当多个程序对同一张表进行操作时,容易出现超时现象,导致数据读取或写入失败。以下是针对此问题的一些解决方法。
一、代码优化
在进行大量数据读写操作时,代码的效率显得尤为重要。因此,进行代码优化是一个有效的解决方法。常用的代码优化方法有:
1、使用缓存,将频繁读写的数据存入缓存。这样可以减少数据库的读写次数,提高程序的效率。
public class CacheHelper {
private static Map cache = new HashMap();
public static Object get(String key){
return cache.get(key);
}
public static void set(String key, Object value){
cache.put(key, value);
}
public static void remove(String key){
cache.remove(key);
}
}
//使用示例
Object obj = CacheHelper.get("key");
if (obj == null) {
obj = getDataFromDatabase();
CacheHelper.set("key", obj);
}
return obj;
2、批量操作数据库,将多条SQL语句合并为一条。这样可以减少数据库的连接次数,在处理大批量数据时效果更加明显。
public void batchInsert(List data) {
try {
Connection conn = getConnection();
conn.setAutoCommit(false);
String sql = "insert into TABLE_NAME (col1, col2, col3, ... ) values(?, ?, ?, ...)";
PreparedStatement ps = conn.prepareStatement(sql);
for (int i=0; i
二、分布式锁
在多个程序同时操作一张表时,避免重复写入数据是一个重要的问题。使用分布式锁可以保证在任意时刻只有一个程序对数据进行写操作。
1、使用Redis实现分布式锁:
public class RedisLock {
private RedisTemplate redisTemplate;
public RedisLock(RedisTemplate redisTemplate) {
this.redisTemplate = redisTemplate;
}
public boolean lock(String key) {
ValueOperations valueOperations = redisTemplate.opsForValue();
return valueOperations.setIfAbsent(key, "1");
}
public void unlock(String key) {
redisTemplate.delete(key);
}
}
//使用示例
RedisLock lock = new RedisLock(redisTemplate);
if (lock.lock("lock_key")) {
//do something
lock.unlock("lock_key");
} else {
//wait or return directly
}
2、使用ZooKeeper实现分布式锁:
public class ZooKeeperLock {
private ZooKeeper zooKeeper;
public ZooKeeperLock(String zkUrl) throws IOException, KeeperException, InterruptedException {
CountDownLatch connectedSignal = new CountDownLatch(1);
zooKeeper = new ZooKeeper(zkUrl, 1000, new Watcher() {
public void process(WatchedEvent we) {
if (we.getState() == Event.KeeperState.SyncConnected) {
connectedSignal.countDown();
}
}
});
connectedSignal.await();
}
public boolean lock(String key) throws KeeperException, InterruptedException {
String path = "/locks/" + key;
String createdPath = zooKeeper.create(path, null, ZooDefs.Ids.OPEN_ACL_UNSAFE, CreateMode.EPHEMERAL_SEQUENTIAL);
List siblings = zooKeeper.getChildren("/locks", false);
//重载排序方法(compareTo是从小到大排序)
Collections.sort(siblings, new Comparator() {
public int compare(String s1, String s2) {
return s1.substring(s1.indexOf("-")+1).compareTo(s2.substring(s2.indexOf("-")+1));
}
});
if (createdPath.equals("/locks/" + siblings.get(0))) {
return true;
} else {
String prevSibling = siblings.get(siblings.indexOf(createdPath.substring("/locks/".length()))-1);
final CountDownLatch lockSignal = new CountDownLatch(1);
Stat stat = zooKeeper.exists("/locks/"+prevSibling, new Watcher() {
public void process(WatchedEvent we) {
if (we.getType() == Event.EventType.NodeDeleted) {
lockSignal.countDown();
} else if (we.getType() == Event.EventType.None) {
if (we.getState() == Event.KeeperState.Expired) {
lockSignal.countDown();
}
}
}
});
if (stat != null) {
lockSignal.await();
}
return true;
}
}
public void unlock(String key) throws KeeperException, InterruptedException {
String path = "/locks/" + key;
zooKeeper.delete(path, -1);
}
}
//使用示例
ZooKeeperLock lock = new ZooKeeperLock("zk_url");
if (lock.lock("lock_key")) {
//do something
lock.unlock("lock_key");
} else {
//wait or return directly
}
三、读写分离
当大量的读操作与写操作同时进行时,读写分离是一个有效的解决方法。将读操作与写操作分别交给不同的节点进行处理,可以有效减轻单个数据库节点的负担。
1、使用MySQL实现读写分离:
jdbc:mysql://master_server/database?useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&rewriteBatchedStatements=true
jdbc:mysql:loadbalance://master_server,slave_server1,slave_server2/database?loadBalanceConnection=true&useUnicode=true&characterEncoding=UTF-8&autoReconnect=true&rewriteBatchedStatements=true&readFromMasterWhenNoSlaves=true
2、使用MongoDB实现读写分离:
@Bean
public MongoClient mongoClient() {
MongoClientURI uri = new MongoClientURI("mongodb://username:password@host1,host2,host3/?replicaSet=rs0");
return new MongoClient(uri);
}
@Bean(name = "mongoTemplate")
public MongoTemplate mongoTemplate() throws Exception {
MongoTemplate template = new MongoTemplate(mongoClient(), "database_name");
return template;
}
四、分区表
当数据量过大时,将数据分成几个小的表格是一种很好的解决方法。这样可以避免单个表格过大,导致读写效率下降。
1、使用MySQL实现分区表:
CREATE TABLE sales (
sale_id INT NOT NULL AUTO_INCREMENT,
sale_date DATE NOT NULL,
sale_amount DECIMAL(12,2) NOT NULL,
PRIMARY KEY (sale_id,sale_date)
)
PARTITION BY RANGE( YEAR(sale_date) ) (
PARTITION p0 VALUES LESS THAN (2010),
PARTITION p1 VALUES LESS THAN (2011),
PARTITION p2 VALUES LESS THAN (2012),
PARTITION p3 VALUES LESS THAN MAXVALUE
);
2、使用MongoDB实现分区表:
db.runCommand( { shardCollection: "db.collection", key: { "_id": "hashed" } } );
五、数据库连接池
在进行大量数据读写操作时,数据库连接池是一个非常重要的组件。它可以保证在并发高峰期,数据库连接的利用率最大。
1、使用Druid实现数据库连接池:
@Bean
public DataSource dataSource() {
DruidDataSource dataSource = new DruidDataSource();
dataSource.setDriverClassName("com.mysql.jdbc.Driver");
dataSource.setUrl("jdbc:mysql://localhost:3306/test");
dataSource.setUsername("root");
dataSource.setPassword("root");
dataSource.setMaxActive(20);
dataSource.setInitialSize(1);
dataSource.setMinIdle(1);
dataSource.setMaxWait(60000);
dataSource.setTimeBetweenEvictionRunsMillis(60000);
dataSource.setMinEvictableIdleTimeMillis(300000);
dataSource.setValidationQuery("SELECT 1 FROM DUAL");
dataSource.setTestWhileIdle(true);
dataSource.setTestOnBorrow(false);
dataSource.setTestOnReturn(false);
return dataSource;
}
@Bean
public JdbcTemplate jdbcTemplate() {
return new JdbcTemplate(dataSource());
}
2、使用HikariCP实现数据库连接池:
@Bean
public DataSource dataSource() {
HikariConfig config = new HikariConfig();
config.setDriverClassName("com.mysql.cj.jdbc.Driver");
config.setJdbcUrl("jdbc:mysql://localhost:3306/test");
config.setUsername("root");
config.setPassword("root");
config.setMaximumPoolSize(20);
config.setMinimumIdle(1);
config.setConnectionTestQuery("SELECT 1 FROM DUAL");
config.setAutoCommit(false);
config.addDataSourceProperty("cachePrepStmts", "true");
config.addDataSourceProperty("prepStmtCacheSize", "250");
config.addDataSourceProperty("prepStmtCacheSqlLimit", "2048");
config.addDataSourceProperty("useServerPrepStmts", "true");
config.addDataSourceProperty("useLocalSessionState", "true");
config.addDataSourceProperty("rewriteBatchedStatements", "true");
config.addDataSourceProperty("cacheResultSetMetadata", "true");
config.addDataSourceProperty("cacheServerConfiguration", "true");
config.addDataSourceProperty("elideSetAutoCommits", "true");
config.addDataSourceProperty("maintainTimeStats", "false");
return new HikariDataSource(config);
}
@Bean
public JdbcTemplate jdbcTemplate() {
return new JdbcTemplate(dataSource());
}