使用Redis实现延时任务的解决方案
最近在生产环境刚好遇到了延时任务的场景，调研了一下目前主流的方案，分析了一下优劣并且敲定了最终的方案。这篇文章记录了调研的过程，以及初步方案的实现。
候选方案对比
下面是想到的几种实现延时任务的方案，总结了一下相应的优势和劣势。
方案
优势
劣势
选用场景
JDK 内置的延迟队列 DelayQueue
实现简单
数据内存态，不可靠
一致性相对低的场景
调度框架和 MySQL 进行短间隔轮询
实现简单，可靠性高
存在明显的性能瓶颈
数据量较少实时性相对低的场景
RabbitMQ 的 DLX 和 TTL，一般称为 死信队列 方案
异步交互可以削峰
延时的时间长度不可控，如果数据需要持久化则性能会降低
-
调度框架和 Redis 进行短间隔轮询
数据持久化，高性能
实现难度大
常见于支付结果回调方案
时间轮
实时性高
实现难度大，内存消耗大
实时性高的场景
如果应用的数据量不高，实时性要求比较低，选用调度框架和 MySQL 进行短间隔轮询这个方案是最优的方案。但是笔者遇到的场景数据量相对比较大，实时性并不高，采用扫库的方案一定会对 MySQL 实例造成比较大的压力。记得很早之前，看过一个PPT叫《盒子科技聚合支付系统演进》，其中里面有一张图片给予笔者一点启发：
里面刚好用到了调度框架和 Redis 进行短间隔轮询实现延时任务的方案，不过为了分摊应用的压力，图中的方案还做了分片处理。鉴于笔者当前业务紧迫，所以在第一期的方案暂时不考虑分片，只做了一个简化版的实现。
由于PPT中没有任何的代码或者框架贴出，有些需要解决的技术点需要自行思考，下面会重现一次整个方案实现的详细过程。
场景设计
实际的生产场景是笔者负责的某个系统需要对接一个外部的资金方，每一笔资金下单后需要延时30分钟推送对应的附件。这里简化为一个订单信息数据延迟处理的场景，就是每一笔下单记录一条订单消息(暂时叫做 OrderMessage )，订单消息需要延迟5到15秒后进行异步处理。
否决的候选方案实现思路
下面介绍一下其它四个不选用的候选方案，结合一些伪代码和流程分析一下实现过程。
JDK内置延迟队列
DelayQueue 是一个阻塞队列的实现，它的队列元素必须是 Delayed 的子类，这里做个简单的例子：
public class DelayQueueMain {
private static final Logger LOGGER = LoggerFactory.getLogger(DelayQueueMain.class);
public static void main(String[] args) throws Exception {
DelayQueue<OrderMessage> queue = new DelayQueue<>();
// 默认延迟5秒
OrderMessage message = new OrderMessage("ORDER_ID_10086");
queue.add(message);
// 延迟6秒
message = new OrderMessage("ORDER_ID_10087", 6);
queue.add(message);
// 延迟10秒
message = new OrderMessage("ORDER_ID_10088", 10);
queue.add(message);
ExecutorService executorService = Executors.newSingleThreadExecutor(r -> {
Thread thread = new Thread(r);
thread.setName("DelayWorker");
thread.setDaemon(true);
return thread;
});
LOGGER.info("开始执行调度线程...");
executorService.execute(() -> {
while (true) {
try {
OrderMessage task = queue.take();
LOGGER.info("延迟处理订单消息,{}", task.getDescription());
} catch (Exception e) {
LOGGER.error(e.getMessage(), e);
}
}
});
Thread.sleep(Integer.MAX_VALUE);
}
private static class OrderMessage implements Delayed {
private static final DateTimeFormatter F = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
/**
* 默认延迟5000毫秒
*/
private static final long DELAY_MS = 1000L * 5;
/**
* 订单ID
*/
private final String orderId;
/**
* 创建时间戳
*/
private final long timestamp;
/**
* 过期时间
*/
private final long expire;
/**
* 描述
*/
private final String description;
public OrderMessage(String orderId, long expireSeconds) {
this.orderId = orderId;
this.timestamp = System.currentTimeMillis();
this.expire = this.timestamp + expireSeconds * 1000L;
this.description = String.format("订单[%s]-创建时间为:%s,超时时间为:%s", orderId,
LocalDateTime.ofInstant(Instant.ofEpochMilli(timestamp), ZoneId.systemDefault()).format(F),
LocalDateTime.ofInstant(Instant.ofEpochMilli(expire), ZoneId.systemDefault()).format(F));
}
public OrderMessage(String orderId) {
this.orderId = orderId;
this.timestamp = System.currentTimeMillis();
this.expire = this.timestamp + DELAY_MS;
this.description = String.format("订单[%s]-创建时间为:%s,超时时间为:%s", orderId,
LocalDateTime.ofInstant(Instant.ofEpochMilli(timestamp), ZoneId.systemDefault()).format(F),
LocalDateTime.ofInstant(Instant.ofEpochMilli(expire), ZoneId.systemDefault()).format(F));
}
public String getOrderId() {
return orderId;
}
public long getTimestamp() {
return timestamp;
}
public long getExpire() {
return expire;
}
public String getDescription() {
return description;
}
@Override
public long getDelay(TimeUnit unit) {
return unit.convert(this.expire - System.currentTimeMillis(), TimeUnit.MILLISECONDS);
}
@Override
public int compareTo(Delayed o) {
return (int) (this.getDelay(TimeUnit.MILLISECONDS) - o.getDelay(TimeUnit.MILLISECONDS));
}
}
}
注意一下， OrderMessage 实现 Delayed 接口，关键是需要实现 Delayed#getDelay() 和 Delayed#compareTo() 。运行一下 main() 方法：
10:16:08.240 [main] INFO club.throwable.delay.DelayQueueMain - 开始执行调度线程... 10:16:13.224 [DelayWorker] INFO club.throwable.delay.DelayQueueMain - 延迟处理订单消息,订单[ORDER_ID_10086]-创建时间为:2019-08-20 10:16:08,超时时间为:2019-08-20 10:16:13 10:16:14.237 [DelayWorker] INFO club.throwable.delay.DelayQueueMain - 延迟处理订单消息,订单[ORDER_ID_10087]-创建时间为:2019-08-20 10:16:08,超时时间为:2019-08-20 10:16:14 10:16:18.237 [DelayWorker] INFO club.throwable.delay.DelayQueueMain - 延迟处理订单消息,订单[ORDER_ID_10088]-创建时间为:2019-08-20 10:16:08,超时时间为:2019-08-20 10:16:18
调度框架 + MySQL
使用调度框架对 MySQL 表进行短间隔轮询是实现难度比较低的方案，通常服务刚上线，表数据不多并且实时性不高的情况下应该首选这个方案。不过要注意以下几点：
MySQL
引入 Quartz 、 MySQL 的Java驱动包和 spring-boot-starter-jdbc （这里只是为了方便用相对轻量级的框架实现，生产中可以按场景按需选择其他更合理的框架）：
<dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.48</version> <scope>test</scope> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-jdbc</artifactId> <version>2.1.7.RELEASE</version> <scope>test</scope> </dependency> <dependency> <groupId>org.quartz-scheduler</groupId> <artifactId>quartz</artifactId> <version>2.3.1</version> <scope>test</scope> </dependency>
假设表设计如下：
CREATE DATABASE `delayTask` CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_520_ci;
USE `delayTask`;
CREATE TABLE `t_order_message`
(
id BIGINT UNSIGNED PRIMARY KEY AUTO_INCREMENT,
order_id VARCHAR(50) NOT NULL COMMENT '订单ID',
create_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建日期时间',
edit_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改日期时间',
retry_times TINYINT NOT NULL DEFAULT 0 COMMENT '重试次数',
order_status TINYINT NOT NULL DEFAULT 0 COMMENT '订单状态',
INDEX idx_order_id (order_id),
INDEX idx_create_time (create_time)
) COMMENT '订单信息表';
# 写入两条测试数据
INSERT INTO t_order_message(order_id) VALUES ('10086'),('10087');
编写代码：
// 常量
public class OrderConstants {
public static final int MAX_RETRY_TIMES = 5;
public static final int PENDING = 0;
public static final int SUCCESS = 1;
public static final int FAIL = -1;
public static final int LIMIT = 10;
}
// 实体
@Builder
@Data
public class OrderMessage {
private Long id;
private String orderId;
private LocalDateTime createTime;
private LocalDateTime editTime;
private Integer retryTimes;
private Integer orderStatus;
}
// DAO
@RequiredArgsConstructor
public class OrderMessageDao {
private final JdbcTemplate jdbcTemplate;
private static final ResultSetExtractor<List<OrderMessage M = r -> {
List<OrderMessage> list = Lists.newArrayList();
while (r.next()) {
list.add(OrderMessage.builder()
.id(r.getLong("id"))
.orderId(r.getString("order_id"))
.createTime(r.getTimestamp("create_time").toLocalDateTime())
.editTime(r.getTimestamp("edit_time").toLocalDateTime())
.retryTimes(r.getInt("retry_times"))
.orderStatus(r.getInt("order_status"))
.build());
}
return list;
};
public List<OrderMessage> selectPendingRecords(LocalDateTime start,
LocalDateTime end,
List<Integer> statusList,
int maxRetryTimes,
int limit) {
StringJoiner joiner = new StringJoiner(",");
statusList.forEach(s -> joiner.add(String.valueOf(s)));
return jdbcTemplate.query("SELECT * FROM t_order_message WHERE create_time >= " +
"AND order_status IN (",
p -> {
p.setTimestamp(1, Timestamp.valueOf(start));
p.setTimestamp(2, Timestamp.valueOf(end));
p.setString(3, joiner.toString());
p.setInt(4, maxRetryTimes);
p.setInt(5, limit);
}, M);
}
public int updateOrderStatus(Long id, int status) {
return jdbcTemplate.update("UPDATE t_order_message SET order_status = ",
p -> {
p.setInt(1, status);
p.setTimestamp(2, Timestamp.valueOf(LocalDateTime.now()));
p.setLong(3, id);
});
}
}
// Service
@RequiredArgsConstructor
public class OrderMessageService {
private static final Logger LOGGER = LoggerFactory.getLogger(OrderMessageService.class);
private final OrderMessageDao orderMessageDao;
private static final List<Integer> STATUS = Lists.newArrayList();
static {
STATUS.add(OrderConstants.PENDING);
STATUS.add(OrderConstants.FAIL);
}
public void executeDelayJob() {
LOGGER.info("订单处理定时任务开始执行......");
LocalDateTime end = LocalDateTime.now();
// 一天前
LocalDateTime start = end.minusDays(1);
List<OrderMessage> list = orderMessageDao.selectPendingRecords(start, end, STATUS, OrderConstants.MAX_RETRY_TIMES, OrderConstants.LIMIT);
if (!list.isEmpty()) {
for (OrderMessage m : list) {
LOGGER.info("处理订单[{}],状态由{}更新为{}", m.getOrderId(), m.getOrderStatus(), OrderConstants.SUCCESS);
// 这里其实可以优化为批量更新
orderMessageDao.updateOrderStatus(m.getId(), OrderConstants.SUCCESS);
}
}
LOGGER.info("订单处理定时任务开始完毕......");
}
}
// Job
@DisallowConcurrentExecution
public class OrderMessageDelayJob implements Job {
@Override
public void execute(JobExecutionContext jobExecutionContext) throws JobExecutionException {
OrderMessageService service = (OrderMessageService) jobExecutionContext.getMergedJobDataMap().get("orderMessageService");
service.executeDelayJob();
}
public static void main(String[] args) throws Exception {
HikariConfig config = new HikariConfig();
config.setJdbcUrl("jdbc:mysql://localhost:3306/delayTask");
config.setDriverClassName(Driver.class.getName());
config.setUsername("root");
config.setPassword("root");
HikariDataSource dataSource = new HikariDataSource(config);
OrderMessageDao orderMessageDao = new OrderMessageDao(new JdbcTemplate(dataSource));
OrderMessageService service = new OrderMessageService(orderMessageDao);
// 内存模式的调度器
StdSchedulerFactory factory = new StdSchedulerFactory();
Scheduler scheduler = factory.getScheduler();
// 这里没有用到IOC容器,直接用Quartz数据集合传递服务引用
JobDataMap jobDataMap = new JobDataMap();
jobDataMap.put("orderMessageService", service);
// 新建Job
JobDetail job = JobBuilder.newJob(OrderMessageDelayJob.class)
.withIdentity("orderMessageDelayJob", "delayJob")
.usingJobData(jobDataMap)
.build();
// 新建触发器,10秒执行一次
Trigger trigger = TriggerBuilder.newTrigger()
.withIdentity("orderMessageDelayTrigger", "delayJob")
.withSchedule(SimpleScheduleBuilder.simpleSchedule().withIntervalInSeconds(10).repeatForever())
.build();
scheduler.scheduleJob(job, trigger);
// 启动调度器
scheduler.start();
Thread.sleep(Integer.MAX_VALUE);
}
}
这个例子里面用了 create_time 做轮询，实际上可以添加一个调度时间 schedule_time 列做轮询，这样子才能更容易定制空闲时和忙碌时候的调度策略。上面的示例的运行效果如下：
11:58:27.202 [main] INFO org.quartz.core.QuartzScheduler - Scheduler meta-data: Quartz Scheduler (v2.3.1) 'DefaultQuartzScheduler' with instanceId 'NON_CLUSTERED' Scheduler class: 'org.quartz.core.QuartzScheduler' - running locally. NOT STARTED. Currently in standby mode. Number of jobs executed: 0 Using thread pool 'org.quartz.simpl.SimpleThreadPool' - with 10 threads. Using job-store 'org.quartz.simpl.RAMJobStore' - which does not support persistence. and is not clustered. 11:58:27.202 [main] INFO org.quartz.impl.StdSchedulerFactory - Quartz scheduler 'DefaultQuartzScheduler' initialized from default resource file in Quartz package: 'quartz.properties' 11:58:27.202 [main] INFO org.quartz.impl.StdSchedulerFactory - Quartz scheduler version: 2.3.1 11:58:27.209 [main] INFO org.quartz.core.QuartzScheduler - Scheduler DefaultQuartzScheduler_$_NON_CLUSTERED started. 11:58:27.212 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 1 triggers 11:58:27.217 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.simpl.PropertySettingJobFactory - Producing instance of Job 'delayJob.orderMessageDelayJob', class=club.throwable.jdbc.OrderMessageDelayJob 11:58:27.219 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@10eb8c53 11:58:27.220 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 0 triggers 11:58:27.221 [DefaultQuartzScheduler_Worker-1] DEBUG org.quartz.core.JobRunShell - Calling execute on job delayJob.orderMessageDelayJob 11:58:34.440 [DefaultQuartzScheduler_Worker-1] INFO club.throwable.jdbc.OrderMessageService - 订单处理定时任务开始执行...... 11:58:34.451 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@3d27ece4 11:58:34.459 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@64e808af 11:58:34.470 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@79c8c2b7 11:58:34.477 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@19a62369 11:58:34.485 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@1673d017 11:58:34.485 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - After adding stats (total=10, active=0, idle=10, waiting=0) 11:58:34.559 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL query 11:58:34.565 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL statement [SELECT * FROM t_order_message WHERE create_time >= "color: #ff0000">RabbitMQ死信队列使用
RabbitMQ死信队列依赖于RabbitMQ的两个特性：TTL和DLX。
TTL：Time To Live，消息存活时间，包括两个维度：队列消息存活时间和消息本身的存活时间。
DLX：Dead Letter Exchange，死信交换器。画个图描述一下这两个特性：
下面为了简单起见，
TTL使用了针对队列的维度。引入RabbitMQ的Java驱动：<dependency> <groupId>com.rabbitmq</groupId> <artifactId>amqp-client</artifactId> <version>5.7.3</version> <scope>test</scope> </dependency>代码如下：
public class DlxMain { private static final DateTimeFormatter F = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"); private static final Logger LOGGER = LoggerFactory.getLogger(DlxMain.class); public static void main(String[] args) throws Exception { ConnectionFactory factory = new ConnectionFactory(); Connection connection = factory.newConnection(); Channel producerChannel = connection.createChannel(); Channel consumerChannel = connection.createChannel(); // dlx交换器名称为dlx.exchange,类型是direct,绑定键为dlx.key,队列名为dlx.queue producerChannel.exchangeDeclare("dlx.exchange", "direct"); producerChannel.queueDeclare("dlx.queue", false, false, false, null); producerChannel.queueBind("dlx.queue", "dlx.exchange", "dlx.key"); Map<String, Object> queueArgs = new HashMap<>(); // 设置队列消息过期时间,5秒 queueArgs.put("x-message-ttl", 5000); // 指定DLX相关参数 queueArgs.put("x-dead-letter-exchange", "dlx.exchange"); queueArgs.put("x-dead-letter-routing-key", "dlx.key"); // 声明业务队列 producerChannel.queueDeclare("business.queue", false, false, false, queueArgs); ExecutorService executorService = Executors.newSingleThreadExecutor(r -> { Thread thread = new Thread(r); thread.setDaemon(true); thread.setName("DlxConsumer"); return thread; }); // 启动消费者 executorService.execute(() -> { try { consumerChannel.basicConsume("dlx.queue", true, new DlxConsumer(consumerChannel)); } catch (IOException e) { LOGGER.error(e.getMessage(), e); } }); OrderMessage message = new OrderMessage("10086"); producerChannel.basicPublish("", "business.queue", MessageProperties.TEXT_PLAIN, message.getDescription().getBytes(StandardCharsets.UTF_8)); LOGGER.info("发送消息成功,订单ID:{}", message.getOrderId()); message = new OrderMessage("10087"); producerChannel.basicPublish("", "business.queue", MessageProperties.TEXT_PLAIN, message.getDescription().getBytes(StandardCharsets.UTF_8)); LOGGER.info("发送消息成功,订单ID:{}", message.getOrderId()); message = new OrderMessage("10088"); producerChannel.basicPublish("", "business.queue", MessageProperties.TEXT_PLAIN, message.getDescription().getBytes(StandardCharsets.UTF_8)); LOGGER.info("发送消息成功,订单ID:{}", message.getOrderId()); Thread.sleep(Integer.MAX_VALUE); } private static class DlxConsumer extends DefaultConsumer { DlxConsumer(Channel channel) { super(channel); } @Override public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException { LOGGER.info("处理消息成功:{}", new String(body, StandardCharsets.UTF_8)); } } private static class OrderMessage { private final String orderId; private final long timestamp; private final String description; OrderMessage(String orderId) { this.orderId = orderId; this.timestamp = System.currentTimeMillis(); this.description = String.format("订单[%s],订单创建时间为:%s", orderId, LocalDateTime.ofInstant(Instant.ofEpochMilli(timestamp), ZoneId.systemDefault()).format(F)); } public String getOrderId() { return orderId; } public long getTimestamp() { return timestamp; } public String getDescription() { return description; } } }运行
main()方法结果如下：16:35:58.638 [main] INFO club.throwable.dlx.DlxMain - 发送消息成功,订单ID:10086 16:35:58.641 [main] INFO club.throwable.dlx.DlxMain - 发送消息成功,订单ID:10087 16:35:58.641 [main] INFO club.throwable.dlx.DlxMain - 发送消息成功,订单ID:10088 16:36:03.646 [pool-1-thread-4] INFO club.throwable.dlx.DlxMain - 处理消息成功:订单[10086],订单创建时间为:2019-08-20 16:35:58 16:36:03.670 [pool-1-thread-5] INFO club.throwable.dlx.DlxMain - 处理消息成功:订单[10087],订单创建时间为:2019-08-20 16:35:58 16:36:03.670 [pool-1-thread-6] INFO club.throwable.dlx.DlxMain - 处理消息成功:订单[10088],订单创建时间为:2019-08-20 16:35:58时间轮
时间轮
TimingWheel是一种高效、低延迟的调度数据结构，底层采用数组实现存储任务列表的环形队列，示意图如下：
这里暂时不对时间轮和其实现作分析，只简单举例说明怎么使用时间轮实现延时任务。这里使用
Netty提供的HashedWheelTimer，引入依赖：<dependency> <groupId>io.netty</groupId> <artifactId>netty-common</artifactId> <version>4.1.39.Final</version> </dependency>代码如下：
public class HashedWheelTimerMain { private static final DateTimeFormatter F = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS"); public static void main(String[] args) throws Exception { AtomicInteger counter = new AtomicInteger(); ThreadFactory factory = r -> { Thread thread = new Thread(r); thread.setDaemon(true); thread.setName("HashedWheelTimerWorker-" + counter.getAndIncrement()); return thread; }; // tickDuration - 每tick一次的时间间隔, 每tick一次就会到达下一个槽位 // unit - tickDuration的时间单位 // ticksPerWhee - 时间轮中的槽位数 Timer timer = new HashedWheelTimer(factory, 1, TimeUnit.SECONDS, 60); TimerTask timerTask = new DefaultTimerTask("10086"); timer.newTimeout(timerTask, 5, TimeUnit.SECONDS); timerTask = new DefaultTimerTask("10087"); timer.newTimeout(timerTask, 10, TimeUnit.SECONDS); timerTask = new DefaultTimerTask("10088"); timer.newTimeout(timerTask, 15, TimeUnit.SECONDS); Thread.sleep(Integer.MAX_VALUE); } private static class DefaultTimerTask implements TimerTask { private final String orderId; private final long timestamp; public DefaultTimerTask(String orderId) { this.orderId = orderId; this.timestamp = System.currentTimeMillis(); } @Override public void run(Timeout timeout) throws Exception { System.out.println(String.format("任务执行时间:%s,订单创建时间:%s,订单ID:%s", LocalDateTime.now().format(F), LocalDateTime.ofInstant(Instant.ofEpochMilli(timestamp), ZoneId.systemDefault()).format(F), orderId)); } } }运行结果：
任务执行时间:2019-08-20 17:19:49.310,订单创建时间:2019-08-20 17:19:43.294,订单ID:10086
任务执行时间:2019-08-20 17:19:54.297,订单创建时间:2019-08-20 17:19:43.301,订单ID:10087
任务执行时间:2019-08-20 17:19:59.297,订单创建时间:2019-08-20 17:19:43.301,订单ID:10088一般来说，任务执行的时候应该使用另外的业务线程池，以免阻塞时间轮本身的运动。
选用的方案实现过程
最终选用了基于
Redis的有序集合Sorted Set和Quartz短轮询进行实现。具体方案是：
- 订单创建的时候，订单ID和当前时间戳分别作为
Sorted Set的member和score添加到订单队列Sorted Set中。 - 订单创建的时候，订单ID和推送内容
JSON字符串分别作为field和value添加到订单队列内容Hash中。 - 第1步和第2步操作的时候用
Lua脚本保证原子性。 - 使用一个异步线程通过
Sorted Set的命令ZREVRANGEBYSCORE弹出指定数量的订单ID对应的订单队列内容Hash中的订单推送内容数据进行处理。
对于第4点处理有两种方案：
- 方案一：弹出订单内容数据的同时进行数据删除，也就是
ZREVRANGEBYSCORE、ZREM和HDEL命令要在同一个Lua脚本中执行，这样的话Lua脚本的编写难度大，并且由于弹出数据已经在Redis中删除，如果数据处理失败则可能需要从数据库重新查询补偿。 - 方案二：弹出订单内容数据之后，在数据处理完成的时候再主动删除订单队列
Sorted Set和订单队列内容Hash中对应的数据，这样的话需要控制并发，有重复执行的可能性。
最终暂时选用了方案一，也就是从 Sorted Set 弹出订单ID并且从 Hash 中获取完推送数据之后马上删除这两个集合中对应的数据。方案的流程图大概是这样：
这里先详细说明一下用到的 Redis 命令。
Sorted Set相关命令
ZADD 命令 - 将一个或多个成员元素及其分数值加入到有序集当中。
ZADD KEY SCORE1 VALUE1.. SCOREN VALUEN
ZREVRANGEBYSCORE 命令 - 返回有序集中指定分数区间内的所有的成员。有序集成员按分数值递减(从大到小)的次序排列。
ZREVRANGEBYSCORE key max min [WITHSCORES] [LIMIT offset count]
max：分数区间 - 最大分数。 min：分数区间 - 最小分数。 WITHSCORES：可选参数，是否返回分数值，指定则会返回得分值。 LIMIT：可选参数，offset和count原理和 MySQL 的 LIMIT offset,size 一致，如果不指定此参数则返回整个集合的数据。 ZREM 命令 - 用于移除有序集中的一个或多个成员，不存在的成员将被忽略。
ZREM key member [member ...]
Hash相关命令 HMSET 命令 - 同时将多个field-value(字段-值)对设置到哈希表中。
HMSET KEY_NAME FIELD1 VALUE1 ...FIELDN VALUEN
HDEL 命令 - 删除哈希表key中的一个或多个指定字段，不存在的字段将被忽略。
HDEL KEY_NAME FIELD1.. FIELDN
Lua相关 加载 Lua 脚本并且返回脚本的 SHA-1 字符串： SCRIPT LOAD script 。 执行已经加载的 Lua 脚本： EVALSHA sha1 numkeys key [key ...] arg [arg ...] 。 unpack 函数可以把 table 类型的参数转化为可变参数，不过需要注意的是 unpack 函数必须使用在非变量定义的函数调用的最后一个参数，否则会失效，详细见 Stackoverflow 的提问 table.unpack() only returns the first element 。
PS：如果不熟悉Lua语言，建议系统学习一下，因为想用好Redis，一定离不开Lua。
引入依赖：
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-dependencies</artifactId>
<version>2.1.7.RELEASE</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<dependencies>
<dependency>
<groupId>org.quartz-scheduler</groupId>
<artifactId>quartz</artifactId>
<version>2.3.1</version>
</dependency>
<dependency>
<groupId>redis.clients</groupId>
<artifactId>jedis</artifactId>
<version>3.1.0</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-jdbc</artifactId>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-context-support</artifactId>
<version>5.1.9.RELEASE</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.8</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.59</version>
</dependency>
</dependencies>
编写 Lua 脚本 /lua/enqueue.lua 和 /lua/dequeue.lua ：
-- /lua/enqueue.lua
local zset_key = KEYS[1]
local hash_key = KEYS[2]
local zset_value = ARGV[1]
local zset_score = ARGV[2]
local hash_field = ARGV[3]
local hash_value = ARGV[4]
redis.call('ZADD', zset_key, zset_score, zset_value)
redis.call('HSET', hash_key, hash_field, hash_value)
return nil
-- /lua/dequeue.lua
-- 参考jesque的部分Lua脚本实现
local zset_key = KEYS[1]
local hash_key = KEYS[2]
local min_score = ARGV[1]
local max_score = ARGV[2]
local offset = ARGV[3]
local limit = ARGV[4]
-- TYPE命令的返回结果是{'ok':'zset'}这样子,这里利用next做一轮迭代
local status, type = next(redis.call('TYPE', zset_key))
if status ~= nil and status == 'ok' then
if type == 'zset' then
local list = redis.call('ZREVRANGEBYSCORE', zset_key, max_score, min_score, 'LIMIT', offset, limit)
if list ~= nil and #list > 0 then
-- unpack函数能把table转化为可变参数
redis.call('ZREM', zset_key, unpack(list))
local result = redis.call('HMGET', hash_key, unpack(list))
redis.call('HDEL', hash_key, unpack(list))
return result
end
end
end
return nil
编写核心API代码：
// Jedis提供者
@Component
public class JedisProvider implements InitializingBean {
private JedisPool jedisPool;
@Override
public void afterPropertiesSet() throws Exception {
jedisPool = new JedisPool();
}
public Jedis provide(){
return jedisPool.getResource();
}
}
// OrderMessage
@Data
public class OrderMessage {
private String orderId;
private BigDecimal amount;
private Long userId;
}
// 延迟队列接口
public interface OrderDelayQueue {
void enqueue(OrderMessage message);
List<OrderMessage> dequeue(String min, String max, String offset, String limit);
List<OrderMessage> dequeue();
String enqueueSha();
String dequeueSha();
}
// 延迟队列实现类
@RequiredArgsConstructor
@Component
public class RedisOrderDelayQueue implements OrderDelayQueue, InitializingBean {
private static final String MIN_SCORE = "0";
private static final String OFFSET = "0";
private static final String LIMIT = "10";
private static final String ORDER_QUEUE = "ORDER_QUEUE";
private static final String ORDER_DETAIL_QUEUE = "ORDER_DETAIL_QUEUE";
private static final String ENQUEUE_LUA_SCRIPT_LOCATION = "/lua/enqueue.lua";
private static final String DEQUEUE_LUA_SCRIPT_LOCATION = "/lua/dequeue.lua";
private static final AtomicReference<String> ENQUEUE_LUA_SHA = new AtomicReference<>();
private static final AtomicReference<String> DEQUEUE_LUA_SHA = new AtomicReference<>();
private static final List<String> KEYS = Lists.newArrayList();
private final JedisProvider jedisProvider;
static {
KEYS.add(ORDER_QUEUE);
KEYS.add(ORDER_DETAIL_QUEUE);
}
@Override
public void enqueue(OrderMessage message) {
List<String> args = Lists.newArrayList();
args.add(message.getOrderId());
args.add(String.valueOf(System.currentTimeMillis()));
args.add(message.getOrderId());
args.add(JSON.toJSONString(message));
try (Jedis jedis = jedisProvider.provide()) {
jedis.evalsha(ENQUEUE_LUA_SHA.get(), KEYS, args);
}
}
@Override
public List<OrderMessage> dequeue() {
// 30分钟之前
String maxScore = String.valueOf(System.currentTimeMillis() - 30 * 60 * 1000);
return dequeue(MIN_SCORE, maxScore, OFFSET, LIMIT);
}
@SuppressWarnings("unchecked")
@Override
public List<OrderMessage> dequeue(String min, String max, String offset, String limit) {
List<String> args = new ArrayList<>();
args.add(max);
args.add(min);
args.add(offset);
args.add(limit);
List<OrderMessage> result = Lists.newArrayList();
try (Jedis jedis = jedisProvider.provide()) {
List<String> eval = (List<String>) jedis.evalsha(DEQUEUE_LUA_SHA.get(), KEYS, args);
if (null != eval) {
for (String e : eval) {
result.add(JSON.parseObject(e, OrderMessage.class));
}
}
}
return result;
}
@Override
public String enqueueSha() {
return ENQUEUE_LUA_SHA.get();
}
@Override
public String dequeueSha() {
return DEQUEUE_LUA_SHA.get();
}
@Override
public void afterPropertiesSet() throws Exception {
// 加载Lua脚本
loadLuaScript();
}
private void loadLuaScript() throws Exception {
try (Jedis jedis = jedisProvider.provide()) {
ClassPathResource resource = new ClassPathResource(ENQUEUE_LUA_SCRIPT_LOCATION);
String luaContent = StreamUtils.copyToString(resource.getInputStream(), StandardCharsets.UTF_8);
String sha = jedis.scriptLoad(luaContent);
ENQUEUE_LUA_SHA.compareAndSet(null, sha);
resource = new ClassPathResource(DEQUEUE_LUA_SCRIPT_LOCATION);
luaContent = StreamUtils.copyToString(resource.getInputStream(), StandardCharsets.UTF_8);
sha = jedis.scriptLoad(luaContent);
DEQUEUE_LUA_SHA.compareAndSet(null, sha);
}
}
public static void main(String[] as) throws Exception {
DateTimeFormatter f = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS");
JedisProvider jedisProvider = new JedisProvider();
jedisProvider.afterPropertiesSet();
RedisOrderDelayQueue queue = new RedisOrderDelayQueue(jedisProvider);
queue.afterPropertiesSet();
// 写入测试数据
OrderMessage message = new OrderMessage();
message.setAmount(BigDecimal.valueOf(10086));
message.setOrderId("ORDER_ID_10086");
message.setUserId(10086L);
message.setTimestamp(LocalDateTime.now().format(f));
List<String> args = Lists.newArrayList();
args.add(message.getOrderId());
// 测试需要,score设置为30分钟之前
args.add(String.valueOf(System.currentTimeMillis() - 30 * 60 * 1000));
args.add(message.getOrderId());
args.add(JSON.toJSONString(message));
try (Jedis jedis = jedisProvider.provide()) {
jedis.evalsha(ENQUEUE_LUA_SHA.get(), KEYS, args);
}
List<OrderMessage> dequeue = queue.dequeue();
System.out.println(dequeue);
}
}
这里先执行一次 main() 方法验证一下延迟队列是否生效：
[OrderMessage(orderId=ORDER_ID_10086, amount=10086, userId=10086, timestamp=2019-08-21 08:32:22.885)]
确定延迟队列的代码没有问题，接着编写一个 Quartz 的 Job 类型的消费者 OrderMessageConsumer ：
@DisallowConcurrentExecution
@Component
public class OrderMessageConsumer implements Job {
private static final AtomicInteger COUNTER = new AtomicInteger();
private static final ExecutorService BUSINESS_WORKER_POOL = Executors.newFixedThreadPool(Runtime.getRuntime().availableProcessors(), r -> {
Thread thread = new Thread(r);
thread.setDaemon(true);
thread.setName("OrderMessageConsumerWorker-" + COUNTER.getAndIncrement());
return thread;
});
private static final Logger LOGGER = LoggerFactory.getLogger(OrderMessageConsumer.class);
@Autowired
private OrderDelayQueue orderDelayQueue;
@Override
public void execute(JobExecutionContext jobExecutionContext) throws JobExecutionException {
StopWatch stopWatch = new StopWatch();
stopWatch.start();
LOGGER.info("订单消息处理定时任务开始执行......");
List<OrderMessage> messages = orderDelayQueue.dequeue();
if (!messages.isEmpty()) {
// 简单的列表等分放到线程池中执行
List<List<OrderMessage partition = Lists.partition(messages, 2);
int size = partition.size();
final CountDownLatch latch = new CountDownLatch(size);
for (List<OrderMessage> p : partition) {
BUSINESS_WORKER_POOL.execute(new ConsumeTask(p, latch));
}
try {
latch.await();
} catch (InterruptedException ignore) {
//ignore
}
}
stopWatch.stop();
LOGGER.info("订单消息处理定时任务执行完毕,耗时:{} ms......", stopWatch.getTotalTimeMillis());
}
@RequiredArgsConstructor
private static class ConsumeTask implements Runnable {
private final List<OrderMessage> messages;
private final CountDownLatch latch;
@Override
public void run() {
try {
// 实际上这里应该单条捕获异常
for (OrderMessage message : messages) {
LOGGER.info("处理订单信息,内容:{}", message);
}
} finally {
latch.countDown();
}
}
}
}
上面的消费者设计的时候需要有以下考量：
- 使用
@DisallowConcurrentExecution注解不允许Job并发执行，其实多个Job并发执行意义不大，因为我们采用的是短间隔的轮询，而Redis是单线程处理命令，在客户端做多线程其实效果不佳。 - 线程池
BUSINESS_WORKER_POOL的线程容量或者队列应该综合LIMIT值、等分订单信息列表中使用的size值以及ConsumeTask里面具体的执行时间进行考虑，这里只是为了方便使用了固定容量的线程池。 ConsumeTask中应该对每一条订单信息的处理单独捕获异常和吞并异常，或者把处理单个订单信息的逻辑封装成一个不抛出异常的方法。
其他 Quartz 相关的代码：
// Quartz配置类
@Configuration
public class QuartzAutoConfiguration {
@Bean
public SchedulerFactoryBean schedulerFactoryBean(QuartzAutowiredJobFactory quartzAutowiredJobFactory) {
SchedulerFactoryBean factory = new SchedulerFactoryBean();
factory.setAutoStartup(true);
factory.setJobFactory(quartzAutowiredJobFactory);
return factory;
}
@Bean
public QuartzAutowiredJobFactory quartzAutowiredJobFactory() {
return new QuartzAutowiredJobFactory();
}
public static class QuartzAutowiredJobFactory extends AdaptableJobFactory implements BeanFactoryAware {
private AutowireCapableBeanFactory autowireCapableBeanFactory;
@Override
public void setBeanFactory(BeanFactory beanFactory) throws BeansException {
this.autowireCapableBeanFactory = (AutowireCapableBeanFactory) beanFactory;
}
@Override
protected Object createJobInstance(TriggerFiredBundle bundle) throws Exception {
Object jobInstance = super.createJobInstance(bundle);
// 这里利用AutowireCapableBeanFactory从新建的Job实例做一次自动装配,得到一个原型(prototype)的JobBean实例
autowireCapableBeanFactory.autowireBean(jobInstance);
return jobInstance;
}
}
}
这里暂时使用了内存态的 RAMJobStore 去存放任务和触发器的相关信息，如果在生产环境最好替换成基于 MySQL 也就是 JobStoreTX 进行集群化，最后是启动函数和 CommandLineRunner 的实现：
@SpringBootApplication(exclude = {DataSourceAutoConfiguration.class, TransactionAutoConfiguration.class})
public class Application implements CommandLineRunner {
@Autowired
private Scheduler scheduler;
@Autowired
private JedisProvider jedisProvider;
public static void main(String[] args) {
SpringApplication.run(Application.class, args);
}
@Override
public void run(String... args) throws Exception {
// 准备一些测试数据
prepareOrderMessageData();
JobDetail job = JobBuilder.newJob(OrderMessageConsumer.class)
.withIdentity("OrderMessageConsumer", "DelayTask")
.build();
// 触发器5秒触发一次
Trigger trigger = TriggerBuilder.newTrigger()
.withIdentity("OrderMessageConsumerTrigger", "DelayTask")
.withSchedule(SimpleScheduleBuilder.simpleSchedule().withIntervalInSeconds(5).repeatForever())
.build();
scheduler.scheduleJob(job, trigger);
}
private void prepareOrderMessageData() throws Exception {
DateTimeFormatter f = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS");
try (Jedis jedis = jedisProvider.provide()) {
List<OrderMessage> messages = Lists.newArrayList();
for (int i = 0; i < 100; i++) {
OrderMessage message = new OrderMessage();
message.setAmount(BigDecimal.valueOf(i));
message.setOrderId("ORDER_ID_" + i);
message.setUserId((long) i);
message.setTimestamp(LocalDateTime.now().format(f));
messages.add(message);
}
// 这里暂时不使用Lua
Map<String, Double> map = Maps.newHashMap();
Map<String, String> hash = Maps.newHashMap();
for (OrderMessage message : messages) {
// 故意把score设计成30分钟前
map.put(message.getOrderId(), Double.valueOf(String.valueOf(System.currentTimeMillis() - 30 * 60 * 1000)));
hash.put(message.getOrderId(), JSON.toJSONString(message));
}
jedis.zadd("ORDER_QUEUE", map);
jedis.hmset("ORDER_DETAIL_QUEUE", hash);
}
}
}
输出结果如下：
2019-08-21 22:45:59.518 INFO 33000 --- [ryBean_Worker-1] club.throwable.OrderMessageConsumer : 订单消息处理定时任务开始执行......
2019-08-21 22:45:59.525 INFO 33000 --- [onsumerWorker-4] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_91, amount=91, userId=91, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.525 INFO 33000 --- [onsumerWorker-2] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_95, amount=95, userId=95, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.525 INFO 33000 --- [onsumerWorker-1] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_97, amount=97, userId=97, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.525 INFO 33000 --- [onsumerWorker-0] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_99, amount=99, userId=99, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.525 INFO 33000 --- [onsumerWorker-3] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_93, amount=93, userId=93, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.539 INFO 33000 --- [onsumerWorker-2] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_94, amount=94, userId=94, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.539 INFO 33000 --- [onsumerWorker-1] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_96, amount=96, userId=96, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.539 INFO 33000 --- [onsumerWorker-3] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_92, amount=92, userId=92, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.539 INFO 33000 --- [onsumerWorker-0] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_98, amount=98, userId=98, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.539 INFO 33000 --- [onsumerWorker-4] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_90, amount=90, userId=90, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:45:59.540 INFO 33000 --- [ryBean_Worker-1] club.throwable.OrderMessageConsumer : 订单消息处理定时任务执行完毕,耗时:22 ms......
2019-08-21 22:46:04.515 INFO 33000 --- [ryBean_Worker-2] club.throwable.OrderMessageConsumer : 订单消息处理定时任务开始执行......
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-5] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_89, amount=89, userId=89, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-6] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_87, amount=87, userId=87, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-7] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_85, amount=85, userId=85, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-5] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_88, amount=88, userId=88, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-2] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_83, amount=83, userId=83, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-1] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_81, amount=81, userId=81, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-6] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_86, amount=86, userId=86, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-2] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_82, amount=82, userId=82, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-7] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_84, amount=84, userId=84, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-1] club.throwable.OrderMessageConsumer : 处理订单信息,内容:OrderMessage(orderId=ORDER_ID_80, amount=80, userId=80, timestamp=2019-08-21 22:45:59.475)
2019-08-21 22:46:04.516 INFO 33000 --- [ryBean_Worker-2] club.throwable.OrderMessageConsumer : 订单消息处理定时任务执行完毕,耗时:1 ms......
......
首次执行的时候涉及到一些组件的初始化，会比较慢，后面看到由于我们只是简单打印订单信息，所以定时任务执行比较快。如果在不调整当前架构的情况下，生产中需要注意：
- 切换
JobStore为JDBC模式，Quartz官方有完整教程，或者看笔者之前翻译的Quartz文档。 - 需要监控或者收集任务的执行状态，添加预警等等。
这里其实有一个性能隐患，命令 ZREVRANGEBYSCORE 的时间复杂度可以视为为 O(N) ， N 是集合的元素个数，由于这里把所有的订单信息都放进了同一个 Sorted Set ( ORDER_QUEUE )中，所以在一直有新增数据的时候， dequeue 脚本的时间复杂度一直比较高，后续订单量升高之后会此处一定会成为性能瓶颈，后面会给出解决的方案。
小结
这篇文章主要从一个实际生产案例的仿真例子入手，分析了当前延时任务的一些实现方案，还基于 Redis 和 Quartz 给出了一个完整的示例。当前的示例只是处于可运行的状态，有些问题尚未解决。下一篇文章会着眼于解决两个方面的问题：
- 分片。
- 监控。
还有一点， 架构是基于业务形态演进出来的，很多东西需要结合场景进行方案设计和改进，思路仅供参考，切勿照搬代码 。
以上所述是小编给大家介绍的使用Redis实现延时任务的解决方案，非常不错，具有一定的参考借鉴价值，需要的朋友参考下吧！
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