利用Sqoop实现HDFS的数据与MySQL数据的互导

2023年8月2日 273次阅读 来源: CoderJed

利用Sqoop实现HDFS的数据与MySQL数据的互导

1. 查看帮助

[root@repo bin]# ./sqoop help

Available commands:
  codegen            Generate code to interact with database records
  create-hive-table  Import a table definition into Hive
  eval               Evaluate a SQL statement and display the results
  export             Export an HDFS directory to a database table
  help               List available commands
  import             Import a table from a database to HDFS
  import-all-tables  Import tables from a database to HDFS
  import-mainframe   Import datasets from a mainframe server to HDFS
  job                Work with saved jobs
  list-databases     List available databases on a server
  list-tables        List available tables in a database
  merge              Merge results of incremental imports
  metastore          Run a standalone Sqoop metastore
  version            Display version information
  
See 'sqoop help COMMAND' for information on a specific command.

2. 查看mysql数据中有哪些数据库

[root@repo bin]# ./sqoop list-databases \
--connect jdbc:mysql://192.168.9.100:3306/ \
--username root \
--password 123456

# 结果：
information_schema
hive_more_users
hive_single_user
mysql
test

3. 导入数据到HDFS

(1) 配置概述

Common arguments:
   --connect <jdbc-uri>                         Specify JDBC connect string
   --connection-manager <class-name>            Specify connection manager class name
   --connection-param-file <properties-file>    Specify connection parameters file
   --driver <class-name>                        Manually specify JDBC driver class to use
   --hadoop-home <hdir>                         Override $HADOOP_MAPRED_HOME_ARG
   --hadoop-mapred-home <dir>                   Override $HADOOP_MAPRED_HOME_ARG
   --help

(2) mysql表准备

mysql> select * from Ownerinfo;
+--------------------+--------+------+
| Ownerid            | Name   | Age  |
+--------------------+--------+------+
| 110101200001010001 | 刘备   |   53 |
| 110101200001010002 | 关羽   |   42 |
| 110101200001010003 | 张飞   |   35 |
| 110101200001010004 | 赵云   |   33 |
| 110101200001010005 | 马超   |   38 |
| 110101200001010006 | 黄忠   |   70 |
+--------------------+--------+------+

(3) Mysql表中数据导入HDFS的默认路径下

[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--split-by Ownerid

# 结果：

[root@repo bin]# hdfs dfs -cat /user/root/Ownerinfo/*
110101200001010001,刘备,53
110101200001010002,关羽,42
110101200001010003,张飞,35
110101200001010004,赵云,33
110101200001010005,马超,38
110101200001010006,黄忠,70

注意：

  1. 如果不指定存储在HDFS哪个路径下，会直接存到/user/user_name/table_name/下
  2. 如果表中没有主键，需要使用–split-by选项来指定
  3. 默认用4个map task并行导入

(4) Mysql表中数据导入HDFS的指定路径下，并指定导入时的map task个数

[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--target-dir /user/root/SQOOP/import/Ownerinfo_common \
--num-mappers 1 \
--split-by Ownerid

结果：
[root@repo bin]# hdfs dfs -cat SQOOP/import/Ownerinfo_common/*
110101200001010001,刘备,53
110101200001010002,关羽,42
110101200001010003,张飞,35
110101200001010004,赵云,33
110101200001010005,马超,38
110101200001010006,黄忠,70

(5) Mysql表中数据导入HDFS时设置数据存储格式为parquet

命令：
[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--target-dir /user/root/SQOOP/import/Ownerinfo_parquet \
--num-mappers 1 \
--as-parquetfile \
--split-by Ownerid

HDFS的存储文件有以下3种格式：

  • textfile(默认)
  • sequencefile
  • parquetfile

创建hive表来解读刚才导入的parquetfile：

drop table if exists hive_Ownerinfo;

create table hive_Ownerinfo (Ownerid string,Name string,Age int)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ','
STORED AS PARQUET;

load data inpath '/user/root/SQOOP/import/Ownerinfo_parquet/3283c8d3-a239-44b9-80dd-0df4bdcebea1.parquet' into table hive_Ownerinfo;

hive> select * from hive_ownerinfo;
110101200001010001  刘备  53
110101200001010002  关羽  42
110101200001010003  张飞  35
110101200001010004  赵云  33
110101200001010005  马超  38
110101200001010006  黄忠  70

(6) 按条件过滤Mysql表中数据后再导入HDFS

--<1> 指定要导入的列
[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--target-dir /user/root/SQOOP/import/Ownerinfo_column \
--num-mappers 1 \
--columns Ownerid,Age \
--split-by Ownerid

--<2> 指定查询语句
[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--query 'select Ownerid, Age from Ownerinfo where $CONDITIONS' \
--target-dir /user/root/SQOOP/import/Ownerinfo_select \
--num-mappers 1 \
--split-by Ownerid

# 结果：

[root@repo bin]# hdfs dfs -cat SQOOP/import/Ownerinfo_select/*
110101200001010001,53
110101200001010002,42
110101200001010003,35
110101200001010004,33
110101200001010005,38
110101200001010006,70

注意：
查询语句必须包含where条件，即使不需要where条件，也需要写上”where $CONDITIONS”来表示没有select语句没有where条件

(7) Mysql表中数据导入HDFS时设置数据压缩格为snappy，并自动判断输出路径是否存在，存在则删除

[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--target-dir /user/root/SQOOP/import/Ownerinfo_compress \
--num-mappers 1 \
--columns Ownerid,Age \
--compress \
--compression-codec org.apache.hadoop.io.compress.SnappyCodec \
--delete-target-dir
--split-by Ownerid

创建hive表来解读刚才导入的snappy压缩格式文件：

drop table if exists hive_Ownerinfo;

create table hive_Ownerinfo (Ownerid string, Age int)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',';

load data inpath '/user/root/SQOOP/import/Ownerinfo_compress/part-m-00000.snappy' into table hive_Ownerinfo;

hive> select * from hive_Ownerinfo;
110101200001010001  53
110101200001010002  42
110101200001010003  35
110101200001010004  33
110101200001010005  38
110101200001010006  70

4. 增量导入

(1) 逐条导入

-- 方法一：指定查询语句
[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--query 'select * from Ownerinfo where Age > 30 and Age < 50 \
--target-dir /user/root/SQOOP/import/Ownerinfo_select \
--num-mappers 1 \
--split-by Ownerid

-- 方法二：使用相关的选项参数(用追加的方式导入Age>33的数据)
[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--target-dir /user/root/SQOOP/import/Ownerinfo_incremental \
--num-mappers 1 \
--incremental append \
--check-column Age \
--last-value 33

# 结果

[root@repo bin]# hdfs dfs -cat /user/root/SQOOP/import/Ownerinfo_incremental/part-m-00000
110101200001010001,刘备,53
110101200001010002,关羽,42
110101200001010003,张飞,35
110101200001010005,马超,38
110101200001010006,黄忠,70

注意：
(1) 非数值型的值不能当做增量

Error during import: Character column (Ownerid) can not be used to determine which rows to incrementally import.

(2) 增量导入不能与–delete-target-dir参数一起使用

(2) direct方式：直接从数据库导出数据，效率高

[root@repo bin]# ./sqoop import \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table Ownerinfo \
--target-dir /user/root/SQOOP/import/Ownerinfo_direct \
--columns Ownerid,Age \
--direct \
--delete-target-dir \
--split-by Ownerid

5. 把HDFS上的数据导出到MySQL表中

/user/root/SQOOP/export/users.txt内容：

1,Jed,15
2,Tom,16
3,Tony,17
4,Bob,18
5,Harry,19
6,Jack,20

[root@repo bin]# ./sqoop export \
--connect jdbc:mysql://192.168.9.100:3306/test \
--username root \
--password 123456 \
--table users \
--export-dir /user/root/SQOOP/export \
--num-mappers 1


mysql> select * from users;
+----+-------+------+
| Id | name  | age  |
+----+-------+------+
|  1 | Jed   |   15 |
|  2 | Tom   |   16 |
|  3 | Tony  |   17 |
|  4 | Bob   |   18 |
|  5 | Harry |   19 |
|  6 | Jack  |   20 |
+----+-------+------+

注意：
MySQL表需要提前创建好

    原文作者：CoderJed
    原文地址: https://www.jianshu.com/p/ec9032f50a86
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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