前置条件
已经成功安装配置Hadoop和Mysql数据库服务器,如果将数据导入或从Hbase导出,还应该已经成功安装配置Hbase。
下载sqoop和Mysql的JDBC驱动
sqoop-1.2.0-CDH3B4.tar.gz :http://archive.cloudera.com/cdh/3/sqoop-1.2.0-CDH3B4.tar.gz
mysql-connector-java-5.1.28
安装sqoop
[hadoop@appserver ~]$ tar-zxvf sqoop-1.2.0-CDH3B4.tar.gz
配置环境变量
拷贝Hadoop核心包和MYSQL驱动包到sqoop的lib目录
[hadoop@appserver ~]$ cp hadoop-1.1.2/hadoop-core-1.1.2.jar sqoop-1.2.0-CDH3B4/lib/
[hadoop@appserver ~]$ cp mysql-connector-java-5.1.28-bin.jar sqoop-1.2.0-CDH3B4/lib/
配置sqoop-1.2.0-CDH3B4/bin/configure-sqoop文件
注释掉hbase和zookeeper检查(除非准备使用HABASE等HADOOP组件)
启动hadoop集群
启动mysql
创建sqoop用户
建立sqoop库,test表,并构造测试数据
测试sqoop连接
[hadoop@appserver ~]$ sqoop list-databases --connect jdbc:mysql://10.120.10.11:3306/ --username sqoop --password sqoop
列出mysql中所有数据库的名称
从mysql导入到hdfs中
sqoop ##sqoop命令
import ##表示导入
--connect jdbc:mysql://ip:3306/sqoop ##告诉jdbc,连接mysql的url
--username sqoop ##连接mysql的用户名
--password sqoop ##连接mysql的密码
--table test ##从mysql导出的表名称
--fields-terminated-by '/t' ##指定输出文件中的行的字段分隔符
-m 1 ##复制过程使用1个map作业
[hadoop@appserver ~]$ sqoop import --connect jdbc:mysql://10.120.10.11:3306/sqoop --username sqoop --password sqoop --table test --fields-terminated-by ':' -m 1
Hadoop中查看导入结果
从hdfs导出到mysql中
sqoop
export ##表示数据从hive复制到mysql中
--connect jdbc:mysql://ip:3306/sqoop
--username sqoop
--password sqoop
--table test ##mysql中的表,即将被导入的表名称
--export-dir '/user/root/aa/part-m-00000' ##hive中被导出的文件
--fields-terminated-by '/t' ##hive中被导出的文件字段的分隔符
[hadoop@appserver ~]$ sqoop export --connect jdbc:mysql://10.120.10.11:3306/sqoop --username sqoop --password sqoop --table test --export-dir '/user/hadoop/test/part-m-00000' --fields-terminated-by ':' -m 1
Mysql中查看导出结果
从Mysql导入到Hbase中
参数说明:
Ø hbase_tablename指定要导成hbase的表名
Ø key_col_name指定mysql数据库表中哪一列作为hbase新表的rowkey
Ø col_fam_name是除rowkey之外的所有列的列族名
[hadoop@appserver ~]$ sqoop import --connect jdbc:mysql://10.120.10.11:3306/sqoop --username sqoop --password sqoop --table test --hbase-create-table --hbase-table mysql_sqoop_test --column-family info --hbase-row-key id -m 1
在Hbase中查看结果

InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Key metrics for EXPLAIN commands include type, key, rows, and Extra. 1) The type reflects the access type of the query. The higher the value, the higher the efficiency, such as const is better than ALL. 2) The key displays the index used, and NULL indicates no index. 3) rows estimates the number of scanned rows, affecting query performance. 4) Extra provides additional information, such as Usingfilesort prompts that it needs to be optimized.

Usingtemporary indicates that the need to create temporary tables in MySQL queries, which are commonly found in ORDERBY using DISTINCT, GROUPBY, or non-indexed columns. You can avoid the occurrence of indexes and rewrite queries and improve query performance. Specifically, when Usingtemporary appears in EXPLAIN output, it means that MySQL needs to create temporary tables to handle queries. This usually occurs when: 1) deduplication or grouping when using DISTINCT or GROUPBY; 2) sort when ORDERBY contains non-index columns; 3) use complex subquery or join operations. Optimization methods include: 1) ORDERBY and GROUPB

MySQL/InnoDB supports four transaction isolation levels: ReadUncommitted, ReadCommitted, RepeatableRead and Serializable. 1.ReadUncommitted allows reading of uncommitted data, which may cause dirty reading. 2. ReadCommitted avoids dirty reading, but non-repeatable reading may occur. 3.RepeatableRead is the default level, avoiding dirty reading and non-repeatable reading, but phantom reading may occur. 4. Serializable avoids all concurrency problems but reduces concurrency. Choosing the appropriate isolation level requires balancing data consistency and performance requirements.

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.


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