sqoop 原理中文手册 split by --m理解

逃离我推掉我的手 2022-06-04 07:51 259阅读 0赞

### MySQL里的数据）通过Sqoop Import HDFS 里 和 通过Sqoop Export HDFS 里的数据到（MySQL）（五） ###

下面我们结合 HDFS，介绍 Sqoop 从关系型数据库的导入和导出

一、MySQL里的数据通过Sqoop import HDFS  
　　它的功能是将数据从关系型数据库导入 HDFS 中，其流程图如下所示。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_6c2040c7ffb1700501d855cd8414ed83_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201611_2F855959_2D20161130093710990_2D563518867_2Epng][]

我们来分析一下 Sqoop 数据导入流程，首先用户输入一个 Sqoop import 命令，Sqoop 会从关系型数据库中获取元数据信息，  
比如要操作数据库表的 schema是什么样子，这个表有哪些字段，这些字段都是什么数据类型等。  
　　它获取这些信息之后，会将输入命令转化为基于 Map 的 MapReduce作业。  
这样 MapReduce作业中有很多 Map 任务，每个 Map 任务从数据库中读取一片数据，这样多个 Map 任务实现并发的拷贝，把整个数据快速的拷贝到 HDFS 上。

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场景：   
  
sqoop import --connect jdbc:postgresql://...../..... --username ....  --query "select \* from retail\_tb\_order\_qiulp\_test where status = 'TRADE\_FINISHED' or status = 'TRADE\_CLOSED' or status = 'TRADE\_CLOSED\_BY\_TAOBAO' and \\$CONDITIONS"  --hive-import -m 6 --hive-table custom\_analyse\_db.retail\_tb\_order\_main --hive-overwrite --hive-partition-key part --hive-partition-value qiulp --target-dir /user/qiulp/test1 --split-by tid   
注意红色字体部分， retail\_tb\_order\_qiulp\_test表原有1000条数据，导入hive后成了6000条，是map的倍数关系。   
如下图，一条数据在hive中有6条相同的数据：   
  
理解   
1.split-by 根据不同的参数类型有不同的切分方法，如int型，Sqoop会取最大和最小split-by字段值，然后根据传入的num-mappers来 确定划分几个区域。比如select max(split\_by),min(split-by) from得到的max(split-by)和min(split-by)分别为1000和1，而num-mappers（-m）为2的话，则会分成两个区域 (1,500)和(501-1000),同时也会分成2个sql给2个map去进行导入操作，分别为select XXX from table where split-by>=1 and split-by<500和select XXX from table where split-by>=501 and split-by<=1000.最后每个map各自获取各自SQL中的数据进行导入工作。   
  
2.当split-by不是int型时出现如上场景中的问题。目前想到的解决办法是：将-m 设置称1，split-by不设置，即只有一个map运行，缺点是不能并行map录入数据。（注意，当-m 设置的值大于1时，split-by必须设置字段）   
  
3.split-by即便是int型，若不是连续有规律递增的话，各个map分配的数据是不均衡的，可能会有些map很忙，有些map几乎没有数据处理的情况

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### Sqoop Import HDFS（带着官网） ###

![timg_pa_quality_100_size_w4096_sec_1512694762_di_058b298797993d36cc35e0207604912a_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317151646213_2D1060525675_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_eb0d90ca6e9addc6743d072441793c9b_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317151657010_2D1167891294_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_bcf95b782e986ffdc3bb5d7a1d03f372_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317151904651_2D555620603_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_f55ce12765725d4531ff16562ebc2f8c_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317151923151_2D1044515795_2Epng][]

具体，自己去尝试做吧！

在这之前，先启动hadoop集群,sbin/start-all.sh。这里不多赘述。

同时，开启MySQL数据库。这里，不多赘述。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_56e572aacfea4316786f412725077bf3_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317154035995_2D1848706261_2Epng][]

同时，因为后续的sqoop运行啊，会产生一些日志等，我这里先新建一个目录，用来专门存放它。在哪个目录下运行后续的sqoop操作，就在哪个目录下新建就好。（因为，已经配置了环境变量，在任何路径下都是可以运行的）

![timg_pa_quality_100_size_w4096_sec_1512694762_di_1d09b783fc581723f3687e36b6f34ba5_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317153344073_2D678125301_2Epng][]

[hadoop@djt002 sqoop]$ pwd
    /usr/local/sqoop
    [hadoop@djt002 sqoop]$ ll
    total 4
    drwxr-xr-x. 9 hadoop hadoop 4096 Apr 27  2015 sqoop-1.4.6
    [hadoop@djt002 sqoop]$ mkdir sqoopRunCreate
    [hadoop@djt002 sqoop]$ ll
    total 8
    drwxr-xr-x. 9 hadoop hadoop 4096 Apr 27  2015 sqoop-1.4.6
    drwxrwxr-x. 2 hadoop hadoop 4096 Mar 17 23:33 sqoopRunCreate
    [hadoop@djt002 sqoop]$ cd sqoopRunCreate/
    [hadoop@djt002 sqoopRunCreate]$ pwd
    /usr/local/sqoop/sqoopRunCreate
    [hadoop@djt002 sqoopRunCreate]$

比如，以后我就在这个目录下运行操作sqoop，/usr/local/sqoop/sqoopRunCreate。

Sqoop Import 应用场景——密码访问

（1）明码访问

![timg_pa_quality_100_size_w4096_sec_1512694762_di_be0bfd9137cef3fd18b7cb7e1eb8f4d9_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317154135166_2D996598484_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop list-databases \ > --connect jdbc:mysql://192.168.80.200/ \
    > --username hive \ > --password hive

![timg_pa_quality_100_size_w4096_sec_1512694762_di_9e6a57c7b40e7cbc092a5eaf2c4dae59_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317154302698_2D538589942_2Epng][]

（2）交互式密码访问

![timg_pa_quality_100_size_w4096_sec_1512694762_di_45adc1b27ce3130ff377d1c2717c8dbb_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317154512807_2D1842108799_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop list-databases \
    > --connect jdbc:mysql://192.168.80.200/ \
    > --username hive \
    > -P
    
    Enter password: （输入hive）

（3）文件授权密码访问

因为，官网上是这么给的，在家目录，且需赋予400权限。所以

![timg_pa_quality_100_size_w4096_sec_1512694762_di_e735a32b922431b0a4424760fcebe0c2_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317160306291_2D92846813_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_f357b52b3e0eb0dd224bf464cbf65fbd_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317160748698_2D1573164105_2Epng][]

[hadoop@djt002 ~]$ pwd
    /home/hadoop
    [hadoop@djt002 ~]$ echo -n "hive" > .password
    [hadoop@djt002 ~]$ ls -a
    .            .bash_history  .cache   djt        flume    .gnote           .gvfs            .local          .nautilus  .pulse         Videos       .xsession-errors
    ..           .bash_logout   .config  Documents  .gconf   .gnupg           .hivehistory     .mozilla        .password  .pulse-cookie  .vim         .xsession-errors.old
    .abrt        .bash_profile  .dbus    Downloads  .gconfd  .gstreamer-0.10  .ICEauthority    Music           Pictures   .ssh           .viminfo
    anagram.jar  .bashrc        Desktop  .esd_auth  .gnome2  .gtk-bookmarks   .imsettings.log  .mysql_history  Public     Templates      .Xauthority
    [hadoop@djt002 ~]$ more .passwordhive
    [hadoop@djt002 ~]$

![timg_pa_quality_100_size_w4096_sec_1512694762_di_67a2eb0c20ab379bde5bdea3fc8d23b1_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317161036463_2D12227862_2Epng][]

[hadoop@djt002 ~]$ chmod 400 .password

![timg_pa_quality_100_size_w4096_sec_1512694762_di_20c9936ba4a2bcd4c9a93d8b1513e741_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317162124588_2D1002194798_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop list-databases \ > --connect jdbc:mysql://192.168.80.200/ \
    > --username hive \ > --password-file /home/hadoop/.password
    
    
    java.io.IOException: The provided password file /home/hadoop/.password does not exist!

![timg_pa_quality_100_size_w4096_sec_1512694762_di_adb3c3efd4adfffebc94cb454823483d_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317162459182_2D1569907363_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a398436303dd251d85c8b61e87d54964_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317162535338_2D164746296_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_89cfd41d4021ad1b6fbd56fac40ba95a_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317162923120_2D40283844_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_40d9862877c1e7563432f4ee3706d041_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317162938354_2D1379820390_2Epng][]

[hadoop@djt002 local]$ $HADOOP_HOME/bin/hadoop dfs -put /home/hadoop/.password /user/hadoop
    
    
    
    [hadoop@djt002 local]$ $HADOOP_HOME/bin/hadoop dfs -chmod 400 /user/hadoop/.password

![timg_pa_quality_100_size_w4096_sec_1512694762_di_5c69738a141d8b4bec51f3354f66f228_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317163047635_2D1871960059_2Epng][]

[hadoop@djt002 ~]$ rm .password 
    rm: remove write-protected regular file `.password'? y

![timg_pa_quality_100_size_w4096_sec_1512694762_di_fb121bb992eba909cab8879f8ba9dcbc_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317163232057_2D44827794_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop list-databases \
    > --connect jdbc:mysql://192.168.80.200/ \
    > --username hive \
    > --password-file /user/hadoop/.password

Sqoop Import 应用场景——导入全表

（1）不指定目录 （则默认是在/user/hadoop/下）

![timg_pa_quality_100_size_w4096_sec_1512694762_di_bc54a8668e42e94ce48c6b1ac6734a79_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317163727166_2D470860338_2Epng][]

我这里啊，给大家尝试另一个软件。（为什么，要这样带大家使用，是为了你们的多适应和多自学能力）（别嫌麻烦！）

# [SQLyog之MySQL客户端的下载、安装和使用][SQLyog_MySQL] #

![timg_pa_quality_100_size_w4096_sec_1512694762_di_0ab4aeb1c6d2e9333b80bb378edb0980_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317163758432_2D1927047739_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_9d7e6c5771b377389c60a68ff871c5ec_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317163842823_2D1863849188_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_7d00b01fb3289f0ff07db86aabfe9abb_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317163954745_2D2073350737_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_f1dc6ca5531bc9ee2687921af2bda4da_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317164026338_2D1485723800_2Epng][]

这里，我们选择在hive这个数据库里，创建新的表，命名为

![timg_pa_quality_100_size_w4096_sec_1512694762_di_7fa00012bae8eee25748a470ce11c0be_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317164421245_2D1217974438_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_82f418ab8693bcf4605dc61d0e54af65_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317170458104_2D1317033458_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_7046bb75cbbf7e0edd68493357408f19_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317170605416_2D1686280964_2Epng][]

如果，面对 SQLyog不能正常显示中文数据的情况：在SQLyog下输入SET character\_set\_results = gb2312（或 gbk），执行，重新启动SQLyog，显示应该也可以看到你所插入的中文数据了。

# [SQLyog软件里无法插入中文（即由默认的latin1改成UTF8编码格式）][SQLyog_latin1_UTF8] #

![timg_pa_quality_100_size_w4096_sec_1512694762_di_903b7dd32c9fca296da3d30da550b3be_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317194103416_2D1337812991_2Epng][]

注意，我的数据表是djt-user。我这里改名啦！

![timg_pa_quality_100_size_w4096_sec_1512694762_di_7befa6ddf8214d0fa862a0b04672f1e3_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317194959120_2D594050557_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \ > --connect jdbc:mysql://192.168.80.200/hive \
    > --username hive \ > --password-file /user/hadoop/.password \ > --table djt-user

![timg_pa_quality_100_size_w4096_sec_1512694762_di_572b61d937dc65a22626cb73bf23acde_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195253463_2D1901919778_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_063ff2c3edaabccf0d6e1bd1fb3770c6_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195317729_2D1433161927_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c2127eeca0748a08bd01ce05999bab27_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195329713_2D1494989071_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_2f6cdf930016f2f348fe7a5e7c0c6cc0_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195445526_2D1065239176_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_8408e0e80fb60e76bf02e16bafe8ce2b_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195613026_2D1067734310_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a342c6108f9a73cca9a13536e82083b8_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195747245_2D1261181150_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c583c3856fae82af0d5effac7c934f09_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317195939620_2D1938654478_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a53a87fef144a872fc0bf157d2c75138_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317200013854_2D1280524807_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_30fd5437701509af9b61335f604a2997_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317200237823_2D1808587684_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_41ba0b4943d0e1bc12e45f111832533e_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317200345135_2D1809729816_2Epng][]

SET character_set_database=utf8;
    SET character_set_server=utf8;
    SHOW VARIABLES LIKE 'character%';

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b49a08579bd8d2cbb7735cc4bf3988df_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317200428635_2D1172285145_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_14e81b92c240c2382525f5ba47c06e0d_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317200950166_2D770936517_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_f0aeb295cb7810a59ec2eedfb3d2e03a_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317201147401_2D1401706302_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a7912d6d862f0030aefb26555e97efaf_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317201403260_2D495666053_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_2d32a0ab18ef42a36f2d29b29514a82c_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317201500151_2D1523296263_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_19c947462da944c31c00c67a6de98fdb_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317201639401_2D1241540328_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -rmr /user/hadoop/djt-user

![timg_pa_quality_100_size_w4096_sec_1512694762_di_6f371b67aa55a6492947e9ca7921e897_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317201715026_2D2029749844_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import --connect jdbc:mysql://192.168.80.200/hive --username hive --password-file /user/hadoop/.password --table djt-user

\[hadoop@djt002 sqoopRunCreate\]$ sqoop import --connect jdbc:mysql://192.168.80.200/hive --username hive --password-file /user/hadoop/.password --table djt-user  
Warning: /usr/local/sqoop/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.  
Please set $HCAT\_HOME to the root of your HCatalog installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.  
Please set $ACCUMULO\_HOME to the root of your Accumulo installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../zookeeper does not exist! Accumulo imports will fail.  
Please set $ZOOKEEPER\_HOME to the root of your Zookeeper installation.  
17/03/18 04:17:10 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6  
SLF4J: Class path contains multiple SLF4J bindings.  
SLF4J: Found binding in \[jar:file:/usr/local/hadoop/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]  
SLF4J: Found binding in \[jar:file:/usr/local/hbase/hbase-1.2.3/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]  
SLF4J: See http://www.slf4j.org/codes.html\#multiple\_bindings for an explanation.  
SLF4J: Actual binding is of type \[org.slf4j.impl.Log4jLoggerFactory\]  
17/03/18 04:17:14 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.  
17/03/18 04:17:14 INFO tool.CodeGenTool: Beginning code generation  
17/03/18 04:17:15 INFO manager.SqlManager: Executing SQL statement: SELECT t.\* FROM \`djt-user\` AS t LIMIT 1  
17/03/18 04:17:15 INFO manager.SqlManager: Executing SQL statement: SELECT t.\* FROM \`djt-user\` AS t LIMIT 1  
17/03/18 04:17:15 INFO orm.CompilationManager: HADOOP\_MAPRED\_HOME is /usr/local/hadoop/hadoop-2.6.0  
Note: /tmp/sqoop-hadoop/compile/38104c9fe28c7f43fdb42c26826dbf91/djt\_user.java uses or overrides a deprecated API.  
Note: Recompile with -Xlint:deprecation for details.  
17/03/18 04:17:21 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/38104c9fe28c7f43fdb42c26826dbf91/djt-user.jar  
17/03/18 04:17:21 WARN manager.MySQLManager: It looks like you are importing from mysql.  
17/03/18 04:17:21 WARN manager.MySQLManager: This transfer can be faster! Use the --direct  
17/03/18 04:17:21 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.  
17/03/18 04:17:21 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)  
17/03/18 04:17:21 INFO mapreduce.ImportJobBase: Beginning import of djt-user  
17/03/18 04:17:21 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar  
17/03/18 04:17:21 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps  
17/03/18 04:17:22 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

17/03/18 04:17:30 INFO db.DBInputFormat: Using read commited transaction isolation  
17/03/18 04:17:30 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(\`id\`), MAX(\`id\`) FROM \`djt-user\`  
17/03/18 04:17:31 INFO mapreduce.JobSubmitter: number of splits:3  
17/03/18 04:17:32 INFO mapreduce.JobSubmitter: Submitting tokens for job: job\_1489767532299\_0002  
17/03/18 04:17:33 INFO impl.YarnClientImpl: Submitted application application\_1489767532299\_0002  
17/03/18 04:17:33 INFO mapreduce.Job: The url to track the job: http://djt002:8088/proxy/application\_1489767532299\_0002/  
17/03/18 04:17:33 INFO mapreduce.Job: Running job: job\_1489767532299\_0002  
17/03/18 04:18:03 INFO mapreduce.Job: Job job\_1489767532299\_0002 running in uber mode : false  
17/03/18 04:18:03 INFO mapreduce.Job: map 0% reduce 0%

17/03/18 04:19:09 INFO mapreduce.Job: map 67% reduce 0%  
17/03/18 04:19:12 INFO mapreduce.Job: map 100% reduce 0%  
17/03/18 04:19:13 INFO mapreduce.Job: Job job\_1489767532299\_0002 completed successfully  
17/03/18 04:19:13 INFO mapreduce.Job: Counters: 30  
File System Counters  
FILE: Number of bytes read=0  
FILE: Number of bytes written=370638  
FILE: Number of read operations=0  
FILE: Number of large read operations=0  
FILE: Number of write operations=0  
HDFS: Number of bytes read=295  
HDFS: Number of bytes written=105  
HDFS: Number of read operations=12  
HDFS: Number of large read operations=0  
HDFS: Number of write operations=6  
Job Counters   
Launched map tasks=3  
Other local map tasks=3  
Total time spent by all maps in occupied slots (ms)=174022  
Total time spent by all reduces in occupied slots (ms)=0  
Total time spent by all map tasks (ms)=174022  
Total vcore-seconds taken by all map tasks=174022  
Total megabyte-seconds taken by all map tasks=178198528  
Map-Reduce Framework  
Map input records=3  
Map output records=3  
Input split bytes=295  
Spilled Records=0  
Failed Shuffles=0  
Merged Map outputs=0  
GC time elapsed (ms)=5172  
CPU time spent (ms)=9510

Physical memory (bytes) snapshot=362741760  
Virtual memory (bytes) snapshot=2535641088  
Total committed heap usage (bytes)=181862400  
File Input Format Counters   
Bytes Read=0  
File Output Format Counters   
Bytes Written=105  
17/03/18 04:19:13 INFO mapreduce.ImportJobBase: Transferred 105 bytes in 111.9157 seconds (0.9382 bytes/sec)  
17/03/18 04:19:13 INFO mapreduce.ImportJobBase: Retrieved 3 records.  
\[hadoop@djt002 sqoopRunCreate\]$

![timg_pa_quality_100_size_w4096_sec_1512694762_di_0190cee48ac77aa9c385a31544fb3932_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317202029463_2D2112966837_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_e7b48494bffec975a09c04a00932f940_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317202111041_2D499302194_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_e608f7d925d097792c8575c3d0949e3f_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317202125135_2D1450561021_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_97c0b79bf32403f4f01d1b22b3e6ce72_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317202222776_2D87625310_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_14bcb753cbe47ecd21c5f18d5fe45e85_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317202550041_2D1485470653_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /user/hadoop/djt-user/part-m-*
    1,王菲,female,36,歌手
    2,谢霆锋,male,30,歌手
    3,周杰伦,male,33,导演
    [hadoop@djt002 ~]$

总结

不指定目录  
    
    sqoop import \ --connect 'jdbc:mysql://192.168.128.200/hive \ --username hive \ --password-file /user/hadoop/.password \ --table djt_user

不指定目录    （推荐这种）
    
    sqoop import \
    --connect 'jdbc:mysql://192.168.128.200/hive?useUnicode=true&characterEncoding=utf-8' \
    --username hive \
    --password-file /user/hadoop/.password \
    --table djt_user

即，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，这里是在/user/hadoop/djt\_user。

（2）指定目录

任意可以指定的。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b5a1bc121cae8c1a2e5087aec3273a57_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317203039995_2D1855560756_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_30003d547a0037e60124697ae223fd55_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317203344745_2D658172514_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*
    1,王菲,female,36,歌手
    2,谢霆锋,male,30,歌手
    3,周杰伦,male,33,导演
    [hadoop@djt002 ~]$

这里，为统一标准和规范化，用数据表djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_740f4cfa88b0996c7fa0dc5b100012e5_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317203439979_2D308484914_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a3d7087c5b76cf1a9d2c02d5048c05cc_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317203458979_2D729033056_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_70baff54da78a561db679296d06b053d_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317203517073_2D908787561_2Epng][]

（3）目录已存在

![timg_pa_quality_100_size_w4096_sec_1512694762_di_6f14c54d554dd56a327b904a3a4cc9f4_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317204403620_2D1174277263_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect jdbc:mysql://192.168.80.200/hive \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user \
    > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir

Warning: /usr/local/sqoop/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.  
Please set $HCAT\_HOME to the root of your HCatalog installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.  
Please set $ACCUMULO\_HOME to the root of your Accumulo installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../zookeeper does not exist! Accumulo imports will fail.  
Please set $ZOOKEEPER\_HOME to the root of your Zookeeper installation.  
17/03/18 04:43:40 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6  
SLF4J: Class path contains multiple SLF4J bindings.  
SLF4J: Found binding in \[jar:file:/usr/local/hadoop/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]  
SLF4J: Found binding in \[jar:file:/usr/local/hbase/hbase-1.2.3/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]  
SLF4J: See http://www.slf4j.org/codes.html\#multiple\_bindings for an explanation.  
SLF4J: Actual binding is of type \[org.slf4j.impl.Log4jLoggerFactory\]  
17/03/18 04:43:45 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.  
17/03/18 04:43:45 INFO tool.CodeGenTool: Beginning code generation  
17/03/18 04:43:46 INFO manager.SqlManager: Executing SQL statement: SELECT t.\* FROM \`djt\_user\` AS t LIMIT 1  
17/03/18 04:43:46 INFO manager.SqlManager: Executing SQL statement: SELECT t.\* FROM \`djt\_user\` AS t LIMIT 1  
17/03/18 04:43:46 INFO orm.CompilationManager: HADOOP\_MAPRED\_HOME is /usr/local/hadoop/hadoop-2.6.0  
Note: /tmp/sqoop-hadoop/compile/1fae17dd362476d95608e216756efa34/djt\_user.java uses or overrides a deprecated API.  
Note: Recompile with -Xlint:deprecation for details.  
17/03/18 04:43:52 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/1fae17dd362476d95608e216756efa34/djt\_user.jar  
17/03/18 04:43:52 INFO tool.ImportTool: Destination directory /sqoop/test/djt\_user deleted.  
17/03/18 04:43:52 WARN manager.MySQLManager: It looks like you are importing from mysql.  
17/03/18 04:43:52 WARN manager.MySQLManager: This transfer can be faster! Use the --direct  
17/03/18 04:43:52 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.

17/03/18 04:43:52 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)  
17/03/18 04:43:52 INFO mapreduce.ImportJobBase: Beginning import of djt\_user  
17/03/18 04:43:52 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar  
17/03/18 04:43:52 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps  
17/03/18 04:43:53 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032  
17/03/18 04:44:02 INFO db.DBInputFormat: Using read commited transaction isolation  
17/03/18 04:44:02 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(\`id\`), MAX(\`id\`) FROM \`djt\_user\`  
17/03/18 04:44:02 INFO mapreduce.JobSubmitter: number of splits:3  
17/03/18 04:44:03 INFO mapreduce.JobSubmitter: Submitting tokens for job: job\_1489767532299\_0005  
17/03/18 04:44:03 INFO impl.YarnClientImpl: Submitted application application\_1489767532299\_0005  
17/03/18 04:44:03 INFO mapreduce.Job: The url to track the job: http://djt002:8088/proxy/application\_1489767532299\_0005/  
17/03/18 04:44:03 INFO mapreduce.Job: Running job: job\_1489767532299\_0005  
17/03/18 04:44:23 INFO mapreduce.Job: Job job\_1489767532299\_0005 running in uber mode : false  
17/03/18 04:44:23 INFO mapreduce.Job: map 0% reduce 0%

17/03/18 04:45:21 INFO mapreduce.Job: map 67% reduce 0%  
17/03/18 04:45:23 INFO mapreduce.Job: map 100% reduce 0%  
17/03/18 04:45:23 INFO mapreduce.Job: Job job\_1489767532299\_0005 completed successfully  
17/03/18 04:45:24 INFO mapreduce.Job: Counters: 30  
File System Counters  
FILE: Number of bytes read=0  
FILE: Number of bytes written=370635  
FILE: Number of read operations=0  
FILE: Number of large read operations=0  
FILE: Number of write operations=0  
HDFS: Number of bytes read=295  
HDFS: Number of bytes written=80  
HDFS: Number of read operations=12  
HDFS: Number of large read operations=0  
HDFS: Number of write operations=6  
Job Counters   
Launched map tasks=3  
Other local map tasks=3  
Total time spent by all maps in occupied slots (ms)=163316  
Total time spent by all reduces in occupied slots (ms)=0  
Total time spent by all map tasks (ms)=163316  
Total vcore-seconds taken by all map tasks=163316  
Total megabyte-seconds taken by all map tasks=167235584  
Map-Reduce Framework  
Map input records=3  
Map output records=3  
Input split bytes=295  
Spilled Records=0  
Failed Shuffles=0  
Merged Map outputs=0  
GC time elapsed (ms)=3240  
CPU time spent (ms)=8480

Physical memory (bytes) snapshot=356696064  
Virtual memory (bytes) snapshot=2535596032  
Total committed heap usage (bytes)=181862400  
File Input Format Counters   
Bytes Read=0  
File Output Format Counters   
Bytes Written=80  
17/03/18 04:45:24 INFO mapreduce.ImportJobBase: Transferred 80 bytes in 91.6189 seconds (0.8732 bytes/sec)  
17/03/18 04:45:24 INFO mapreduce.ImportJobBase: Retrieved 3 records.  
\[hadoop@djt002 sqoopRunCreate\]$

![timg_pa_quality_100_size_w4096_sec_1512694762_di_6d8ac07afdaeb6de8bdd9bc5a195c344_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317204634901_2D62973029_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b783c36880be140acd8cee034f28d75b_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317204641870_2D1064599307_2Epng][]

Sqoop Import 应用场景——控制并行度

（1）控制并行度

默认是4个，当然我这里数据量小，指定1个就行了。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_1517a17e4220cea74858ab114c9ec675_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317205133291_2D1609446237_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -rm  /sqoop/test/djt_user/part-m-*

![timg_pa_quality_100_size_w4096_sec_1512694762_di_5c3b473c799206b3817a0061f38f6939_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317214533151_2D2143735353_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user \
    > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir \
    > -m 1

在这里，可能会遇到这个问题。

# [Sqoop异常解决ERROR tool.ImportTool: Encountered IOException running import job: java.io.IOException: No columns to generate for ClassWriter问题][Sqoop_ERROR tool.ImportTool_ Encountered IOException running import job_ java.io.IOException_ No columns to generate for ClassWriter] #

![timg_pa_quality_100_size_w4096_sec_1512694762_di_46667a41ef423379154b2bcb8cf41c6e_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317214937120_2D1311813870_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_14b6249a2acbb4408b8cb1ff63e4ca75_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317215108838_2D776994484_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*

Sqoop Import 应用场景——控制字段分隔符

（1）控制字段分隔符

注意，默认的控制分段分隔符是逗号，我们这里自定义。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c6d7bb15df401356719ae86b115719a1_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317215700229_2D2089355861_2Epng][]

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_af07c4dcfb44df9041228fbf900368a3_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317215824338_2D386173046_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*
    1@王菲@female@36@歌手
    2@谢霆锋@male@30@歌手
    3@周杰伦@male@33@导演
    [hadoop@djt002 ~]$

（2）手动增量导入

![timg_pa_quality_100_size_w4096_sec_1512694762_di_d3aebc2247916671ded7ef53ca8031dc_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317220239338_2D679598221_2Epng][]

我们加入，4、5和6。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_ce6f8580a2e9758ed78478afcf0397ce_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317220448916_2D1932980517_2Epng][]

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_24af736c0ec1d6a62a5636241230864c_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317220542541_2D1633732505_2Epng][]

（3）自动增量导入

![timg_pa_quality_100_size_w4096_sec_1512694762_di_cb0b037f8023b1f3effa11e596b1352f_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317221230995_2D1130081137_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_d8fff533d92557d10ba4a0cde48380ed_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317224729541_2D1242132200_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop job \
    > --create job_import_djt_user \
    > -- import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user \
    > --target-dir /sqoop/test/djt_user \
    > -m 1 \
    > --fields-terminated-by "@" \
    > --append \
    > --check-column 'id' \
    > --incremental append \
    > --last-value 6
    [hadoop@djt002 sqoopRunCreate]$ sqoop job --exec job_import_djt_user

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

删除某个job

![timg_pa_quality_100_size_w4096_sec_1512694762_di_94dc172ef33050b45f96f8d4e0d77ded_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317224300995_2D1845994775_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop job --delete job_import_djt_user

查看当前可用的job

\[hadoop@djt002 sqoopRunCreate\]$ sqoop job --list

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c8a77de7ef1cd5ee50ce00465269e502_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317225031995_2D1725922359_2Epng][]

查看某个具体job的信息

\[hadoop@djt002 sqoopRunCreate\]$  sqoop job --show job\_import\_djt\_user

![timg_pa_quality_100_size_w4096_sec_1512694762_di_9810e5c3b2fd199b802c732d5d963ae9_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170317225223166_2D17449009_2Epng][]

\[hadoop@djt002 sqoopRunCreate\]$ sqoop job --show  
Warning: /usr/local/sqoop/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.  
Please set $HCAT\_HOME to the root of your HCatalog installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.  
Please set $ACCUMULO\_HOME to the root of your Accumulo installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../zookeeper does not exist! Accumulo imports will fail.  
Please set $ZOOKEEPER\_HOME to the root of your Zookeeper installation.  
17/03/18 06:50:58 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6  
Missing argument for option: show  
\[hadoop@djt002 sqoopRunCreate\]$ clear  
\[hadoop@djt002 sqoopRunCreate\]$ sqoop job --show job\_import\_djt\_user  
Warning: /usr/local/sqoop/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.  
Please set $HCAT\_HOME to the root of your HCatalog installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.  
Please set $ACCUMULO\_HOME to the root of your Accumulo installation.  
Warning: /usr/local/sqoop/sqoop-1.4.6/../zookeeper does not exist! Accumulo imports will fail.  
Please set $ZOOKEEPER\_HOME to the root of your Zookeeper installation.  
17/03/18 06:51:47 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6  
SLF4J: Class path contains multiple SLF4J bindings.  
SLF4J: Found binding in \[jar:file:/usr/local/hadoop/hadoop-2.6.0/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]  
SLF4J: Found binding in \[jar:file:/usr/local/hbase/hbase-1.2.3/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class\]  
SLF4J: See http://www.slf4j.org/codes.html\#multiple\_bindings for an explanation.  
SLF4J: Actual binding is of type \[org.slf4j.impl.Log4jLoggerFactory\]  
Job: job\_import\_djt\_user  
Tool: import  
Options:  
\----------------------------  
verbose = false  
incremental.last.value = 10  
db.connect.string = jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8  
codegen.output.delimiters.escape = 0  
codegen.output.delimiters.enclose.required = false

codegen.input.delimiters.field = 0  
hbase.create.table = false  
hdfs.append.dir = true  
db.table = djt\_user  
codegen.input.delimiters.escape = 0  
import.fetch.size = null  
accumulo.create.table = false  
codegen.input.delimiters.enclose.required = false  
db.username = hive  
reset.onemapper = false  
codegen.output.delimiters.record = 10  
import.max.inline.lob.size = 16777216  
hbase.bulk.load.enabled = false  
hcatalog.create.table = false  
db.clear.staging.table = false  
incremental.col = id  
codegen.input.delimiters.record = 0  
db.password.file = /user/hadoop/.password  
enable.compression = false  
hive.overwrite.table = false  
hive.import = false  
codegen.input.delimiters.enclose = 0  
accumulo.batch.size = 10240000  
hive.drop.delims = false  
codegen.output.delimiters.enclose = 0  
hdfs.delete-target.dir = false

codegen.output.dir = .  
codegen.auto.compile.dir = true  
relaxed.isolation = false  
mapreduce.num.mappers = 1  
accumulo.max.latency = 5000  
import.direct.split.size = 0  
codegen.output.delimiters.field = 64  
export.new.update = UpdateOnly  
incremental.mode = AppendRows  
hdfs.file.format = TextFile  
codegen.compile.dir = /tmp/sqoop-hadoop/compile/d81bf23cb3eb8eb11e7064a16df0b92b  
direct.import = false  
hdfs.target.dir = /sqoop/test/djt\_user  
hive.fail.table.exists = false  
db.batch = false  
\[hadoop@djt002 sqoopRunCreate\]$

Sqoop Import 应用场景——启动压缩

启动压缩

默认是gzip压缩，具体去看官网

![timg_pa_quality_100_size_w4096_sec_1512694762_di_80ab8d039568c7ab811a5bd2747acda7_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318100604370_2D28645877_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > -table djt_user \
    > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir \
    > -m 1 \
    > --fields-terminated-by "@" \
    > -z

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_308588afaf49ff7230555361516b4ddd_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318100708354_2D1549750068_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_04f2017bac17956b3693e6993e9715aa_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318100822448_2D322725849_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*

Sqoop Import 应用场景——导入空值处理

（1）导入空值处理

![timg_pa_quality_100_size_w4096_sec_1512694762_di_5fd83abfcfd9a723c9a424183d3c30de_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318100947198_2D294554899_2Epng][]

先，不加空值处理，看是怎样的。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_8e4647457912b203147b8bf2e3aa7a09_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318101253713_2D585199366_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > -table djt_user \
    > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir \
    > -m 1 \
    > --fields-terminated-by "@"
    >

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c64c76d9cae82c8110df4ee581192d27_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318101342026_2D76139679_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*

所以，一般需要对null进行转换，即需对空值进行处理。比如年龄那列，要么给他假如是18岁定死，要么就是0等。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c080372e7e1690810f5ad3f2e52f2d9d_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318101739323_2D352826193_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > -table djt_user \
    > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir \
    > -m 1 \
    > --fields-terminated-by "@" \
    > --null-non-string "###" \
    > --null-string "###"

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

我这里，将空值null转换成\#\#\#，这个，大家可以根据自己的需要，可以转换成其它的，不多赘述。自行去举一反三。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b1428c362574d47d61c4fe360848221a_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318101911041_2D257399661_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*

下面呢，这个场景，比如，如下，我不需全部的字段导出，非空值的某部分字段呢，该如何操作啊?

Sqoop Import 应用场景——导入部分数据

（1）使用–columns

即，指定某个或某些字段导入

![timg_pa_quality_100_size_w4096_sec_1512694762_di_eb1f37be66743bd7c74b01c97499bb1d_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318102204182_2D774492321_2Epng][]

比如，我这里，指定只导入id和name，当然，你可以去指定更多，我这里只是个参考和带入门的引子实例罢了。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a761d8162a88f7c2f3def2c5dd1666f2_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318102857901_2D1493254228_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > -table djt_user \
    > --columns id,name \ > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir \
    > -m 1 \
    > --fields-terminated-by "@" \
    > --null-non-string "###" \
    > --null-string "###"

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_fda77b4ed91b734d50b5bcd72a8521dc_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318102957307_2D1598946798_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*
    1@王菲
    2@谢霆锋
    3@周杰伦
    4@王力宏
    5@张三
    6@李四
    7@王五
    8@王六
    9@小王
    10@小林
    [hadoop@djt002 ~]$

（2）使用–where

刚是导入指定的字段，也可以用筛选来导入达到目的。

比如，我这里，只想导入sex=female的。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b84e571dc330cf1a54c0b85dd4e81aa5_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318103406166_2D1568093501_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b933bf4f38b482479c8e8fef01cd6f3f_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318103242182_2D1901781537_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user \
    > --where "sex='female'" \ > --target-dir /sqoop/test/djt_user \
    > --delete-target-dir \
    > -m 1 \
    > --fields-terminated-by "@" \
    > --null-non-string "###" \
    > --null-string "###"

这里，djt\_user是在MySQL里，通过Sqoop工具，导入到HDFS里，是在/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b9e3409dc0523a89bc232c885ba17b14_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318103450979_2D64903011_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*
    1@王菲@female@36@歌手
    6@李四@female@18@学生
    9@小王@female@24@hadoop运维
    10@小林@female@30@###
    [hadoop@djt002 ~]$

（3）使用–query

比如，导入比较复杂更实用。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_80d395e076e94f27d0727a6b9bab8824_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318103754088_2D1502659131_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_e0d5907f0edbc32174dc01aaaca8053c_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318104140932_2D467632306_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_651c7736bbb4148f300d5ea261a26240_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318104457370_2D252605012_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*
    2@谢霆锋@male@30@歌手
    6@李四@female@18@学生
    9@小王@female@24@hadoop运维
    10@小林@female@30@###
    [hadoop@djt002 ~]$

注意  
　　若，从MySQL数据库导入数据到HDFS里，出现中断情况了怎么办?

答：好比MapReduce作业丢失一样，有个容错机制。但是这里，我们不用担心，任务中断导致数据重复插入，这个不需担心。

它这里呢，要么就是全部导入才成功，要么就是一条都导不进不成功。

即，Sqoop Import HDFS 里没有“脏数据”的情况发生。

MySQL里的数据通过Sqoop import HDFS（作为扩展）

下面我们看一下 Sqoop 如何使用命令行来导入数据的，其命令行语法如下所示。

sqoop import \\  
\--connect jdbc:mysql://192.168.80.128:3306/db\_hadoop \\  
\--username sqoop \\  
\--password sqoop \\  
\--table user \\  
\--target-dir /junior/sqoop/ \\ //可选，不指定目录，数据默认导入到/user下  
\--where "sex='female'" \\ //可选  
\--as-sequencefile \\ //可选，不指定格式，数据格式默认为 Text 文本格式  
\--num-mappers 10 \\ //可选，这个数值不宜太大  
\--null-string '\\\\N' \\ //可选   
\--null-non-string '\\\\N' \\ //可选

\--connect：指定 JDBC URL。  
\--username/password：mysql 数据库的用户名。  
\--table：要读取的数据库表。  
\--target-dir：将数据导入到指定的 HDFS 目录下，文件名称如果不指定的话，会默认数据库的表名称。  
\--where：过滤从数据库中要导入的数据。  
\--as-sequencefile：指定数据导入数据格式。  
\--num-mappers：指定 Map 任务的并发度。  
\--null-string，--null-non-string：同时使用可以将数据库中的空字段转化为'\\N'，因为数据库中字段为 null，会占用很大的空间。

下面我们介绍几种 Sqoop 数据导入的特殊应用（作为扩展）

1、Sqoop 每次导入数据的时候，不需要把以往的所有数据重新导入 HDFS，只需要把新增的数据导入 HDFS 即可，下面我们来看看如何导入新增数据。

sqoop import \ --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \--username sqoop \ --password sqoop \ --table user \ --incremental append \//代表只导入增量数据--check-column id \//以主键id作为判断条件--last-value 999//导入id大于999的新增数据

上述三个组合使用，可以实现数据的增量导入。

2、Sqoop 数据导入过程中，直接输入明码存在安全隐患，我们可以通过下面两种方式规避这种风险。  
　　1)-P:sqoop 命令行最后使用 -P，此时提示用户输入密码，而且用户输入的密码是看不见的，起到安全保护作用。密码输入正确后，才会执行 sqoop 命令。

sqoop import \
    --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \
    --username sqoop \
    --table user \
    -P

2)--password-file：指定一个密码保存文件，读取密码。我们可以将这个文件设置为只有自己可读的文件，防止密码泄露。

sqoop import \
    --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \
    --username sqoop \
    --table user \
    --password-file my-sqoop-password

二、通过Sqoop Export HDFS里的数据到MySQL  
　　它的功能是将数据从 HDFS 导入关系型数据库表中，其流程图如下所示。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_4f1848711523b43c06e993f323480a2b_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201611_2F855959_2D20161130094401506_2D1657159659_2Epng][]

我们来分析一下 Sqoop 数据导出流程，首先用户输入一个 Sqoop export 命令，它会获取关系型数据库的 schema，  
建立 Hadoop 字段与数据库表字段的映射关系。 然后会将输入命令转化为基于 Map 的 MapReduce作业，  
　　这样 MapReduce作业中有很多 Map 任务，它们并行的从 HDFS 读取数据，并将整个数据拷贝到数据库中。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_146f467fc3804503178fda7f87349c70_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318105314276_2D1571435584_2Epng][]

大家，必须要去看官网！

Sqoop Export 应用场景——直接导出

直接导出

请去看我下面的这篇博客，对你有好处。我不多赘述。

# [SQLyog普通版与SQLyog企业版对比分析][SQLyog_SQLyog] #

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c9e6fe4f4a8e443cdc2b03dd6d2509b3_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318113211776_2D1906460104_2Epng][]

CREATE TABLE djt_user_copy SELECT * FROM djt_user WHERE 1=2;

![timg_pa_quality_100_size_w4096_sec_1512694762_di_703abb75d3327a5962024f8803ac6556_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318112202995_2D1758363934_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop export \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user_copy \
    > --export-dir /sqoop/test/djt_user \
    > --input-fields-terminated-by "@"

这里，HDFS里，是在/sqoop/test/djt\_user，通过Sqoop工具，导出到djt\_user\_copy是在MySQL里。

因为啊，之前，/sqoop/test/djt\_user的数据如下

![timg_pa_quality_100_size_w4096_sec_1512694762_di_5280067242a5efeb3d9b085c1b689443_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318112129854_2D549281554_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_6e46702ecd7140db0573d4ffe04acd5d_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318113524104_2D64023160_2Epng][]

Sqoop Export 应用场景——指定map数

指定map数

Map Task默认是4个

![timg_pa_quality_100_size_w4096_sec_1512694762_di_cc6df105f04a239e40024336ab0d2261_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318114252010_2D1212759630_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_79b948a81d94d299f7ba709d683f4ae4_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318114310073_2D377143181_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a46140170d5e6ebfbe9bcb6ead744de6_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318113805260_2D1422008133_2Epng][]

这里，HDFS里，是在/sqoop/test/djt\_user，通过Sqoop工具，导出到djt\_user\_copy是在MySQL里。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_f098338c58ece0882c981882e6946d2e_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318114536666_2D857265087_2Epng][]

Sqoop Export 应用场景——插入和更新

插入和更新

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b9150cb020a0613cd9e2e03a3aff8833_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318114623307_2D292949850_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_4f2fb97b51f09fc873e87edb6357098e_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318115518901_2D2093473674_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_5b7a7d42fe7a1afc792133907d2eca05_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318115259510_2D1425214797_2Epng][]

这里，HDFS里，是在/sqoop/test/djt\_user，通过Sqoop工具，导出到djt\_user\_copy是在MySQL里。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_75b8fe0607ccd762bd1db6b4f632b2fe_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318115605604_2D663141806_2Epng][]

Sqoop Export 应用场景——事务处理

事务处理

比如，从HDFS里导出到MySQL。这个时候可能会出现意外情况，如出现中断，则会出现“脏数据”重复情况。

则提供了这个事务处理。

即 HDFS  ->   先导出到  中间表（成功才可以，后续导出） -> MySQL

我这里是，     /sqoop/test/djt\_user（在HDFS里）    ->     djt\_user\_copy\_tmp （在MySQL里）  ->    djt\_user\_copy （在MySQL里）

这里，HDFS里，是在/sqoop/test/djt\_user，通过Sqoop工具，导出到djt\_user\_copy是在MySQL里。

注意这个中间表，需要创建djt\_user\_copy\_tmp

![timg_pa_quality_100_size_w4096_sec_1512694762_di_29a21579555eb93cae8b2c5bc2eeb25b_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318120340916_2D1205403045_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_50e3b59b63864f784afc49fed9cc829a_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318120512338_2D1059304186_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_f1c5b66711c98df8eb31c9e1dfca9151_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318120201432_2D1495293006_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_141b3d3d43e99021540bf2ebc44c76d1_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318120209604_2D1930035827_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_9af0e05f9471f49fecd2cb774e977066_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318121016588_2D1334524115_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop export \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user_copy \
    > --staging-table djt_user_copy_tmp \
    > --clear-staging-table \
    > --export-dir /sqoop/test/djt_user \
    > -input-fields-terminated-by "@"

这里，HDFS里，是在/sqoop/test/djt\_user，通过Sqoop工具，先导出到中间表djt\_user\_copy\_tmp是在MySQL里，再继续导出到djt\_user\_copy是在MySQL里。

因为，此刻HDFS里的

![timg_pa_quality_100_size_w4096_sec_1512694762_di_c5148e2781c2412699533c269d86eecb_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318121403370_2D1576669605_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b10b376e115d5ef405837e83eebbf604_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318121416338_2D1623080436_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_922e9f839b85c97d226719eaf01242cd_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318121428729_2D924524380_2Epng][]

再次做测试，假设，我现在，把MySQL里的djt\_user数据导入到HDFS里的/sqoop/test/djt\_user。

![timg_pa_quality_100_size_w4096_sec_1512694762_di_b482c212d9f1a2a43ad2634eacc10a3f_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318121514354_2D701981786_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_75e617f0a2a1f95845036df0ca382dcd_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318122546635_2D74477842_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop import --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' --username hive --password-file /user/hadoop/.password -table djt_user  --target-dir /sqoop/test/djt_user --delete-target-dir -m 1 --fields-terminated-by "@" --null-non-string "###" --null-string "###"

![timg_pa_quality_100_size_w4096_sec_1512694762_di_4e0bb0f6144a7471742f23d9c8006bff_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318122711448_2D919098539_2Epng][]

[hadoop@djt002 ~]$ $HADOOP_HOME/bin/hadoop fs -cat /sqoop/test/djt_user/part-m-*
    1@王菲@female@36@歌手
    2@谢霆锋@male@30@歌手
    3@周杰伦@male@33@导演
    4@王力宏@male@40@演员
    5@张三@male@39@无业游民
    6@李四@female@18@学生
    7@王五@male@34@Java开发工程师
    8@王六@male@45@hadoop工程师
    9@小王@female@24@hadoop运维
    10@小林@female@30@###
    [hadoop@djt002 ~]$

然后，接着，我们把HDFS里的/sqoop/test/djt\_user    导出到  MySQL里的 djt\_user\_copydjt\_user\_copy。

说白了，就是再次做了一下 Sqoop Export 应用场景——事务处理。（自己好好理清思路去）

即 HDFS  ->   先导出到  中间表（成功才可以，后续导出） -> MySQL

我这里是，      /sqoop/test/djt\_user（在HDFS里）    ->     djt\_user\_copy\_tmp （在MySQL里）  ->    djt\_user\_copydjt\_user\_copy （在MySQL里）

![timg_pa_quality_100_size_w4096_sec_1512694762_di_24121ee898b037cd59668654f75a4c60_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318123353307_2D225800598_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_4d6a47e1646f1f3d6de60ca2fc4b1687_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318123041698_2D1731142354_2Epng][]

[hadoop@djt002 sqoopRunCreate]$ sqoop export \
    > --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' \
    > --username hive \
    > --password-file /user/hadoop/.password \
    > --table djt_user_copy \
    > --staging-table djt_user_copy_tmp \
    > --clear-staging-table \
    > --export-dir /sqoop/test/djt_user \
    > -input-fields-terminated-by "@"

这里，HDFS里，是在/sqoop/test/djt\_user，通过Sqoop工具，先导出到中间表djt\_user\_copy\_tmp是在MySQL里，再继续导出到djt\_user\_copy是在MySQL里。

因为，HDFS里的

![timg_pa_quality_100_size_w4096_sec_1512694762_di_68741320291cc539395fd2d1caab3524_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318123202432_2D1674707464_2Epng][]

得到，

![timg_pa_quality_100_size_w4096_sec_1512694762_di_a666f1a9b751a587a43a0f8640477e10_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318123702823_2D1711413558_2Epng][]

Sqoop Export  HDFS 应用场景——字段不对应问题

字段不对应问题

因为，在Sqoop import时，我们有选择性的导入某个字段或某些字段对吧，那么，同样，对于Sqoop export也是一样！

[hadoop@djt002 sqoopRunCreate]$ sqoop import --connect 'jdbc:mysql://192.168.80.200/hive?useUnicode=true&characterEncoding=utf-8' --username hive --password-file /user/hadoop/.password -table djt_user --columns name,sex,age,profile --target-dir /sqoop/test/djt_user --delete-target-dir -m 1 --fields-terminated-by "@" --null-non-string "###" --null-string "###"

比如，HDFS里（的/sqoop/test/djt_user/）有4列，  数据库里（的djt_user_copy）有5列（因为会多出自增键）。那么，如何来处理这个棘手问题呢?

![timg_pa_quality_100_size_w4096_sec_1512694762_di_50dc5f31513fe48f69f78e3285674e8a_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318124410932_2D1393408953_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_6c42d10bcdfaa7261195f9c7d564588e_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318124451791_2D1770241347_2Epng][]

这样来处理，

照样sqoop export里也有 -columns name,sex,age,profile \\

![timg_pa_quality_100_size_w4096_sec_1512694762_di_0d905753c350591967f29c69c81cd3c7_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318124951604_2D195714814_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_9e469676165f16861acd42c07a740519_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318124837573_2D628639204_2Epng][]

![timg_pa_quality_100_size_w4096_sec_1512694762_di_e551485af44e6fb308f7647469595edb_ref_https_3A_2F_2Fwww_2Ecnblogs_2Ecom_2Fzlslch_2Fp_2F6116476_2Ehtml_src_http_3A_2F_2Fimages2015_2Ecnblogs_2Ecom_2Fblog_2F855959_2F201703_2F855959_2D20170318125218416_2D1081440827_2Epng][]

我的这里，自增键呢?/

通过Sqoop Export HDFS里的数据到MySQL（作为扩展）

下面我们看一下 Sqoop 如何使用命令行来导出数据的，其命令行语法如下所示。

sqoop export \
    --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \
    --username sqoop \
    --password sqoop \
    --table user \
    --export-dir user

\--connect：指定 JDBC URL。  
\--username/password：mysql 数据库的用户名和密码。  
\--table：要导入的数据库表。  
\--export-dir：数据在 HDFS 上的存放目录。

下面我们介绍几种 Sqoop 数据导出的特殊应用（作为扩展）

1、Sqoop export 将数据导入数据库，一般情况下是一条一条导入的，这样导入的效率非常低。这时我们可以使用 Sqoop export 的批量导入提高效率，其具体语法如下。

sqoop export \ --Dsqoop.export.records.per.statement=10 \ --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \ --username sqoop \ --password sqoop \ --table user \ --export-dir user \ --batch

\--Dsqoop.export.records.per.statement：指定每次导入10条数据，--batch：指定是批量导入。

2、在实际应用中还存在这样一个问题，比如导入数据的时候，Map Task 执行失败，   
那么该 Map 任务会转移到另外一个节点执行重新运行，这时候之前导入的数据又要重新导入一份，造成数据重复导入。  
因为 Map Task 没有回滚策略，一旦运行失败，已经导入数据库中的数据就无法恢复。  
Sqoop export 提供了一种机制能保证原子性， 使用--staging-table 选项指定临时导入的表。

Sqoop export 导出数据的时候会分为两步：  
　　第一步，将数据导入数据库中的临时表，如果导入期间 Map Task 失败，会删除临时表数据重新导入；  
　　第二步，确认所有 Map Task 任务成功后，会将临时表名称为指定的表名称。

sqoop export \
    --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \
    --username sqoop \
    --password sqoop \
    --table user \
    --staging-table staging_user

3、在 Sqoop 导出数据过程中，如果我们想更新已有数据，可以采取以下两种方式。

1)通过 --update-key id 更新已有数据。

sqoop export \
    --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \
    --username sqoop \
    --password sqoop \
    --table user \
    --update-key id

2)  使用 --update-key id和--update-mode allowinsert 两个选项的情况下，如果数据已经存在，则更新数据，如果数据不存在，则插入新数据记录。

sqoop export \
    --connect jdbc:mysql://192.168.80.128.:3306/db_hadoop \
    --username sqoop \
    --password sqoop \
    --table user \
    --update-key id \
    --update-mode allowinsert

4、如果 HDFS 中的数据量比较大，很多字段并不需要，我们可以使用 --columns 来指定插入某几列数据。

sqoop export \
    --connect jdbc:mysql://192.168.80.128:3306/db_hadoop \
    --username sqoop \
    --password sqoop \
    --table user \
    --column username,sex

5、当导入的字段数据不存在或者为null的时候，我们使用--input-null-string和--input-null-non-string 来处理。

sqoop export \
    --connect jdbc:mysql://129.168.80.128:3306/db_hadoop \
    --username sqoop \
    --password sqoop \
    --table user \
    --input-null-string '\\N' \
    --input-null-non-string '\\N'

推荐博客

[http://sqoop.apache.org/docs/1.4.6/SqoopUserGuide.html][http_sqoop.apache.org_docs_1.4.6_SqoopUserGuide.html] （sqoop官网文档）

# 1.     概述 #

本文档主要对SQOOP的使用进行了说明，参考内容主要来自于Cloudera SQOOP的官方文档。为了用中文更清楚明白地描述各参数的使用含义，本文档几乎所有参数使用说明都经过了我的实际验证而得到。

# 2.     codegen #

将关系数据库表映射为一个java文件、java class类、以及相关的jar包，作用主要是两方面：

1、  将数据库表映射为一个Java文件，在该Java文件中对应有表的各个字段。

2、  生成的Jar和class文件在metastore功能使用时会用到。

基础语句：

sqoop codegen –connect jdbc:mysql://localhost:3306/hive –username root –password 123456 –table TBLS2

# 3.     create-hive-table #

生成与关系数据库表的表结构对应的HIVE表

基础语句：

sqoop create-hive-table –connect jdbc:mysql://localhost:3306/hive -username root -password 123456 –table TBLS –hive-table h\_tbls2

# 4.     eval #

可以快速地使用SQL语句对关系数据库进行操作，这可以使得在使用import这种工具进行数据导入的时候，可以预先了解相关的SQL语句是否正确，并能将结果显示在控制台。

查询示例：

sqoop eval –connect jdbc:mysql://localhost:3306/hive -username root -password 123456 -query “SELECT \* FROM tbls LIMIT 10″

数据插入示例：

sqoop eval –connect jdbc:mysql://localhost:3306/hive -username root -password 123456 -e “INSERT INTO TBLS2

VALUES(100,1375170308,1,0,’hadoop’,0,1,’guest’,’MANAGED\_TABLE’,’abc’,’ddd’)”

\-e、-query这两个参数经过测试，比如后面分别接查询和插入SQL语句，皆可运行无误，如上。

# 5.     export #

从hdfs中导数据到关系数据库中

sqoop export –connect jdbc:mysql://localhost:3306/hive –username root –password

123456  –table TBLS2 –export-dir sqoop/test

# 6.     import #

将数据库表的数据导入到hive中，如果在hive中没有对应的表，则自动生成与数据库表名相同的表。

sqoop import –connect jdbc:mysql://localhost:3306/hive –username root –password

123456 –table user –split-by id –hive-import

–split-by指定数据库表中的主键字段名，在这里为id。

<table style="border-collapse:collapse; border-spacing:0px; max-width:100%; word-break:break-all; border-width:0px 0px 1px; border-style:solid; border-color:rgb(238,238,238) rgb(238,238,238) rgb(237,237,237); font-family:&quot;Open Sans&quot;,Helvetica,Arial,sans-serif; margin:0px 0px 1.71429rem; padding:0px; font-size:0.857143rem; vertical-align:baseline; color:rgb(117,117,117); line-height:2; width:624px"> 
 <tbody style="margin:0px; padding:0px; border:0px; font-size:12px; vertical-align:baseline"> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 参数</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 说明</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –append</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 将数据追加到hdfs中已经存在的dataset中。使用该参数，sqoop将把数据先导入到一个临时目录中，然后重新给文件命名到一个正式的目录中，以避免和该目录中已存在的文件重名。</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –as-avrodatafile</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 将数据导入到一个Avro数据文件中</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –as-sequencefile</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 将数据导入到一个sequence文件中</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –as-textfile</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 将数据导入到一个普通文本文件中，生成该文本文件后，可以在hive中通过sql语句查询出结果。</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –boundary-query &lt;statement&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 边界查询，也就是在导入前先通过SQL查询得到一个结果集，然后导入的数据就是该结果集内的数据，格式如：–boundary-query ‘select id,creationdate from person where id = 3’，表示导入的数据为id=3的记录，或者select min(&lt;split-by&gt;), max(&lt;split-by&gt;) from &lt;table name&gt;，注意查询的字段中不能有数据类型为字符串的字段，否则会报错：java.sql.SQLException: Invalid value for <p style="margin-top:0px; margin-bottom:1.71429rem; padding-top:0px; padding-bottom:0px; font-family:Arial,&quot;Microsoft YaHei&quot;; font-size:0.7rem; color:rgb(51,51,51); line-height:1.71429; word-break:break-all; border:0px; vertical-align:baseline"> </p> <p style="margin-top:0px; margin-bottom:1.71429rem; padding-top:0px; padding-bottom:0px; font-family:Arial,&quot;Microsoft YaHei&quot;; font-size:0.7rem; color:rgb(51,51,51); line-height:1.71429; word-break:break-all; border:0px; vertical-align:baseline"> getLong()</p> <p style="margin-top:0px; margin-bottom:1.71429rem; padding-top:0px; padding-bottom:0px; font-family:Arial,&quot;Microsoft YaHei&quot;; font-size:0.7rem; color:rgb(51,51,51); line-height:1.71429; word-break:break-all; border:0px; vertical-align:baseline"> 目前问题原因还未知</p> <p style="margin-top:0px; margin-bottom:1.71429rem; padding-top:0px; padding-bottom:0px; font-family:Arial,&quot;Microsoft YaHei&quot;; font-size:0.7rem; color:rgb(51,51,51); line-height:1.71429; word-break:break-all; border:0px; vertical-align:baseline"> &nbsp;</p> </td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –columns&lt;col,col,col…&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 指定要导入的字段值，格式如：–columns id,username</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –direct</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 直接导入模式，使用的是关系数据库自带的导入导出工具。官网上是说这样导入会更快</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –direct-split-size</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 在使用上面direct直接导入的基础上，对导入的流按字节数分块，特别是使用直连模式从PostgreSQL导入数据的时候，可以将一个到达设定大小的文件分为几个独立的文件。</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –inline-lob-limit</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 设定大对象数据类型的最大值</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> -m,–num-mappers</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 启动N个map来并行导入数据，默认是4个，最好不要将数字设置为高于集群的节点数</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –query，-e&lt;statement&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 从查询结果中导入数据，该参数使用时必须指定–target-dir、–hive-table，在查询语句中一定要有where条件且在where条件中需要包含$CONDITIONS，示例：–query ‘select * from person where $CONDITIONS ‘ –target-dir <p style="margin-top:0px; margin-bottom:1.71429rem; padding-top:0px; padding-bottom:0px; font-family:Arial,&quot;Microsoft YaHei&quot;; font-size:0.7rem; color:rgb(51,51,51); line-height:1.71429; word-break:break-all; border:0px; vertical-align:baseline"> </p> <p style="margin-top:0px; margin-bottom:1.71429rem; padding-top:0px; padding-bottom:0px; font-family:Arial,&quot;Microsoft YaHei&quot;; font-size:0.7rem; color:rgb(51,51,51); line-height:1.71429; word-break:break-all; border:0px; vertical-align:baseline"> /user/hive/warehouse/person –hive-table person</p> </td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –split-by&lt;column-name&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 表的列名，用来切分工作单元，一般后面跟主键ID</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –table &lt;table-name&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 关系数据库表名，数据从该表中获取</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –target-dir &lt;dir&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 指定hdfs路径</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –warehouse-dir &lt;dir&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 与–target-dir不能同时使用，指定数据导入的存放目录，适用于hdfs导入，不适合导入hive目录</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –where</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 从关系数据库导入数据时的查询条件，示例：–where ‘id = 2′</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> -z,–compress</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 压缩参数，默认情况下数据是没被压缩的，通过该参数可以使用gzip压缩算法对数据进行压缩，适用于SequenceFile, text文本文件, 和Avro文件</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –compression-codec</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> Hadoop压缩编码，默认是gzip</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –null-string &lt;null-string&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 可选参数，如果没有指定，则字符串null将被使用</td> 
  </tr> 
  <tr style="margin:0px; padding:0px; border:0px; vertical-align:baseline"> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> –null-non-string&lt;null-string&gt;</td> 
   <td style="border-width:1px 0px 0px; border-style:solid; border-color:rgb(237,237,237) rgb(238,238,238) rgb(238,238,238); margin:0px; padding:6px 10px 6px 0px; vertical-align:baseline"> 可选参数，如果没有指定，则字符串null将被使用</td> 
  </tr> 
 </tbody> 
</table>

## 增量导入 ##

对incremental参数，如果是以日期作为追加导入的依据，则使用lastmodified，否则就使用append值。

# 7.     import-all-tables #

将数据库里的所有表导入到HDFS中，每个表在hdfs中都对应一个独立的目录。

sqoop import-all-tables –connect jdbc:mysql://localhost:3306/test

sqoop import-all-tables –connect jdbc:mysql://localhost:3306/test –hive-import

# 8.     job #

用来生成一个sqoop的任务，生成后，该任务并不执行，除非使用命令执行该任务。

sqoop job

# 9.     list-databases #

打印出关系数据库所有的数据库名

sqoop list-databases –connect jdbc:mysql://localhost:3306/ -username root -password 123456

# 10.             list-tables #

打印出关系数据库某一数据库的所有表名

sqoop list-tables –connect jdbc:mysql://localhost:3306/zihou -username root -password 123456

# 11.             merge #

将HDFS中不同目录下面的数据合在一起，并存放在指定的目录中，示例如：

sqoop merge –new-data /test/p1/person –onto /test/p2/person –target-dir /test/merged –jar-file /opt/data/sqoop/person/Person.jar –class-name Person –merge-key id

其中，–class-name所指定的class名是对应于Person.jar中的Person类，而Person.jar是通过Codegen生成的

# 12.             metastore #

记录sqoop job的元数据信息，如果不启动metastore实例，则默认的元数据存储目录为：~/.sqoop，如果要更改存储目录，可以在配置文件sqoop-site.xml中进行更改。

metastore实例启动：sqoop metastore

# 13.             version #

显示sqoop版本信息

语句：sqoop version

# 14.             help #

打印sqoop帮助信息

语句：sqoop help

# 15.             公共参数 #

## Hive参数 ##

## 数据库连接参数 ##

## 文件输出参数 ##

用于import场景。

示例如：

sqoop import –connect jdbc:mysql://localhost:3306/test –username root –P –table person –split-by id –check-column id –incremental append  –last-value 1 –enclosed-by ‘\\”‘

–escaped-by \\\# –fields-terminated-by .

## 文件输入参数 ##

对数据格式的解析，用于export场景，与文件输出参数相对应。

示例如：

sqoop export –connect jdbc:mysql://localhost:3306/test –username root –password

123456  –table person2 –export-dir /user/hadoop/person –staging-table person3

–clear-staging-table –input-fields-terminated-by ‘,’

在hdfs中存在某一格式的数据，在将这样的数据导入到关系数据库中时，必须要按照该格式来解析出相应的字段值，比如在hdfs中有这样格式的数据：

3,jimsss,dd@dd.com,1,2013-08-07 16:00:48.0,”hehe”,测试

上面的各字段是以逗号分隔的，那么在解析时，必须要以逗号来解析出各字段值，如：

–input-fields-terminated-by ‘,’

这些内容是从sqoop的官网整理出来的，是1.4.3版本的Document，如果有错误，希望大家指正。

1.使用sqoop导入数据

sqoop import --connect jdbc:mysql://localhost/db --username foo --table TEST

2.账号密码

sqoop import --connect jdbc:mysql://database.example.com/employees \
        --username aaron --password 12345

3.驱动

sqoop import --driver com.microsoft.jdbc.sqlserver.SQLServerDriver \
        --connect <connect-string> ...

4.写sql语句导入的方式

sqoop import \
      --query 'SELECT a.*, b.* FROM a JOIN b on (a.id == b.id) WHERE $CONDITIONS' \
      --split-by a.id --target-dir /user/foo/joinresults

如果是顺序导入的话，可以只开一个线程

sqoop import \
      --query 'SELECT a.*, b.* FROM a JOIN b on (a.id == b.id) WHERE $CONDITIONS' \
      -m 1 --target-dir /user/foo/joinresults

如果where语句中有要用单引号的，就像这样子写就可以啦"SELECT \* FROM x WHERE a='foo' AND \\$CONDITIONS"

5.  1.4.3版本的sqoop不支持复杂的sql语句，不支持or语句

6. --split-by <column-name>

默认是主键，假设有100行数据，它会执行那个SELECT \* FROM sometable WHERE id >= lo AND id < hi, with (lo, hi)  会分为4次导入（0,250），（250,500），(500,750),(750,1001)

如果这个字段不能达到实际的划分区域的效果，可以用别的字段。如果没有索引列或者是组合主键的表，需要手动设置一个划分列。

7. --direct 是为了利用某些数据库本身提供的快速导入导出数据的工具，比如mysql的mysqldump性能比jdbc更好，但是不知大对象的列，使用的时候，那些快速导入的工具的客户端必须的shell脚本的目录下。

8.导入数据到hdfs目录，这个命令会把数据写到/shared/foo/ 目录。

sqoop import --connnect <connect-str> --table foo --warehouse-dir /shared \

或者

sqoop import --connnect <connect-str> --table foo --target-dir /dest \

9.传递参数给快速导入的工具，使用--开头，下面这句命令传递给mysql默认的字符集是latin1。

sqoop import --connect jdbc:mysql://server.foo.com/db --table bar \
        --direct -- --default-character-set=latin1

10.转换为对象

--map-column-java <mapping>  转换为java数据类型

--map-column-hive <mapping>  转转为hive数据类型

11.增加导入

--check-column (col)  Specifies the column to be examined when determining which rows to import.

--incremental (mode)  Specifies how Sqoop determines which rows are new. Legal values for mode include append and lastmodified.

--last-value (value)  Specifies the maximum value of the check column from the previous import.

增加导入支持两种模式append和lastmodified，用--incremental来指定。

12.导入大对象，比如BLOB和CLOB列时需要特殊处理，小于16MB的大对象可以和别的数据一起存储，超过这个值就存储在\_lobs的子目录当中。

它们采用的是为大对象做过优化的存储格式，最大能存储2^63字节的数据，我们可以用--inline-lob-limit参数来指定每个lob文件最大的限制是多少。如果设置为0，则大对象使用外部存储。

13.分隔符、转移字符

下面的这句话

Some string, with a comma.

Another "string with quotes"

使用这句命令导入$ sqoop import --fields-terminated-by , --escaped-by \\\\ --enclosed-by '\\"' ...

会有下面这个结果

"Some string, with a comma.","1","2","3"...

"Another \\"string with quotes\\"","4","5","6"...

使用这句命令导入$ sqoop import --optionally-enclosed-by '\\"' (the rest as above)...

"Some string, with a comma.",1,2,3...

"Another \\"string with quotes\\"",4,5,6...

14.hive导入参数

--hive-home <dir>  重写$HIVE\_HOME

--hive-import          插入数据到hive当中，使用hive的默认分隔符

--hive-overwrite  重写插入

--create-hive-table  建表，如果表已经存在，该操作会报错！

--hive-table <table-name>  设置到hive当中的表名

--hive-drop-import-delims  导入到hive时删除 \\n, \\r, and \\01

--hive-delims-replacement  导入到hive时用自定义的字符替换掉 \\n, \\r, and \\01

--hive-partition-key          hive分区的key

--hive-partition-value <v>  hive分区的值

--map-column-hive <map>          类型匹配，sql类型对应到hive类型

15.hive空值处理

sqoop会自动把NULL转换为null处理，但是hive中默认是把\\N来表示null，因为预先处理不会生效的。我们需要使用 --null-string 和 --null-non-string来处理空值 把\\N转为\\\\N。

sqoop import  ... --null-string '\\N' --null-non-string '\\N'

16.导入数据到hbase

导入的时候加上--hbase-table，它就会把内容导入到hbase当中，默认是用主键作为split列。也可以用--hbase-row-key来指定，列族用--column-family来指定,它不支持--direct。如果不想手动建表或者列族，就用--hbase-create-table参数。

17.代码生成参数，没看懂

--bindir <dir>  Output directory for compiled objects

--class-name <name>  Sets the generated class name. This overrides --package-name. When combined with --jar-file, sets the input class.

--jar-file <file>  Disable code generation; use specified jar

--outdir <dir>  Output directory for generated code

--package-name <name>  Put auto-generated classes in this package

--map-column-java <m>  Override default mapping from SQL type to Java type for configured columns.

18.通过配置文件conf/sqoop-site.xml来配置常用参数

<property>
        <name>property.name</name>
        <value>property.value</value>
     </property>

如果不在这里面配置的话，就需要像这样写命令

sqoop import -D property.name=property.value ...

19.两个特别的参数

sqoop.bigdecimal.format.string  大decimal是否保存为string，如果保存为string就是 0.0000007,否则则为1E7。sqoop.hbase.add.row.key          是否把作为rowkey的列也加到行数据当中，默认是false的。

20.例子

#指定列
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --columns "employee_id,first_name,last_name,job_title"
    #使用8个线程
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        -m 8
    #快速模式
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --direct
    #使用sequencefile作为存储方式
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --class-name com.foocorp.Employee --as-sequencefile
    #分隔符
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --fields-terminated-by '\t' --lines-terminated-by '\n' \
        --optionally-enclosed-by '\"'
    #导入到hive
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --hive-import
    #条件过滤
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --where "start_date > '2010-01-01'"
    #用dept_id作为分个字段
    $ sqoop import --connect jdbc:mysql://db.foo.com/corp --table EMPLOYEES \
        --split-by dept_id
    #追加导入
    $ sqoop import --connect jdbc:mysql://db.foo.com/somedb --table sometable \
        --where "id > 100000" --target-dir /incremental_dataset --append

21.导入所有的表sqoop-import-all-tables

每个表都要有主键，不能使用where条件过滤

sqoop import-all-tables --connect jdbc:mysql://db.foo.com/corp

22.export

我们采用sqoop-export插入数据的时候，如果数据已经存在了，插入会失败。

如果我们使用--update-key，它会认为每个数据都是更新，比如我们使用下面这条语句：

sqoop-export --table foo --update-key id --export-dir /path/to/data --connect …
    　　UPDATE foo SET msg='this is a test', bar=42 WHERE id=0;
    　　UPDATE foo SET msg='some more data', bar=100 WHERE id=1;
    　　...

这样即使找不到它也不会报错。

23.如果存在就更新，不存在就插入

加上这个参数就可以啦--update-mode allowinsert。

24.事务的处理

它会一次statement插入100条数据，然后每100个statement提交一次，所以一次就会提交10000条数据。

25.例子

$ sqoop export --connect jdbc:mysql://db.example.com/foo --table bar  \
        --export-dir /results/bar_data
    
    $ sqoop export --connect jdbc:mysql://db.example.com/foo --table bar  \
        --export-dir /results/bar_data --validate
    
    $ sqoop export --connect jdbc:mysql://db.example.com/foo --call barproc \
        --export-dir /results/bar_data

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[SQLyog_MySQL]: http://www.cnblogs.com/zlslch/p/5971271.html
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[SQLyog_latin1_UTF8]: http://www.cnblogs.com/zlslch/p/6567641.html
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[SQLyog_SQLyog]: http://www.cnblogs.com/zlslch/p/6572293.html
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[http_sqoop.apache.org_docs_1.4.6_SqoopUserGuide.html]: http://sqoop.apache.org/docs/1.4.6/SqoopUserGuide.html