使用sqoop把mysql数据导入hive

export HADOOP_COMMON_HOME=/hadoop

export HADOOP_MAPRED_HOME=/hadoop

 

cp /hive/lib/mysql-connector-java-5.1.25-bin.jar     /sqoop/lib/

share表第一列为自增主键 share_id，share_id<1000的数据共有999条：

mysql> SELECT COUNT(*) FROM share WHERE share_id<1000;

+----------+

| COUNT(*) |

+----------+

|      999 |

+----------+

1 ROW IN SET (0.00 sec)

使用sqoop导入到hive

[hduser@www lib]$ /sqoop/bin/sqoop import --connect jdbc:mysql://localhost/shipincon --table share --username root --password xxx --hive-import --where "share_id<1000"  -- --default-character-set=utf8

 

 

Warning: /usr/lib/hbase does not exist! HBase imports will fail.

Please set $HBASE_HOME to the root of your HBase installation.

		--password 密码   比较危险，可以改成 -P   (Read password from console)

13/07/12 18:42:58 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.

		默认是用hive的分隔符 ctrl+a 可以使用 --fields-terminated-by 来更改

		插一句：在console vim中如何输入ctrl+a：  在输入模式下先按ctrl+v，再按ctrl+a即可，会显示成^A

13/07/12 18:42:58 INFO tool.BaseSqoopTool: Using Hive-specific delimiters for output. You can override

13/07/12 18:42:58 INFO tool.BaseSqoopTool: delimiters with --fields-terminated-by, etc.

13/07/12 18:42:58 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

13/07/12 18:42:58 INFO tool.CodeGenTool: Beginning code generation

13/07/12 18:42:58 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `share` AS t LIMIT 1

13/07/12 18:42:58 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `share` AS t LIMIT 1

13/07/12 18:42:58 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /hadoop

Note: /tmp/sqoop-hduser/compile/720a3072de33c8a826cdf96d9eced686/share.java uses or overrides a deprecated API.

Note: Recompile with -Xlint:deprecation for details.

13/07/12 18:43:00 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hduser/compile/720a3072de33c8a826cdf96d9eced686/share.jar

13/07/12 18:43:00 WARN manager.MySQLManager: It looks like you are importing from mysql.

	如果没有blob clob longvarbinary字段，可以使用 --direct 速度也许会快一些。    text字段不清楚。

13/07/12 18:43:00 WARN manager.MySQLManager: This transfer can be faster! Use the --direct

13/07/12 18:43:00 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.

13/07/12 18:43:00 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)

13/07/12 18:43:00 INFO mapreduce.ImportJobBase: Beginning import of share

13/07/12 18:43:02 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`share_id`), MAX(`share_id`) FROM `share` WHERE ( share_id<1000 )

13/07/12 18:43:02 INFO mapred.JobClient: Running job: job_201307121751_0001

13/07/12 18:43:03 INFO mapred.JobClient:  map 0% reduce 0%

13/07/12 18:43:12 INFO mapred.JobClient:  map 50% reduce 0%

13/07/12 18:43:17 INFO mapred.JobClient:  map 100% reduce 0%

13/07/12 18:43:18 INFO mapred.JobClient: Job complete: job_201307121751_0001

13/07/12 18:43:18 INFO mapred.JobClient: Counters: 18

13/07/12 18:43:18 INFO mapred.JobClient:   Job Counters

13/07/12 18:43:18 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=20723

13/07/12 18:43:18 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0

13/07/12 18:43:18 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0

		################################ map tasks  =4 就会生成四个文件

13/07/12 18:43:18 INFO mapred.JobClient:     Launched map tasks=4

13/07/12 18:43:18 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=0

13/07/12 18:43:18 INFO mapred.JobClient:   File Output Format Counters

13/07/12 18:43:18 INFO mapred.JobClient:     Bytes Written=202094

13/07/12 18:43:18 INFO mapred.JobClient:   FileSystemCounters

13/07/12 18:43:18 INFO mapred.JobClient:     HDFS_BYTES_READ=455

13/07/12 18:43:18 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=260292

13/07/12 18:43:18 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=202094

13/07/12 18:43:18 INFO mapred.JobClient:   File Input Format Counters

13/07/12 18:43:18 INFO mapred.JobClient:     Bytes Read=0

13/07/12 18:43:18 INFO mapred.JobClient:   Map-Reduce Framework

13/07/12 18:43:18 INFO mapred.JobClient:     Map input records=999

13/07/12 18:43:18 INFO mapred.JobClient:     Physical memory (bytes) snapshot=179216384

13/07/12 18:43:18 INFO mapred.JobClient:     Spilled Records=0

13/07/12 18:43:18 INFO mapred.JobClient:     CPU time spent (ms)=3360

13/07/12 18:43:18 INFO mapred.JobClient:     Total committed heap usage (bytes)=65011712

13/07/12 18:43:18 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=1519448064

13/07/12 18:43:18 INFO mapred.JobClient:     Map output records=999

13/07/12 18:43:18 INFO mapred.JobClient:     SPLIT_RAW_BYTES=455

13/07/12 18:43:18 INFO mapreduce.ImportJobBase: Transferred 197.3574 KB in 18.0516 seconds (10.9329 KB/sec)

		共999行记录

13/07/12 18:43:18 INFO mapreduce.ImportJobBase: Retrieved 999 records.

13/07/12 18:43:19 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `share` AS t LIMIT 1

13/07/12 18:43:19 WARN hive.TableDefWriter: Column add_time had to be cast to a less precise type in Hive

		add_time的精度变小了。。为嘛？  mysql中 `add_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,

mysql> select add_time from share limit 1;

+---------------------+

| add_time            |

+---------------------+

| 2012-04-08 17:17:13 |

+---------------------+

1 row in set (0.00 sec)

到了hive中会变成：

2012-04-08 17:17:13.0

	貌似精度是变大了。

不过据说某些数据库的timestamp字段支持到了毫秒的精度。

13/07/12 18:43:19 INFO hive.HiveImport: Removing temporary files from import process: hdfs://localhost:54310/user/hduser/share/_logs

13/07/12 18:43:19 INFO hive.HiveImport: Loading uploaded data into Hive

13/07/12 18:43:21 INFO hive.HiveImport: WARNING: org.apache.hadoop.metrics.jvm.EventCounter is deprecated. Please use org.apache.hadoop.log.metrics.EventCounter in all the log4j.properties files.

13/07/12 18:43:22 INFO hive.HiveImport: Logging initialized using configuration in jar:file:/usr/local/hive/lib/hive-common-0.10.0.jar!/hive-log4j.properties

13/07/12 18:43:22 INFO hive.HiveImport: Hive history file=/tmp/hduser/hive_job_log_hduser_201307121843_1382245494.txt

13/07/12 18:43:25 INFO hive.HiveImport: OK

13/07/12 18:43:25 INFO hive.HiveImport: Time taken: 2.389 seconds

13/07/12 18:43:25 INFO hive.HiveImport: Loading data to table default.share

13/07/12 18:43:26 INFO hive.HiveImport: Table default.share stats: [num_partitions: 0, num_files: 5, num_rows: 0, total_size: 202094, raw_data_size: 0]

13/07/12 18:43:26 INFO hive.HiveImport: OK

13/07/12 18:43:26 INFO hive.HiveImport: Time taken: 0.726 seconds

13/07/12 18:43:26 INFO hive.HiveImport: Hive import complete.

13/07/12 18:43:26 INFO hive.HiveImport: Export directory is empty, removing it.

[hduser@www lib]$

 

 

 

--default-character-set=utf8之前还有 --

 

Arguments to mysqldump and other subprograms may be supplied

after a '--' on the command line.

去hive中看看数据：

[hduser@www lib]$ /hive/bin/hive

WARNING: org.apache.hadoop.metrics.jvm.EventCounter IS deprecated. Please USE org.apache.hadoop.log.metrics.EventCounter IN ALL the log4j.properties files.

Logging initialized USING configuration IN jar:file:/usr/LOCAL/hive/lib/hive-common-0.10.0.jar!/hive-log4j.properties

Hive history file=/tmp/hduser/hive_job_log_hduser_201307121854_1566000179.txt

hive> USE shipincon;

FAILED: Error IN metadata: ERROR: The DATABASE shipincon does NOT exist.

FAILED: Execution Error, RETURN code 1 FROM org.apache.hadoop.hive.ql.EXEC.DDLTask

hive> SHOW DATABASES;

OK

DEFAULT

TIME taken: 0.244 seconds

hive> USE DEFAULT;

OK

TIME taken: 0.018 seconds

hive> SHOW TABLES;

OK

a

cleanmaster

cm

login_info

login_interm

share

TIME taken: 0.155 seconds

hive> SELECT COUNT(*) FROM sahre;

FAILED: SemanticException [Error 10001]: Line 1:21 TABLE NOT found 'sahre'

hive> SELECT COUNT(*) FROM share

    > ;

Total MapReduce jobs = 1

Launching Job 1 OUT OF 1

NUMBER OF reduce tasks determined at compile TIME: 1

IN ORDER TO CHANGE the average LOAD FOR a reducer (IN bytes):

  SET hive.EXEC.reducers.bytes.per.reducer=<number>

IN ORDER TO LIMIT the maximum NUMBER OF reducers:

  SET hive.EXEC.reducers.MAX=<number>

IN ORDER TO SET a constant NUMBER OF reducers:

  SET mapred.reduce.tasks=<number>

Starting Job = job_201307121751_0002, Tracking URL = http://localhost:50030/jobdetails.jsp?jobid=job_201307121751_0002

KILL Command = /usr/LOCAL/hadoop/libexec/../bin/hadoop job  -KILL job_201307121751_0002

Hadoop job information FOR Stage-1: NUMBER OF mappers: 1; NUMBER OF reducers: 1

2013-07-12 18:54:37,120 Stage-1 map = 0%,  reduce = 0%

2013-07-12 18:54:40,143 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:41,154 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:42,162 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:43,168 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:44,175 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:45,181 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:46,192 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:47,199 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 0.82 sec

2013-07-12 18:54:48,208 Stage-1 map = 100%,  reduce = 33%, Cumulative CPU 0.82 sec

2013-07-12 18:54:49,219 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.74 sec

2013-07-12 18:54:50,238 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 1.74 sec

MapReduce Total cumulative CPU TIME: 1 seconds 740 msec

Ended Job = job_201307121751_0002

MapReduce Jobs Launched:

Job 0: Map: 1  Reduce: 1   Cumulative CPU: 1.74 sec   HDFS READ: 202547 HDFS WRITE: 4 SUCCESS

Total MapReduce CPU TIME Spent: 1 seconds 740 msec

OK

999

TIME taken: 18.573 seconds

hive> SHOW CREATE TABLE share;

OK

CREATE  TABLE share(

  share_id BIGINT,

  add_time string,

  tag string)

COMMENT 'Imported by sqoop on 2013/07/12 19:48:33'

ROW FORMAT DELIMITED

  FIELDS TERMINATED BY '\u0001'

  LINES TERMINATED BY '\n'

STORED AS INPUTFORMAT

  'org.apache.hadoop.mapred.TextInputFormat'

OUTPUTFORMAT

  'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'

LOCATION

  'hdfs://localhost:54310/user/hive/warehouse2/share'

TBLPROPERTIES (

  'numPartitions'='0',

  'numFiles'='4',

  'transient_lastDdlTime'='1373629961',

  'totalSize'='227161',

  'numRows'='0',

  'rawDataSize'='0')

TIME taken: 0.292 seconds

 

hive>

再去dfs里面看看：

[hduser@www lib]$ /hadoop/bin/hadoop dfs -ls /user/hive/warehouse2/share

Found 5 items

-rw-r--r--   1 hduser supergroup          0 2013-07-12 18:43 /user/hive/warehouse2/share/_SUCCESS

-rw-r--r--   1 hduser supergroup      53226 2013-07-12 18:43 /user/hive/warehouse2/share/part-m-00000

-rw-r--r--   1 hduser supergroup      50185 2013-07-12 18:43 /user/hive/warehouse2/share/part-m-00001

-rw-r--r--   1 hduser supergroup      50634 2013-07-12 18:43 /user/hive/warehouse2/share/part-m-00002

-rw-r--r--   1 hduser supergroup      48049 2013-07-12 18:43 /user/hive/warehouse2/share/part-m-00003

乍一看，像是4个partition。可是细看文件名，并不是partition的那种结构：
比如：/user/hive/warehouse2/cm/country_name=VN_en

把文件复制到本地：

/hadoop/bin/hadoop dfs -get /user/hive/warehouse2/share  ~/

[hduser@www share]$ cd ~/share

[hduser@www share]$ wc -l *

   250 part-m-00000

   250 part-m-00001

   249 part-m-00002

   250 part-m-00003

     0 _SUCCESS

   999 total

–hive-overwrite 可以覆盖hive中的现有数据。
–hive-table
Sets the table name to use when importing to Hive.
可以把数据import到某个分区中：
–hive-partition-key Name of a hive field to partition are sharded on
–hive-partition-value String-value that serves as partition key for this imported into hive in this job.

可以写个cron，周期性地把share表的新增数据导入到hive中了: –append

参考：

http://sqoop.apache.org/docs/1.4.3/SqoopUserGuide.html

http://www.mysqlperformanceblog.com/2013/07/11/mysql-and-hadoop/

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