使用Redis实现延时任务的解决方案

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));
    }
  }
}

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

<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>

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');

// 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);
  }
}

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！