin/writer/ -type f -name "._*er" | xargs rm -rf

这里的/datax/plugin/writer/要改为你自己的目录

原文链接：https://blog.csdn.net/dz77dz/article/details/127055299

2.3读取Mysql中的数据写入到HDFS

准备
创建数据库和表并加载测试数据

create database test;
use test;
create table c_s(
   id   varchar(100) null,
    c_id int          null,
    s_id varchar(20)  null
);
INSERT INTO test.c_s (id, c_id, s_id) VALUES ('123', 1, '201967');
INSERT INTO test.c_s (id, c_id, s_id) VALUES ('123', 2, '201967');
INSERT INTO test.c_s (id, c_id, s_id) VALUES ('123', 3, '201967');
INSERT INTO test.c_s (id, c_id, s_id) VALUES ('123', 5, '201967');
INSERT INTO test.c_s (id, c_id, s_id) VALUES ('123', 6, '201967');

查看官方提供的模板

[root@node01 datax]# bin/datax.py -r mysqlreader -w hdfswriter

DataX (DATAX-OPENSOURCE-3.0), From Alibaba !
Copyright (C) 2010-2017, Alibaba Group. All Rights Reserved.

Please refer to the mysqlreader document:
     https://github.com/alibaba/DataX/blob/master/mysqlreader/doc/mysqlreader.md

Please refer to the hdfswriter document:
     https://github.com/alibaba/DataX/blob/master/hdfswriter/doc/hdfswriter.md

Please save the following configuration as a json file and  use
     python {DATAX_HOME}/bin/datax.py {JSON_FILE_NAME}.json
to run the job.

{
    "job": {
        "content": [
            {
                "reader": {
                    "name": "mysqlreader",
                    "parameter": {
                        "column": [],
                        "connection": [
                            {
                                "jdbcUrl": [],
                                "table": []
                            }
                        ],
                        "password": "",
                        "username": "",
                        "where": ""
                    }
                },
                "writer": {
                    "name": "hdfswriter",
                    "parameter": {
                        "column": [],
                        "compress": "",
                        "defaultFS": "",
                        "fieldDelimiter": "",
                        "fileName": "",
                        "fileType": "",
                        "path": "",
                        "writeMode": ""
                    }
                }
            }
        ],
        "setting": {
            "speed": {
                "channel": ""
            }
        }
    }
}

根据官网模板进行修改

[root@node01 datax]# vim job/mysqlToHDFS.json
{
    "job": {
        "content": [
            {
                "reader": {
                    "name": "mysqlreader",
                    "parameter": {
                        "column": [
                            "id",
                            "c_id",
                            "s_id"
                        ],
                        "connection": [
                            {
                                "jdbcUrl": [
                                    "jdbc:mysql://node02:3306/test"
                                ],
                                "table": [
                                    "c_s"
                                ]
                            }
                        ],
                        "password": "123456",
                        "username": "root"
                    }
                },
                "writer": {
                    "name": "hdfswriter",
                    "parameter": {
                        "column": [
                            {
                                "name": "id",
                                "type": "string"
                            },
                            {
                                "name": "c_id",
                                "type": "int"
                            },
                            {
                                "name": "s_id",
                                "type": "string"
                            }
                        ],
                        "defaultFS": "hdfs://node01:8020",
                        "fieldDelimiter": "\t",
                        "fileName": "c_s.txt",
                        "fileType": "text",
                        "path": "/",
                        "writeMode": "append"
                    }
                }
            }
        ],
        "setting": {
            "speed": {
                "channel": "1"
            }
        }
    }
}

HDFS的端口号注意版本，2.7.4 是9000；hdfs://node01:9000

MySQL的参数介绍
在这里插入图片描述
HDFS参数介绍
在这里插入图片描述
运行脚本

[root@node01 datax]# bin/datax.py  job/mysqlToHDFS.json
2020-10-02 16:12:16.358 [job-0] INFO  HookInvoker - No hook invoked, because base dir not exists or is a file: /export/servers/datax/hook
2020-10-02 16:12:16.359 [job-0] INFO  JobContainer -
         [total cpu info] =>
                averageCpu                     | maxDeltaCpu                    | minDeltaCpu
                -1.00%                         | -1.00%                         | -1.00%

         [total gc info] =>
                 NAME                 | totalGCCount       | maxDeltaGCCount    | minDeltaGCCount    | totalGCTime        | maxDeltaGCTime     | minDeltaGCTime
                 PS MarkSweep         | 1                  | 1                  | 1                  | 0.245s             | 0.245s             | 0.245s
                 PS Scavenge          | 1                  | 1                  | 1                  | 0.155s             | 0.155s             | 0.155s

2020-10-02 16:12:16.359 [job-0] INFO  JobContainer - PerfTrace not enable!
2020-10-02 16:12:16.359 [job-0] INFO  StandAloneJobContainerCommunicator - Total 5 records, 50 bytes | Speed 5B/s, 0 records/s | Error 0 records, 0 bytes |  All Task WaitWriterTime 0.000s |  All Task WaitReaderTime 0.000s | Percentage 100.00%
2020-10-02 16:12:16.360 [job-0] INFO  JobContainer -
任务启动时刻                    : 2020-10-02 16:12:04
任务结束时刻                    : 2020-10-02 16:12:16
任务总计耗时                    :                 12s
任务平均流量                    :                5B/s
记录写入速度                    :              0rec/s
读出记录总数                    :                   5
读写失败总数                    :                   0

2.4 读取HDFS中的数据写入到Mysql

准备工作

create database test;
use test;
create table c_s2(
   id   varchar(100) null,
    c_id int          null,
    s_id varchar(20)  null
);

查看官方提供的模板

[root@node01 datax]# bin/datax.py -r hdfsreader -w mysqlwriter

DataX (DATAX-OPENSOURCE-3.0), From Alibaba !
Copyright (C) 2010-2017, Alibaba Group. All Rights Reserved.

Please refer to the hdfsreader document:
     https://github.com/alibaba/DataX/blob/master/hdfsreader/doc/hdfsreader.md

Please refer to the mysqlwriter document:
     https://github.com/alibaba/DataX/blob/master/mysqlwriter/doc/mysqlwriter.md

Please save the following configuration as a json file and  use
     python {DATAX_HOME}/bin/datax.py {JSON_FILE_NAME}.json
to run the job.

{
    "job": {
        "content": [
            {
                "reader": {
                    "name": "hdfsreader",
                    "parameter": {
                        "column": [],
                        "defaultFS": "",
                        "encoding": "UTF-8",
                        "fieldDelimiter": ",",
                        "fileType": "orc",
                        "path": ""
                    }
                },
                "writer": {
                    "name": "mysqlwriter",
                    "parameter": {
                        "column": [],
                        "connection": [
                            {
                                "jdbcUrl": "",
                                "table": []
                            }
                        ],
                        "password": "",
                        "preSql": [],
                        "session": [],
                        "username": "",
                        "writeMode": ""
                    }
                }
            }
        ],
        "setting": {
            "speed": {
                "channel": ""
            }
        }
    }
}

根据官方提供模板进行修改

[root@node01 datax]# vim job/hdfsTomysql.json
{
    "job": {
        "content": [
            {
                "reader": {
                    "name": "hdfsreader",
                    "parameter": {
                        "column": [
                            "*"
                        ],
                        "defaultFS": "hdfs://node01:8020",
                        "encoding": "UTF-8",
                        "fieldDelimiter": "\t",
                        "fileType": "text",
                        "path": "/c_s.txt"
                    }
                },
                "writer": {
                    "name": "mysqlwriter",
                    "parameter": {
                        "column": [
                            "id",
                            "c_id",
                            "s_id"
                        ],
                        "connection": [
                            {
                                "jdbcUrl": "jdbc:mysql://node02:3306/test",
                                "table": [
                                    "c_s2"
                                ]
                            }
                        ],
                        "password": "123456",
                        "username": "root",
                        "writeMode": "replace"
                    }
                }
            }
        ],
        "setting": {
            "speed": {
                "channel": "1"
            }
        }
    }
}

脚本运行

[root@node01 datax]# bin/datax.py job/hdfsTomysql.json

         [total cpu info] =>
                averageCpu                     | maxDeltaCpu                    | minDeltaCpu
                -1.00%                         | -1.00%                         | -1.00%

         [total gc info] =>
                 NAME                 | totalGCCount       | maxDeltaGCCount    | minDeltaGCCount    | totalGCTime        | maxDeltaGCTime     | minDeltaGCTime
                 PS MarkSweep         | 1                  | 1                  | 1                  | 0.026s             | 0.026s             | 0.026s
                 PS Scavenge          | 1                  | 1                  | 1                  | 0.015s             | 0.015s             | 0.015s

2020-10-02 16:57:13.152 [job-0] INFO  JobContainer - PerfTrace not enable!
2020-10-02 16:57:13.152 [job-0] INFO  StandAloneJobContainerCommunicator - Total 5 records, 50 bytes | Speed 5B/s, 0 records/s | Error 0 records, 0 bytes |  All Task WaitWriterTime 0.000s |  All Task WaitReaderTime 0.033s | Percentage 100.00%
2020-10-02 16:57:13.153 [job-0] INFO  JobContainer -
任务启动时刻                    : 2020-10-02 16:57:02
任务结束时刻                    : 2020-10-02 16:57:13
任务总计耗时                    :                 11s
任务平均流量                    :                5B/s
记录写入速度                    :              0rec/s
读出记录总数                    :                   5
读写失败总数                    :                   0

2.5将Mysql表导入Hive

1.在hive中建表

-- hive建表
CREATE TABLE student2 (
    classNo string,
    stuNo string,
    score int) 
row format delimited fields terminated by ',';

-- 构造点mysql数据
create table if not exists student2(
    classNo varchar ( 50 ),
    stuNo   varchar ( 50 ),
    score    int 
)
insert into student2 values('1001','1012ww10087',63);
insert into student2 values('1002','1012aa10087',63);
insert into student2 values('1003','1012bb10087',63);
insert into student2 values('1004','1012cc10087',63);
insert into student2 values('1005','1012dd10087',63);
insert into student2 values('1006','1012ee10087',63);

2.编写mysql2hive.json配置文件

{
    "job": {
        "setting": {
            "speed": {
                "channel": 1
            }
        },
        "content": [
            {
                "reader": {
                    "name": "mysqlreader",
                    "parameter": {
                        "username": "root",
                        "password": "root",
                        "connection": [
                            {
                                "table": [
                                    "student2"
                                ],
                                "jdbcUrl": [
                                    "jdbc:mysql://192.168.43.10:3306/mytestmysql"
                                ]
                            }
                        ],
                        "column": [
                            "classNo",
                            "stuNo",
                            "score"
                        ]
                    }
                },
                "writer": {
                    "name": "hdfswriter",
                    "parameter": {
                        "defaultFS": "hdfs://192.168.43.10:9000",
                        "path": "/hive/warehouse/home/myhive.db/student2",
                        "fileName": "myhive",
                        "writeMode": "append",
                        "fieldDelimiter": ",",
                        "fileType": "text",
                        "column": [
                            {
                                "name": "classNo",
                                "type": "string"
                            },
                            {
                                "name": "stuNo",
                                "type": "string"
                            },
                            {
                                "name": "score",
                                "type": "int"
                            }
                        ]
                    }
                }
            }
        ]
    }
}

3.运行脚本

bin/datax.py job/mysql2hive.json

4.查看hive表是否有数据

2.6将Hive表数据导入Mysql

1.要先在mysql建好表

create table if not exists student(
    classNo varchar ( 50 ),
    stuNo   varchar ( 50 ),
    score    int 
)

2.hive2mysql.json配置文件

{
    "job": {
        "setting": {
            "speed": {
                "channel": 3
            }
        },
        "content": [
            {
                "reader": {
                    "name": "hdfsreader",
                    "parameter": {
                        "path": "/hive/warehouse/home/myhive.db/student/*",
                        "defaultFS": "hdfs://192.168.43.10:9000",
                        "column": [
                               {
                                "index": 0,
                                "type": "string"
                               },
                                                           {
                                "index": 1,
                                "type": "string"
                               },
                               {
                                "index": 2,
                                "type": "Long"
                               }
                        ],
                        "fileType": "text",
                        "encoding": "UTF-8",
                        "fieldDelimiter": ","
                    }

                },
                "writer": {
                    "name": "mysqlwriter",
                    "parameter": {
                        "writeMode": "insert",
                        "username": "root",
                        "password": "root",
                        "column": [
                            "classNo",
                            "stuNo",
                            "score"
                        ],
                        "preSql": [
                            "delete from student"
                        ],
                        "connection": [
                            {
                                "jdbcUrl": "jdbc:mysql://192.168.43.10:3306/mytestmysql?useUnicode=true&characterEncoding=utf8",
                                "table": [
                                    "student"
                                ]
                            }
                        ]
                    }
                }
            }
        ]
    }
}

注意事项：

在Hive的ODS层建表语句中，以“,”为分隔符；
fields terminated by ','
在DataX的json文件中，也以“,”为分隔符。
"fieldDelimiter": "," 与hive表里面的分隔符保持一致即可

由于DataX不能完全支持所有Hive表的数据类型，应将DataX启动文件中的hdfsreader中的column字段的类型改成DataX支持的类型

标签： 大数据
本文转载自: https://blog.csdn.net/qq_35224503/article/details/132008213
版权归原作者 Bobby Wang 所有， 如有侵权，请联系我们删除。
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