sqoop的安装与使用

1.什么是Sqoop

Sqoop即 SQL to Hadoop ，是一款方便的在传统型数据库与Hadoop之间进行数据迁移的工具。充分利用MapReduce并行特点以批处理的方式加快传输数据。发展至今主要演化了二大版本号。Sqoop1和Sqoop2。

Sqoop工具是hadoop下连接关系型数据库和Hadoop的桥梁，支持关系型数据库和hive、hdfs。hbase之间数据的相互导入，能够使用全表导入和增量导入。

那么为什么选择Sqoop呢？ 

高效可控的利用资源，任务并行度，超时时间。 数据类型映射与转化，可自己主动进行，用户也可自己定义 支持多种主流数据库。MySQL,Oracle，SQL Server，DB2等等 

2.Sqoop1和Sqoop2对照的异同之处

两个不同的版本号。全然不兼容 版本号号划分差别。Apache版本号：1.4.x(Sqoop1); 1.99.x(Sqoop2)     CDH版本号 : Sqoop-1.4.3-cdh4(Sqoop1) ; Sqoop2-1.99.2-cdh4.5.0 (Sqoop2)Sqoop2比Sqoop1的改进 引入Sqoop server。集中化管理connector等 多种訪问方式：CLI,Web UI，REST API 引入基于角色的安全机制 

3.Sqoop1与Sqoop2的架构图

Sqoop架构图1

Sqoop架构图2

4.Sqoop1与Sqoop2的优缺点

5.Sqoop1的安装部署

5.0 安装环境

hadoop：hadoop-2.3.0-cdh5.1.2

sqoop：sqoop-1.4.4-cdh5.1.2

5.1 下载安装包及解压

tar -zxvf  sqoop-1.4.4-cdh5.1.2.tar.gz

ln -s sqoop-1.4.4-cdh5.1.2  sqoop

5.2 配置环境变量和配置文件

<span style="font-size:18px;">cd sqoop/conf/

cat  sqoop-env-template.sh  >> sqoop-env.sh

vi sqoop-env.sh </span>

在sqoop-env.sh中加入例如以下代码

<span style="font-size:18px;"># Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# included in all the hadoop scripts with source command
# should not be executable directly
# also should not be passed any arguments, since we need original $*

# Set Hadoop-specific environment variables here.

#Set path to where bin/hadoop is available
export HADOOP_COMMON_HOME=/home/hadoop/hadoop

#Set path to where hadoop-*-core.jar is available
export HADOOP_MAPRED_HOME=/home/hadoop/hadoop

#set the path to where bin/hbase is available
export HBASE_HOME=/home/hadoop/hbase

#Set the path to where bin/hive is available
export HIVE_HOME=/home/hadoop/hive

#Set the path for where zookeper config dir is
export ZOOCFGDIR=/home/hadoop/zookeeper
</span>

该配置文件里仅仅有HADOOP_COMMON_HOME的配置是必须的 另外关于hbase和hive的配置 假设用到须要配置 不用的话就不用配置

5.3 加入须要的jar包到lib以下

这里的jar包指的是连接关系型数据库的jar 比方mysql oracle  这些jar包是须要自己加入到lib文件夹以下去的

<span style="font-size:18px;"> cp  ~/hive/lib/mysql-connector-java-5.1.30.jar   ~/sqoop/lib/</span>

5.4 加入环境变量

vi ~/.profile

加入例如以下内容

<span style="font-size:18px;">export SQOOP_HOME=/home/hadoop/sqoop

export SBT_HOME=/home/hadoop/sbt


export PATH=$PATH:$SBT_HOME/bin:$SQOOP_HOME/bin
export CLASSPATH=$CLASSPATH:$SQOOP_HOME/lib
</span>

source ~/.profile使配置文件生效

5.5 測试mysql数据库的连接使用

①连接mysql数据库，列出全部的数据库

<span style="font-size:18px;">hadoop@caozw:~/sqoop/conf$ sqoop list-databases --connect jdbc:mysql://127.0.0.1:3306/ --username root -P
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:15:15 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password: 
14/10/21 18:15:19 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
information_schema
XINGXUNTONG
XINGXUNTONG_HIVE
amon
hive
hmon
mahout
mysql
oozie
performance_schema
realworld
rman
scm
smon
</span>

-P表示输入password 能够直接使用–password来制定password

②mysql数据库的表导入到HDFS

hadoop@caozw:~/sqoop/conf$ sqoop import -m 1  --connect jdbc:mysql://127.0.0.1:3306/realworld --username root -P --table weblogs --target-dir /user/sqoop/test1
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:19:18 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password: 
14/10/21 18:19:21 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
14/10/21 18:19:21 INFO tool.CodeGenTool: Beginning code generation
14/10/21 18:19:22 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:19:22 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:19:22 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/hadoop
Note: /tmp/sqoop-hadoop/compile/15cb67e2b315154cdf02e3a17cf32bbe/weblogs.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
14/10/21 18:19:23 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/15cb67e2b315154cdf02e3a17cf32bbe/weblogs.jar
14/10/21 18:19:23 WARN manager.MySQLManager: It looks like you are importing from mysql.
14/10/21 18:19:23 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
14/10/21 18:19:23 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
14/10/21 18:19:23 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
14/10/21 18:19:23 INFO mapreduce.ImportJobBase: Beginning import of weblogs
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/hadoop-2.3.0-cdh5.1.2/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/hbase-0.98.1-cdh5.1.2/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]
14/10/21 18:19:24 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/10/21 18:19:24 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/10/21 18:19:25 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/10/21 18:19:25 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/10/21 18:19:40 INFO db.DBInputFormat: Using read commited transaction isolation
14/10/21 18:19:41 INFO mapreduce.JobSubmitter: number of splits:1
14/10/21 18:19:42 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1413879907572_0002
14/10/21 18:19:46 INFO impl.YarnClientImpl: Submitted application application_1413879907572_0002
14/10/21 18:19:46 INFO mapreduce.Job: The url to track the job: N/A
14/10/21 18:19:46 INFO mapreduce.Job: Running job: job_1413879907572_0002
14/10/21 18:20:12 INFO mapreduce.Job: Job job_1413879907572_0002 running in uber mode : false
14/10/21 18:20:12 INFO mapreduce.Job:  map 0% reduce 0%
14/10/21 18:20:41 INFO mapreduce.Job:  map 100% reduce 0%
14/10/21 18:20:45 INFO mapreduce.Job: Job job_1413879907572_0002 completed successfully
14/10/21 18:20:46 INFO mapreduce.Job: Counters: 30
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=107189
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=87
		HDFS: Number of bytes written=251130
		HDFS: Number of read operations=4
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters 
		Launched map tasks=1
		Other local map tasks=1
		Total time spent by all maps in occupied slots (ms)=22668
		Total time spent by all reduces in occupied slots (ms)=0
		Total time spent by all map tasks (ms)=22668
		Total vcore-seconds taken by all map tasks=22668
		Total megabyte-seconds taken by all map tasks=23212032
	Map-Reduce Framework
		Map input records=3000
		Map output records=3000
		Input split bytes=87
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=41
		CPU time spent (ms)=1540
		Physical memory (bytes) snapshot=133345280
		Virtual memory (bytes) snapshot=1201442816
		Total committed heap usage (bytes)=76021760
	File Input Format Counters 
		Bytes Read=0
	File Output Format Counters 
		Bytes Written=251130
14/10/21 18:20:46 INFO mapreduce.ImportJobBase: Transferred 245.2441 KB in 80.7974 seconds (3.0353 KB/sec)
14/10/21 18:20:46 INFO mapreduce.ImportJobBase: Retrieved 3000 records.

-m 表示启动几个map任务来读取数据   假设数据库中的表没有主键这个參数是必须设置的并且仅仅能设定为1   否则会提示

14/10/21 18:18:27 ERROR tool.ImportTool: Error during import: No primary key could be found for table weblogs. Please specify one with --split-by or perform a sequential import with '-m 1'.

而这个參数设置为几会直接决定导入的文件在hdfs上面是分成几块的 比方 设置为1 则会产生一个数据文件

14/10/21 18:23:54 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 2 items
-rw-r--r--   1 hadoop supergroup          0 2014-10-21 18:20 /user/sqoop/test1/_SUCCESS
-rw-r--r--   1 hadoop supergroup     251130 2014-10-21 18:20 /user/sqoop/test1/part-m-00000

这里加入主键：

mysql> desc weblogs;
+--------------+-------------+------+-----+---------+-------+
| Field        | Type        | Null | Key | Default | Extra |
+--------------+-------------+------+-----+---------+-------+
| md5          | varchar(32) | YES  |     | NULL    |       |
| url          | varchar(64) | YES  |     | NULL    |       |
| request_date | date        | YES  |     | NULL    |       |
| request_time | time        | YES  |     | NULL    |       |
| ip           | varchar(15) | YES  |     | NULL    |       |
+--------------+-------------+------+-----+---------+-------+
5 rows in set (0.00 sec)


mysql> alter table weblogs add primary key(md5,ip);
Query OK, 3000 rows affected (1.60 sec)
Records: 3000  Duplicates: 0  Warnings: 0

mysql> desc weblogs;
+--------------+-------------+------+-----+---------+-------+
| Field        | Type        | Null | Key | Default | Extra |
+--------------+-------------+------+-----+---------+-------+
| md5          | varchar(32) | NO   | PRI |         |       |
| url          | varchar(64) | YES  |     | NULL    |       |
| request_date | date        | YES  |     | NULL    |       |
| request_time | time        | YES  |     | NULL    |       |
| ip           | varchar(15) | NO   | PRI |         |       |
+--------------+-------------+------+-----+---------+-------+
5 rows in set (0.02 sec)

然后指定-m

hadoop@caozw:~/sqoop/conf$ sqoop import -m 2  --connect jdbc:mysql://127.0.0.1:3306/realworld --username root -P --table weblogs --target-dir /user/sqoop/test2
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:22:40 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password: 
14/10/21 18:24:04 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
14/10/21 18:24:04 INFO tool.CodeGenTool: Beginning code generation
14/10/21 18:24:04 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:24:04 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:24:04 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/hadoop
Note: /tmp/sqoop-hadoop/compile/7061f445f29510afa2b89729126a57b9/weblogs.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
14/10/21 18:24:07 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/7061f445f29510afa2b89729126a57b9/weblogs.jar
14/10/21 18:24:07 WARN manager.MySQLManager: It looks like you are importing from mysql.
14/10/21 18:24:07 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
14/10/21 18:24:07 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
14/10/21 18:24:07 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
14/10/21 18:24:07 ERROR tool.ImportTool: Error during import: No primary key could be found for table weblogs. Please specify one with --split-by or perform a sequential import with '-m 1'.
hadoop@caozw:~/sqoop/conf$ sqoop import -m 2  --connect jdbc:mysql://127.0.0.1:3306/realworld --username root -P --table weblogs --target-dir /user/sqoop/test2
Warning: /home/hadoop/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
14/10/21 18:30:04 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.1.2
Enter password: 
14/10/21 18:30:07 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
14/10/21 18:30:07 INFO tool.CodeGenTool: Beginning code generation
14/10/21 18:30:07 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:30:07 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `weblogs` AS t LIMIT 1
14/10/21 18:30:07 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/hadoop
Note: /tmp/sqoop-hadoop/compile/6dbf2401c1a51b81c5b885e6f7d43137/weblogs.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
14/10/21 18:30:09 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/6dbf2401c1a51b81c5b885e6f7d43137/weblogs.jar
14/10/21 18:30:09 WARN manager.MySQLManager: It looks like you are importing from mysql.
14/10/21 18:30:09 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
14/10/21 18:30:09 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
14/10/21 18:30:09 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
14/10/21 18:30:09 WARN manager.CatalogQueryManager: The table weblogs contains a multi-column primary key. Sqoop will default to the column md5 only for this job.
14/10/21 18:30:09 WARN manager.CatalogQueryManager: The table weblogs contains a multi-column primary key. Sqoop will default to the column md5 only for this job.
14/10/21 18:30:09 INFO mapreduce.ImportJobBase: Beginning import of weblogs
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/hadoop-2.3.0-cdh5.1.2/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/hbase-0.98.1-cdh5.1.2/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]
14/10/21 18:30:09 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/10/21 18:30:09 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/10/21 18:30:10 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/10/21 18:30:10 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/10/21 18:30:17 INFO db.DBInputFormat: Using read commited transaction isolation
14/10/21 18:30:17 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`md5`), MAX(`md5`) FROM `weblogs`
14/10/21 18:30:17 WARN db.TextSplitter: Generating splits for a textual index column.
14/10/21 18:30:17 WARN db.TextSplitter: If your database sorts in a case-insensitive order, this may result in a partial import or duplicate records.
14/10/21 18:30:17 WARN db.TextSplitter: You are strongly encouraged to choose an integral split column.
14/10/21 18:30:18 INFO mapreduce.JobSubmitter: number of splits:4
14/10/21 18:30:18 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1413879907572_0003
14/10/21 18:30:19 INFO impl.YarnClientImpl: Submitted application application_1413879907572_0003
14/10/21 18:30:19 INFO mapreduce.Job: The url to track the job: N/A
14/10/21 18:30:19 INFO mapreduce.Job: Running job: job_1413879907572_0003
14/10/21 18:30:32 INFO mapreduce.Job: Job job_1413879907572_0003 running in uber mode : false
14/10/21 18:30:32 INFO mapreduce.Job:  map 0% reduce 0%
14/10/21 18:31:12 INFO mapreduce.Job:  map 50% reduce 0%
14/10/21 18:31:13 INFO mapreduce.Job:  map 75% reduce 0%
14/10/21 18:31:15 INFO mapreduce.Job:  map 100% reduce 0%
14/10/21 18:31:21 INFO mapreduce.Job: Job job_1413879907572_0003 completed successfully
14/10/21 18:31:22 INFO mapreduce.Job: Counters: 30
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=429312
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=532
		HDFS: Number of bytes written=251209
		HDFS: Number of read operations=16
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=8
	Job Counters 
		Launched map tasks=4
		Other local map tasks=4
		Total time spent by all maps in occupied slots (ms)=160326
		Total time spent by all reduces in occupied slots (ms)=0
		Total time spent by all map tasks (ms)=160326
		Total vcore-seconds taken by all map tasks=160326
		Total megabyte-seconds taken by all map tasks=164173824
	Map-Reduce Framework
		Map input records=3001
		Map output records=3001
		Input split bytes=532
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=806
		CPU time spent (ms)=5450
		Physical memory (bytes) snapshot=494583808
		Virtual memory (bytes) snapshot=4805771264
		Total committed heap usage (bytes)=325058560
	File Input Format Counters 
		Bytes Read=0
	File Output Format Counters 
		Bytes Written=251209
14/10/21 18:31:22 INFO mapreduce.ImportJobBase: Transferred 245.3213 KB in 72.5455 seconds (3.3816 KB/sec)

这里产生的文件跟主键的字段个数以及-m的參数是相关的 大致是-m的值乘以主键字段数，有待考证

hadoop@caozw:~/study/cdh5$ hadoop fs -ls /user/sqoop/test2/
14/10/21 18:32:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 5 items
-rw-r--r--   1 hadoop supergroup          0 2014-10-21 18:31 /user/sqoop/test2/_SUCCESS
-rw-r--r--   1 hadoop supergroup          0 2014-10-21 18:31 /user/sqoop/test2/part-m-00000
-rw-r--r--   1 hadoop supergroup     251130 2014-10-21 18:31 /user/sqoop/test2/part-m-00001
-rw-r--r--   1 hadoop supergroup          0 2014-10-21 18:31 /user/sqoop/test2/part-m-00002
-rw-r--r--   1 hadoop supergroup         79 2014-10-21 18:31 /user/sqoop/test2/part-m-00003

这里的主键设计的不合理导致数据分布不均匀～～  有待改进

③数据导出Oracle和HBase

使用export可将hdfs中数据导入到远程数据库中

           export –connect jdbc:oracle:thin:@192.168.**.**:**:**–username **–password=** -m1table VEHICLE–export-dir /user/root/VEHICLE

向Hbase导入数据

           sqoop import –connect jdbc:oracle:thin:@192.168.**.**:**:**–username**–password=**–m 1 –table VEHICLE –hbase-create-table –hbase-table VEHICLE–hbase-row-key ID –column-family VEHICLEINFO –split-by ID

5.6 測试Mysql数据库的使用

前提：导入mysql jdbc的jar包

①測试数据库连接
sqoop list-databases –connect jdbc:mysql://192.168.10.63 –username root–password 123456
②Sqoop的使用
下面全部的命令每行之后都存在一个空格，不要忘记
（下面6中命令都没有进行过成功測试）

<1>mysql–>hdfs
sqoop export –connect
jdbc:mysql://192.168.10.63/ipj
–username root
–password 123456
–table ipj_flow_user
–export-dir hdfs://192.168.10.63:8020/user/flow/part-m-00000
前提：
(1)hdfs中文件夹/user/flow/part-m-00000必须存在
(2)假设集群设置了压缩方式lzo。那么本机必须得安装且配置成功lzo
(3)hadoop集群中每一个节点都要有对mysql的操作权限

<2>hdfs–>mysql
sqoop import –connect
jdbc:mysql://192.168.10.63/ipj
–table ipj_flow_user

<3>mysql–>hbase
sqoop  import  –connect
jdbc:mysql://192.168.10.63/ipj
–table ipj_flow_user
–hbase-table ipj_statics_test
–hbase-create-table
–hbase-row-key id
–column-family imei

<4>hbase–>mysql
关于将Hbase的数据导入到mysql里，Sqoop并非直接支持的，一般採用例如以下3种方法：
第一种：将Hbase数据扁平化成HDFS文件，然后再由Sqoop导入.
另外一种：将Hbase数据导入Hive表中，然后再导入mysql。

第三种：直接使用Hbase的Java API读取表数据。直接向mysql导入
不须要使用Sqoop。

<5>mysql–>hive
sqoop import –connect
jdbc:mysql://192.168.10.63/ipj
–table hive_table_test
–hive-import 
–hive-table hive_test_table 或–create-hive-table hive_test_table

<6>hive–>mysql
sqoop export –connect
jdbc:mysql://192.168.10.63/ipj
–username hive 
–password 123456 
–table target_table 
–export-dir /user/hive/warehouse/uv/dt=mytable
前提：mysql中表必须存在

③Sqoop其它操作
<1>列出mysql中的全部数据库
sqoop list-databases –connect jdbc:mysql://192.168.10.63:3306/ –usernameroot –password 123456 
<2>列出mysql中某个库下全部表
sqoop list-tables –connect jdbc:mysql://192.168.10.63:3306/ipj –usernameroot –password 123456

6 Sqoop1的性能

 測试数据：

表名：tb_keywords
行数：11628209
数据文件大小：1.4G
測试结果：

从结果上来看，以FILE作为中转方式性能是要高于SQOOP的,原因例如以下：

本质上SQOOP使用的是JDBC，效率不会比MYSQL自带的导入\导出工具效率高以导入数据到DB为例。SQOOP的设计思想是分阶段提交，也就是说如果一个表有1K行。那么它会先读出100行（默认值），然后插入，提交。再读取100行……如此往复

即便如此。SQOOP也是有优势的。比方说使用的便利性，任务运行的容错性等。在一些測试环境中假设须要的话能够考虑把它拿来作为一个工具使用。