使用Sqoop能够极大简化MySQL数据迁移至Hive之流程，并降低Hadoop处理分析任务时的难度。
先决条件:安装并运行有Sqoop与Hive的Hadoop环境。为了加快处理速度，我们还将使用Cloudera Quickstart VM(要求至少4 GB内存)，不过大家也可以使用Hortonworks Data Platform(至少要求8 GB内存)。由于我的笔记本电脑只有8 GB内存，因此我在这里使用CLoudera VM镜像。
如果大家需要使用Virtualbox运行Cloudera/HDP VM，则可以轻松使用其它多种Hadoop生态系统预装软件包(包括MySQL、Oozie、Hadoop、Hive、Zookeeper、Storm、Kafka以及Spark等等)。
在MySQL中创建表
在Cloudera VM中，打开命令界面并确保MySQL已经安装完毕。
- shell> mysql --version
- mysql Ver 14.14 Distrib 5.1.66, for redhat-linux-gnu (x86_64) using readline 5.
示例当中自然要使用自己的数据库，因此使用以下命令在MySQL中创建一套数据库：
- mysql> create database sqoop;
接下来：
- mysql> use sqoop;
- mysql> create table customer(id varchar(3), name varchar(20), age varchar(3), salary integer(10));
- Query OK, 0 rows affected (0.09 sec)
- mysql> desc customer;
- +--------+-------------+------+-----+---------+-------+
- | Field | Type | Null | Key | Default | Extra |
- +--------+-------------+------+-----+---------+-------+
- | id | varchar(3) | YES | | NULL | |
- | name | varchar(20) | YES | | NULL | |
- | age | varchar(3) | YES | | NULL | |
- | salary | int(10) | YES | | NULL | |
- +--------+-------------+------+-----+---------+-------+
- mysql> select * from customer;
- +------+--------+------+--------+
- | id | name | age | salary |
- +------+--------+------+--------+
- | 1 | John | 30 | 80000 |
- | 2 | Kevin | 33 | 84000 |
- | 3 | Mark | 28 | 90000 |
- | 4 | Jenna | 34 | 93000 |
- | 5 | Robert | 32 | 100000 |
- | 6 | Zoya | 40 | 60000 |
- | 7 | Sam | 37 | 75000 |
- | 8 | George | 31 | 67000 |
- | 9 | Peter | 23 | 70000 |
- | 19 | Alex | 26 | 74000 |
- +------+--------+------+-----
开始Sqoop之旅
如大家所见，其中customer表中并不包含主键。我在该表中并未添加多少记录。默认情况下，Sqoop能够识别出表中的主键列(如果有的话)，并将其作为划分列。该划分列的低值与高值检索自该数据库，而映射任务则指向符合区间要求的均匀部分。
如果主键并未均匀分布在该区间当中，那么任务将出现不平衡状况。这时，大家应当明确选定一个与--split-by参数不同的列，例如--split-by id。
由于我们希望将此表直接导入至Hive中，因此需要在Sqoop命令中添加–hive-import：
- sqoop import --connect jdbc:mysql://localhost:3306/sqoop
- --username root
- -P
- --split-by id
- --columns id,name
- --table customer
- --target-dir /user/cloudera/ingest/raw/customers
- --fields-terminated-by ","
- --hive-import
- --create-hive-table
- --hive-table sqoop_workspace.customers
下面来看Sqoop命令各选项的具体作用：
connect – 提供jdbc字符串
username – 数据库用户名
-P – 将在控制台中询问密码。大家也可以使用-passwaord，但并不推荐这种作法，因为其会显示在任务执行日志中并可能导致问题。解决办法之一在于将数据库密码存储在HDFS中的文件内，并将其向运行时交付。
- table – 告知计算机我们希望导入哪个MySQL表。在这里，表名称为customer。
- split-by – 指定划分列。在这里我们指定id列。
- target-dir – HDFS目标目录。
- fields-terminated-by – 我已经指定了逗号作为分隔值(默认情况下，导入HDFS的数据以逗号作为分隔值)。
- hive-import – 将表导入Hive(如果不加设置，则使用Hive的默认分隔符)。
- create-hive-table – 检查如果已经存在一个Hive表，任务设置是否会因此失败。
- hive-table – 指定.。本示例中为sqoop_workspace.customers，其中sqoop_workspace为数据库名称，而customers则为表名称。
如下所示，Sqoop为一项map-reduce任务。请注意，这里我使用-P作为密码选项。除了这种方式，我们也可以使用-password实现参数化，并从文件中读取密码内容。
- sqoop import --connect jdbc:mysql://localhost:3306/sqoop --username root -P --split-by id --columns id,name --table customer --target-dir /user/cloudera/ingest/raw/customers --fields-terminated-by "," --hive-import --create-hive-table --hive-table sqoop_workspace.customers
- Warning: /usr/lib/sqoop/../accumulo does not exist! Accumulo imports will fail.
- Please set $ACCUMULO_HOME to the root of your Accumulo installation.
- 16/03/01 12:59:44 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6-cdh5.5.0
- Enter password:
- 16/03/01 12:59:54 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
- 16/03/01 12:59:54 INFO tool.CodeGenTool: Beginning code generation
- 16/03/01 12:59:55 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `customer` AS t LIMIT 1
- 16/03/01 12:59:56 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `customer` AS t LIMIT 1
- 16/03/01 12:59:56 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/lib/hadoop-mapreduce
- Note: /tmp/sqoop-cloudera/compile/6471c43b5c867834458d3bf5a67eade2/customer.java uses or overrides a deprecated API.
- Note: Recompile with -Xlint:deprecation for details.
- 16/03/01 13:00:01 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-cloudera/compile/6471c43b5c867834458d3bf5a67eade2/customer.jar
- 16/03/01 13:00:01 WARN manager.MySQLManager: It looks like you are importing from mysql.
- 16/03/01 13:00:01 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
- 16/03/01 13:00:01 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
- 16/03/01 13:00:01 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
- 16/03/01 13:00:01 INFO mapreduce.ImportJobBase: Beginning import of customer
- 16/03/01 13:00:01 INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address
- 16/03/01 13:00:02 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
- 16/03/01 13:00:04 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
- 16/03/01 13:00:05 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
- 16/03/01 13:00:11 INFO db.DBInputFormat: Using read commited transaction isolation
- 16/03/01 13:00:11 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`id`), MAX(`id`) FROM `customer`
- 16/03/01 13:00:11 WARN db.TextSplitter: Generating splits for a textual index column.
- 16/03/01 13:00:11 WARN db.TextSplitter: If your database sorts in a case-insensitive order, this may result in a partial import or duplicate records.
- 16/03/01 13:00:11 WARN db.TextSplitter: You are strongly encouraged to choose an integral split column.
- 16/03/01 13:00:11 INFO mapreduce.JobSubmitter: number of splits:4
- 16/03/01 13:00:12 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1456782715090_0004
- 16/03/01 13:00:13 INFO impl.YarnClientImpl: Submitted application application_1456782715090_0004
- 16/03/01 13:00:13 INFO mapreduce.Job: The url to track the job: http://quickstart.cloudera:8088/proxy/application_1456782715090_0004/
- 16/03/01 13:00:13 INFO mapreduce.Job: Running job: job_1456782715090_0004
- 16/03/01 13:00:47 INFO mapreduce.Job: Job job_1456782715090_0004 running in uber mode : false
- 16/03/01 13:00:48 INFO mapreduce.Job: map 0% reduce 0%
- 16/03/01 13:01:43 INFO mapreduce.Job: map 25% reduce 0%
- 16/03/01 13:01:46 INFO mapreduce.Job: map 50% reduce 0%
- 16/03/01 13:01:48 INFO mapreduce.Job: map 100% reduce 0%
- 16/03/01 13:01:48 INFO mapreduce.Job: Job job_1456782715090_0004 completed successfully
- 16/03/01 13:01:48 INFO mapreduce.Job: Counters: 30
- File System Counters
- FILE: Number of bytes read=0
- FILE: Number of bytes written=548096
- FILE: Number of read operations=0
- FILE: Number of large read operations=0
- FILE: Number of write operations=0
- HDFS: Number of bytes read=409
- HDFS: Number of bytes written=77
- HDFS: Number of read operations=16
- HDFS: Number of large read operations=0
- HDFS: Number of write operations=8
- Job Counters
- Launched map tasks=4
- Other local map tasks=5
- Total time spent by all maps in occupied slots (ms)=216810
- Total time spent by all reduces in occupied slots (ms)=0
- Total time spent by all map tasks (ms)=216810
- Total vcore-seconds taken by all map tasks=216810
- Total megabyte-seconds taken by all map tasks=222013440
- Map-Reduce Framework
- Map input records=10
- Map output records=10
- Input split bytes=409
- Spilled Records=0
- Failed Shuffles=0
- Merged Map outputs=0
- GC time elapsed (ms)=2400
- CPU time spent (ms)=5200
- Physical memory (bytes) snapshot=418557952
- Virtual memory (bytes) snapshot=6027804672
- Total committed heap usage (bytes)=243007488
- File Input Format Counters
- Bytes Read=0
- File Output Format Counters
- Bytes Written=77
- 16/03/01 13:01:48 INFO mapreduce.ImportJobBase: Transferred 77 bytes in 104.1093 seconds (0.7396 bytes/sec)
- 16/03/01 13:01:48 INFO mapreduce.ImportJobBase: Retrieved 10 records.
- 16/03/01 13:01:49 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `customer` AS t LIMIT 1
- 16/03/01 13:01:49 INFO hive.HiveImport: Loading uploaded data into Hive
- Logging initialized using configuration in jar:file:/usr/jars/hive-common-1.1.0-cdh5.5.0.jar!/hive-log4j.properties
- OK
- Time taken: 2.163 seconds
- Loading data to table sqoop_workspace.customers
- chgrp: changing ownership of 'hdfs://quickstart.cloudera:8020/user/hive/warehouse/sqoop_workspace.db/customers/part-m-00000': User does not belong to supergroup
- chgrp: changing ownership of 'hdfs://quickstart.cloudera:8020/user/hive/warehouse/sqoop_workspace.db/customers/part-m-00001': User does not belong to supergroup
- chgrp: changing ownership of 'hdfs://quickstart.cloudera:8020/user/hive/warehouse/sqoop_workspace.db/customers/part-m-00002': User does not belong to supergroup
- chgrp: changing ownership of 'hdfs://quickstart.cloudera:8020/user/hive/warehouse/sqoop_workspace.db/customers/part-m-00003': User does not belong to supergroup
- Table sqoop_workspace.customers stats: [numFiles=4, totalSize=77]
- OK
- Time taken: 1.399 seconds
***，让我们验证Hive中的输出结果：
- hive> show databases;
- OK
- default
- sqoop_workspace
- Time taken: 0.034 seconds, Fetched: 2 row(s)
- hive> use sqoop_workspace;
- OK
- Time taken: 0.063 seconds
- hive> show tables;
- OK
- customers
- Time taken: 0.036 seconds, Fetched: 1 row(s)
- hive> show create table customers;
- OK
- CREATE TABLE `customers`(
- `id` string,
- `name` string)
- COMMENT 'Imported by sqoop on 2016/03/01 13:01:49'
- ROW FORMAT DELIMITED
- FIELDS TERMINATED BY ','
- LINES TERMINATED BY '\n'
- STORED AS INPUTFORMAT
- 'org.apache.hadoop.mapred.TextInputFormat'
- OUTPUTFORMAT
- 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat'
- LOCATION
- 'hdfs://quickstart.cloudera:8020/user/hive/warehouse/sqoop_workspace.db/customers'
- TBLPROPERTIES (
- 'COLUMN_STATS_ACCURATE'='true',
- 'numFiles'='4',
- 'totalSize'='77',
- 'transient_lastDdlTime'='1456866115')
- Time taken: 0.26 seconds, Fetched: 18 row(s)
hive> select * from customers;
OK
1 John
2 Kevin
19 Alex
3 Mark
4 Jenna
5 Robert
6 Zoya
7 Sam
8 George
9 Peter
Time taken: 1.123 seconds, Fetched: 10 row(s).
到此完成!从MySQL到Hive，数据迁移工作就是这么简单。
