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
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<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
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<value>${yarn.home.dir}/etc/hadoop/fairscheduler.xml</value> 1
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<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
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<value>30720</value> 1
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--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！
