Scoop是用来实现HDFS文件系统和关系型数据库如MySQL之间数据传输和转换的工具。

从MySQL导出到HDFS可以通过--table, --columns and --where等设置数据抽出的条件。但是同时也只是自由sql语句(Free-form Query )的方式抽出数据。此时我们用--query加sql语句方式自由抽取数据。

1,必须制定目标文件的位置--target-dir

2,必须使用$CONDITIONS关键字,

3,你也可以选择使用--split-by分片(分区,结果分成多个小文件,请参考mapreduce分区)

我们主要讨论$CONDITIONS关键字的作用是什么。

1如果直接输出,这里面是空的条件

2,我们在执行log中发现被替换成了1=0

sqoop import   --connect jdbc:mysql://server74:3306/Server74   --username root  --password 123456  --target-dir /sqoopout2  --m 1 --delete-target-dir 
--query 'select id,name,deg from emp where id>1202 and $CONDITIONS'
[root@server72 sqoop]# sqoop import   --connect jdbc:mysql://server74:3306/Server74   --username root  --password 123456  --target-dir /sqoopout2  
--m 1 --delete-target-dir  --query 'select id,name,deg from emp where id>1202 and $CONDITIONS'
Warning: /usr/local/sqoop/../hbase does not exist! HBase imports will fail.
Please set $HBASE_HOME to the root of your HBase installation.
Warning: /usr/local/sqoop/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /usr/local/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
17/11/10 13:42:14 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
17/11/10 13:42:14 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
17/11/10 13:42:16 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
17/11/10 13:42:16 INFO tool.CodeGenTool: Beginning code generation
17/11/10 13:42:18 INFO manager.SqlManager: Executing SQL statement: select id,name,deg from emp where id>1202 and  (1 = 0)
17/11/10 13:42:18 INFO manager.SqlManager: Executing SQL statement: select id,name,deg from emp where id>1202 and  (1 = 0)
17/11/10 13:42:18 INFO manager.SqlManager: Executing SQL statement: select id,name,deg from emp where id>1202 and  (1 = 0)
17/11/10 13:42:18 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/local/hadoop
Note: /tmp/sqoop-root/compile/ac7745794cf5f0bf5859e7e8369a8c5f/QueryResult.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
17/11/10 13:42:31 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-root/compile/ac7745794cf5f0bf5859e7e8369a8c5f/QueryResult.jar
17/11/10 13:42:33 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/11/10 13:42:41 INFO tool.ImportTool: Destination directory /sqoopout2 deleted.
17/11/10 13:42:41 INFO mapreduce.ImportJobBase: Beginning query import.
17/11/10 13:42:41 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
17/11/10 13:42:41 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
17/11/10 13:42:43 INFO client.RMProxy: Connecting to ResourceManager at server71/192.168.32.71:8032
17/11/10 13:42:58 INFO db.DBInputFormat: Using read commited transaction isolation
17/11/10 13:42:58 INFO mapreduce.JobSubmitter: number of splits:1
17/11/10 13:43:00 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1510279795921_0011
17/11/10 13:43:03 INFO impl.YarnClientImpl: Submitted application application_1510279795921_0011
17/11/10 13:43:04 INFO mapreduce.Job: The url to track the job: http://server71:8088/proxy/application_1510279795921_0011/
17/11/10 13:43:04 INFO mapreduce.Job: Running job: job_1510279795921_0011
17/11/10 13:44:01 INFO mapreduce.Job: Job job_1510279795921_0011 running in uber mode : false
17/11/10 13:44:01 INFO mapreduce.Job:  map 0% reduce 0%
17/11/10 13:44:58 INFO mapreduce.Job:  map 100% reduce 0%
17/11/10 13:45:00 INFO mapreduce.Job: Job job_1510279795921_0011 completed successfully
17/11/10 13:45:01 INFO mapreduce.Job: Counters: 30
    File System Counters
        FILE: Number of bytes read=0
        FILE: Number of bytes written=124473
        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=61
        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)=45099
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=45099
        Total vcore-milliseconds taken by all map tasks=45099
        Total megabyte-milliseconds taken by all map tasks=46181376
    Map-Reduce Framework
        Map input records=3
        Map output records=3
        Input split bytes=87
        Spilled Records=0
        Failed Shuffles=0
        Merged Map outputs=0
        GC time elapsed (ms)=370
        CPU time spent (ms)=6380
        Physical memory (bytes) snapshot=106733568
        Virtual memory (bytes) snapshot=842854400
        Total committed heap usage (bytes)=16982016
    File Input Format Counters
        Bytes Read=0
    File Output Format Counters
        Bytes Written=61
17/11/10 13:45:01 INFO mapreduce.ImportJobBase: Transferred 61 bytes in 139.3429 seconds (0.4378 bytes/sec)
17/11/10 13:45:01 INFO mapreduce.ImportJobBase: Retrieved 3 records. 输出结果查看,发现1202以上的数据被正常抽出
[root@server72 sqoop]# hdfs dfs -cat /sqoopout2/part-m-00000
17/11/10 13:48:48 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
1203,khalil,php dev
1204,prasanth,php dev
1205,kranthi,admin

通过以上过程,我们得知一点:$CONTITONS是linux系统的变量,在执行过程中被赋值为(1=0),虽然实际执行的这个sql很奇怪。

现在正式开始研究CONTITONS到底是什么,所以我们先查看官方文档。

If you want to import the results of a query in parallel, then each map task will need to execute a copy of the query, with results partitioned by bounding conditions inferred by Sqoop. Your query must include the token $CONDITIONS which each Sqoop process will replace with a unique condition expression. You must also select a splitting column with --split-by.

如果你想通过并行的方式导入结果,每个map task需要执行sql查询语句的副本,结果会根据sqoop推测的边界条件分区。query必须包含$CONDITIONS。这样每个scoop程序都会被替换为一个独立的条件。同时你必须指定--split-by.分区

For example:

$ 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

直接理解可能有点困难,我先修改一些条件,大家观察joblog的区别。

sqoop import   --connect jdbc:mysql://server74:3306/Server74   --username root  --password 123456  --target-dir /sqoopout2

      --m 2 --delete-target-dir  --query 'select id,name,deg from emp where id>1202 and $CONDITIONS'

      --split-by id

我按照要求添加了--split-by id 分区,并设置map task数量为2

[root@server72 sqoop]# sqoop import   --connect jdbc:mysql://server74:3306/Server74   --username root 
--password 123456 --target-dir /sqoopout2 --m 2 --delete-target-dir --query 'select id,name,deg from emp where id>1202 and $CONDITIONS' --split-by id
Warning: /usr/local/sqoop/../hbase does not exist! HBase imports will fail.
Please set $HBASE_HOME to the root of your HBase installation.
Warning: /usr/local/sqoop/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /usr/local/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
// :: INFO sqoop.Sqoop: Running Sqoop version: 1.4.
// :: WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
// :: INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
// :: INFO tool.CodeGenTool: Beginning code generation
// :: INFO manager.SqlManager: Executing SQL statement: select id,name,deg from emp where id> and ( = )
// :: INFO manager.SqlManager: Executing SQL statement: select id,name,deg from emp where id> and ( = )
// :: INFO manager.SqlManager: Executing SQL statement: select id,name,deg from emp where id> and ( = )
// :: INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/local/hadoop
Note: /tmp/sqoop-root/compile/1024341fa58082466565e5bd648cb10e/QueryResult.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
// :: INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-root/compile/1024341fa58082466565e5bd648cb10e/QueryResult.jar
// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
// :: INFO tool.ImportTool: Destination directory /sqoopout2 deleted.
// :: INFO mapreduce.ImportJobBase: Beginning query import.
// :: INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
// :: INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
// :: INFO client.RMProxy: Connecting to ResourceManager at server71/192.168.32.71:
// :: INFO db.DBInputFormat: Using read commited transaction isolation
// :: INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(id), MAX(id) FROM (select id,name,deg from emp where id>1202 and (1 = 1) ) AS t1
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1510279795921_0012
// :: INFO impl.YarnClientImpl: Submitted application application_1510279795921_0012
// :: INFO mapreduce.Job: The url to track the job: http://server71:8088/proxy/application_1510279795921_0012/
// :: INFO mapreduce.Job: Running job: job_1510279795921_0012
// :: INFO mapreduce.Job: Job job_1510279795921_0012 running in uber mode : false
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: Job job_1510279795921_0012 completed successfully
// :: INFO mapreduce.Job: Counters:
File System Counters
FILE: Number of bytes read=
FILE: Number of bytes written=
FILE: Number of read operations=
FILE: Number of large read operations=
FILE: Number of write operations=
HDFS: Number of bytes read=
HDFS: Number of bytes written=
HDFS: Number of read operations=
HDFS: Number of large read operations=
HDFS: Number of write operations=
Job Counters
Killed map tasks=
Launched map tasks=
Other local map tasks=
Total time spent by all maps in occupied slots (ms)=
Total time spent by all reduces in occupied slots (ms)=
Total time spent by all map tasks (ms)=
Total vcore-milliseconds taken by all map tasks=
Total megabyte-milliseconds taken by all map tasks=
Map-Reduce Framework
Map input records=
Map output records=
Input split bytes=
Spilled Records=
Failed Shuffles=
Merged Map outputs=
GC time elapsed (ms)=
CPU time spent (ms)=
Physical memory (bytes) snapshot=
Virtual memory (bytes) snapshot=
Total committed heap usage (bytes)=
File Input Format Counters
Bytes Read=
File Output Format Counters
Bytes Written=
												

Sqoop--Free-form Query Imports 自由查询模式下$CONDITIONS关键字的作用的更多相关文章

  1. Query Object--查询对象模式(下)

    回顾 上一篇对模式进行了介绍,并基于ADO.NET进行了实现,虽然现在ORM框架越来越流行,但是很多中小型的公司仍然是使用ADO.NET来进行数据库操作的,随着项目的需求不断增加,业务不断变化,ADO ...

  2. Query Object--查询对象模式(上)

    回顾 上两篇文章主要讲解了我对于数据层的Unit Of Work(工作单元模式)的理解,其中包括了CUD的操作,那么今天就来谈谈R吧,文章包括以下几点: 什么是Query Object 基于SQL的实 ...

  3. Oracle ADF VO排序及VO的查询模式

    常规应用中,当需要使用Table向终端用户展示数据时,Table中数据的显示排序一致性极大程度的影响到了客户体验.通常希望诸如多次查询结果显示顺序相同.插入数据在原数据上方等的实现. ADF为开发人员 ...

  4. 重构改善既有代码设计--重构手法04:Replace Temp with Query (以查询取代临时变量)

    所谓的以查询取代临时变量:就是当你的程序以一个临时变量保存某一个表达式的运算效果.将这个表达式提炼到一个独立函数中.将这个临时变量的所有引用点替换为对新函数的调用.此后,新函数就可以被其他函数调用. ...

  5. iPhone CSS media query(媒体查询)

    iPhone5  iPhone6  iPhone6Plus iPad设备 media query(媒体查询)代码. iPhone < 5: @media screen and (device-a ...

  6. Elasticsearch(入门篇)——Query DSL与查询行为

    ES提供了丰富多彩的查询接口,可以满足各种各样的查询要求.更多内容请参考:ELK修炼之道 Query DSL结构化查询 Query DSL是一个Java开源框架用于构建类型安全的SQL查询语句.采用A ...

  7. Spring Boot 整合 Elasticsearch,实现 function score query 权重分查询

    摘要: 原创出处 www.bysocket.com 「泥瓦匠BYSocket 」欢迎转载,保留摘要,谢谢! 『 预见未来最好的方式就是亲手创造未来 – <史蒂夫·乔布斯传> 』 运行环境: ...

  8. 重构手法之Replace Temp with Query(以查询取代临时变量)

    返回总目录 6.4Replace Temp with Query(以查询取代临时变量) 概要 你的程序以一个临时变量保存某一表达式的运算结果. 将这个表达式提炼到一个独立函数中.将这个临时变量的所有引 ...

  9. GIS-010-ArcGIS JS 三种查询模式(转)

    QueryTask.FindTask.IdentifyTask都是继承自ESRI.ArcGIS.Client.Tasks: 1.QueryTask:是一个进行空间和属性查询的功能类,它可以在某个地图服 ...

随机推荐

  1. Linux基本命令 网络命令

    概述 网络和监控命令类似于这些: hostname, ping, ifconfig, iwconfig, netstat, nslookup, traceroute, finger, telnet, ...

  2. CSS3手风琴下拉菜单

    在线演示 本地下载

  3. bash脚本之读取数据

    题目: 一个tab间隔的文件,读取时一行为一个循环,依次读取每行的参数. 比如第一行为:a b c ,输出为a+b+c #/bin/bash while read id do a=($id) b=${ ...

  4. JDK各个版本的新特性jdk1.5-jdk8[转]

    JDK各个版本的新特性 对于很多刚接触java语言的初学者来说,要了解一门语言,最好的方式就是要能从基础的版本进行了解,升级的过程,以及升级的新特性,这样才能循序渐进的学好一门语言.今天先为大家介绍一 ...

  5. java深入探究10-文件上传组件FileUpload,邮件开发

    1.文件上传组件FileUpload 1)java提供了文件上传的工具包 需要引入:commons-fileupload-1.2.1.jar(文件上床组件核心包) commons-oi-1.4(封装了 ...

  6. org.apache.http.NoHttpResponseException: XX.XX.XX.XX:80 failed to respond

    解决: Finally I fix the issue and it is caused by buffer size. By default, buffer size of httpclient i ...

  7. Java-集合类源码List篇(三)

    前言 前面分析了ArrayList和LinkedList的实现,分别是基于数组和双向链表的List实现.但看之前那张图,还有两个实现类,一个是Vector,另一个是Stack,接下里一起走进它们的源码 ...

  8. review28

    前面介绍了指向文件的输入流和输出流.随机流是既能读文件也能写文件. RandomAccessFile类创建的流称做随机流,与前面的输入.输出流不同的是,RandomAccessFile类既不是Inpu ...

  9. php get_magic_quotes_gpc()

    magic_quotes_gpc函数在php中的作用是判断解析用户输入的数据,如包括有:post.get.cookie过来的数据增加转义字符“\”,以确保这些数据不会引起程序异常,特别是数据库语句因为 ...

  10. 怎么用API网关构建微服务

    选择将应用程序构建为微服务时,需要确定应用程序客户端如何与微服务交互.在单体应用程序中,只有一组端点.而在微服务架构中,每个微服务都会暴露一组通常是细粒度的端点.在本文中,我们将讨论一下这对客户端与应 ...