一次消息消费服务的内存泄漏排查小记

时间 2021-02-19

标签 java spring apache app 框架分布式 ide this spa 栏目 Java 繁體版

原文原文链接

线上有一个消息消费服务xxx-consumer，使用spring-kafka框架，主线程批量从消费队列（kafka）拉取交易系统生产的消息，而后提交到子线程池中挨个处理消费。java

public abstract class AbstractMessageDispatchListener implements
        BatchAcknowledgingMessageListener<String, Msg>, ApplicationListener<ApplicationReadyEvent> {

    private ThreadPoolExecutor executor;

    public abstract MessageWorker chooseWorker(ConsumerRecord<String, Msg> data);

    @Override
    public void onMessage(List<ConsumerRecord<String, Msg>> datas, Acknowledgment acknowledgment) {
        List<Future<?>> futureList = new ArrayList<>(datas.size());
        try {
            CountDownLatch countDownLatch = new CountDownLatch(datas.size());
            for (ConsumerRecord<String, Msg> data : datas) {
                Future<?> future = executor.submit(new Worker(data, countDownLatch));
                futureList.add(future);
            }

            countDownLatch.await(20000L - 2000, TimeUnit.MILLISECONDS);
            long countDownLatchCount = countDownLatch.getCount();
            if (countDownLatchCount > 0) {
                return;
            }
            acknowledgment.acknowledge();
        } catch (Exception e) {
            logger.error("onMessage error ", e);
        } finally {
            for (Future<?> future : futureList) {
                if (future.isDone() || future.isCancelled()) {
                    continue;
                }
                future.cancel(true);
            }
        }
    }

    @Override
    public void onApplicationEvent(ApplicationReadyEvent event) {
        ThreadFactoryBuilder builder = new ThreadFactoryBuilder();
        builder.setNameFormat(this.getClass().getSimpleName() + "-pool-%d");
        builder.setDaemon(false);
        executor = new ThreadPoolExecutor(12,
                12 * 2,
                60L,
                TimeUnit.SECONDS,
                new ArrayBlockingQueue<>(100),
                builder.build());
    }

    private class Worker implements Runnable {
        private ConsumerRecord<String, Msg> data;
        private CountDownLatch countDownLatch;

        Worker(ConsumerRecord<String, Msg> data, CountDownLatch countDownLatch) {
            this.data = data;
            this.countDownLatch = countDownLatch;
        }

        @Override
        public void run() {
            try {
                MessageWorker worker = chooseWorker(data);
                worker.work(data.value());
            } finally {
                countDownLatch.countDown();
            }
        }
    }
}

1. 问题背景

有一天早上xxx-consumer服务出现大量报警，人工排查发现30w+的消息未处理，业务日志正常，gc日志有大量Full gc，初步判断由于Full gc致使消息处理慢，大量的消息积压。spring

2. 堆栈分析

查看了近一个月的JVM内存信息，发现老年代内存没法被回收（9月22号的降低是由于服务有一次上线重启），初步判断发生了内存泄漏。apache

经过<jmap -dump:format=b,file=/home/work/app/xxx-consumer/logs/jmap_dump.hprof -F>命令导出内存快照，使用Memory Analyzer解析内存快照文件jmap_dump.hprof，发现有很明显的内存泄漏提示：app

进一步查看线程细节，发现建立了大量的ThreadLocalScope对象且循环引用：框架

同时咱们也看到了分布式追踪（dd-trace-java）jar包中的FakeSpan类，初步判断是dd-trace-java中自研扩展的kafka插件存在内存泄漏bug。分布式

3. 代码分析

继续查看dd-trace-java中kafka插件的代码，其处理流程以下：ide

第一批消息ui

（SpringKafkaConsumerInstrumentation:L22）BatchAcknowledgingMessageListener.onMessage进入时，主线程会建立一个scope00=ThreadLocalScope（Type_BatchMessageListener_Value，toRestore=null）
（ExecutorInstrumentation:L21L47）消息被submit到线程池中处理时，子线程会建立一个scope10=ThreadLocalScope（Type_BatchMessageListener_Value，toRestore=null）
（SpringKafkaConsumerInstrumentation:L68）子线程处理消息时（ConsumerRecord.value），会建立一个scope11=ThreadLocalScope（Type_ConsumberRecord_Value，toRestore=scope10）
（ExecutorInstrumentation:L54）子线程处理完消息后，执行scope10.close()，而scopeManager.tlsScope.get()=scope11，命中ThreadLocalScope:L19，scope10和scope11均没法被GC
（SpringKafkaConsumerInstrumentation:L42）BatchAcknowledgingMessageListener.onMessage退出时，主线程会执行scope00.close()，scope00会被GC

第二批消息this

（SpringKafkaConsumerInstrumentation:L22）BatchAcknowledgingMessageListener.onMessage进入时，主线程会建立一个scope01=ThreadLocalScope（Type_BatchMessageListener_Value，toRestore=null）
（ExecutorInstrumentation:L21L47）消息被submit到线程池中处理时，子线程会建立一个scope12=ThreadLocalScope（Type_BatchMessageListener_Value，toRestore=scope11）
（SpringKafkaConsumerInstrumentation:L68）子线程处理消息时（ConsumerRecord.value），会建立一个scope13=ThreadLocalScope（Type_ConsumberRecord_Value，toRestore=scope12）
（ExecutorInstrumentation:L54）子线程处理完消息后，执行scope12.close()，而scopeManager.tlsScope.get()=scope13，命中ThreadLocalScope:L19，scope12和scope13均没法被GC
（SpringKafkaConsumerInstrumentation:L42）BatchAcknowledgingMessageListener.onMessage退出时，主线程会执行scope01.close()，scope01会被GC

从上能够看到，主线程建立的ThreadLocalScope能被正确GC，而线程池中建立的ThreadLocalScope被循环引用，没法被正确GC，从而形成内存泄漏。spa

@AutoService(Instrumenter.class)
public final class SpringKafkaConsumerInstrumentation extends Instrumenter.Configurable {
 
    @Override
    public AgentBuilder apply(final AgentBuilder agentBuilder) {
        return agentBuilder
                .type(hasSuperType(named("org.springframework.kafka.listener.BatchAcknowledgingMessageListener")))
                .transform(DDAdvice.create().advice(isMethod().and(isPublic()).and(named("onMessage")),
                        BatchMessageListenerAdvice.class.getName()))
                .type(named("org.apache.kafka.clients.consumer.ConsumerRecord"))
                .transform(DDAdvice.create().advice(isMethod().and(isPublic()).and(named("value")),
                        RecoredValueAdvice.class.getName()))
                .asDecorator();
    }
 
    public static class BatchMessageListenerAdvice {
 
        @Advice.OnMethodEnter(suppress = Throwable.class)
        public static Scope before() {
            FakeSpan span = new FakeSpan();
            span.setKind(FakeSpan.Type_BatchMessageListener_Value);
            Scope scope = GlobalTracer.get().scopeManager().activate(span, false);
            return scope;
        }
 
        @Advice.OnMethodExit(suppress = Throwable.class)
        public static void after(@Advice.Enter Scope scope) {
            while (true) {
                Span span = GlobalTracer.get().activeSpan();
                if (span != null && span instanceof FakeSpan) {
                    FakeSpan fakeSpan = (FakeSpan) span;
                    if (fakeSpan.getKind().equals(FakeSpan.Type_ConsumberRecord_Value)) {
                        GlobalTracer.get().scopeManager().active().close();
                    } else {
                        break;
                    }
                } else {
                    break;
                }
            }
            if (scope != null) {
                scope.close();
            }
        }
    }
 
    public static class RecoredValueAdvice {
 
        @Advice.OnMethodEnter(suppress = Throwable.class)
        public static void before(@Advice.This ConsumerRecord record) {
            Span activeSpan = GlobalTracer.get().activeSpan();
            if (activeSpan instanceof FakeSpan) {
                FakeSpan proxy = (FakeSpan) activeSpan;
                if (proxy.getKind().equals(FakeSpan.Type_ConsumberRecord_Value)) {
                    GlobalTracer.get().scopeManager().active().close();
                    activeSpan = GlobalTracer.get().activeSpan();
                    if (activeSpan instanceof FakeSpan) {
                        proxy = (FakeSpan) activeSpan;
                    }
                }
 
                if (proxy.getKind().equals(FakeSpan.Type_BatchMessageListener_Value)) {
                    final SpanContext spanContext = TracingKafkaUtils.extractSecond(record.headers(), GlobalTracer.get());
                    if (spanContext != null) {
                        FakeSpan consumerProxy = new FakeSpan();
                        consumerProxy.setContext(spanContext);
                        consumerProxy.setKind(FakeSpan.Type_ConsumberRecord_Value);
                        GlobalTracer.get().scopeManager().activate(consumerProxy, false);
                    }
                }
            }
        }
    }
}

@AutoService(Instrumenter.class)
public final class ExecutorInstrumentation extends Instrumenter.Configurable {
 
    @Override
    public AgentBuilder apply(final AgentBuilder agentBuilder) {
        return agentBuilder
                .type(not(isInterface()).and(hasSuperType(named(ExecutorService.class.getName()))))
                .transform(DDAdvice.create().advice(named("submit").and(takesArgument(0, Runnable.class)),
                        SubmitTracedRunnableAdvice.class.getName()))
                .asDecorator();
    }
 
 
    public static class SubmitTracedRunnableAdvice {
 
        @Advice.OnMethodEnter(suppress = Throwable.class)
        public static TracedRunnable wrapJob(
                @Advice.This Object dis,
                @Advice.Argument(value = 0, readOnly = false) Runnable task) {
            if (task != null && (!dis.getClass().getName().startsWith("slick.util.AsyncExecutor"))) {
                task = new TracedRunnable(task, GlobalTracer.get());
                return (TracedRunnable) task;
            }
            return null;
        }
    }
 
    public static class TracedRunnable implements Runnable {
        private final Runnable delegate;
        private final Span span;
        private final Tracer tracer;
 
        public TracedRunnable(Runnable delegate, Tracer tracer) {
            this.delegate = delegate;
            this.tracer = tracer;
            if (tracer != null) {
                this.span = tracer.activeSpan();
            } else {
                this.span = null;
            }
        }
 
        @Override
        public void run() {
            Scope scope = null;
            if (span != null && tracer != null) {
                scope = tracer.scopeManager().activate(span, false);
            }
 
            try {
                delegate.run();
            } finally {
                if (scope != null) {
                    scope.close();
                }
            }
        }
    }
}

public class ThreadLocalScopeManager implements ScopeManager {
 
    final ThreadLocal<ThreadLocalScope> tlsScope = new ThreadLocal<ThreadLocalScope>();
 
    @Override
    public Scope activate(Span span, boolean finishOnClose) {
        return new ThreadLocalScope(this, span, finishOnClose);
    }
 
    @Override
    public Scope active() {
        return tlsScope.get();
    }
}

public class ThreadLocalScope implements Scope {
    private final ThreadLocalScopeManager scopeManager;
    private final Span wrapped;
    private final boolean finishOnClose;
    private final ThreadLocalScope toRestore;
 
    ThreadLocalScope(ThreadLocalScopeManager scopeManager, Span wrapped, boolean finishOnClose) {
        this.scopeManager = scopeManager;
        this.wrapped = wrapped;
        this.finishOnClose = finishOnClose;
        this.toRestore = scopeManager.tlsScope.get();
        scopeManager.tlsScope.set(this);
    }
 
    @Override
    public void close() {
        if (scopeManager.tlsScope.get() != this) {
            // This shouldn't happen if users call methods in the expected order. Bail out.
            return;
        }
 
        if (finishOnClose) {
            wrapped.finish();
        }
 
        scopeManager.tlsScope.set(toRestore);
    }
 
    @Override
    public Span span() {
        return wrapped;
    }
}

End

RecoredValueAdvice没有销毁本身建立的对象，而是寄但愿于BatchMessageListenerAdvice去销毁。

但（SpringKafkaConsumerInstrumentation:L27）BatchAcknowledgingMessageListener.onMessage退出时，只会close主线程建立的ThreadLocalScope，不会close线程池中建立的ThreadLocalScope，致使子线程建立的ThreadLocalScope被循环引用，没法被正确GC，从而形成内存泄漏。