在上一篇文章中《使用DbUtils实现增删改查》,发现执行runner.query()这行代码时,需要自己去处理查询到的结果集,比较麻烦。这行代码的原型是: public Object query(Connection conn, String sql, ResultSetHandlerT rsh, Object... params) 其中ResultSet
在上一篇文章中《使用DbUtils实现增删改查》,发现执行runner.query()这行代码时,需要自己去处理查询到的结果集,比较麻烦。这行代码的原型是:
public Object query(Connection conn, String sql, ResultSetHandler<T> rsh, Object... params)
其中ResultSetHandler是一个接口,实际上,万能的Apache已经为我们提供了众多好用的实现类,现在举例如下:
public class RSHanlderDemo { //ScalarHandler:获取结果集中第一行数据指定列的值,常用来进行单值查询 @Test public void tes9() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); Long count = (Long)runner.query("select count(*) from account",new ScalarHandler()); System.out.println(count); } //KeyedHandler(name):将结果集中的每一行数据都封装到一个Map里(List<Map>),再把这些map再存到一个map里,其key为指定的列。 @Test public void tes8() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); Map<Object, Map<String, Object>> map = runner.query("select * from account where money>?", new KeyedHandler("id"),500); System.out.println(map); } //ColumnListHandler:将结果集中某一列的数据存放到List中。 @Test public void tes7() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); List<Object>list = runner.query("select * from account where money>?", new ColumnListHandler(3),500); System.out.println(list); } //MapListHandler:将结果集中的每一行数据都封装到一个Map里,然后再存放到List @Test public void tes6() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); List<Map<String, Object>> list = runner.query("select * from account where money>?", new MapListHandler(),500); System.out.println(list); } //MapHandler:将结果集中的第一行数据封装到一个Map里,key是列名,value就是对应的值。 @Test public void tes5() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); Map<String, Object> map = runner.query("select * from account where money>?", new MapHandler(),500); System.out.println(map); } //BeanListHandler:将结果集中的每一行数据都封装到一个对应的JavaBean实例中,存放到List里。 @Test public void tes4() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); List<Account>list = runner.query("select * from account where money>?", new BeanListHandler<Account>(Account.class),500); System.out.println(list); } //BeanHandler:将结果集中的第一行数据封装到一个对应的JavaBean实例中。 @Test public void tes3() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); Account acc = runner.query("select * from account where money>?", new BeanHandler<Account>(Account.class),500); System.out.println(acc); } //ArrayListHandler:把结果集中的每一行数据都转成一个对象数组,再存放到List中。 @Test public void tes2() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); List<Object[]> list = runner.query("select * from account where money>?", new ArrayListHandler(),500); System.out.println(list); } //ArrayHandler:把结果集中的第一行数据转成对象数组。 @Test public void test1() throws SQLException{ QueryRunner runner = new QueryRunner(new ComboPooledDataSource()); Object[] objs = runner.query("select * from account where money>?", new ArrayHandler(),500); System.out.println(objs); } }测试时,可以加断点调试,再执行Debug as JUnit Test。
总结如下:
①ArrayHandler:把结果集中的第一行数据转成对象数组。
②ArrayListHandler:把结果集中的每一行数据都转成一个对象数组,再存放到List中。
③BeanHandler:将结果集中的第一行数据封装到一个对应的JavaBean实例中。
④BeanListHandler:将结果集中的每一行数据都封装到一个对应的JavaBean实例中,存放到List里。
⑤MapHandler:将结果集中的第一行数据封装到一个Map里,key是列名,value就是对应的值。
⑥MapListHandler:将结果集中的每一行数据都封装到一个Map里,然后再存放到List
⑦ColumnListHandler:将结果集中某一列的数据存放到List中。
⑧KeyedHandler(name):将结果集中的每一行数据都封装到一个Map里(List

MySQL is an open source relational database management system, mainly used to store and retrieve data quickly and reliably. Its working principle includes client requests, query resolution, execution of queries and return results. Examples of usage include creating tables, inserting and querying data, and advanced features such as JOIN operations. Common errors involve SQL syntax, data types, and permissions, and optimization suggestions include the use of indexes, optimized queries, and partitioning of tables.

MySQL is an open source relational database management system suitable for data storage, management, query and security. 1. It supports a variety of operating systems and is widely used in Web applications and other fields. 2. Through the client-server architecture and different storage engines, MySQL processes data efficiently. 3. Basic usage includes creating databases and tables, inserting, querying and updating data. 4. Advanced usage involves complex queries and stored procedures. 5. Common errors can be debugged through the EXPLAIN statement. 6. Performance optimization includes the rational use of indexes and optimized query statements.

MySQL is chosen for its performance, reliability, ease of use, and community support. 1.MySQL provides efficient data storage and retrieval functions, supporting multiple data types and advanced query operations. 2. Adopt client-server architecture and multiple storage engines to support transaction and query optimization. 3. Easy to use, supports a variety of operating systems and programming languages. 4. Have strong community support and provide rich resources and solutions.

InnoDB's lock mechanisms include shared locks, exclusive locks, intention locks, record locks, gap locks and next key locks. 1. Shared lock allows transactions to read data without preventing other transactions from reading. 2. Exclusive lock prevents other transactions from reading and modifying data. 3. Intention lock optimizes lock efficiency. 4. Record lock lock index record. 5. Gap lock locks index recording gap. 6. The next key lock is a combination of record lock and gap lock to ensure data consistency.

The main reasons for poor MySQL query performance include not using indexes, wrong execution plan selection by the query optimizer, unreasonable table design, excessive data volume and lock competition. 1. No index causes slow querying, and adding indexes can significantly improve performance. 2. Use the EXPLAIN command to analyze the query plan and find out the optimizer error. 3. Reconstructing the table structure and optimizing JOIN conditions can improve table design problems. 4. When the data volume is large, partitioning and table division strategies are adopted. 5. In a high concurrency environment, optimizing transactions and locking strategies can reduce lock competition.

In database optimization, indexing strategies should be selected according to query requirements: 1. When the query involves multiple columns and the order of conditions is fixed, use composite indexes; 2. When the query involves multiple columns but the order of conditions is not fixed, use multiple single-column indexes. Composite indexes are suitable for optimizing multi-column queries, while single-column indexes are suitable for single-column queries.

To optimize MySQL slow query, slowquerylog and performance_schema need to be used: 1. Enable slowquerylog and set thresholds to record slow query; 2. Use performance_schema to analyze query execution details, find out performance bottlenecks and optimize.

MySQL and SQL are essential skills for developers. 1.MySQL is an open source relational database management system, and SQL is the standard language used to manage and operate databases. 2.MySQL supports multiple storage engines through efficient data storage and retrieval functions, and SQL completes complex data operations through simple statements. 3. Examples of usage include basic queries and advanced queries, such as filtering and sorting by condition. 4. Common errors include syntax errors and performance issues, which can be optimized by checking SQL statements and using EXPLAIN commands. 5. Performance optimization techniques include using indexes, avoiding full table scanning, optimizing JOIN operations and improving code readability.


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