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Spring Cloud Sleuth RC1 Deployed

Recently, I'm mostly focusing on the Spring Cloud Sleuth project and actually, some gigantic changes have happened there since the M5 release. In this short post, I’ll show you the rationale behind and describe briefly the features related to span naming and customizations in relation to span propagation.

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On the 24th of March 2016, we managed to move our release train called Brixton to the next station: RC1. I’m really happy about this... it cost us a lot of energy but was well worth it!

Recently, I'm mostly focusing on the Spring Cloud Sleuth project and actually, some gigantic changes have happened there since the M5 release. In this short post, I’ll show you the rationale behind and describe briefly the features related to span naming and customizations in relation to span propagation.

What is Spring Cloud Sleuth?

For those who don’t know what Spring Cloud Sleuth is—it’s a library that implements a distributed tracing solution for Spring Cloud. You can check out the code at GitHub.

We’re also trying to be aligned with the concepts, terminology, and approaches present in the OpenTracing Project.

Distributed Tracing Terminology

I’ll quote the documentation to present some of the basic concepts of distributed tracing.

Span: The basic unit of work. For example, sending an RPC is a new span, as is sending a response to an RPC. Span’s are identified by a unique 64-bit ID for the span and another 64-bit ID for the trace the span is a part of. Spans also have other data, such as descriptions, timestamped events, key-value annotations (tags), the ID of the span that caused them, and process ID’s (normally IP address).

Spans are started and stopped, and they keep track of their timing information. Once you create a span, you must stop it at some point in the future.

Trace: A set of spans forming a tree-like structure. For example, if you are running a distributed big-data store, a trace might be formed by a put request.

Annotation: is used to record existence of an event in time. Some of the core annotations used to define the start and stop of a request are:

  • cs - Client Sent - The client has made a request. This annotation depicts the start of the span.
  • sr - Server Received - The server side got the request and will start processing it. If one subtracts the cs timestamp from this timestamp one will receive the network latency.
  • ss - Server Sent - Annotated upon completion of request processing (when the response got sent back to the client). If one subtracts the sr timestamp from this timestamp one will receive the time needed by the server side to process the request.
  • cr - Client Received - Signifies the end of the span. The client has successfully received the response from the server side. If one subtracts the cs timestamp from this timestamp one will receive the whole time needed by the client to receive the response from the server.

Okay, since now we’re on the same page with the terminology, let’s see what’s new in Sleuth.

Span Creation and Naming


A really big problem in the distributed tracing world is the issue of naming spans. Actually, this topic can be looked at from two angles.

First, what should the name of the span look like? Should it be a long and descriptive name, or quite the contrary? As we write in the documentation:

The name should be low cardinality (e.g. not include identifiers).

Finding the name for the span is not that big of a problem from a library’s perspective. You just pass on to a span whatever the user provides. But, what about the situation in which some operation is deferred in time? Or scheduled at certain intervals?

The second problem is related to a bigger issue: for the sake of consistency of passing tracing data, should we enforce creating spans? Should we be eager with that or allow the user to control span creation? Because, in that way, we can have a problem with how to name this artificial instance.

For RC1, we’ve decided that we will be eager in creating span names, but we will come back to this topic in future releases.

Naming Spans

Okay, so we know the why, now let’s move on to the how… There is quite a lot of instrumentation going on in Sleuth, so sometimes the names of spans might sound artificial (e.g. async for asynchronous operations). When talking about runnables and callables, often you’re dealing with code similar to this one:

Runnable runnable = new Runnable() {
  @Override public void run() {
      // perform logic
Future<?> future = executorService.submit(runnable);
// ... some additional logic ...

The Runnable is an operation that you would like to wrap in a span. And, what should the name of that span be? How can you pass it to the Tracer so that the span name is set?

To answer those issues we’ve introduced two approaches:

  • @SpanName annotation for an explicit class that implements Runnable or Callable
  • toString() method resolution of an anonymous instance of either of those interfaces

Most likely in the future releases @SpanName or its modification will be used more heavily to provide explicit names of spans.

Anyway, examples could look like those in the documentation. Here's an example for @SpanNameannotated class:

class TaxCountingRunnable implements Runnable {

  @Override public void run() {
      // perform logic

And, an anonymous instance:

new TraceRunnable(tracer, spanNamer, new Runnable() {
  @Override public void run() {
      // perform logic

  @Override public String toString() {
      return "calculateTax";

Both will have the same span name. Remember that both Runnables should be wrapped in aTraceRunnable instance.

Customization of Span Propagation

It’s pretty obvious that there’s a lot of companies that have already created some form of distributed tracing instrumentation. In Spring Cloud Sleuth, we’re expecting the tracing headers to be containing certain names like X-B3-TraceId for the trace id containing headers or X-B3-SpanId for the span related one.

One of the first issues that we created was related to support configurable header names, but actually, we developed it quite late. Anyway, with RC1, it’s possible to customize Sleuth in such a way that it’s compatible with your system’s nomenclature. Let’s define two terms before we go any further: Injector and Extractor.


In Spring Cloud Sleuth, an Injector is actually a functional interface called SpanInjector. It has the following method:

void inject(Span span, T carrier);

Its purpose is to take whatever is necessary from a span and inject it to the carrier. Let’s assume that in your system you don’t set the headers for trace id with the name X-B3-TraceId but you call it correlationId and mySpanId for X-B3-SpanId. Then you would have to override the behavior of Sleuth by registering a custom implementation of theSpanInjector. Let’s look at the following snippets from the documentation:

class CustomHttpServletResponseSpanInjector implements SpanInjector<HttpServletResponse> {

  public void inject(Span span, HttpServletResponse carrier) {
      carrier.addHeader("correlationId", Span.idToHex(span.getTraceId()));
      carrier.addHeader("mySpanId", Span.idToHex(span.getSpanId()));
      // inject the rest of Span values to the header

Note that this approach will work with Zipkin only if your values that you’re passing are Zipkin-compatible. That means that the ids are 64 bit numbers.

Also, you may wonder why we convert values using Span.idToHex. We’ve decided that we want the values of ids in the logs and in the message headers to be the very same values as the ones that you see later in Zipkin. That way you can just copy the value and put it into Zipkin to debug your system.

Once you have the SpanInjector, you have to register it as a bean with @Primaryannotation as presented below:

SpanInjector<HttpServletResponse> customHttpServletResponseSpanInjector() {
  return new CustomHttpServletResponseSpanInjector();


In Spring Cloud Sleuth, an Extractor is actually a functional interface called SpanExtractor. It has the following method:

Span joinTrace(T carrier);

Its purpose is to create a Span from the provided carrier. Let’s make the same assumption as with the SpanInjector and consider a case where traceId header is named correlationId and spanId header is mySpanId. Then we customize the Spring context by providing our own implementation of the SpanExtractor:

class CustomHttpServletRequestSpanExtractor implements SpanExtractor<HttpServletRequest> {

  public Span joinTrace(HttpServletRequest carrier) {
      long traceId = Span.hexToId(carrier.getHeader("correlationId"));
      long spanId = Span.hexToId(carrier.getHeader("mySpanId"));
      // extract all necessary headers
      Span.SpanBuilder builder = Span.builder().traceId(traceId).spanId(spanId);
      // build rest of the Span
      return builder.build();

Again note that we’re considering that the values are Zipkin compatible (64 bit values for ids). Also, note that we’ve assumed that the ids are sent in a hexadecimal form like they are presented in the Zipkin UI. That’s why we used the Span.hexToId method to convert it back to long again.


In this very short post, you saw two major features available in the RC1 release. You can check Spring Cloud Sleuth documentation for more information about the integrations and configurations of Sleuth. Actually, you can see everything that has changed in the RC1 release by checking the closed issues and merged PRs.

In case you have questions, don't hesitate to ping us on the Gitter channel or file an issue on Github.

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spring ,spring cloud ,spring cloud sleuth ,java

Published at DZone with permission of Marcin Grzejszczak, DZone MVB. See the original article here.

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