1.注意win下直接复制进linux 改一下--等
sqoop-list-databases --connect jdbc:mysql://122.206.79.212:3306/ --username root -P
先看一下有什么数据库，发现有些数据库，能查询到的数据库才能导入，很奇怪。
2.导入到hdfs
sqoop import --connect jdbc:mysql://122.206.79.212:3306/dating --username root --password 123456 --table t_rec_top --driver com.mysql.jdbc.Driver
那个数据库 端口号 账户名 密码 那个表 不需要加上驱动 那没指定导入到hdfs的哪，肯定会有默认位置的
可以看出只有map任务 没有reduce任务
Warning: /home/hxsyl/Spark_Relvant/sqoop-1.4.6.bin__hadoop-2.0.4-alpha/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hxsyl/Spark_Relvant/sqoop-1.4.6.bin__hadoop-2.0.4-alpha/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
17/03/15 11:05:12 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
17/03/15 11:05:12 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
17/03/15 11:05:12 WARN sqoop.ConnFactory: Parameter --driver is set to an explicit driver however appropriate connection manager is not being set (via --connection-manager). Sqoop is going to fall back to org.apache.sqoop.manager.GenericJdbcManager. Please specify explicitly which connection manager should be used next time.
17/03/15 11:05:12 INFO manager.SqlManager: Using default fetchSize of 1000
17/03/15 11:05:12 INFO tool.CodeGenTool: Beginning code generation
17/03/15 11:05:13 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM t_rec_top AS t WHERE 1=0
17/03/15 11:05:13 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM t_rec_top AS t WHERE 1=0
17/03/15 11:05:13 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hxsyl/Spark_Relvant/hadoop-2.6.4/share/hadoop/mapreduce
Note: /tmp/sqoop-hxsyl/compile/ddeeb02cdbd25cddc2662317b89c80f1/t_rec_top.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
17/03/15 11:05:18 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hxsyl/compile/ddeeb02cdbd25cddc2662317b89c80f1/t_rec_top.jar
17/03/15 11:05:18 INFO mapreduce.ImportJobBase: Beginning import of t_rec_top
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hxsyl/Spark_Relvant/hadoop-2.6.4/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hxsyl/Spark_Relvant/hbase-1.2.4/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]
17/03/15 11:05:19 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
17/03/15 11:05:19 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM t_rec_top AS t WHERE 1=0
17/03/15 11:05:21 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
17/03/15 11:05:21 INFO client.RMProxy: Connecting to ResourceManager at CentOSMaster/192.168.58.180:8032
17/03/15 11:05:28 INFO db.DBInputFormat: Using read commited transaction isolation
17/03/15 11:05:28 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(id), MAX(id) FROM t_rec_top
17/03/15 11:05:28 INFO mapreduce.JobSubmitter: number of splits:1
17/03/15 11:05:29 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1489547007191_0001
17/03/15 11:05:30 INFO impl.YarnClientImpl: Submitted application application_1489547007191_0001
17/03/15 11:05:31 INFO mapreduce.Job: The url to track the job: http://CentOSMaster:8088/proxy/application_1489547007191_0001/
17/03/15 11:05:31 INFO mapreduce.Job: Running job: job_1489547007191_0001
17/03/15 11:05:48 INFO mapreduce.Job: Job job_1489547007191_0001 running in uber mode : false
17/03/15 11:05:48 INFO mapreduce.Job: map 0% reduce 0%
17/03/15 11:06:06 INFO mapreduce.Job: map 100% reduce 0%
17/03/15 11:06:07 INFO mapreduce.Job: Job job_1489547007191_0001 completed successfully
17/03/15 11:06:07 INFO mapreduce.Job: Counters: 30
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=127058
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=99
HDFS: Number of bytes written=21
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)=13150
Total time spent by all reduces in occupied slots (ms)=0
Total time spent by all map tasks (ms)=13150
Total vcore-milliseconds taken by all map tasks=13150
Total megabyte-milliseconds taken by all map tasks=13465600
Map-Reduce Framework
Map input records=1
Map output records=1
Input split bytes=99
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=183
CPU time spent (ms)=1200
Physical memory (bytes) snapshot=107761664
Virtual memory (bytes) snapshot=2069635072
Total committed heap usage (bytes)=30474240
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=21
17/03/15 11:06:07 INFO mapreduce.ImportJobBase: Transferred 21 bytes in 46.7701 seconds (0.449 bytes/sec)
17/03/15 11:06:07 INFO mapreduce.ImportJobBase: Retrieved 1 records.
创建一个user/yonhumig的目录，其中t_rec_top里就是我们的数据，不过没有标头，可以看出只是以m，表示map任务就结束了
wc00是配置文件
"AS 1
"License"); 1
${yarn.nodemanager.local-dirs}/usercache/${user}/appcache/application_${appid}. 1
(the 1
--> 3
2.0 1
<!-- 3
</configuration> 1
</description> 1
</property> 15
<?xml 1
<configuration> 1
<description>Amount 1
<description>List 1
<description>Number 1
<description>The 7
<description>Where 1
<description>Whether 1
<description>fair-scheduler 1
<description>the 1
<name>yarn.log-aggregation-enable</name> 1
<name>yarn.nodemanager.aux-services</name> 1
<name>yarn.nodemanager.local-dirs</name> 1
<name>yarn.nodemanager.remote-app-log-dir</name> 1
<name>yarn.nodemanager.resource.cpu-vcores</name> 1
<name>yarn.nodemanager.resource.memory-mb</name> 1
<name>yarn.resourcemanager.address</name> 1
<name>yarn.resourcemanager.admin.address</name> 1
<name>yarn.resourcemanager.hostname</name> 1
<name>yarn.resourcemanager.resource-tracker.address</name> 1
<name>yarn.resourcemanager.scheduler.address</name> 1
<name>yarn.resourcemanager.scheduler.class</name> 1
<name>yarn.resourcemanager.webapp.address</name> 1
<name>yarn.resourcemanager.webapp.https.address</name> 1
<name>yarn.scheduler.fair.allocation.file</name> 1
<property> 15
<value>${yarn.home.dir}/etc/hadoop/fairscheduler.xml</value> 1
<value>${yarn.resourcemanager.hostname}:8030</value> 1
<value>${yarn.resourcemanager.hostname}:8031</value> 1
<value>${yarn.resourcemanager.hostname}:8032</value> 1
<value>${yarn.resourcemanager.hostname}:8033</value> 1
<value>${yarn.resourcemanager.hostname}:8088</value> 1
<value>${yarn.resourcemanager.hostname}:8090</value> 1
<value>/home/hxsyl/Spark_Relvant/yarn/local</value> 1
<value>/tmp/logs</value> 1
<value>12</value> 1
<value>30720</value> 1
<value>CentOSMaster</value> 1
<value>mapreduce_shuffle</value> 1
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value> 1
<value>true</value> 1
ANY 1
An 1
Apache 1
BASIS, 1
CONDITIONS 1
CPU 1
Configs 1
IS" 1
Individual 1
KIND, 1
LICENSE 1
License 3
License, 1
License. 2
Licensed 1
MB, 1
Manager 1
OF 1
OR 1
RM 3
RM.</description> 2
Resource 1
See 2
Site 1
Unless 1
Version 1
WARRANTIES 1
WITHOUT 1
YARN 1
You 1
a 1
a-zA-Z0-9_ 1
accompanying 1
adddress 1
address 4
admin 1
aggregate 1
aggregation</description> 1
agreed 1
allocated 2
an 1
and 2
applicable 1
application's 1
application.</description> 2
applications 1
as 1
at 1
be 4
by 1
called 1
can 3
class 1
compliance 1
conf 1
configuration 1
contain 1
container_${contid}, 1
containers' 1
containers.</description> 2
copy 1
cores 1
directories 1
directories, 1
directory 1
distributed 2
either 1
enable 1
except 1
express 1
file 2
file. 1
files 1
for 3
found 1
governing 1
hostname 1
http 1
http://www.apache.org/licenses/LICENSE-2.0 1
https 1
implied. 1
in 4
in. 1
in: 1
interface 1
interface.</description> 2
is 1
language 1
law 1
limitations 1
localized 2
location</description> 1
log 1
logs 1
manager 1
may 2
memory, 1
name 1
not 2
numbers</description> 1
obtain 1
of 11
on 1
only 1
or 2
permissions 1
physical 1
properties 1
required 1
resource 1
scheduler 1
scheduler.</description> 1
service 1
should 1
software 1
specific 2
start 1
store 1
subdirectories 1
that 2
the 15
this 1
this. 1
to 5
to.</description> 1
under 3
use 2
valid 1
version="1.0"?> 1
web 2
will 2
with 2
work 1
writing, 1
you 1
--target-dir /path 放到那个路径 -m :标书numberMapper
从hdfs上打开的文件可以看出 默认是逗号 --fields-terminated-by '\t' 这个分隔符不是为了写入到hdfs来分割，而是原始数据的分隔符
--columns 'id,account,income' 只导入某些特定的列
符合特定条件的列才被导入，--where "id>2 and id <9"
从多个表查询或者指定查询语句 --query "select * form t_detail where id >5 and $CONDITIONS" $那个必须加
但是如果-m大于1 就需要指定各个Mapper读取几条记录或者找分隔符 --split-by t_detail.id $CONDITIONS就是根据分割的信息找到记录条数，进而切分数据，
建议使用单引号 使用双引号需要转义， --后边跟的是全称 -是简写
单引号与双引号的最大不同在于双引号仍然可以保有变量的内容，但单引号内仅能是
一般字符 ，而不会有特殊符号。我们以底下的例子做说明：假设您定义了一个变量，
name=VBird ，现在想以 name 这个变量的内容定义出 myname 显示 VBird its me 这
个内容，要如何订定呢？
[root@linux ~]# name=VBird
[root@linux ~]# echo $name
VBird
[root@linux ~]# myname="$name its me"
[root@linux ~]# echo $myname
VBird its me
[root@linux ~]# myname='$name its me'
[root@linux ~]# echo $myname
$name its me
发现了吗？没错！使用了单引号的时候，那么 $name 将失去原有的变量内容， 仅为
一般字符的显示型态而已！这里必需要特别小心在意！