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Sqoop学习之路 (一)

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Sqoop学习之路 (一)

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正文

一、概述

sqoop 是 apache 旗下一款“Hadoop 和关系数据库服务器之间传送数据”的工具。

核心的功能有两个:

导入、迁入

导出、迁出

导入数据:MySQL,Oracle 导入数据到 Hadoop 的 HDFS、HIVE、HBASE 等数据存储系统

导出数据:从 Hadoop 的文件系统中导出数据到关系数据库 mysql 等 Sqoop 的本质还是一个命令行工具,和 HDFS,Hive 相比,并没有什么高深的理论。

sqoop:

工具:本质就是迁移数据, 迁移的方式:就是把sqoop的迁移命令转换成MR程序

hive

工具,本质就是执行计算,依赖于HDFS存储数据,把SQL转换成MR程序

二、工作机制

将导入或导出命令翻译成 MapReduce 程序来实现 在翻译出的 MapReduce 中主要是对 InputFormat 和 OutputFormat 进行定制

三、安装

1、前提概述

将来sqoop在使用的时候有可能会跟那些系统或者组件打交道?

HDFS, MapReduce, YARN, ZooKeeper, Hive, HBase, MySQL

sqoop就是一个工具, 只需要在一个节点上进行安装即可。

 

补充一点: 如果你的sqoop工具将来要进行hive或者hbase等等的系统和MySQL之间的交互

 

你安装的SQOOP软件的节点一定要包含以上你要使用的集群或者软件系统的安装包

 

补充一点: 将来要使用的azakban这个软件 除了会调度 hadoop的任务或者hbase或者hive的任务之外, 还会调度sqoop的任务

 

azkaban这个软件的安装节点也必须包含以上这些软件系统的客户端/2、

2、软件下载

下载地址http://mirrors.hust.edu.cn/apache/

sqoop版本说明

绝大部分企业所使用的sqoop的版本都是 sqoop1

sqoop-1.4.6 或者 sqoop-1.4.7 它是 sqoop1

sqoop-1.99.4—-都是 sqoop2

此处使用sqoop-1.4.6版本sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz

3、安装步骤

(1)上传解压缩安装包到指定目录

因为之前hive只是安装在hadoop3机器上,所以sqoop也同样安装在hadoop3机器上

[hadoop@hadoop3 ~]$ tar -zxvf sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz -C apps/

(2)进入到 conf 文件夹,找到 sqoop-env-template.sh,修改其名称为 sqoop-env.sh cd conf

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[hadoop@hadoop3 ~]$ cd apps/
[hadoop@hadoop3 apps]$ ls
apache-hive-2.3.3-bin  hadoop-2.7.5  hbase-1.2.6  sqoop-1.4.6.bin__hadoop-2.0.4-alpha  zookeeper-3.4.10
[hadoop@hadoop3 apps]$ mv sqoop-1.4.6.bin__hadoop-2.0.4-alpha/ sqoop-1.4.6
[hadoop@hadoop3 apps]$ cd sqoop-1.4.6/conf/
[hadoop@hadoop3 conf]$ ls
oraoop-site-template.xml  sqoop-env-template.sh    sqoop-site.xml
sqoop-env-template.cmd    sqoop-site-template.xml
[hadoop@hadoop3 conf]$ mv sqoop-env-template.sh sqoop-env.sh
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(3)修改 sqoop-env.sh

[hadoop@hadoop3 conf]$ vi sqoop-env.sh 
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export HADOOP_COMMON_HOME=/home/hadoop/apps/hadoop-2.7.5

#Set path to where hadoop-*-core.jar is available
export HADOOP_MAPRED_HOME=/home/hadoop/apps/hadoop-2.7.5

#set the path to where bin/hbase is available
export HBASE_HOME=/home/hadoop/apps/hbase-1.2.6

#Set the path to where bin/hive is available
export HIVE_HOME=/home/hadoop/apps/apache-hive-2.3.3-bin

#Set the path for where zookeper config dir is
export ZOOCFGDIR=/home/hadoop/apps/zookeeper-3.4.10/conf
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为什么在sqoop-env.sh 文件中会要求分别进行 common和mapreduce的配置呢???

在apache的hadoop的安装中;四大组件都是安装在同一个hadoop_home中的

但是在CDH, HDP中, 这些组件都是可选的。

在安装hadoop的时候,可以选择性的只安装HDFS或者YARN,

CDH,HDP在安装hadoop的时候,会把HDFS和MapReduce有可能分别安装在不同的地方。

(4)加入 mysql 驱动包到 sqoop1.4.6/lib 目录下

[hadoop@hadoop3 ~]$ cp mysql-connector-java-5.1.40-bin.jar apps/sqoop-1.4.6/lib/

(5)配置系统环境变量

[hadoop@hadoop3 ~]$ vi .bashrc 
#Sqoop
export SQOOP_HOME=/home/hadoop/apps/sqoop-1.4.6
export PATH=$PATH:$SQOOP_HOME/bin

保存退出使其立即生效

[hadoop@hadoop3 ~]$ source .bashrc 

(6)验证安装是否成功

sqoop-version 或者 sqoop version

四、Sqoop的基本命令

基本操作

首先,我们可以使用 sqoop help 来查看,sqoop 支持哪些命令

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[hadoop@hadoop3 ~]$ sqoop help
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 13:37:19 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
usage: sqoop COMMAND [ARGS]

Available commands:
  codegen            Generate code to interact with database records
  create-hive-table  Import a table definition into Hive
  eval               Evaluate a SQL statement and display the results
  export             Export an HDFS directory to a database table
  help               List available commands
  import             Import a table from a database to HDFS
  import-all-tables  Import tables from a database to HDFS
  import-mainframe   Import datasets from a mainframe server to HDFS
  job                Work with saved jobs
  list-databases     List available databases on a server
  list-tables        List available tables in a database
  merge              Merge results of incremental imports
  metastore          Run a standalone Sqoop metastore
  version            Display version information

See 'sqoop help COMMAND' for information on a specific command.
[hadoop@hadoop3 ~]$
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然后得到这些支持了的命令之后,如果不知道使用方式,可以使用 sqoop command 的方式 来查看某条具体命令的使用方式,比如:

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[hadoop@hadoop3 ~]$ sqoop help import
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 13:38:29 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
usage: sqoop import [GENERIC-ARGS] [TOOL-ARGS]

Common arguments:
   --connect <jdbc-uri>                         Specify JDBC connect
                                                string
   --connection-manager <class-name>            Specify connection manager
                                                class name
   --connection-param-file <properties-file>    Specify connection
                                                parameters file
   --driver <class-name>                        Manually specify JDBC
                                                driver class to use
   --hadoop-home <hdir>                         Override
                                                $HADOOP_MAPRED_HOME_ARG
   --hadoop-mapred-home <dir>                   Override
                                                $HADOOP_MAPRED_HOME_ARG
   --help                                       Print usage instructions
-P                                              Read password from console
   --password <password>                        Set authentication
                                                password
   --password-alias <password-alias>            Credential provider
                                                password alias
   --password-file <password-file>              Set authentication
                                                password file path
   --relaxed-isolation                          Use read-uncommitted
                                                isolation for imports
   --skip-dist-cache                            Skip copying jars to
                                                distributed cache
   --username <username>                        Set authentication
                                                username
   --verbose                                    Print more information
                                                while working

Import control arguments:
   --append                                                   Imports data
                                                              in append
                                                              mode
   --as-avrodatafile                                          Imports data
                                                              to Avro data
                                                              files
   --as-parquetfile                                           Imports data
                                                              to Parquet
                                                              files
   --as-sequencefile                                          Imports data
                                                              to
                                                              SequenceFile
                                                              s
   --as-textfile                                              Imports data
                                                              as plain
                                                              text
                                                              (default)
   --autoreset-to-one-mapper                                  Reset the
                                                              number of
                                                              mappers to
                                                              one mapper
                                                              if no split
                                                              key
                                                              available
   --boundary-query <statement>                               Set boundary
                                                              query for
                                                              retrieving
                                                              max and min
                                                              value of the
                                                              primary key
   --columns <col,col,col...>                                 Columns to
                                                              import from
                                                              table
   --compression-codec <codec>                                Compression
                                                              codec to use
                                                              for import
   --delete-target-dir                                        Imports data
                                                              in delete
                                                              mode
   --direct                                                   Use direct
                                                              import fast
                                                              path
   --direct-split-size <n>                                    Split the
                                                              input stream
                                                              every 'n'
                                                              bytes when
                                                              importing in
                                                              direct mode
-e,--query <statement>                                        Import
                                                              results of
                                                              SQL
                                                              'statement'
   --fetch-size <n>                                           Set number
                                                              'n' of rows
                                                              to fetch
                                                              from the
                                                              database
                                                              when more
                                                              rows are
                                                              needed
   --inline-lob-limit <n>                                     Set the
                                                              maximum size
                                                              for an
                                                              inline LOB
-m,--num-mappers <n>                                          Use 'n' map
                                                              tasks to
                                                              import in
                                                              parallel
   --mapreduce-job-name <name>                                Set name for
                                                              generated
                                                              mapreduce
                                                              job
   --merge-key <column>                                       Key column
                                                              to use to
                                                              join results
   --split-by <column-name>                                   Column of
                                                              the table
                                                              used to
                                                              split work
                                                              units
   --table <table-name>                                       Table to
                                                              read
   --target-dir <dir>                                         HDFS plain
                                                              table
                                                              destination
   --validate                                                 Validate the
                                                              copy using
                                                              the
                                                              configured
                                                              validator
   --validation-failurehandler <validation-failurehandler>    Fully
                                                              qualified
                                                              class name
                                                              for
                                                              ValidationFa
                                                              ilureHandler
   --validation-threshold <validation-threshold>              Fully
                                                              qualified
                                                              class name
                                                              for
                                                              ValidationTh
                                                              reshold
   --validator <validator>                                    Fully
                                                              qualified
                                                              class name
                                                              for the
                                                              Validator
   --warehouse-dir <dir>                                      HDFS parent
                                                              for table
                                                              destination
   --where <where clause>                                     WHERE clause
                                                              to use
                                                              during
                                                              import
-z,--compress                                                 Enable
                                                              compression

Incremental import arguments:
   --check-column <column>        Source column to check for incremental
                                  change
   --incremental <import-type>    Define an incremental import of type
                                  'append' or 'lastmodified'
   --last-value <value>           Last imported value in the incremental
                                  check column

Output line formatting arguments:
   --enclosed-by <char>               Sets a required field enclosing
                                      character
   --escaped-by <char>                Sets the escape character
   --fields-terminated-by <char>      Sets the field separator character
   --lines-terminated-by <char>       Sets the end-of-line character
   --mysql-delimiters                 Uses MySQL's default delimiter set:
                                      fields: ,  lines: \n  escaped-by: \
                                      optionally-enclosed-by: '
   --optionally-enclosed-by <char>    Sets a field enclosing character

Input parsing arguments:
   --input-enclosed-by <char>               Sets a required field encloser
   --input-escaped-by <char>                Sets the input escape
                                            character
   --input-fields-terminated-by <char>      Sets the input field separator
   --input-lines-terminated-by <char>       Sets the input end-of-line
                                            char
   --input-optionally-enclosed-by <char>    Sets a field enclosing
                                            character

Hive arguments:
   --create-hive-table                         Fail if the target hive
                                               table exists
   --hive-database <database-name>             Sets the database name to
                                               use when importing to hive
   --hive-delims-replacement <arg>             Replace Hive record \0x01
                                               and row delimiters (\n\r)
                                               from imported string fields
                                               with user-defined string
   --hive-drop-import-delims                   Drop Hive record \0x01 and
                                               row delimiters (\n\r) from
                                               imported string fields
   --hive-home <dir>                           Override $HIVE_HOME
   --hive-import                               Import tables into Hive
                                               (Uses Hive's default
                                               delimiters if none are
                                               set.)
   --hive-overwrite                            Overwrite existing data in
                                               the Hive table
   --hive-partition-key <partition-key>        Sets the partition key to
                                               use when importing to hive
   --hive-partition-value <partition-value>    Sets the partition value to
                                               use when importing to hive
   --hive-table <table-name>                   Sets the table name to use
                                               when importing to hive
   --map-column-hive <arg>                     Override mapping for
                                               specific column to hive
                                               types.

HBase arguments:
   --column-family <family>    Sets the target column family for the
                               import
   --hbase-bulkload            Enables HBase bulk loading
   --hbase-create-table        If specified, create missing HBase tables
   --hbase-row-key <col>       Specifies which input column to use as the
                               row key
   --hbase-table <table>       Import to <table> in HBase

HCatalog arguments:
   --hcatalog-database <arg>                        HCatalog database name
   --hcatalog-home <hdir>                           Override $HCAT_HOME
   --hcatalog-partition-keys <partition-key>        Sets the partition
                                                    keys to use when
                                                    importing to hive
   --hcatalog-partition-values <partition-value>    Sets the partition
                                                    values to use when
                                                    importing to hive
   --hcatalog-table <arg>                           HCatalog table name
   --hive-home <dir>                                Override $HIVE_HOME
   --hive-partition-key <partition-key>             Sets the partition key
                                                    to use when importing
                                                    to hive
   --hive-partition-value <partition-value>         Sets the partition
                                                    value to use when
                                                    importing to hive
   --map-column-hive <arg>                          Override mapping for
                                                    specific column to
                                                    hive types.

HCatalog import specific options:
   --create-hcatalog-table            Create HCatalog before import
   --hcatalog-storage-stanza <arg>    HCatalog storage stanza for table
                                      creation

Accumulo arguments:
   --accumulo-batch-size <size>          Batch size in bytes
   --accumulo-column-family <family>     Sets the target column family for
                                         the import
   --accumulo-create-table               If specified, create missing
                                         Accumulo tables
   --accumulo-instance <instance>        Accumulo instance name.
   --accumulo-max-latency <latency>      Max write latency in milliseconds
   --accumulo-password <password>        Accumulo password.
   --accumulo-row-key <col>              Specifies which input column to
                                         use as the row key
   --accumulo-table <table>              Import to <table> in Accumulo
   --accumulo-user <user>                Accumulo user name.
   --accumulo-visibility <vis>           Visibility token to be applied to
                                         all rows imported
   --accumulo-zookeepers <zookeepers>    Comma-separated list of
                                         zookeepers (host:port)

Code generation arguments:
   --bindir <dir>                        Output directory for compiled
                                         objects
   --class-name <name>                   Sets the generated class name.
                                         This overrides --package-name.
                                         When combined with --jar-file,
                                         sets the input class.
   --input-null-non-string <null-str>    Input null non-string
                                         representation
   --input-null-string <null-str>        Input null string representation
   --jar-file <file>                     Disable code generation; use
                                         specified jar
   --map-column-java <arg>               Override mapping for specific
                                         columns to java types
   --null-non-string <null-str>          Null non-string representation
   --null-string <null-str>              Null string representation
   --outdir <dir>                        Output directory for generated
                                         code
   --package-name <name>                 Put auto-generated classes in
                                         this package

Generic Hadoop command-line arguments:
(must preceed any tool-specific arguments)
Generic options supported are
-conf <configuration file>     specify an application configuration file
-D <property=value>            use value for given property
-fs <local|namenode:port>      specify a namenode
-jt <local|resourcemanager:port>    specify a ResourceManager
-files <comma separated list of files>    specify comma separated files to be copied to the map reduce cluster
-libjars <comma separated list of jars>    specify comma separated jar files to include in the classpath.
-archives <comma separated list of archives>    specify comma separated archives to be unarchived on the compute machines.

The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]

At minimum, you must specify --connect and --table
Arguments to mysqldump and other subprograms may be supplied
after a '--' on the command line.
[hadoop@hadoop3 ~]$
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示例

列出MySQL数据有哪些数据库

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[hadoop@hadoop3 ~]$ sqoop list-databases \
> --connect jdbc:mysql://hadoop1:3306/ \
> --username root \
> --password root
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 13:43:51 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
18/04/12 13:43:51 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/04/12 13:43:51 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
information_schema
hivedb
mysql
performance_schema
test
[hadoop@hadoop3 ~]$
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列出MySQL中的某个数据库有哪些数据表:


[hadoop@hadoop3 ~]$ sqoop list-tables \
–connect jdbc:mysql://hadoop1:3306/mysql \
–username root \
–password root

 

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[hadoop@hadoop3 ~]$ sqoop list-tables \
> --connect jdbc:mysql://hadoop1:3306/mysql \
> --username root \
> --password root
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 13:46:21 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
18/04/12 13:46:21 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/04/12 13:46:21 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
columns_priv
db
event
func
general_log
help_category
help_keyword
help_relation
help_topic
innodb_index_stats
innodb_table_stats
ndb_binlog_index
plugin
proc
procs_priv
proxies_priv
servers
slave_master_info
slave_relay_log_info
slave_worker_info
slow_log
tables_priv
time_zone
time_zone_leap_second
time_zone_name
time_zone_transition
time_zone_transition_type
user
[hadoop@hadoop3 ~]$
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创建一张跟mysql中的help_keyword表一样的hive表hk:

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sqoop create-hive-table \
--connect jdbc:mysql://hadoop1:3306/mysql \
--username root \
--password root \
--table help_keyword \
--hive-table hk
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[hadoop@hadoop3 ~]$ sqoop create-hive-table \
> --connect jdbc:mysql://hadoop1:3306/mysql \
> --username root \
> --password root \
> --table help_keyword \
> --hive-table hk
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 13:50:20 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
18/04/12 13:50:20 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/04/12 13:50:20 INFO tool.BaseSqoopTool: Using Hive-specific delimiters for output. You can override
18/04/12 13:50:20 INFO tool.BaseSqoopTool: delimiters with --fields-terminated-by, etc.
18/04/12 13:50:20 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
18/04/12 13:50:21 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM help_keyword AS t LIMIT 1
18/04/12 13:50:21 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM help_keyword AS t LIMIT 1
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/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]
18/04/12 13:50:23 INFO hive.HiveImport: Loading uploaded data into Hive
18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Class path contains multiple SLF4J bindings.
18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/home/hadoop/apps/apache-hive-2.3.3-bin/lib/log4j-slf4j-impl-2.6.2.jar!/org/slf4j/impl/StaticLoggerBinder.class]
18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
18/04/12 13:50:34 INFO hive.HiveImport: SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory]
18/04/12 13:50:36 INFO hive.HiveImport: 
18/04/12 13:50:36 INFO hive.HiveImport: Logging initialized using configuration in jar:file:/home/hadoop/apps/apache-hive-2.3.3-bin/lib/hive-common-2.3.3.jar!/hive-log4j2.properties Async: true
18/04/12 13:50:50 INFO hive.HiveImport: OK
18/04/12 13:50:50 INFO hive.HiveImport: Time taken: 11.651 seconds
18/04/12 13:50:51 INFO hive.HiveImport: Hive import complete.
[hadoop@hadoop3 ~]$
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五、Sqoop的数据导入

“导入工具”导入单个表从 RDBMS 到 HDFS。表中的每一行被视为 HDFS 的记录。所有记录 都存储为文本文件的文本数据(或者 Avro、sequence 文件等二进制数据)

1、从RDBMS导入到HDFS中

语法格式

sqoop import (generic-args) (import-args)

常用参数

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--connect <jdbc-uri> jdbc 连接地址
--connection-manager <class-name> 连接管理者
--driver <class-name> 驱动类
--hadoop-mapred-home <dir> $HADOOP_MAPRED_HOME
--help help 信息
-P 从命令行输入密码
--password <password> 密码
--username <username> 账号
--verbose 打印流程信息
--connection-param-file <filename> 可选参数
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示例

普通导入:导入mysql库中的help_keyword的数据到HDFS上

导入的默认路径:/user/hadoop/help_keyword

复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/mysql   \
--username root  \
--password root   \
--table help_keyword   \
-m 1
复制代码

复制代码
[hadoop@hadoop3 ~]$ sqoop import   \
> --connect jdbc:mysql://hadoop1:3306/mysql   \
> --username root  \
> --password root   \
> --table help_keyword   \
> -m 1
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 13:53:48 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
18/04/12 13:53:48 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/04/12 13:53:48 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
18/04/12 13:53:48 INFO tool.CodeGenTool: Beginning code generation
18/04/12 13:53:49 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM help_keyword AS t LIMIT 1
18/04/12 13:53:49 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM help_keyword AS t LIMIT 1
18/04/12 13:53:49 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/apps/hadoop-2.7.5
注: /tmp/sqoop-hadoop/compile/979d87b9521d0a09ee6620060a112d60/help_keyword.java使用或覆盖了已过时的 API。
注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。
18/04/12 13:53:51 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/979d87b9521d0a09ee6620060a112d60/help_keyword.jar
18/04/12 13:53:51 WARN manager.MySQLManager: It looks like you are importing from mysql.
18/04/12 13:53:51 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
18/04/12 13:53:51 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
18/04/12 13:53:51 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
18/04/12 13:53:51 INFO mapreduce.ImportJobBase: Beginning import of help_keyword
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/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]
18/04/12 13:53:52 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
18/04/12 13:53:53 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
18/04/12 13:53:58 INFO db.DBInputFormat: Using read commited transaction isolation
18/04/12 13:53:58 INFO mapreduce.JobSubmitter: number of splits:1
18/04/12 13:53:59 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1523510178850_0001
18/04/12 13:54:00 INFO impl.YarnClientImpl: Submitted application application_1523510178850_0001
18/04/12 13:54:00 INFO mapreduce.Job: The url to track the job: http://hadoop3:8088/proxy/application_1523510178850_0001/
18/04/12 13:54:00 INFO mapreduce.Job: Running job: job_1523510178850_0001
18/04/12 13:54:17 INFO mapreduce.Job: Job job_1523510178850_0001 running in uber mode : false
18/04/12 13:54:17 INFO mapreduce.Job:  map 0% reduce 0%
18/04/12 13:54:33 INFO mapreduce.Job:  map 100% reduce 0%
18/04/12 13:54:34 INFO mapreduce.Job: Job job_1523510178850_0001 completed successfully
18/04/12 13:54:35 INFO mapreduce.Job: Counters: 30
    File System Counters
        FILE: Number of bytes read=0
        FILE: Number of bytes written=142965
        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=8264
        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)=12142
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=12142
        Total vcore-milliseconds taken by all map tasks=12142
        Total megabyte-milliseconds taken by all map tasks=12433408
    Map-Reduce Framework
        Map input records=619
        Map output records=619
        Input split bytes=87
        Spilled Records=0
        Failed Shuffles=0
        Merged Map outputs=0
        GC time elapsed (ms)=123
        CPU time spent (ms)=1310
        Physical memory (bytes) snapshot=93212672
        Virtual memory (bytes) snapshot=2068234240
        Total committed heap usage (bytes)=17567744
    File Input Format Counters 
        Bytes Read=0
    File Output Format Counters 
        Bytes Written=8264
18/04/12 13:54:35 INFO mapreduce.ImportJobBase: Transferred 8.0703 KB in 41.8111 seconds (197.6507 bytes/sec)
18/04/12 13:54:35 INFO mapreduce.ImportJobBase: Retrieved 619 records.
[hadoop@hadoop3 ~]$
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查看导入的文件

[hadoop@hadoop4 ~]$ hadoop fs -cat /user/hadoop/help_keyword/part-m-00000

导入: 指定分隔符和导入路径

 

复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/mysql   \
--username root  \
--password root   \
--table help_keyword   \
--target-dir /user/hadoop11/my_help_keyword1  \
--fields-terminated-by '\t'  \
-m 2
复制代码

 

导入数据:带where条件

复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/mysql   \
--username root  \
--password root   \
--where "name='STRING' " \
--table help_keyword   \
--target-dir /sqoop/hadoop11/myoutport1  \
-m 1
复制代码

 

查询指定列

复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/mysql   \
--username root  \
--password root   \
--columns "name" \
--where "name='STRING' " \
--table help_keyword  \
--target-dir /sqoop/hadoop11/myoutport22  \
-m 1
selct name from help_keyword where name = "string"
复制代码

 

导入:指定自定义查询SQL

复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/  \
--username root  \
--password root   \
--target-dir /user/hadoop/myimport33_1  \
--query 'select help_keyword_id,name from mysql.help_keyword where $CONDITIONS and name = "STRING"' \
--split-by  help_keyword_id \
--fields-terminated-by '\t'  \
-m 4
复制代码

 

在以上需要按照自定义SQL语句导出数据到HDFS的情况下:
1、引号问题,要么外层使用单引号,内层使用双引号,$CONDITIONS的$符号不用转义, 要么外层使用双引号,那么内层使用单引号,然后$CONDITIONS的$符号需要转义
2、自定义的SQL语句中必须带有WHERE \$CONDITIONS

2、把MySQL数据库中的表数据导入到Hive中

Sqoop 导入关系型数据到 hive 的过程是先导入到 hdfs,然后再 load 进入 hive

普通导入:数据存储在默认的default hive库中,表名就是对应的mysql的表名:

复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/mysql   \
--username root  \
--password root   \
--table help_keyword   \
--hive-import \
-m 1
复制代码

导入过程

第一步:导入mysql.help_keyword的数据到hdfs的默认路径
第二步:自动仿造mysql.help_keyword去创建一张hive表, 创建在默认的default库中
第三步:把临时目录中的数据导入到hive表中

查看数据

[hadoop@hadoop3 ~]$ hadoop fs -cat /user/hive/warehouse/help_keyword/part-m-00000

指定行分隔符和列分隔符,指定hive-import,指定覆盖导入,指定自动创建hive表,指定表名,指定删除中间结果数据目录

复制代码
sqoop import  \
--connect jdbc:mysql://hadoop1:3306/mysql  \
--username root  \
--password root  \
--table help_keyword  \
--fields-terminated-by "\t"  \
--lines-terminated-by "\n"  \
--hive-import  \
--hive-overwrite  \
--create-hive-table  \
--delete-target-dir \
--hive-database  mydb_test \
--hive-table new_help_keyword
复制代码

报错原因是hive-import 当前这个导入命令。 sqoop会自动给创建hive的表。 但是不会自动创建不存在的库

手动创建mydb_test数据块

hive> create database mydb_test;
OK
Time taken: 6.147 seconds
hive>

之后再执行上面的语句没有报错

查询一下

select * from new_help_keyword limit 10;

上面的导入语句等价于

复制代码
sqoop import  \
--connect jdbc:mysql://hadoop1:3306/mysql  \
--username root  \
--password root  \
--table help_keyword  \
--fields-terminated-by "\t"  \
--lines-terminated-by "\n"  \
--hive-import  \
--hive-overwrite  \
--create-hive-table  \ 
--hive-table  mydb_test.new_help_keyword  \
--delete-target-dir
复制代码

增量导入

执行增量导入之前,先清空hive数据库中的help_keyword表中的数据

truncate table help_keyword;
复制代码
sqoop import   \
--connect jdbc:mysql://hadoop1:3306/mysql   \
--username root  \
--password root   \
--table help_keyword  \
--target-dir /user/hadoop/myimport_add  \
--incremental  append  \
--check-column  help_keyword_id \
--last-value 500  \
-m 1
复制代码

语句执行成功

复制代码
[hadoop@hadoop3 ~]$ sqoop import   \
> --connect jdbc:mysql://hadoop1:3306/mysql   \
> --username root  \
> --password root   \
> --table help_keyword  \
> --target-dir /user/hadoop/myimport_add  \
> --incremental  append  \
> --check-column  help_keyword_id \
> --last-value 500  \
> -m 1
Warning: /home/hadoop/apps/sqoop-1.4.6/../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /home/hadoop/apps/sqoop-1.4.6/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
18/04/12 22:01:07 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
18/04/12 22:01:08 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/04/12 22:01:08 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
18/04/12 22:01:08 INFO tool.CodeGenTool: Beginning code generation
18/04/12 22:01:08 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM help_keyword AS t LIMIT 1
18/04/12 22:01:08 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM help_keyword AS t LIMIT 1
18/04/12 22:01:08 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/apps/hadoop-2.7.5
注: /tmp/sqoop-hadoop/compile/a51619d1ef8c6e4b112a209326ed9e0f/help_keyword.java使用或覆盖了已过时的 API。
注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。
18/04/12 22:01:11 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/a51619d1ef8c6e4b112a209326ed9e0f/help_keyword.jar
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hadoop-2.7.5/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/hadoop/apps/hbase-1.2.6/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]
18/04/12 22:01:12 INFO tool.ImportTool: Maximal id query for free form incremental import: SELECT MAX(help_keyword_id) FROM help_keyword
18/04/12 22:01:12 INFO tool.ImportTool: Incremental import based on column help_keyword_id
18/04/12 22:01:12 INFO tool.ImportTool: Lower bound value: 500
18/04/12 22:01:12 INFO tool.ImportTool: Upper bound value: 618
18/04/12 22:01:12 WARN manager.MySQLManager: It looks like you are importing from mysql.
18/04/12 22:01:12 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
18/04/12 22:01:12 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
18/04/12 22:01:12 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
18/04/12 22:01:12 INFO mapreduce.ImportJobBase: Beginning import of help_keyword
18/04/12 22:01:12 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
18/04/12 22:01:12 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
18/04/12 22:01:17 INFO db.DBInputFormat: Using read commited transaction isolation
18/04/12 22:01:17 INFO mapreduce.JobSubmitter: number of splits:1
18/04/12 22:01:17 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1523510178850_0010
18/04/12 22:01:19 INFO impl.YarnClientImpl: Submitted application application_1523510178850_0010
18/04/12 22:01:19 INFO mapreduce.Job: The url to track the job: http://hadoop3:8088/proxy/application_1523510178850_0010/
18/04/12 22:01:19 INFO mapreduce.Job: Running job: job_1523510178850_0010
18/04/12 22:01:30 INFO mapreduce.Job: Job job_1523510178850_0010 running in uber mode : false
18/04/12 22:01:30 INFO mapreduce.Job:  map 0% reduce 0%
18/04/12 22:01:40 INFO mapreduce.Job:  map 100% reduce 0%
18/04/12 22:01:40 INFO mapreduce.Job: Job job_1523510178850_0010 completed successfully
18/04/12 22:01:41 INFO mapreduce.Job: Counters: 30
    File System Counters
        FILE: Number of bytes read=0
        FILE: Number of bytes written=143200
        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=1576
        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)=7188
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=7188
        Total vcore-milliseconds taken by all map tasks=7188
        Total megabyte-milliseconds taken by all map tasks=7360512
    Map-Reduce Framework
        Map input records=118
        Map output records=118
        Input split bytes=87
        Spilled Records=0
        Failed Shuffles=0
        Merged Map outputs=0
        GC time elapsed (ms)=86
        CPU time spent (ms)=870
        Physical memory (bytes) snapshot=95576064
        Virtual memory (bytes) snapshot=2068234240
        Total committed heap usage (bytes)=18608128
    File Input Format Counters 
        Bytes Read=0
    File Output Format Counters 
        Bytes Written=1576
18/04/12 22:01:41 INFO mapreduce.ImportJobBase: Transferred 1.5391 KB in 28.3008 seconds (55.6875 bytes/sec)
18/04/12 22:01:41 INFO mapreduce.ImportJobBase: Retrieved 118 records.
18/04/12 22:01:41 INFO util.AppendUtils: Creating missing output directory - myimport_add
18/04/12 22:01:41 INFO tool.ImportTool: Incremental import complete! To run another incremental import of all data following this import, supply the following arguments:
18/04/12 22:01:41 INFO tool.ImportTool:  --incremental append
18/04/12 22:01:41 INFO tool.ImportTool:   --check-column help_keyword_id
18/04/12 22:01:41 INFO tool.ImportTool:   --last-value 618
18/04/12 22:01:41 INFO tool.ImportTool: (Consider saving this with 'sqoop job --create')
[hadoop@hadoop3 ~]$
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查看结果

3、把MySQL数据库中的表数据导入到hbase

普通导入

复制代码
sqoop import \
--connect jdbc:mysql://hadoop1:3306/mysql \
--username root \
--password root \
--table help_keyword \
--hbase-table new_help_keyword \
--column-family person \
--hbase-row-key help_keyword_id
复制代码

 

此时会报错,因为需要先创建Hbase里面的表,再执行导入的语句

hbase(main):001:0> create 'new_help_keyword', 'base_info'
0 row(s) in 3.6280 seconds

=> Hbase::Table - new_help_keyword
hbase(main):002:0>

 

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