Threads, Agents & Duty Cycles (线程、代理与职责周期)
概述
本文深入探讨 Agrona 中线程、Agent 和 Duty Cycle 三者之间的关系,以及如何通过合理的设计实现高性能系统。
三者关系
graph TB
T[Thread 线程] --> AR[AgentRunner]
AR --> IS[IdleStrategy 空闲策略]
AR --> A[Agent 代理]
A --> DC[Duty Cycle 职责周期]
DC --> DW[doWork 方法]
style T fill:#4a90e2,stroke:#2e5c8a,color:#fff
style AR fill:#50c878,stroke:#2d7a4a,color:#fff
style A fill:#f39c12,stroke:#b8730a,color:#fff
style DC fill:#e74c3c,stroke:#a93529,color:#fff
- Thread: 提供执行上下文
- AgentRunner: 管理Agent的生命周期
- Agent: 定义工作逻辑
- Duty Cycle: Agent的单次工作周期
- IdleStrategy: 控制空闲时的行为
线程模型
1. 专用线程模式
每个Agent运行在独占的线程上:
// 创建Agent
final Agent agent = new MyHighPriorityAgent();
final IdleStrategy idleStrategy = new BusySpinIdleStrategy();
// 创建Runner
final AgentRunner runner = new AgentRunner(
idleStrategy,
Throwable::printStackTrace,
null,
agent
);
// 启动专用线程
final Thread thread = AgentRunner.startOnThread(runner);
thread.setName("HighPriority-Agent");
thread.setPriority(Thread.MAX_PRIORITY);
优势: 最低延迟、可预测的性能 劣势: 资源消耗大
2. 共享线程模式
多个Agent共享一个线程:
// 创建复合Agent
final Agent compositeAgent = new CompositeAgent(
"SharedThread",
new ReceiveAgent(),
new ProcessAgent(),
new SendAgent()
);
final AgentRunner runner = new AgentRunner(
new BackoffIdleStrategy(100, 10, 1, 1_000_000),
Throwable::printStackTrace,
null,
compositeAgent
);
AgentRunner.startOnThread(runner);
优势: 资源利用率高 劣势: 可能存在优先级问题
实战示例
高性能消息处理系统
import org.agrona.concurrent.*;
/**
* 完整的消息处理系统示例
*/
public class MessageProcessingSystem
{
// 消息队列
private final ManyToOneConcurrentArrayQueue<Message> inputQueue;
private final ManyToOneConcurrentArrayQueue<Message> outputQueue;
// Agents
private final Agent receiverAgent;
private final Agent processorAgent;
private final Agent senderAgent;
// Runners
private final AgentRunner receiverRunner;
private final AgentRunner processorRunner;
private final AgentRunner senderRunner;
public MessageProcessingSystem(final int queueSize)
{
this.inputQueue = new ManyToOneConcurrentArrayQueue<>(queueSize);
this.outputQueue = new ManyToOneConcurrentArrayQueue<>(queueSize);
// 创建Agents
this.receiverAgent = new ReceiverAgent(inputQueue);
this.processorAgent = new ProcessorAgent(inputQueue, outputQueue);
this.senderAgent = new SenderAgent(outputQueue);
// 创建Runners (使用不同的空闲策略)
this.receiverRunner = new AgentRunner(
new BusySpinIdleStrategy(), // 接收器需要最低延迟
this::handleError,
null,
receiverAgent
);
this.processorRunner = new AgentRunner(
new BackoffIdleStrategy(100, 10, 1, 1_000_000),
this::handleError,
null,
processorAgent
);
this.senderRunner = new AgentRunner(
new YieldingIdleStrategy(),
this::handleError,
null,
senderAgent
);
}
public void start()
{
// 启动所有Agent
AgentRunner.startOnThread(receiverRunner)
.setName("Receiver-Thread");
AgentRunner.startOnThread(processorRunner)
.setName("Processor-Thread");
AgentRunner.startOnThread(senderRunner)
.setName("Sender-Thread");
}
public void shutdown()
{
receiverRunner.close();
processorRunner.close();
senderRunner.close();
}
private void handleError(final Throwable throwable)
{
System.err.println("Error: " + throwable.getMessage());
throwable.printStackTrace();
}
/**
* 接收器Agent
*/
static class ReceiverAgent implements Agent
{
private final ManyToOneConcurrentArrayQueue<Message> outputQueue;
ReceiverAgent(final ManyToOneConcurrentArrayQueue<Message> outputQueue)
{
this.outputQueue = outputQueue;
}
@Override
public int doWork()
{
// 从网络接收消息
final Message message = receiveFromNetwork();
if (message != null)
{
outputQueue.offer(message);
return 1;
}
return 0;
}
@Override
public String roleName()
{
return "Receiver";
}
private Message receiveFromNetwork()
{
// 实际的网络接收逻辑
return null;
}
}
/**
* 处理器Agent
*/
static class ProcessorAgent implements Agent
{
private final ManyToOneConcurrentArrayQueue<Message> inputQueue;
private final ManyToOneConcurrentArrayQueue<Message> outputQueue;
ProcessorAgent(
final ManyToOneConcurrentArrayQueue<Message> inputQueue,
final ManyToOneConcurrentArrayQueue<Message> outputQueue)
{
this.inputQueue = inputQueue;
this.outputQueue = outputQueue;
}
@Override
public int doWork()
{
int workDone = 0;
// 批量处理
for (int i = 0; i < 10; i++)
{
final Message message = inputQueue.poll();
if (message == null)
{
break;
}
// 处理消息
processMessage(message);
// 放入输出队列
outputQueue.offer(message);
workDone++;
}
return workDone;
}
@Override
public String roleName()
{
return "Processor";
}
private void processMessage(final Message message)
{
// 实际的处理逻辑
}
}
/**
* 发送器Agent
*/
static class SenderAgent implements Agent
{
private final ManyToOneConcurrentArrayQueue<Message> inputQueue;
SenderAgent(final ManyToOneConcurrentArrayQueue<Message> inputQueue)
{
this.inputQueue = inputQueue;
}
@Override
public int doWork()
{
int workDone = 0;
for (int i = 0; i < 10; i++)
{
final Message message = inputQueue.poll();
if (message == null)
{
break;
}
sendToNetwork(message);
workDone++;
}
return workDone;
}
@Override
public String roleName()
{
return "Sender";
}
private void sendToNetwork(final Message message)
{
// 实际的网络发送逻辑
}
}
static class Message
{
// 消息内容
}
}
性能优化技巧
1. CPU亲和性
// 将线程绑定到特定CPU核心
public static void setThreadAffinity(final Thread thread, final int cpuId)
{
// 使用JNI或Agrona的ThreadHints
ThreadHints.onSpinWait();
}
2. 批量处理
@Override
public int doWork()
{
int work = 0;
// 批量处理提高吞吐量
for (int i = 0; i < BATCH_SIZE; i++)
{
final Task task = queue.poll();
if (task == null) break;
processTask(task);
work++;
}
return work;
}
3. 预取优化
@Override
public int doWork()
{
int totalWork = 0;
// 预取多个数据源
totalWork += processInput1();
totalWork += processInput2();
totalWork += processOutput();
return totalWork;
}
最佳实践
- 专用线程用于低延迟: 超低延迟场景使用专用线程+BusySpinIdleStrategy
- 批量处理提高吞吐: 适当批量处理以摊销开销
- 监控和调优: 持续监控并根据实际情况调整
- 避免阻塞: doWork方法绝对不能阻塞
总结
理解线程、Agent和Duty Cycle的关系是构建高性能Agrona应用的关键。通过合理的设计和配置,可以实现极致的性能表现。