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使用Redis实现延时任务的解决方案

发表于：2024-11-29 作者：千家信息网编辑

千家信息网最后更新 2024年11月29日，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

千家信息网最后更新 2024年11月29日使用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 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 10:16:1310:16:14.237 [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 10:16:1410:16:18.237 [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）：

  mysql  mysql-connector-java  5.1.48  test  org.springframework.boot  spring-boot-starter-jdbc  2.1.7.RELEASE  test  org.quartz-scheduler  quartz  2.3.1  test

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@Datapublic class OrderMessage {  private Long id;  private String orderId;  private LocalDateTime createTime;  private LocalDateTime editTime;  private Integer retryTimes;  private Integer orderStatus;}// DAO@RequiredArgsConstructorpublic class OrderMessageDao {  private final JdbcTemplate jdbcTemplate;  private static final ResultSetExtractor> M = r -> {    List 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 selectPendingRecords(LocalDateTime start,                          LocalDateTime end,                          List 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 create_time <= ? " +            "AND order_status IN (?) AND retry_times < ? LIMIT ?",        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 = ?,edit_time = ? WHERE id =?",        p -> {          p.setInt(1, status);          p.setTimestamp(2, Timestamp.valueOf(LocalDateTime.now()));          p.setLong(3, id);        });  }}// Service@RequiredArgsConstructorpublic class OrderMessageService {  private static final Logger LOGGER = LoggerFactory.getLogger(OrderMessageService.class);  private final OrderMessageDao orderMessageDao;  private static final List 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 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@DisallowConcurrentExecutionpublic 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?useSSL=false&characterEncoding=utf8");    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: 2.3.111:58:27.209 [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 triggers11:58:27.217 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.simpl.PropertySettingJobFactory - Producing instance of Job 'delayJob.orderMessageDelayJob', class=club.throwable.jdbc.OrderMessageDelayJob11:58:27.219 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@10eb8c5311:58:27.220 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 0 triggers11:58:27.221 [DefaultQuartzScheduler_Worker-1] DEBUG org.quartz.core.JobRunShell - Calling execute on job delayJob.orderMessageDelayJob11:58:34.440 [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@3d27ece411:58:34.459 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@64e808af11:58:34.470 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@79c8c2b711:58:34.477 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@19a6236911:58:34.485 [HikariPool-1 connection adder] DEBUG com.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Added connection com.mysql.jdbc.JDBC4Connection@1673d01711:58:34.485 [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 query11:58:34.565 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL statement [SELECT * FROM t_order_message WHERE create_time >= ? AND create_time <= ? AND order_status IN (?) AND retry_times < ? LIMIT ?]11:58:34.645 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.datasource.DataSourceUtils - Fetching JDBC Connection from DataSource11:58:35.210 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - SQLWarning ignored: SQL state '22007', error code '1292', message [Truncated incorrect DOUBLE value: '0,-1']11:58:35.335 [DefaultQuartzScheduler_Worker-1] INFO club.throwable.jdbc.OrderMessageService - 处理订单[10086],状态由0更新为111:58:35.342 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL update11:58:35.346 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL statement [UPDATE t_order_message SET order_status = ?,edit_time = ? WHERE id =?]11:58:35.347 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.datasource.DataSourceUtils - Fetching JDBC Connection from DataSource11:58:35.354 [DefaultQuartzScheduler_Worker-1] INFO club.throwable.jdbc.OrderMessageService - 处理订单[10087],状态由0更新为111:58:35.355 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL update11:58:35.355 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL statement [UPDATE t_order_message SET order_status = ?,edit_time = ? WHERE id =?]11:58:35.355 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.datasource.DataSourceUtils - Fetching JDBC Connection from DataSource11:58:35.361 [DefaultQuartzScheduler_Worker-1] INFO club.throwable.jdbc.OrderMessageService - 订单处理定时任务开始完毕......11:58:35.363 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 1 triggers11:58:37.206 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.simpl.PropertySettingJobFactory - Producing instance of Job 'delayJob.orderMessageDelayJob', class=club.throwable.jdbc.OrderMessageDelayJob11:58:37.206 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 0 triggers

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：

  com.rabbitmq  amqp-client  5.7.3  test

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 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:1008616:35:58.641 [main] INFO club.throwable.dlx.DlxMain - 发送消息成功,订单ID:1008716:35:58.641 [main] INFO club.throwable.dlx.DlxMain - 发送消息成功,订单ID:1008816:36:03.646 [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 16:35:5816:36:03.670 [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 16:35:5816:36:03.670 [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：

  io.netty  netty-common  4.1.39.Final

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：

            org.springframework.boot      spring-boot-dependencies      2.1.7.RELEASE      pom      import            org.quartz-scheduler    quartz    2.3.1        redis.clients    jedis    3.1.0        org.springframework.boot    spring-boot-starter-web        org.springframework.boot    spring-boot-starter-jdbc          org.springframework    spring-context-support    5.1.9.RELEASE         org.projectlombok    lombok    1.18.8    provided        com.alibaba    fastjson    1.2.59

编写 Lua 脚本 /lua/enqueue.lua 和 /lua/dequeue.lua ：

-- /lua/enqueue.lualocal 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  endendreturn nil

编写核心API代码：

// Jedis提供者@Componentpublic class JedisProvider implements InitializingBean {  private JedisPool jedisPool;  @Override  public void afterPropertiesSet() throws Exception {    jedisPool = new JedisPool();  }  public Jedis provide(){    return jedisPool.getResource();  }}// OrderMessage@Datapublic 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 dequeue(String min, String max, String offset, String limit);  List dequeue();  String enqueueSha();  String dequeueSha();}// 延迟队列实现类@RequiredArgsConstructor@Componentpublic 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 ENQUEUE_LUA_SHA = new AtomicReference<>();  private static final AtomicReference DEQUEUE_LUA_SHA = new AtomicReference<>();  private static final List KEYS = Lists.newArrayList();  private final JedisProvider jedisProvider;  static {    KEYS.add(ORDER_QUEUE);    KEYS.add(ORDER_DETAIL_QUEUE);  }  @Override  public void enqueue(OrderMessage message) {    List 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 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 dequeue(String min, String max, String offset, String limit) {    List args = new ArrayList<>();    args.add(max);    args.add(min);    args.add(offset);    args.add(limit);    List result = Lists.newArrayList();    try (Jedis jedis = jedisProvider.provide()) {      List eval = (List) 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 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 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@Componentpublic 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 messages = orderDelayQueue.dequeue();    if (!messages.isEmpty()) {      // 简单的列表等分放到线程池中执行      List> partition = Lists.partition(messages, 2);      int size = partition.size();      final CountDownLatch latch = new CountDownLatch(size);      for (List 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 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@Configurationpublic 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 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 map = Maps.newHashMap();      Map 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：