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Brave是Java版的Zipkin客户端，它将收集的跟踪信息，以Span的形式上报给Zipkin系统。
（Zipkin是基于Google的一篇论文，名为Dapper，Dapper在荷兰语里是“勇敢的”的意思，这也是Brave的命名的原因）
Brave目前版本为4.9.1，兼容zipkin1和2的协议，github地址：https://github.com/openzipkin/brave
我们一般不会手动编写Trace相关的代码，Brave提供了一些开箱即用的库，来帮助我们对某些特定的库类来进行追踪，比如servlet，springmvc，mysql，okhttp3，httpclient等，这些都可以在下面页面中找到：
https://github.com/openzipkin/brave/tree/master/instrumentation
我们先来看看一个简单的Demo来演示下Brave的基本使用，这对我们后续分析Brave的原理和其他类库的使用有很大帮助
TraceDemo
package tracing;
import brave.Span;
import brave.Tracer;
import brave.Tracing;
import brave.context.log4j2.ThreadContextCurrentTraceContext;
import brave.propagation.B3Propagation;
import brave.propagation.ExtraFieldPropagation;
import zipkin2.codec.SpanBytesEncoder;
import zipkin2.reporter.AsyncReporter;
import zipkin2.reporter.Sender;
import zipkin2.reporter.okhttp3.OkHttpSender;
import java.util.concurrent.TimeUnit;
public class TraceDemo {
public static void main(String[] args) {
Sender sender = OkHttpSender.create("http://localhost:9411/api/v2/spans");
AsyncReporter asyncReporter = AsyncReporter.builder(sender)
.closeTimeout(500, TimeUnit.MILLISECONDS)
.build(SpanBytesEncoder.JSON_V2);
Tracing tracing = Tracing.newBuilder()
.localServiceName("tracer-demo")
.spanReporter(asyncReporter)
.propagationFactory(ExtraFieldPropagation.newFactory(B3Propagation.FACTORY, "user-name"))
.currentTraceContext(ThreadContextCurrentTraceContext.create())
.build();
Tracer tracer = tracing.tracer();
Span span = tracer.newTrace().name("encode").start();
try {
doSomethingExpensive();
} finally {
span.finish();
}
Span twoPhase = tracer.newTrace().name("twoPhase").start();
try {
Span prepare = tracer.newChild(twoPhase.context()).name("prepare").start();
try {
prepare();
} finally {
prepare.finish();
}
Span commit = tracer.newChild(twoPhase.context()).name("commit").start();
try {
commit();
} finally {
commit.finish();
}
} finally {
twoPhase.finish();
}
sleep(1000);
}
private static void doSomethingExpensive() {
sleep(500);
}
private static void commit() {
sleep(500);
}
private static void prepare() {
sleep(500);
}
private static void sleep(long milliseconds) {
try {
TimeUnit.MILLISECONDS.sleep(milliseconds);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
启动Zipkin，然后运行TraceDemo，在Zipkin的UI界面中能查到两条跟踪信息 
点击第一条跟踪信息，可以看到有一条Span(encode)，耗时500ms左右 
本条跟踪信息对应的代码片段为：
Tracer tracer = tracing.tracer();
Span span = tracer.newTrace().name("encode").start();
try {
doSomethingExpensive();
} finally {
span.finish();
}
由Tracer创建一个新的Span，名为encode，然后调用start方法开始计时，之后运行一个比较耗时的方法doSomethingExpensive，最后调用finish方法结束计时，完成并记录一条跟踪信息。
这段代码实际上向Zipkin上报的数据为：
[
{
"traceId": "16661f6cb5d58903",
"id": "16661f6cb5d58903",
"name": "encode",
"timestamp": 1510043590522358,
"duration": 499867,
"binaryAnnotations": [
{
"key": "lc",
"value": "",
"endpoint": {
"serviceName": "tracer-demo",
"ipv4": "192.168.99.1"
}
}
]
}
]
然后我们再来看第二条稍微复杂的跟踪信息，可以看到一条名为twoPhase的Span，总耗时为1000ms，它有2个子Span，分别名为prepare和commit，两者分别耗时500ms
这条跟踪信息对应的代码片段为
Span twoPhase = tracer.newTrace().name("twoPhase").start();
try {
Span prepare = tracer.newChild(twoPhase.context()).name("prepare").start();
try {
prepare();
} finally {
prepare.finish();
}
Span commit = tracer.newChild(twoPhase.context()).name("commit").start();
try {
commit();
} finally {
commit.finish();
}
} finally {
twoPhase.finish();
}
这段代码实际上向Zipkin上报的数据为：
[
{
"traceId": "89e051d5394b90b1",
"id": "89e051d5394b90b1",
"name": "twophase",
"timestamp": 1510043591038983,
"duration": 1000356,
"binaryAnnotations": [
{
"key": "lc",
"value": "",
"endpoint": {
"serviceName": "tracer-demo",
"ipv4": "192.168.99.1"
}
}
]
},
{
"traceId": "89e051d5394b90b1",
"id": "60568c4903793b8d",
"name": "prepare",
"parentId": "89e051d5394b90b1",
"timestamp": 1510043591039919,
"duration": 499246,
"binaryAnnotations": [
{
"key": "lc",
"value": "",
"endpoint": {
"serviceName": "tracer-demo",
"ipv4": "192.168.99.1"
}
}
]
},
{
"traceId": "89e051d5394b90b1",
"id": "ce14448169d01d2f",
"name": "commit",
"parentId": "89e051d5394b90b1",
"timestamp": 1510043591539304,
"duration": 499943,
"binaryAnnotations": [
{
"key": "lc",
"value": "",
"endpoint": {
"serviceName": "tracer-demo",
"ipv4": "192.168.99.1"
}
}
]
}
]
Span
首先看下Span的实现类RealSpan
该类依赖几个核心类
Recorder，用于记录Span
Reporter，用于上报Span给Zipkin
MutableSpan，Span的包装类，提供各种API操作Span
MutableSpanMap，以TraceContext为Key，MutableSpan为Value的Map结构，用于内存中存放所有的Span
RealSpan两个核心方法start, finish
public Span start(long timestamp) {
recorder().start(context(), timestamp);
return this;
}
public void finish(long timestamp) {
recorder().finish(context(), timestamp);
}
分别调用Recorder的start和finish方法，获取跟TraceContext绑定的Span信息，记录开始时间和结束时间，并在结束时，调用reporter的report方法，上报给Zipkin
public void start(TraceContext context, long timestamp) {
if (noop.get()) return;
spanMap.getOrCreate(context).start(timestamp);
}
public void finish(TraceContext context, long finishTimestamp) {
MutableSpan span = spanMap.remove(context);
if (span == null || noop.get()) return;
synchronized (span) {
span.finish(finishTimestamp);
reporter.report(span.toSpan());
}
}
BoundedAsyncReporter
Reporter的实现类AsyncReporter，而AsyncReporter的实现类是BoundedAsyncReporter
static final class BoundedAsyncReporter extends AsyncReporter {
static final Logger logger = Logger.getLogger(BoundedAsyncReporter.class.getName());
final AtomicBoolean closed = new AtomicBoolean(false);
final BytesEncoder encoder;
final ByteBoundedQueue pending;
final Sender sender;
final int messageMaxBytes;
final long messageTimeoutNanos;
final long closeTimeoutNanos;
final CountDownLatch close;
final ReporterMetrics metrics;
BoundedAsyncReporter(Builder builder, BytesEncoder encoder) {
this.pending = new ByteBoundedQueue(builder.queuedMaxSpans, builder.queuedMaxBytes);
this.sender = builder.sender;
this.messageMaxBytes = builder.messageMaxBytes;
this.messageTimeoutNanos = builder.messageTimeoutNanos;
this.closeTimeoutNanos = builder.closeTimeoutNanos;
this.close = new CountDownLatch(builder.messageTimeoutNanos > 0 ? 1 : 0);
this.metrics = builder.metrics;
this.encoder = encoder;
}
}
BoundedAsyncReporter中的几个重要的类：
- BytesEncoder - Span的编码器，将Span编码成二进制，便于sender发送给Zipkin
- ByteBoundedQueue - 类似于BlockingQueue，是一个既有数量限制，又有字节数限制的阻塞队列
- Sender - 将编码后的二进制数据，发送给Zipkin
- ReporterMetrics - Span的report相关的统计信息
- BufferNextMessage - Consumer，Span信息的消费者，依靠Sender上报Span信息
public AsyncReporter build(BytesEncoder encoder) {
if (encoder == null) throw new NullPointerException("encoder == null");
if (encoder.encoding() != sender.encoding()) {
throw new IllegalArgumentException(String.format(
"Encoder doesn't match Sender: %s %s", encoder.encoding(), sender.encoding()));
}
final BoundedAsyncReporter result = new BoundedAsyncReporter<>(this, encoder);
if (messageTimeoutNanos > 0) { // Start a thread that flushes the queue in a loop.
final BufferNextMessage consumer =
new BufferNextMessage(sender, messageMaxBytes, messageTimeoutNanos);
final Thread flushThread = new Thread(() -> {
try {
while (!result.closed.get()) {
result.flush(consumer);
}
} finally {
for (byte[] next : consumer.drain()) result.pending.offer(next);
result.close.countDown();
}
}, "AsyncReporter(" + sender + ")");
flushThread.setDaemon(true);
flushThread.start();
}
return result;
}
当messageTimeoutNanos大于0时，启动一个守护线程flushThread，一直循环调用BoundedAsyncReporter的flush方法，将内存中的Span信息上报给Zipkin 而当messageTimeoutNanos等于0时，客户端需要手动调用flush方法来上报Span信息
再来看下BoundedAsyncReporter中的close方法
@Override public void close() {
if (!closed.compareAndSet(false, true)) return; // already closed
try {
// wait for in-flight spans to send
if (!close.await(closeTimeoutNanos, TimeUnit.NANOSECONDS)) {
logger.warning("Timed out waiting for in-flight spans to send");
}
} catch (InterruptedException e) {
logger.warning("Interrupted waiting for in-flight spans to send");
Thread.currentThread().interrupt();
}
int count = pending.clear();
if (count > 0) {
metrics.incrementSpansDropped(count);
logger.warning("Dropped " + count + " spans due to AsyncReporter.close()");
}
}
这个close方法和FlushThread中while循环相呼应，在close方法中，首先将closed变量置为true，然后调用close.await()，等待close信号量(CountDownLatch)的释放，此处代码会阻塞，一直到FlushThread中finally中调用result.close.countDown(); 而在close方法中将closed变量置为true后，FlushThread中的while循环将结束执行，然后执行finally代码块，系统会将内存中还未上报的Span，添加到queue（result.pending）中，然后调用result.close.countDown(); close方法中阻塞的代码会继续执行，将调用metrics.incrementSpansDropped(count)将这些Span的数量添加到metrics统计信息中
@Override public void report(S span) {
if (span == null) throw new NullPointerException("span == null");
metrics.incrementSpans(1);
byte[] next = encoder.encode(span);
int messageSizeOfNextSpan = sender.messageSizeInBytes(Collections.singletonList(next));
metrics.incrementSpanBytes(next.length);
if (closed.get() ||
// don't enqueue something larger than we can drain
messageSizeOfNextSpan > messageMaxBytes ||
!pending.offer(next)) {
metrics.incrementSpansDropped(1);
}
}
前面看到在Recorder的finish方法中，会调用Reporter的report方法，此处report方法，将span转化成字节数组，然后计算出messageSize，添加到queue(pending)中，并记录相应的统计信息
接下来看看两个flush方法，其中flush()方法，是public的，供外部手动调用，而flush(BufferNextMessage bundler)是在FlushThread中循环调用
@Override public final void flush() {
flush(new BufferNextMessage(sender, messageMaxBytes, 0));
}
void flush(BufferNextMessage bundler) {
if (closed.get()) throw new IllegalStateException("closed");
//将队列中的数据，全部提取到BufferNextMessage中，直到buffer(bundler)满为止
pending.drainTo(bundler, bundler.remainingNanos());
// record after flushing reduces the amount of gauge events vs on doing this on report
metrics.updateQueuedSpans(pending.count);
metrics.updateQueuedBytes(pending.sizeInBytes);
// loop around if we are running, and the bundle isn't full
// if we are closed, try to send what's pending
if (!bundler.isReady() && !closed.get()) return;
// Signal that we are about to send a message of a known size in bytes
metrics.incrementMessages();
metrics.incrementMessageBytes(bundler.sizeInBytes());
List nextMessage = bundler.drain();
try {
sender.sendSpans(nextMessage).execute();
} catch (IOException | RuntimeException | Error t) {
// In failure case, we increment messages and spans dropped.
int count = nextMessage.size();
Call.propagateIfFatal(t);
metrics.incrementMessagesDropped(t);
metrics.incrementSpansDropped(count);
if (logger.isLoggable(FINE)) {
logger.log(FINE,
format("Dropped %s spans due to %s(%s)", count, t.getClass().getSimpleName(),
t.getMessage() == null ? "" : t.getMessage()), t);
}
// Raise in case the sender was closed out-of-band.
if (t instanceof IllegalStateException) throw (IllegalStateException) t;
}
}
flush中大致分下面几步
- 先将队列pending中的数据，全部提取到BufferNextMessage（bundler）中，直到bundler满为止
- 当bundler准备好，即isReady()返回true，将bundler中的message全部取出来
- 将取出来的所有message，调用Sender的sendSpans方法，发送到Zipkin
ByteBoundedQueue
类似于BlockingQueue，是一个既有数量限制，又有字节数限制的阻塞队列，提供了offer，drainTo，clear三个方法，供调用者向queue里存放，提取和清空数据
final class ByteBoundedQueue {
final ReentrantLock lock = new ReentrantLock(false);
final Condition available = lock.newCondition();
final int maxSize;
final int maxBytes;
final byte[][] elements;
int count;
int sizeInBytes;
int writePos;
int readPos;
ByteBoundedQueue(int maxSize, int maxBytes) {
this.elements = new byte[maxSize][];
this.maxSize = maxSize;
this.maxBytes = maxBytes;
}
}
ByteBoundedQueue接受两个int参数，maxSize是queue接受的最大数量，maxBytes是queue接受的最大字节数 ByteBoundedQueue中使用一个二维byte数组elements来存储message，并使用writePos和readPos两个游标，分别记录写和读的位置 ByteBoundedQueue中使用了最典型的可重入锁ReentrantLock，使offer，drainTo，clear等方法是线程安全的
/**
* Returns true if the element could be added or false if it could not due to its size.
*/
boolean offer(byte[] next) {
lock.lock();
try {
if (count == elements.length) return false;
if (sizeInBytes + next.length > maxBytes) return false;
elements[writePos++] = next;
if (writePos == elements.length) writePos = 0; // circle back to the front of the array
count++;
sizeInBytes += next.length;
available.signal(); // alert any drainers
return true;
} finally {
lock.unlock();
}
}
offer方法是添加message到queue中，使用了标准的try-lock结构，即先获取锁，然后finally里释放锁，在获取锁以后 当count等于elements.length时，意味着queue是满的，则不能继续添加 当sizeInBytes + next.length > maxBytes时，意味着该消息加进队列会超出队列字节大小限制，也不能添加新message 如果上面两个条件都不满足，则表明可以继续添加message，将writePos+1，并将message放于writePos+1处 当writePos到达数组尾部，则将writePos置为0，让下一次添加从数组头部开始 然后将count计数器加1，并更新字节总数 最后调用available.signal()来通知其他在lock上等待的线程（在drainTo方法中阻塞的线程）继续竞争线程资源
/** Blocks for up to nanosTimeout for elements to appear. Then, consume as many as possible. */
int drainTo(Consumer consumer, long nanosTimeout) {
try {
// This may be called by multiple threads. If one is holding a lock, another is waiting. We
// use lockInterruptibly to ensure the one waiting can be interrupted.
lock.lockInterruptibly();
try {
long nanosLeft = nanosTimeout;
while (count == 0) {
if (nanosLeft <= 0) return 0;
nanosLeft = available.awaitNanos(nanosLeft);
}
return doDrain(consumer);
} finally {
lock.unlock();
}
} catch (InterruptedException e) {
return 0;
}
}
drainTo方法是提取message到Consumer中消费，如果当时queue里没有消息，则每次等待nanosTimeout，直到queue里存入消息为止 当while循环退出，表明queue中已经有新的message添加进来，可以消费，则调用doDrain方法。
int doDrain(Consumer consumer) {
int drainedCount = 0;
int drainedSizeInBytes = 0;
while (drainedCount < count) {
byte[] next = elements[readPos];
if (next == null) break;
if (consumer.accept(next)) {
drainedCount++;
drainedSizeInBytes += next.length;
elements[readPos] = null;
if (++readPos == elements.length) readPos = 0; // circle back to the front of the array
} else {
break;
}
}
count -= drainedCount;
sizeInBytes -= drainedSizeInBytes;
return drainedCount;
}
doDrain里依然是一个while循环，当drainedCount小于count，即提取的message数量总数小于queue里消息总数时，尝试调用consumer.accept方法 如果accept方法返回true，则将drainedCount加1，并且drainedSizeInBytes加上当前消息的字节数 如果accept方法返回false，则跳出循环，将queue的count减掉提取的总消息数drainedCount，sizeInBytes减去提取的总字节数drainedSizeInBytes
int clear() {
lock.lock();
try {
int result = count;
count = sizeInBytes = readPos = writePos = 0;
Arrays.fill(elements, null);
return result;
} finally {
lock.unlock();
}
}
clear方法，清空队列，这个方法比较简单，就是将所有东西清零，该方法在Reporter的close方法中会被使用
BufferNextMessage
BufferNextMessage是ByteBoundedQueue.Consumer的默认实现
final class BufferNextMessage implements ByteBoundedQueue.Consumer {
private final Sender sender;
private final int maxBytes;
private final long timeoutNanos;
private final List buffer = new LinkedList<>();
long deadlineNanoTime;
int sizeInBytes;
boolean bufferFull;
BufferNextMessage(Sender sender, int maxBytes, long timeoutNanos) {
this.sender = sender;
this.maxBytes = maxBytes;
this.timeoutNanos = timeoutNanos;
}
}
BufferNextMessage中使用一个LinkedList来存储接收的messages
@Override
public boolean accept(byte[] next) {
buffer.add(next); // speculatively add to the buffer so we can size it
int x = sender.messageSizeInBytes(buffer);
int y = maxBytes;
int includingNextVsMaxBytes = (x < y) ? -1 : ((x == y) ? 0 : 1);
// If we can fit queued spans and the next into one message...
if (includingNextVsMaxBytes <= 0) {
sizeInBytes = x;
if (includingNextVsMaxBytes == 0) {
bufferFull = true;
}
return true;
} else {
buffer.remove(buffer.size() - 1);
return false; // we couldn't fit the next message into this buffer
}
}
accept方法，先将message放入buffer，然后调用sender的messageSizeInBytes方法统计下所有buffer消息的总字节数includingNextVsMaxBytes 当includingNextVsMaxBytes大于该buffer的最大字节数maxBytes，则将加入到buffer的message移除 当includingNextVsMaxBytes等于该buffer的最大字节数maxBytes，则将该buffer标记为已满状态，即bufferFull = true
long remainingNanos() {
if (buffer.isEmpty()) {
deadlineNanoTime = System.nanoTime() + timeoutNanos;
}
return Math.max(deadlineNanoTime - System.nanoTime(), 0);
}
boolean isReady() {
return bufferFull || remainingNanos() <= 0;
}
remainingNanos方法中，当buffer为空，则重置一个deadlineNanoTime，其值为当前系统时间加上timeoutNanos，当系统时间超过这个时间或者buffer满了的时候， isReady会返回true，即buffer为准备就绪状态
List drain() {
if (buffer.isEmpty()) return Collections.emptyList();
ArrayList result = new ArrayList<>(buffer);
buffer.clear();
sizeInBytes = 0;
bufferFull = false;
deadlineNanoTime = 0;
return result;
}
drain方法返回buffer里的所有数据，并将buffer清空
isReady方法和drain方法，在BoundedAsyncReporter的flush方法中会被使用
void flush(BufferNextMessage bundler) {
// ...
if (!bundler.isReady() && !closed.get()) return;
// ...
List nextMessage = bundler.drain();
// ...
sender.sendSpans(nextMessage).execute();
}
因为flush是会一直不间断被调用，而这里先调用bundler.isReady()方法，当返回true后才取出所有堆积的消息，一起打包发送给zipkin提高效率
再回过头来看看BoundedAsyncReporter里手动flush方法
@Override public final void flush() {
flush(new BufferNextMessage(sender, messageMaxBytes, 0));
}
在我们分析完BufferNextMessage源代码后，我们很容易得出结论：这里构造BufferNextMessage传入的timeoutNanos为0，所以BufferNextMessage的isReady()方法会永远返回true。 这意味着每次我们手动调用flush方法，会立即将queue的数据用BufferNextMessage填满，并打包发送给Zipkin，至于queue里剩下的数据，需要等到下次FlushThread循环执行flush方法的时候被发送
至此，我们已经分析过Tracer和Span相关的源代码，这对我们后续看Brave和其他框架整合有很大帮助： Span/RealSpan Recorder Reporter/AsyncReporter/BoundedAsyncReporter BufferNextMessage ByteBoundedQueue
在下一篇博文中，会继续分析Tracing的初始化过程，以及相关源代码