sqoop安装与简单实用

Posted 2021-01-07 ontheway-0518

一，sqoop安装　　 　　

1.解压源码包
2.配置环境变量
3.在bin目录下的
　　/bin/configsqoop 注释掉check报错信息
4.配置conf目录下
　　/conf/sqoop-env.sh 配置hadoop和hive家目录
5.导入依赖的jar包至lib目录下
　　mysql-connector-java-5.1.46-bin.jar
   /share/hadoop/common/hadoop-common-2.7.2.jar
6.连接测试

 

二、命令介绍

 　　

version
import
list-databases
list-tables
create-hive-table　　　　在hive中仅创建表结构

--connect　　　　　　　　　连接url ，在hive-site.xml中配置连接参数　　
--username
--password

--table
--columns
--query　　　　　　　　　　　　　　　　where  and $CONDITIONS (没有就会报错)

--delete-target-dir　　　　　　　　　目标文件若存在在删除
--target-dir
--fields-terminated-by　　　　　　　默认逗号
--lines-terminated-by　　　　　　　　默认换行符
--hive-import 　　　　　　　　　　　　以Hive的方式进行导入
--hive-database 　　　　　　　　　　导入到指定的Hive库中
--external-table-dir <hdfs path>：以外部表的形式创建
--hive-table 　　　　　　　　　　　　导入到指定的Hive表中
--hive-overwrite 　　　　　　　　　　是否覆盖导入
--split-by　　　　　　　　　　　　　　指定分任务的操作列名
-m {d}　　　　　　　　　　　　　　　　指定maptask数目（默认4）

 

三、数据导入

 　　1.数据导入到HDFS　　　　

1.指定命令 -- （声明配置项 - 指定参数）
2.命令语句 -> 翻译成程序 -> 打成jar包
3.将jar包(核心程序 - 依赖jar包)提交至集群
4.简单的数据导出：只有Map阶段 -> 数据源:关系型数据库的数据(结构化的数据)
5.单表数据导出：将数据全部查出，拼接分隔符输出，Reduce阶段原样输出
6.默认使用4个MapTask执行 -> 产生了四个结果文件 -> 通过-m参数实现
7.默认使用第一列作为分割任务的列 -> 1(MIN(id))-100(MAX(id)) -> 确定固定的区间 -> --split-by指定分割列
8.在相应的目录下生成结果 -> 使用SQL(聚合函数运算或UDAF) -> -m 1

 

 　　2.数据导入到HIVE

• 先将数据文件导入到HDFS上，产生一个临时文件
• 成功后将数据上传到HIVE，成功则删除hdfs上的临时文件　

  1.导入至HDFS ->临时文件的路径:当前执行sqoop命令的家目录 -> 目录名称：不指定自动生成/也可以手动指定
  2.导入为HDFS时指定的部分参数会被Hive导入时进行读取(分隔符)
  3.如果目标表不存在则创建(数据表结构信息(数据源的表结构) - 命令指定的相关参数)
  4.如果目标表存在:默认追加导入，数据结构保持一致，目标Hive表的相关参数(分隔符)
  5.导入数据 -> 将中间目录的文件夹下的数据文件移动至内部表目录下
  

   

 　　3.打印信息相关理解

 1 sqoop import --connect jdbc:mysql://sz01:3306/test --username root --password root --table make --delete-target-dir --target-dir /home/haha
 2 18/09/18 11:36:00 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7                                                                                                                                                                        1.sqoop版本
 3 18/09/18 11:36:00 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
 4 18/09/18 11:36:00 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.                                                                                                                    2.获取mysql的结果集
 5 18/09/18 11:36:00 INFO tool.CodeGenTool: Beginning code generation                                                                                                                                                                    3.sql代码生成
 6 18/09/18 11:36:01 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `make` AS t LIMIT 1
 7 18/09/18 11:36:01 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `make` AS t LIMIT 1
 8 18/09/18 11:36:01 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/bigdata/hadoop-2.7.2                                                                                                            4.hadoop-->MR
 9 Note: /tmp/sqoop-bigdata/compile/5b8add033feeb0f72f4b3eac44ff00e9/make.java uses or overrides a deprecated API.
10 Note: Recompile with -Xlint:deprecation for details.
11 18/09/18 11:36:04 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-bigdata/compile/5b8add033feeb0f72f4b3eac44ff00e9/make.jar                                5生成jar文件
12 18/09/18 11:36:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
13 18/09/18 11:36:05 INFO tool.ImportTool: Destination directory /home/haha is not present, hence not deleting.                                                                                6.确认目标路径
14 18/09/18 11:36:05 WARN manager.MySQLManager: It looks like you are importing from mysql.
15 18/09/18 11:36:05 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
16 18/09/18 11:36:05 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
17 18/09/18 11:36:05 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
18 18/09/18 11:36:05 INFO mapreduce.ImportJobBase: Beginning import of make                                                                                                                                                        7.开始导入数据
19 18/09/18 11:36:05 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar                                                                                    8.开始替换MR
20 18/09/18 11:36:05 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
21 18/09/18 11:36:06 INFO client.RMProxy: Connecting to ResourceManager at sz01/192.168.18.130:8032                                                                                                        9.连接RM                                
22 18/09/18 11:36:11 INFO db.DBInputFormat: Using read commited transaction isolation
23 18/09/18 11:36:11 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`id`), MAX(`id`) FROM `make`                                                                                10.划分任务
24 18/09/18 11:36:11 INFO db.IntegerSplitter: Split size: 67; Num splits: 4 from: 1 to: 272
25 18/09/18 11:36:11 INFO mapreduce.JobSubmitter: number of splits:4
26 18/09/18 11:36:11 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1537076834131_0011                                                                                                        11.提交JOB
27 18/09/18 11:36:12 INFO impl.YarnClientImpl: Submitted application application_1537076834131_0011
28 18/09/18 11:36:12 INFO mapreduce.Job: The url to track the job: http://sz01:8088/proxy/application_1537076834131_0011/    
29 18/09/18 11:36:12 INFO mapreduce.Job: Running job: job_1537076834131_0011
30 18/09/18 11:36:27 INFO mapreduce.Job: Job job_1537076834131_0011 running in uber mode : false                                                                                                                12.执行job
31 18/09/18 11:36:27 INFO mapreduce.Job:  map 0% reduce 0%
32 18/09/18 11:36:40 INFO mapreduce.Job:  map 25% reduce 0%                                                                        
33 18/09/18 11:36:41 INFO mapreduce.Job:  map 50% reduce 0%
34 18/09/18 11:36:48 INFO mapreduce.Job:  map 75% reduce 0%
35 18/09/18 11:36:50 INFO mapreduce.Job:  map 100% reduce 0%
36 18/09/18 11:36:51 INFO mapreduce.Job: Job job_1537076834131_0011 completed successfully
37 18/09/18 11:36:52 INFO mapreduce.Job: Counters: 31                                                                                                                                                                                                    13.打印counters
38     File System Counters
39         FILE: Number of bytes read=0
40         FILE: Number of bytes written=548044
41         FILE: Number of read operations=0
42         FILE: Number of large read operations=0
43         FILE: Number of write operations=0
44         HDFS: Number of bytes read=405
45         HDFS: Number of bytes written=2279
46         HDFS: Number of read operations=16
47         HDFS: Number of large read operations=0
48         HDFS: Number of write operations=8
49     Job Counters 
50         Killed map tasks=1
51         Launched map tasks=4
52         Other local map tasks=4
53         Total time spent by all maps in occupied slots (ms)=34012
54         Total time spent by all reduces in occupied slots (ms)=0
55         Total time spent by all map tasks (ms)=34012
56         Total vcore-milliseconds taken by all map tasks=34012
57         Total megabyte-milliseconds taken by all map tasks=34828288
58     Map-Reduce Framework
59         Map input records=184
60         Map output records=184
61         Input split bytes=405
62         Spilled Records=0
63         Failed Shuffles=0
64         Merged Map outputs=0
65         GC time elapsed (ms)=470
66         CPU time spent (ms)=5830
67         Physical memory (bytes) snapshot=417902592
68         Virtual memory (bytes) snapshot=8252805120
69         Total committed heap usage (bytes)=121896960
70     File Input Format Counters 
71         Bytes Read=0
72     File Output Format Counters 
73         Bytes Written=2279
74 18/09/18 11:36:52 INFO mapreduce.ImportJobBase: Transferred 2.2256 KB in 46.2286 seconds (49.2985 bytes/sec)
75 18/09/18 11:36:52 INFO mapreduce.ImportJobBase: Retrieved 184 records.
76 
77 
78 18/09/18 13:24:59 INFO mapreduce.ImportJobBase: Publishing Hive/Hcat import job data to Listeners for table make
79 18/09/18 13:24:59 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `make` AS t LIMIT 1
80 18/09/18 13:24:59 INFO hive.HiveImport: Loading uploaded data into Hive
81 18/09/18 13:25:00 DEBUG hdfs.LeaseRenewer: Lease renewer daemon for [] with renew id 1 executed
82 
83 Logging initialized using configuration in jar:file:/home/bigdata/apache-hive-1.2.2-bin/lib/hive-jdbc-1.2.2-standalone.jar!/hive-log4j.properties
84 OK
85 Time taken: 5.158 seconds
86 Loading data to table user1.make
87 Table user1.make stats: [numFiles=4, totalSize=2279]
88 OK
89 Time taken: 1.244 seconds

View Code

 

四、日志记录

　　1.自己配置
　　　　conf目录下放入log4j.properties -> 日志级别，日志文件产生的目录以及名称
　　　　lib目录下放入log4j相关jar包

　　2.sqoop拉取数据时，实际是MR和HIVE数据导入导出

　　　　可查看hadoop的logs/userlogs 和hive的日志