最近在生產環境剛好遇到了延時任務的場景，調研了一下目前主流的方案，分析了一下優劣並且敲定了最終的方案。這篇文章記錄了調研的過程，以及初步方案的實現。

候選方案對比

下面是想到的幾種實現延時任務的方案，總結了一下相應的優勢和劣勢。

方案	優勢	劣勢	選用場景
JDK內建的延遲佇列DelayQueue	實現簡單	資料記憶體態，不可靠	一致性相對低的場景
排程框架和MySQL進行短間隔輪詢	實現簡單，可靠性高	存在明顯的效能瓶頸	資料量較少實時性相對低的場景
RabbitMQ的DLX和TTL，一般稱為死信佇列方案	非同步互動可以削峰	延時的時間長度不可控，如果資料需要持久化則效能會降低	-
Redis進行短間隔輪詢	資料持久化，高效能	實現難度大	常見於支付結果回撥方案
時間輪	實時性高	實現難度大，記憶體消耗大	實時性高的場景

如果應用的資料量不高，實時性要求比較低，選用排程框架和 MySQL

裡面剛好用到了排程框架和 Redis 進行短間隔輪詢實現延時任務的方案，不過為了分攤應用的壓力，圖中的方案還做了分片處理。鑑於筆者當前業務緊迫，所以在第一期的方案暫時不考慮分片，只做了一個簡化版的實現。

由於PPT中沒有任何的程式碼或者框架貼出，有些需要解決的技術點需要自行思考，下面會重現一次整個方案實現的詳細過程。

場景設計

實際的生產場景是筆者負責的某個系統需要對接一個外部的資金方，每一筆資金下單後需要延時30分鐘推送對應的附件。這裡簡化為一個訂單資訊資料延遲處理的場景，就是每一筆下單記錄一條訂單訊息(暫時叫做 OrderMessage

否決的候選方案實現思路

下面介紹一下其它四個不選用的候選方案，結合一些虛擬碼和流程分析一下實現過程。

JDK內建延遲佇列

DelayQueue 是一個阻塞佇列的實現，它的佇列元素必須是 Delayed 的子類，這裡做個簡單的例子：

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<>();
    // 預設延遲5秒
    OrderMessage message = new OrderMessage("ORDER_ID_10086");
    queue.add(message);
    // 延遲6秒
    message = new OrderMessage("ORDER_ID_10087",6);
    queue.add(message);
    // 延遲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("開始執行排程執行緒...");
    executorService.execute(() -> {
      while (true) {
        try {
          OrderMessage task = queue.take();
          LOGGER.info("延遲處理訂單訊息,{}",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");
    /**
     * 預設延遲5000毫秒
     */
    private static final long DELAY_MS = 1000L * 5;
    /**
     * 訂單ID
     */
    private final String orderId;
    /**
     * 建立時間戳
     */
    private final long timestamp;
    /**
     * 過期時間
     */
    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("訂單[%s]-建立時間為:%s,超時時間為:%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("訂單[%s]-建立時間為:%s,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 實現 Delayed 介面，關鍵是需要實現 Delayed#getDelay() 和 Delayed#compareTo() 。執行一下 main() 方法：

10:16:08.240 [main] INFO club.throwable.delay.DelayQueueMain - 開始執行排程執行緒...
10:16:13.224 [DelayWorker] INFO club.throwable.delay.DelayQueueMain - 延遲處理訂單訊息,訂單[ORDER_ID_10086]-建立時間為:2019-08-20 10:16:08,超時時間為:2019-08-20 10:16:13
10:16:14.237 [DelayWorker] INFO club.throwable.delay.DelayQueueMain - 延遲處理訂單訊息,訂單[ORDER_ID_10087]-建立時間為:2019-08-20 10:16:08,超時時間為:2019-08-20 10:16:14
10:16:18.237 [DelayWorker] INFO club.throwable.delay.DelayQueueMain - 延遲處理訂單訊息,訂單[ORDER_ID_10088]-建立時間為:2019-08-20 10:16:08,超時時間為:2019-08-20 10:16:18

排程框架 + MySQL

使用排程框架對 MySQL 表進行短間隔輪詢是實現難度比較低的方案，通常服務剛上線，表資料不多並且實時性不高的情況下應該首選這個方案。不過要注意以下幾點：

MySQL

引入 Quartz 、 MySQL 的Java驅動包和 spring-boot-starter-jdbc （這裡只是為了方便用相對輕量級的框架實現，生產中可以按場景按需選擇其他更合理的框架）：

<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>

假設表設計如下：

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 '訂單ID',create_time DATETIME  NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '建立日期時間',edit_time  DATETIME  NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '修改日期時間',retry_times TINYINT   NOT NULL DEFAULT 0 COMMENT '重試次數',order_status TINYINT   NOT NULL DEFAULT 0 COMMENT '訂單狀態',INDEX idx_order_id (order_id),INDEX idx_create_time (create_time)
) COMMENT '訂單資訊表';

# 寫入兩條測試資料
INSERT INTO t_order_message(order_id) VALUES ('10086'),('10087');

編寫程式碼：

// 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;
}

// 實體
@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 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
@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("訂單處理定時任務開始執行......");
    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("處理訂單[{}],狀態由{}更新為{}",m.getOrderId(),m.getOrderStatus(),OrderConstants.SUCCESS);
        // 這裡其實可以優化為批量更新
        orderMessageDao.updateOrderStatus(m.getId(),OrderConstants.SUCCESS);
      }
    }
    LOGGER.info("訂單處理定時任務開始完畢......");
  }
}

// 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?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);
    // 記憶體模式的排程器
    StdSchedulerFactory factory = new StdSchedulerFactory();
    Scheduler scheduler = factory.getScheduler();
    // 這裡沒有用到IOC容器,直接用Quartz資料集合傳遞服務引用
    JobDataMap jobDataMap = new JobDataMap();
    jobDataMap.put("orderMessageService",service);
    // 新建Job
    JobDetail job = JobBuilder.newJob(OrderMessageDelayJob.class)
        .withIdentity("orderMessageDelayJob","delayJob")
        .usingJobData(jobDataMap)
        .build();
    // 新建觸發器,10秒執行一次
    Trigger trigger = TriggerBuilder.newTrigger()
        .withIdentity("orderMessageDelayTrigger","delayJob")
        .withSchedule(SimpleScheduleBuilder.simpleSchedule().withIntervalInSeconds(10).repeatForever())
        .build();
    scheduler.scheduleJob(job,trigger);
    // 啟動排程器
    scheduler.start();
    Thread.sleep(Integer.MAX_VALUE);
  }
}

這個例子裡面用了 create_time 做輪詢，實際上可以新增一個排程時間 schedule_time 列做輪詢，這樣子才能更容易定製空閒時和忙碌時候的排程策略。上面的示例的執行效果如下：

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 - 訂單處理定時任務開始執行......
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 >= ? 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 DataSource
11:58:35.210 [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 - 處理訂單[10086],狀態由0更新為1
11:58:35.342 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL update
11:58:35.346 [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 DataSource
11:58:35.354 [DefaultQuartzScheduler_Worker-1] INFO club.throwable.jdbc.OrderMessageService - 處理訂單[10087],狀態由0更新為1
11:58:35.355 [DefaultQuartzScheduler_Worker-1] DEBUG org.springframework.jdbc.core.JdbcTemplate - Executing prepared SQL update
11:58:35.355 [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 DataSource
11:58:35.361 [DefaultQuartzScheduler_Worker-1] INFO club.throwable.jdbc.OrderMessageService - 訂單處理定時任務開始完畢......
11:58:35.363 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 1 triggers
11:58:37.206 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.simpl.PropertySettingJobFactory - Producing instance of Job 'delayJob.orderMessageDelayJob',class=club.throwable.jdbc.OrderMessageDelayJob
11:58:37.206 [DefaultQuartzScheduler_QuartzSchedulerThread] DEBUG org.quartz.core.QuartzSchedulerThread - batch acquisition of 0 triggers

RabbitMQ死信佇列

使用 RabbitMQ 死信佇列依賴於 RabbitMQ 的兩個特性： TTL 和 DLX 。

TTL ： Time To Live ，訊息存活時間，包括兩個維度：佇列訊息存活時間和訊息本身的存活時間。

DLX ： Dead Letter Exchange ，死信交換器。

畫個圖描述一下這兩個特性：

下面為了簡單起見， TTL 使用了針對佇列的維度。引入 RabbitMQ 的Java驅動：

<dependency>
  <groupId>com.rabbitmq</groupId>
  <artifactId>amqp-client</artifactId>
  <version>5.7.3</version>
  <scope>test</scope>
</dependency>

程式碼如下：

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交換器名稱為dlx.exchange,型別是direct,繫結鍵為dlx.key,佇列名為dlx.queue
    producerChannel.exchangeDeclare("dlx.exchange","direct");
    producerChannel.queueDeclare("dlx.queue",false,null);
    producerChannel.queueBind("dlx.queue","dlx.exchange","dlx.key");
    Map<String,Object> queueArgs = new HashMap<>();
    // 設定佇列訊息過期時間,5秒
    queueArgs.put("x-message-ttl",5000);
    // 指定DLX相關引數
    queueArgs.put("x-dead-letter-exchange","dlx.exchange");
    queueArgs.put("x-dead-letter-routing-key","dlx.key");
    // 宣告業務佇列
    producerChannel.queueDeclare("business.queue",queueArgs);
    ExecutorService executorService = Executors.newSingleThreadExecutor(r -> {
      Thread thread = new Thread(r);
      thread.setDaemon(true);
      thread.setName("DlxConsumer");
      return thread;
    });
    // 啟動消費者
    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("傳送訊息成功,訂單ID:{}",message.getOrderId());

    message = new OrderMessage("10087");
    producerChannel.basicPublish("",message.getOrderId());

    message = new OrderMessage("10088");
    producerChannel.basicPublish("",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("處理訊息成功:{}",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("訂單[%s],訂單建立時間為:%s",ZoneId.systemDefault()).format(F));
    }

    public String getOrderId() {
      return orderId;
    }

    public long getTimestamp() {
      return timestamp;
    }

    public String getDescription() {
      return description;
    }
  }
}

執行 main() 方法結果如下：

16:35:58.638 [main] INFO club.throwable.dlx.DlxMain - 傳送訊息成功,訂單ID:10086
16:35:58.641 [main] INFO club.throwable.dlx.DlxMain - 傳送訊息成功,訂單ID:10087
16:35:58.641 [main] INFO club.throwable.dlx.DlxMain - 傳送訊息成功,訂單ID:10088
16:36:03.646 [pool-1-thread-4] INFO club.throwable.dlx.DlxMain - 處理訊息成功:訂單[10086],訂單建立時間為:2019-08-20 16:35:58
16:36:03.670 [pool-1-thread-5] INFO club.throwable.dlx.DlxMain - 處理訊息成功:訂單[10087],訂單建立時間為:2019-08-20 16:35:58
16:36:03.670 [pool-1-thread-6] INFO club.throwable.dlx.DlxMain - 處理訊息成功:訂單[10088],訂單建立時間為:2019-08-20 16:35:58

時間輪

時間輪 TimingWheel 是一種高效、低延遲的排程資料結構，底層採用陣列實現儲存任務列表的環形佇列，示意圖如下：

這裡暫時不對時間輪和其實現作分析，只簡單舉例說明怎麼使用時間輪實現延時任務。這裡使用 Netty 提供的 HashedWheelTimer ，引入依賴：

<dependency>
  <groupId>io.netty</groupId>
  <artifactId>netty-common</artifactId>
  <version>4.1.39.Final</version>
</dependency>

程式碼如下：

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一次的時間間隔,每tick一次就會到達下一個槽位
    // unit - tickDuration的時間單位
    // ticksPerWhee - 時間輪中的槽位數
    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("任務執行時間:%s,訂單建立時間:%s,訂單ID:%s",LocalDateTime.now().format(F),orderId));
    }
  }
}

執行結果：

任務執行時間:2019-08-20 17:19:49.310,訂單建立時間:2019-08-20 17:19:43.294,訂單ID:10086
任務執行時間:2019-08-20 17:19:54.297,訂單建立時間:2019-08-20 17:19:43.301,訂單ID:10087
任務執行時間:2019-08-20 17:19:59.297,訂單ID:10088

一般來說，任務執行的時候應該使用另外的業務執行緒池，以免阻塞時間輪本身的運動。

選用的方案實現過程

最終選用了基於 Redis 的有序集合 Sorted Set 和 Quartz 短輪詢進行實現。具體方案是：

• 訂單建立的時候，訂單ID和當前時間戳分別作為 Sorted Set 的member和score新增到訂單佇列 Sorted Set 中。
• 訂單建立的時候，訂單ID和推送內容 JSON 字串分別作為field和value新增到訂單佇列內容 Hash 中。
• 第1步和第2步操作的時候用 Lua 指令碼保證原子性。
• 使用一個非同步執行緒通過 Sorted Set 的命令 ZREVRANGEBYSCORE 彈出指定數量的訂單ID對應的訂單佇列內容 Hash 中的訂單推送內容資料進行處理。

對於第4點處理有兩種方案：

• 方案一：彈出訂單內容資料的同時進行資料刪除，也就是 ZREVRANGEBYSCORE 、 ZREM 和 HDEL 命令要在同一個 Lua 指令碼中執行，這樣的話 Lua 指令碼的編寫難度大，並且由於彈出資料已經在 Redis 中刪除，如果資料處理失敗則可能需要從資料庫重新查詢補償。
• 方案二：彈出訂單內容資料之後，在資料處理完成的時候再主動刪除訂單佇列 Sorted Set 和訂單佇列內容 Hash 中對應的資料，這樣的話需要控制併發，有重複執行的可能性。

最終暫時選用了方案一，也就是從 Sorted Set 彈出訂單ID並且從 Hash 中獲取完推送資料之後馬上刪除這兩個集合中對應的資料。方案的流程圖大概是這樣：

這裡先詳細說明一下用到的 Redis 命令。

Sorted Set相關命令

ZADD 命令 - 將一個或多個成員元素及其分數值加入到有序集當中。

ZADD KEY SCORE1 VALUE1.. SCOREN VALUEN

ZREVRANGEBYSCORE 命令 - 返回有序集中指定分數區間內的所有的成員。有序整合員按分數值遞減(從大到小)的次序排列。

ZREVRANGEBYSCORE key max min [WITHSCORES] [LIMIT offset count]

max：分數區間 - 最大分數。 min：分數區間 - 最小分數。 WITHSCORES：可選引數，是否返回分數值，指定則會返回得分值。 LIMIT：可選引數，offset和count原理和 MySQL 的 LIMIT offset,size 一致，如果不指定此引數則返回整個集合的資料。 ZREM 命令 - 用於移除有序集中的一個或多個成員，不存在的成員將被忽略。

ZREM key member [member ...]

Hash相關命令 HMSET 命令 - 同時將多個field-value(欄位-值)對設定到雜湊表中。

HMSET KEY_NAME FIELD1 VALUE1 ...FIELDN VALUEN

HDEL 命令 - 刪除雜湊表key中的一個或多個指定欄位，不存在的欄位將被忽略。

HDEL KEY_NAME FIELD1.. FIELDN

Lua相關 載入 Lua 指令碼並且返回指令碼的 SHA-1 字串： SCRIPT LOAD script 。 執行已經載入的 Lua 指令碼： EVALSHA sha1 numkeys key [key ...] arg [arg ...] 。 unpack 函式可以把 table 型別的引數轉化為可變引數，不過需要注意的是 unpack 函式必須使用在非變數定義的函式呼叫的最後一個引數，否則會失效，詳細見 Stackoverflow 的提問 table.unpack() only returns the first element 。

PS：如果不熟悉Lua語言，建議系統學習一下，因為想用好Redis，一定離不開Lua。

引入依賴：

<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>

編寫 Lua 指令碼 /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
-- 參考jesque的部分Lua指令碼實現
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命令的返回結果是{'ok':'zset'}這樣子,這裡利用next做一輪迭代
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',max_score,min_score,'LIMIT',offset,limit)
    if list ~= nil and #list > 0 then
      -- unpack函式能把table轉化為可變引數
      redis.call('ZREM',unpack(list))
      local result = redis.call('HMGET',unpack(list))
      redis.call('HDEL',unpack(list))
      return result
    end
  end
end
return nil

編寫核心API程式碼：

// 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;
}

// 延遲佇列介面
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();
}

// 延遲佇列實現類
@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分鐘之前
    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 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(),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 {
    // 載入Lua指令碼
    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();
    // 寫入測試資料
    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());
    // 測試需要,score設定為30分鐘之前
    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(),args);
    }
    List<OrderMessage> dequeue = queue.dequeue();
    System.out.println(dequeue);
  }
}

這裡先執行一次 main() 方法驗證一下延遲佇列是否生效：

[OrderMessage(orderId=ORDER_ID_10086,amount=10086,userId=10086,timestamp=2019-08-21 08:32:22.885)]

確定延遲佇列的程式碼沒有問題，接著編寫一個 Quartz 的 Job 型別的消費者 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("訂單訊息處理定時任務開始執行......");
    List<OrderMessage> messages = orderDelayQueue.dequeue();
    if (!messages.isEmpty()) {
      // 簡單的列表等分放到執行緒池中執行
      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("訂單訊息處理定時任務執行完畢,耗時:{} ms......",stopWatch.getTotalTimeMillis());
  }

  @RequiredArgsConstructor
  private static class ConsumeTask implements Runnable {

    private final List<OrderMessage> messages;
    private final CountDownLatch latch;

    @Override
    public void run() {
      try {
        // 實際上這裡應該單條捕獲異常
        for (OrderMessage message : messages) {
          LOGGER.info("處理訂單資訊,內容:{}",message);
        }
      } finally {
        latch.countDown();
      }
    }
  }
}

上面的消費者設計的時候需要有以下考量：

• 使用 @DisallowConcurrentExecution 註解不允許 Job 併發執行，其實多個 Job 併發執行意義不大，因為我們採用的是短間隔的輪詢，而 Redis 是單執行緒處理命令，在客戶端做多執行緒其實效果不佳。
• 執行緒池 BUSINESS_WORKER_POOL 的執行緒容量或者佇列應該綜合 LIMIT 值、等分訂單資訊列表中使用的 size 值以及 ConsumeTask 裡面具體的執行時間進行考慮，這裡只是為了方便使用了固定容量的執行緒池。
• ConsumeTask 中應該對每一條訂單資訊的處理單獨捕獲異常和吞併異常，或者把處理單個訂單資訊的邏輯封裝成一個不丟擲異常的方法。

其他 Quartz 相關的程式碼：

// Quartz配置類
@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);
      // 這裡利用AutowireCapableBeanFactory從新建的Job例項做一次自動裝配,得到一個原型(prototype)的JobBean例項
      autowireCapableBeanFactory.autowireBean(jobInstance);
      return jobInstance;
    }
  }
}

這裡暫時使用了記憶體態的 RAMJobStore 去存放任務和觸發器的相關資訊，如果在生產環境最好替換成基於 MySQL 也就是 JobStoreTX 進行叢集化，最後是啟動函式和 CommandLineRunner 的實現：

@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 {
    // 準備一些測試資料
    prepareOrderMessageData();
    JobDetail job = JobBuilder.newJob(OrderMessageConsumer.class)
        .withIdentity("OrderMessageConsumer","DelayTask")
        .build();
    // 觸發器5秒觸發一次
    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);
      }
      // 這裡暫時不使用Lua
      Map<String,Double> map = Maps.newHashMap();
      Map<String,String> hash = Maps.newHashMap();
      for (OrderMessage message : messages) {
        // 故意把score設計成30分鐘前
        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);
    }
  }
}

輸出結果如下：

2019-08-21 22:45:59.518 INFO 33000 --- [ryBean_Worker-1] club.throwable.OrderMessageConsumer : 訂單訊息處理定時任務開始執行......
2019-08-21 22:45:59.525 INFO 33000 --- [onsumerWorker-4] club.throwable.OrderMessageConsumer : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 訂單訊息處理定時任務執行完畢,耗時:22 ms......
2019-08-21 22:46:04.515 INFO 33000 --- [ryBean_Worker-2] club.throwable.OrderMessageConsumer : 訂單訊息處理定時任務開始執行......
2019-08-21 22:46:04.516 INFO 33000 --- [onsumerWorker-5] club.throwable.OrderMessageConsumer : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容: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 : 處理訂單資訊,內容:OrderMessage(orderId=ORDER_ID_81,amount=81,userId=81,內容:OrderMessage(orderId=ORDER_ID_86,amount=86,userId=86,內容:OrderMessage(orderId=ORDER_ID_82,amount=82,userId=82,內容:OrderMessage(orderId=ORDER_ID_84,amount=84,userId=84,內容: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 : 訂單訊息處理定時任務執行完畢,耗時:1 ms......
......

首次執行的時候涉及到一些元件的初始化，會比較慢，後面看到由於我們只是簡單列印訂單資訊，所以定時任務執行比較快。如果在不調整當前架構的情況下，生產中需要注意：

• 切換 JobStore 為 JDBC 模式， Quartz 官方有完整教程，或者看筆者之前翻譯的 Quartz 文件。
• 需要監控或者收集任務的執行狀態，新增預警等等。

這裡其實有一個性能隱患，命令 ZREVRANGEBYSCORE 的時間複雜度可以視為為 O(N) ， N 是集合的元素個數，由於這裡把所有的訂單資訊都放進了同一個 Sorted Set ( ORDER_QUEUE )中，所以在一直有新增資料的時候， dequeue 指令碼的時間複雜度一直比較高，後續訂單量升高之後會此處一定會成為效能瓶頸，後面會給出解決的方案。

小結

這篇文章主要從一個實際生產案例的模擬例子入手，分析了當前延時任務的一些實現方案，還基於 Redis 和 Quartz 給出了一個完整的示例。當前的示例只是處於可執行的狀態，有些問題尚未解決。下一篇文章會著眼於解決兩個方面的問題：

1. 分片。
2. 監控。

還有一點， 架構是基於業務形態演進出來的，很多東西需要結合場景進行方案設計和改進，思路僅供參考，切勿照搬程式碼 。

以上所述是小編給大家介紹的使用Redis實現延時任務的解決方案，非常不錯，具有一定的參考借鑑價值，需要的朋友參考下吧！