dubbo+zipkin调用链监控(二)

*:first-child {
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blockquote {
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margin-top: 0; }
blockquote > :last-child {
margin-bottom: 0; }

img {
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去年的时候写过dubbo+zipkin调用链监控,最近看到zipkin2配合brave实现起来会比我之前的实现要简单很多，因为brave将很多交互的内容都封装起来了，不需要自己去写具体的实现，比如如何去构建span，如何去上报数据。

收集器抽象

由于zipkin支持http以及kafka两种方式上报数据，所以在配置上需要做下抽象。

AbstractZipkinCollectorConfiguration

主要是针对下面两种收集方式的一些配置上的定义，最核心的是Sender接口的定义，http与kafka是两类完全不同的实现。

public abstract Sender getSender();

其次是协助性的构造函数，主要是配合构建收集器所需要的一些参数。

zipkinUrl

如果是http收集，那么对应的是zipkin api域名，如果是kafka，对应的是kafka集群的地址

topic

仅在收集方式为kafka是有效，http时传空值即可。

public AbstractZipkinCollectorConfiguration(String serviceName,String zipkinUrl,String topic){

    this.zipkinUrl=zipkinUrl;

    this.serviceName=serviceName;

    this.topic=topic;

    this.tracing=this.tracing();

}

配置上报方式,这里统一采用异常上传，并且配置上报的超时时间。

protected AsyncReporter<Span> spanReporter() {

    return AsyncReporter

            .builder(getSender())

            .closeTimeout(500, TimeUnit.MILLISECONDS)

            .build(SpanBytesEncoder.JSON_V2);

}

下面这两方法，是配合应用构建span使用的。

注意那个sampler()方法，默认是什么也不做的意思，我们要想看到数据就需要配置成Sampler.ALWAYS_SAMPLE，这样才能真正将数据上报到zipkin服务器。

protected Tracing tracing() {

    this.tracing= Tracing

            .newBuilder()

            .localServiceName(this.serviceName)

            .sampler(Sampler.ALWAYS_SAMPLE)

            .spanReporter(spanReporter())

            .build();

    return this.tracing;

}

protected Tracing getTracing(){

    return this.tracing;

}

HttpZipkinCollectorConfiguration

主要是实现getSender方法，可以借用OkHttpSender这个对象来快速构建，api版本采用v2。

public class HttpZipkinCollectorConfiguration extends AbstractZipkinCollectorConfiguration {

    public HttpZipkinCollectorConfiguration(String serviceName,String zipkinUrl) {

        super(serviceName,zipkinUrl,null);

    }

    @Override

    public Sender getSender() {

        return OkHttpSender.create(super.getZipkinUrl()+"/api/v2/spans");

    }

}

OkHttpSender这个类需要引用这个包

<dependency>

    <groupId>io.zipkin.reporter2</groupId>

    <artifactId>zipkin-sender-okhttp3</artifactId>

    <version>${zipkin-reporter2.version}</version>

</dependency>

KafkaZipkinCollectorConfiguration

同样也是实现getSender方法

public class KafkaZipkinCollectorConfiguration extends AbstractZipkinCollectorConfiguration {

    public KafkaZipkinCollectorConfiguration(String serviceName,String zipkinUrl,String topic) {

        super(serviceName,zipkinUrl,topic);

    }

    @Override

    public Sender getSender() {

        return KafkaSender

                .newBuilder()

                .bootstrapServers(super.getZipkinUrl())

                .topic(super.getTopic())

                .encoding(Encoding.JSON)

                .build();

    }

}

KafkaSender这个类需要引用这个包：

<dependency>

    <groupId>io.zipkin.reporter2</groupId>

    <artifactId>zipkin-sender-kafka11</artifactId>

    <version>${zipkin-reporter2.version}</version>

</dependency>

收集器工厂

由于上面创建了两个收集器配置类，使用时只能是其中之一，所以实际运行的实例需要根据配置来动态生成。ZipkinCollectorConfigurationFactory就是负责生成收集器实例的。

private final AbstractZipkinCollectorConfiguration zipkinCollectorConfiguration;

@Autowired

public ZipkinCollectorConfigurationFactory(TraceConfig traceConfig){

    if(Objects.equal("kafka", traceConfig.getZipkinSendType())){

        zipkinCollectorConfiguration=new KafkaZipkinCollectorConfiguration(

                traceConfig.getApplicationName(),

                traceConfig.getZipkinUrl(),

                traceConfig.getZipkinKafkaTopic());

    }

    else {

        zipkinCollectorConfiguration = new HttpZipkinCollectorConfiguration(

                traceConfig.getApplicationName(),

                traceConfig.getZipkinUrl());

    }

}

通过构建函数将我们的配置类TraceConfig注入进来，然后根据发送方式来构建实例。另外提供一个辅助函数：

public Tracing getTracing(){

    return this.zipkinCollectorConfiguration.getTracing();

}

过滤器

在dubbo的过滤器中实现数据上传的功能逻辑相对简单，一般都在invoke方法执行前记录数据，然后方法执行完成后再次记录数据。这个逻辑不变，有变化的是数据上报的实现，上一个版本是通过发http请求实现需要编码，现在可以直接借用brave所提供的span来帮助我们完成，有两重要的方法:

finish

方法源码如下，在完成的时候会填写上完成的时间并上报数据，这一般应用于同步调用场景。

public void finish(TraceContext context, long finishTimestamp) {

    MutableSpan span = this.spanMap.remove(context);

    if(span != null && !this.noop.get()) {

        synchronized(span) {

            span.finish(Long.valueOf(finishTimestamp));

            this.reporter.report(span.toSpan());

        }

    }

}

flush 与上面finish方法的不同点在于，在报数据时没有完成时间，这应该是适用于一些异步调用但不关心结果的场景，比如dubbo所提供的oneway方式调用。

public void flush(TraceContext context) {

    MutableSpan span = this.spanMap.remove(context);

    if(span != null && !this.noop.get()) {

        synchronized(span) {

            span.finish((Long)null);

            this.reporter.report(span.toSpan());

        }

    }

}

消费者

做为消费方，有一个核心功能就是将traceId以及spanId传递到服务提供方，这里还是通过dubbo提供的附加参数方式实现。

@Override

public Result invoke(Invoker<?> invoker, Invocation invocation) throws RpcException {

    if(!RpcTraceContext.getTraceConfig().isEnabled()){

        return invoker.invoke(invocation);

    }

    ZipkinCollectorConfigurationFactory zipkinCollectorConfigurationFactory=

            SpringContextUtils.getApplicationContext().getBean(ZipkinCollectorConfigurationFactory.class);

    Tracer tracer= zipkinCollectorConfigurationFactory.getTracing().tracer();

    if(null==RpcTraceContext.getTraceId()){

        RpcTraceContext.start();

        RpcTraceContext.setTraceId(IdUtils.get());

        RpcTraceContext.setParentId(null);

        RpcTraceContext.setSpanId(IdUtils.get());

    }

    else {

        RpcTraceContext.setParentId(RpcTraceContext.getSpanId());

        RpcTraceContext.setSpanId(IdUtils.get());

    }

    TraceContext traceContext= TraceContext.newBuilder()

            .traceId(RpcTraceContext.getTraceId())

            .parentId(RpcTraceContext.getParentId())

            .spanId(RpcTraceContext.getSpanId())

            .sampled(true)

            .build();

    Span span=tracer.toSpan(traceContext).start();

    invocation.getAttachments().put(RpcTraceContext.TRACE_ID_KEY, String.valueOf(span.context().traceId()));

    invocation.getAttachments().put(RpcTraceContext.SPAN_ID_KEY, String.valueOf(span.context().spanId()));

    Result result = invoker.invoke(invocation);

    span.finish();

    return result;

}

提供者

@Override

    public Result invoke(Invoker<?> invoker, Invocation invocation) throws RpcException {

        if(!RpcTraceContext.getTraceConfig().isEnabled()){

            return invoker.invoke(invocation);

        }

        Map<String, String> attaches = invocation.getAttachments();

        if (!attaches.containsKey(RpcTraceContext.TRACE_ID_KEY)){

            return invoker.invoke(invocation);

        }

        Long traceId = Long.valueOf(attaches.get(RpcTraceContext.TRACE_ID_KEY));

        Long spanId = Long.valueOf(attaches.get(RpcTraceContext.SPAN_ID_KEY));

        attaches.remove(RpcTraceContext.TRACE_ID_KEY);

        attaches.remove(RpcTraceContext.SPAN_ID_KEY);

        RpcTraceContext.start();

        RpcTraceContext.setTraceId(traceId);

        RpcTraceContext.setParentId(spanId);

        RpcTraceContext.setSpanId(IdUtils.get());

        ZipkinCollectorConfigurationFactory zipkinCollectorConfigurationFactory=

                SpringContextUtils.getApplicationContext().getBean(ZipkinCollectorConfigurationFactory.class);

        Tracer tracer= zipkinCollectorConfigurationFactory.getTracing().tracer();

        TraceContext traceContext= TraceContext.newBuilder()

                .traceId(RpcTraceContext.getTraceId())

                .parentId(RpcTraceContext.getParentId())

                .spanId(RpcTraceContext.getSpanId())

                .sampled(true)

                .build();

        Span span = tracer.toSpan(traceContext).start();

        Result result = invoker.invoke(invocation);

        span.finish();

        return result;

    }

异常流程

上面无论是消费者的过滤器还是服务提供者的过滤器，均未考虑服务在调用invoker.invoke时出错的场景，如果出错，后面的span.finish方法将不会按预期执行，也就记录不了信息。所以需要针对此问题做优化：可以在finally块中执行finish方法。

try {

    result = invoker.invoke(invocation);

}

finally {

    span.finish();

}

消费者在调用服务时，异步调用问题

上面过滤器中调用span.finish都是基于同步模式，而由于dubbo除了同步调用外还提供了两种调用方式

异步调用通过callback机制的异步
oneway

只发起请求并不等待结果的异步调用，无callback一说

针对上面两类异步再加上同步调用，我们要想准确记录服务真正的时间，需要在消费方的过滤器中做如下处理：

创建一个用于回调的处理类，它的主要目的是为了在回调成功时记录时间，这里无论是成功还是失败。

private class AsyncSpanCallback implements ResponseCallback{

    private Span span;

    public AsyncSpanCallback(Span span){

        this.span=span;

    }

    @Override

    public void done(Object o) {

        span.finish();

    }

    @Override

    public void caught(Throwable throwable) {

        span.finish();

    }

}

再在调用invoke方法时,如果是oneway方式，则调用flush方法结果，如果是同步则直接调用finish方法，如果是异步则在回调时调用finish方法。



Result result = null;

boolean isOneway = RpcUtils.isOneway(invoker.getUrl(), invocation);

try {

    result = invoker.invoke(invocation);

}

finally {

    if(isOneway) {

        span.flush();

    }

    else if(!isAsync) {

        span.finish();

    }

}

待完善问题

过滤器中生成span的方式应该有更好的方法，还没有对brave做过多研究，后续想办法再优化下。另外我测试的场景是consumer调用provider，provider内部再调用provider2，我测试时发现第三步调用传递的parentId好像有点小问题，后续需要再确认下。

代码下载

https://github.com/jiangmin168168/jim-framework

巴特西