高吞吐框架DISRUPTOR应用场景

Zealot
编程语言
2019-07-11

多年前在并发编程网http://ifeve.com/disruptor 了解到了自认为是黑科技的并发框架DISRUPTOR, 我当时在想NETTY为什么没有和它整合。后来了解过的log4j2, jstorm也慢慢有用到, 而一直以来也并没有机会去使用和了解细节, 大多时候觉得Doug Lea的JDK并发包也足够使用。而近期业务需要基于NETTY简单裹了一个类似vertx的luoying-server, https://github.com/zealzeng/luoying-server, 业务处理线程不适合在event loop中处理, 简单用有界的ThreadPoolExecutor作为worker pool, 想考虑把disruptor整合进来, 看了两天发觉对disruptor的使用场景产生了误解。

具体的入门有需要可以到并发网或官网https://github.com/LMAX-Exchange/disruptor 看下

1.官方的性能测试用例

1.1 JDK BlockingQueue的吞吐测试, 先来个一个生产者, 一个消费者用例

https://github.com/zealzeng/fabric-samples/blob/master/disruptor-demo/src/main/java/com/lmax/disruptor/queue/OneToOneQueueThroughputTest.java

package com.lmax.disruptor.queue;

import com.lmax.disruptor.AbstractPerfTestQueue;
import com.lmax.disruptor.support.ValueAdditionQueueProcessor;
import com.lmax.disruptor.util.DaemonThreadFactory;

import java.util.concurrent.*;

import static com.lmax.disruptor.support.PerfTestUtil.failIf;

/**
 * <pre>
 * UniCast a series of items between 1 publisher and 1 event processor.
 *
 * +----+    +-----+
 * | P1 |--->| EP1 |
 * +----+    +-----+
 *
 * Queue Based:
 * ============
 *
 *        put      take
 * +----+    +====+    +-----+
 * | P1 |--->| Q1 |<---| EP1 |
 * +----+    +====+    +-----+
 *
 * P1  - Publisher 1
 * Q1  - Queue 1
 * EP1 - EventProcessor 1
 *
 * </pre>
 */
public final class OneToOneQueueThroughputTest extends AbstractPerfTestQueue
{
    private static final int BUFFER_SIZE = 1024 * 64;
    private static final long ITERATIONS = 1000L * 1000L * 10L;
    private final ExecutorService executor = Executors.newSingleThreadExecutor(DaemonThreadFactory.INSTANCE);
    private final long expectedResult = ITERATIONS * 3L;

    ///////////////////////////////////////////////////////////////////////////////////////////////

    private final BlockingQueue<Long> blockingQueue = new LinkedBlockingQueue<Long>(BUFFER_SIZE);
    private final ValueAdditionQueueProcessor queueProcessor =
        new ValueAdditionQueueProcessor(blockingQueue, ITERATIONS - 1);

    ///////////////////////////////////////////////////////////////////////////////////////////////

    @Override
    protected int getRequiredProcessorCount()
    {
        return 2;
    }

    @Override
    protected long runQueuePass() throws InterruptedException
    {
        final CountDownLatch latch = new CountDownLatch(1);
        queueProcessor.reset(latch);
        Future<?> future = executor.submit(queueProcessor);
        long start = System.currentTimeMillis();

        for (long i = 0; i < ITERATIONS; i++)
        {
            blockingQueue.put(3L);
        }

        latch.await();
        long opsPerSecond = (ITERATIONS * 1000L) / (System.currentTimeMillis() - start);
        queueProcessor.halt();
        future.cancel(true);

        failIf(expectedResult, 0);

        return opsPerSecond;
    }

    public static void main(String[] args) throws Exception
    {
        OneToOneQueueThroughputTest test = new OneToOneQueueThroughputTest();
        test.testImplementations();
    }
}

主线程往queue增加元素, 一个消费线程执行ValueAdditionQueueProcessor是runnable任务,逻辑简单就是获取队列元素后累加, 时间损耗很很小基本相当于空跑。 在老的四代I5貌似每秒吞吐也蛮高, 百万级。 实际场景ValueAdditionQueueProcessor处理业务也要几十到几百毫秒吧,单线程消费基本就不行了,所以ThreadPoolExecutor基本是要为获取出来的任务分配一个线程,这个是常规的搞法。

Starting Queue tests
Run 0, BlockingQueue=4,539,264 ops/sec
Run 1, BlockingQueue=5,414,185 ops/sec
Run 2, BlockingQueue=4,657,661 ops/sec
Run 3, BlockingQueue=5,288,207 ops/sec
Run 4, BlockingQueue=5,339,028 ops/sec
Run 5, BlockingQueue=5,246,589 ops/sec
Run 6, BlockingQueue=5,197,505 ops/sec

1.2 disruptor的一个生产者, 一个消费者

https://github.com/zealzeng/fabric-samples/blob/master/disruptor-demo/src/main/java/com/lmax/disruptor/sequenced/OneToOneSequencedThroughputTest.java

package com.lmax.disruptor.sequenced;

import static com.lmax.disruptor.RingBuffer.createSingleProducer;
import static com.lmax.disruptor.support.PerfTestUtil.failIfNot;

import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

import com.lmax.disruptor.*;
import com.lmax.disruptor.support.PerfTestUtil;
import com.lmax.disruptor.support.ValueAdditionEventHandler;
import com.lmax.disruptor.support.ValueEvent;
import com.lmax.disruptor.util.DaemonThreadFactory;

/**
 * <pre>
 * UniCast a series of items between 1 publisher and 1 event processor.
 *
 * +----+    +-----+
 * | P1 |--->| EP1 |
 * +----+    +-----+
 *
 * Disruptor:
 * ==========
 *              track to prevent wrap
 *              +------------------+
 *              |                  |
 *              |                  v
 * +----+    +====+    +====+   +-----+
 * | P1 |--->| RB |<---| SB |   | EP1 |
 * +----+    +====+    +====+   +-----+
 *      claim      get    ^        |
 *                        |        |
 *                        +--------+
 *                          waitFor
 *
 * P1  - Publisher 1
 * RB  - RingBuffer
 * SB  - SequenceBarrier
 * EP1 - EventProcessor 1
 *
 * </pre>
 */
public final class OneToOneSequencedThroughputTest extends AbstractPerfTestDisruptor
{
    private static final int BUFFER_SIZE = 1024 * 64;
    private static final long ITERATIONS = 1000L * 1000L * 100L;
    private final ExecutorService executor = Executors.newSingleThreadExecutor(DaemonThreadFactory.INSTANCE);
    private final long expectedResult = PerfTestUtil.accumulatedAddition(ITERATIONS);

    ///////////////////////////////////////////////////////////////////////////////////////////////

    private final RingBuffer<ValueEvent> ringBuffer =
        createSingleProducer(ValueEvent.EVENT_FACTORY, BUFFER_SIZE, new YieldingWaitStrategy());
    private final SequenceBarrier sequenceBarrier = ringBuffer.newBarrier();
    private final ValueAdditionEventHandler handler = new ValueAdditionEventHandler();
    private final BatchEventProcessor<ValueEvent> batchEventProcessor =
        new BatchEventProcessor<ValueEvent>(ringBuffer, sequenceBarrier, handler);

    {
        ringBuffer.addGatingSequences(batchEventProcessor.getSequence());
    }

    ///////////////////////////////////////////////////////////////////////////////////////////////

    @Override
    protected int getRequiredProcessorCount()
    {
        return 2;
    }

    @Override
    protected PerfTestContext runDisruptorPass() throws InterruptedException
    {
        PerfTestContext perfTestContext = new PerfTestContext();
        final CountDownLatch latch = new CountDownLatch(1);
        long expectedCount = batchEventProcessor.getSequence().get() + ITERATIONS;
        handler.reset(latch, expectedCount);
        executor.submit(batchEventProcessor);
        long start = System.currentTimeMillis();

        final RingBuffer<ValueEvent> rb = ringBuffer;

        for (long i = 0; i < ITERATIONS; i++)
        {
            long next = rb.next();
            rb.get(next).setValue(i);
            rb.publish(next);
        }

        latch.await();
        perfTestContext.setDisruptorOps((ITERATIONS * 1000L) / (System.currentTimeMillis() - start));
        perfTestContext.setBatchData(handler.getBatchesProcessed(), ITERATIONS);
        waitForEventProcessorSequence(expectedCount);
        batchEventProcessor.halt();

        failIfNot(expectedResult, handler.getValue());

        return perfTestContext;
    }

    private void waitForEventProcessorSequence(long expectedCount) throws InterruptedException
    {
        while (batchEventProcessor.getSequence().get() != expectedCount)
        {
            Thread.sleep(1);
        }
    }

    public static void main(String[] args) throws Exception
    {
        OneToOneSequencedThroughputTest test = new OneToOneSequencedThroughputTest();
        test.testImplementations();
    }
}

用例没用完整封装的Disruptor类, 而直接用了RingBuffer和BatchEventProcessor处理, 一样的处理逻辑,吞吐是千万级别。但还是那句话, 如果ValueAdditionEventHandler 耗时几十到几百ms, ring buffer再无锁再高效也没用。 所以接下来我们看下disruptor的两种消费方式。

Starting Disruptor tests
Run 0, Disruptor=32,701,111 ops/sec BatchPercent=95.16% AverageBatchSize=20
Run 1, Disruptor=36,805,299 ops/sec BatchPercent=62.61% AverageBatchSize=2
Run 2, Disruptor=69,348,127 ops/sec BatchPercent=86.93% AverageBatchSize=7
Run 3, Disruptor=69,396,252 ops/sec BatchPercent=87.21% AverageBatchSize=7
Run 4, Disruptor=67,430,883 ops/sec BatchPercent=86.10% AverageBatchSize=7
Run 5, Disruptor=69,108,500 ops/sec BatchPercent=86.49% AverageBatchSize=7
Run 6, Disruptor=66,979,236 ops/sec BatchPercent=86.42% AverageBatchSize=7

2.Disruptor消息处理方式

2.1muti-cast 广播消息

官方入门例子给的蛮多都是这个模式, 即使用Disruptor.handleEventsWith(EventHandler… handlers) 这种, 实际会构建一个BatchEventProcessor, 而对应一个线程在跑这个EventProcessor, 这个EventProcessor把ring buffer获取到的任务在同一线程内调用多个EventHandler处理。
多次调用Disruptor.handleEventsWith()就多个BatchEventProcessor消费者线程, 不过这种模式是广播, 每个BatchEventProcessor都可以获取到广播的Event.

// Construct the Disruptor
Disruptor<LongEvent> disruptor = new Disruptor<>(factory, bufferSize, DaemonThreadFactory.INSTANCE);

// Connect the handler
disruptor.handleEventsWith(new LongEventHandler());

不是多个EventProcessor消费者去抢一个Event, 是广播。 如果EventHandler如果是耗时多基本没意义, 又得起个线程池异步处理了。

2.2 Work Pool模式

即调用Disruptor.handleEventsWithWorkerPool, 这样每个WorkHandler会是一个线程,各自处理属于自己的Event, 这样就跟平常用的线程池差不多的用法了。

public final EventHandlerGroup<T> handleEventsWithWorkerPool(final WorkHandler<T>... workHandlers)
{
    return createWorkerPool(new Sequence[0], workHandlers);
}

官方也有若干个例子,百万级别吞吐, 当WorkHandler耗时较多时其实和线程池相差不大。
https://github.com/zealzeng/fabric-samples/blob/master/disruptor-demo/src/main/java/com/lmax/disruptor/workhandler/OneToThreeWorkerPoolThroughputTest.java

Starting Disruptor tests
Run 0, Disruptor=4,349,906 ops/sec BatchPercent=0.00% AverageBatchSize=-1
Run 1, Disruptor=4,591,579 ops/sec BatchPercent=0.00% AverageBatchSize=-1
Run 2, Disruptor=4,590,946 ops/sec BatchPercent=0.00% AverageBatchSize=-1
Run 3, Disruptor=4,662,222 ops/sec BatchPercent=0.00% AverageBatchSize=-1
Run 4, Disruptor=4,695,276 ops/sec BatchPercent=0.00% AverageBatchSize=-1
Run 5, Disruptor=4,690,211 ops/sec BatchPercent=0.00% AverageBatchSize=-1
Run 6, Disruptor=4,713,201 ops/sec BatchPercent=0.00% AverageBatchSize=-1

3.Disruptor使用场景

参考使用到disruptor的一些框架.

3.1 Log4j2

Log4j2异步日志使用到了disruptor, 日志一般是有缓冲区, 满了才写到文件, 增量追加文件结合NIO等应该也比较快, 所以无论是EventHandler还是WorkHandler处理应该延迟比较小的, 写的文件也不多, 所以场景是比较合适的。

3.2 JStorm

在流处理中不同线程中数据交换,数据计算可能蛮多内存中计算, 流计算快进快出,disruptor应该不错的选择。

3.3 百度uid-generator

部分使用ring buffer和去伪共享等思路缓存已生成的uid, 应该也部分参考了disruptor吧。

3.4小结

Luoying-framework在event loop中在使用disruptor作为work pool性能不会有什么提升, 因为服务器实现内部的业务带着数据库查询等操作, disruptor只是数据交换快, 业务慢终究还是慢。

这就好比我要寄给合同到东北,有些快递确实快两天就到了,有些慢些可能3,4天,但快递只负责把东西送收件人手上, 收件人处理合同讲不好要个一两周,快递再快也解决不了合同处理慢的问题。

不同线程间需要快速交换数据, 快速处理数据的场景我从事的领域见得不多, 可能在大数据开发中和一些中间件开发中会有用武之地。

Disruptor也没深入去看,但是源码貌似不多,如果有纰漏请大家指正, 但ring buffer, sequencer等代码值得研究。

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