业务
商品有库存， 如10000 每买一个商品 库存就减一
减库存可以通过mysql来实现 如
update product_stock set stock = stock - 1 where product_id = 1 and stock > 0; 也可以使用redis来实现 如
decr 1_stock
(integer) 99
面对这种场景都说要使用redis 因为redis并发性能更好 想实际验证一下是否这样
思路
设置较大的并发数去更新库存 执行10次 比较redis和mysql花费的时间
代码
@SpringBootApplication
public class CocurrentUpdateStockApplication implements CommandLineRunner {
@Autowired
private JdbcTemplate jdbcTemplate;
@Bean
JedisConnectionFactory jedisConnectionFactory() {
return new JedisConnectionFactory();
}
@Bean
RedisTemplate<String, Long> redisTemplate() {
final RedisTemplate<String, Long> template = new RedisTemplate<String, Long>();
template.setConnectionFactory(jedisConnectionFactory());
template.setKeySerializer(new StringRedisSerializer());
template.setHashValueSerializer(new GenericToStringSerializer<Long>(Long.class));
template.setValueSerializer(new GenericToStringSerializer<Long>(Long.class));
return template;
}
public static void main(String[] args) {
SpringApplication.run(CocurrentUpdateStockApplication.class, args);
}
//mysql减库存任务
private Callable<Void> updateStockInMysqlTask = () -> {
final String sql = "update product_stock set stock = stock-1 where product_id=1 and stock>0";
jdbcTemplate.update(sql);
return null;
};
//redis减库存任务
private Callable<Void> updateStockInRedisTask = () -> {
redisTemplate().execute(new RedisCallback<Long>() {
public Long doInRedis(RedisConnection connection) throws DataAccessException {
Long decr = connection.decr("1_stock".getBytes());
return decr;
}
});
return null;
};
@Override
public void run(String... args) throws Exception {
final String name = "mysql"; // or "redis"
System.out.printf("start concurrent update stock in %s...%n", name);
List<Long> timeList = new ArrayList<>();
for (int i = 0; i < 10; i++) {//分别统计10次
long start = System.currentTimeMillis();
concurrentUpdateStock(name); //
long end = System.currentTimeMillis();
System.out.printf("Done. Take time: %d ms%n", end - start);
timeList.add(end - start);
Thread.sleep(1000); //休眠1秒
}
System.out.println(timeList.stream().collect(Collectors.summarizingLong(t -> t))); //输出统计结果
}
private void concurrentUpdateStock(String name) throws InterruptedException {
// 模拟并发更新库存
int nThreads = 500; //设置一个较大线程数
ExecutorService pool = Executors.newFixedThreadPool(nThreads);
List<Callable<Void>> tasks = new ArrayList<>();
for (int i = 0; i < nThreads * 2; i++) { //2倍于线程数的减库存任务
if ("mysql".equalsIgnoreCase(name))
tasks.add(updateStockInMysqlTask);
else if ("redis".equalsIgnoreCase(name))
tasks.add(updateStockInRedisTask);
}
List<Future<Void>> futureList = pool.invokeAll(tasks); //并发去执行这些任务
while (futureList.stream().anyMatch(f -> !f.isDone())); //等待任务执行完
pool.shutdown();
}
}
输出结果
mysql：
LongSummaryStatistics{count=10, sum=11485, min=897, average=1148.500000, max=1458}
redis：
LongSummaryStatistics{count=10, sum=1706, min=95, average=170.600000, max=493}结果
并发执行1000次减库存操作 mysql要比redis慢差不多7倍
