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Tapper - Zipkin client for Elixir.

Implements an interface for recording traces and sending them to a Zipkin server.

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See also tapper_demo

A Client

A client making a request:

# start a new, sampled, trace, and root span;
# creates a 'client send' annotation on root span
# (defaults to type: :client) and a 'server address' (sa)
# binary annotation (because we pass the remote option with
# an endpoint)

# prepare remote endpoint metadata
service_host = %Tapper.Endpoint{service_name: "my-service"}

id = Tapper.start(name: "fetch", sample: true, remote: service_host, annotations: [
  Tapper.tag("some-key", "some-value"),

# ... do remote call ...

# add response details to span
Tapper.update_span(id, [

# finish the trace (and the top-level span), with some detail about the operation
Tapper.finish(id, annotations: [
    tag("result", some_result)

A Server

A server processing a request (usually performed via integration e.g. Tapper.Plug):

# use propagated trace context (e.g. from Plug integration) and incoming Plug.Conn;
# adds a 'server receive' (sr) annotation (defaults to type: :server)
id = Tapper.join(trace_id, span_id, parent_span_id, sample, debug, annotations: [
  Tapper.client_address(%Tapper.Endpoint{ip: conn.remote_ip}), # equivalent to 'remote:' option

# NB because the server joined the trace, rather than starting it, 
# it must always start child spans for tracing anything it does, 
# rather than using the incoming span

# call another service in a child span, now as a client
id = Tapper.start_span(id, name: "fetch-details", annotations: [
# ...
Tapper.update_span(id, Tapper.client_send())

# ... call service ...

Tapper.update_span(id, Tapper.client_receive())

# finish child span with some details about response
id = Tapper.finish_span(id, annotations: [
    Tapper.tag("userId", 1234),

# perform some expensive local processing in a named local span:
id = Tapper.start_span(id, name: "process", local: "compute-result") # adds 'lc' binary annotation

# ... do processing ...

id = Tapper.finish_span(id)

# ... send response to client ...

# finish trace as far as this process is concerned
Tapper.finish(id, annotations: Tapper.server_send())

NB Tapper.start_span/2 and Tapper.finish_span/2 return an updated id, whereas all other functions return the same id, so you don't need to propagate the id backwards down a call-chain to just add annotations, but you should propagate the id forwards when adding spans, and pair finish_span/2 with the id from the corresponding start_span/2. Parallel spans can share the same starting id.

The Alternative Contextual API

The above API is the functional API: you need the Tapper.Id on-hand whenever you use it. You may complain that this pollutes your API, or creates difficulties for integrations.

Whilst you may mitigate this yourself using process dictionaries, ETS, or pure functional approaches using closures, the Tapper.Ctx interface provides a version of the API that tracks the Tapper.Id for you, using Erlang's process dictionary. Erlang purists might hate it, but it does get the id out of your mainstream code:

def my_main_function() do
  # ...
  Tapper.Ctx.start(name: "main", sample: true)
  # ...
  x = do_something_useful(a_useful_argument)
  # ...

def do_something_useful(a_useful_argument) do  # no Tapper.Id!
  Tapper.Ctx.start_span(name: "do-something", annotations: tag("arg", a_useful_argument))
  # ...
  # ...

It's nearly identical to the functional API, but without explicitly passing the Tapper.Id around.

Behind the scenes, the Tapper.Id is managed using Tapper.Ctx.put_context/1 and Tapper.Ctx.context/0. Use these functions directly to propagate the Tapper.Id across process boundaries.

See the Tapper.Ctx module for details, including details of options for debugging the inevitable incorrect usage in your code!

API Documentation

The API documentation can be found at

See also

Tapper.Plug - Plug integration: decodes incoming B3 trace headers, joining or sampling traces.

tapper_demo - a simple client-server application using Tapper.


The Tapper API starts, and communicates with a supervised GenServer process (Tapper.Tracer.Server), with one server started per trace; all traces are thus isolated.

Once a trace has been started, all span operations and updates are performed asynchronously by sending a message to the server; this way there is minimum processing on the client side. One message is sent per Tapper.start_span/2, Tapper.finish_span/2 or Tapper.update_span/2, tagged with the current timestamp at the point of the call.

When a trace is terminated with Tapper.finish/2, the server sends the trace to the configured collector (e.g. a Zipkin server), and exits normally.

If a trace is not terminated by an API call, Tapper will time-out after a pre-determined time since the last API operation (ttl option on trace creation, default 30s), and terminate the trace as if Tapper.finish/2 was called, annotating the unfinished spans with a timeout annotation. Timeout will will also happen if the client process exits before finishing the trace.

If the API client starts spans in, or around, asynchronous processes, and exits before they have finished, it should call Tapper.start_span/2 or Tapper.update_span/2 with a Tapper.async/0 annotation, or Tapper.finish/2 passing the async: true option or annotation; async spans should be closed as normal by Tapper.finish_span/2, otherwise they will eventually be terminated by the TTL behaviour.

The API client is not effected by the termination, normally or otherwise, of a trace-server, and the trace-server is likewise isolated from the API client, i.e. there is a separate supervision tree. Thus if the API client crashes, then the span can still be reported. The trace-server monitors the API client process for abnormal termination, and annotates the trace with an error (TODO). If the trace-server crashes, any child spans and annotations registered with the server will be lost, but subsequent spans and the trace itself will be reported, since the supervisor will re-start the trace-server using the initial data from Tapper.start/1 or Tapper.join/6.

The id returned from the Tapper API tracks the trace id, enabling messages to be sent to the right server, and span nesting, to ensure annotations are added to the correct span.

Tapper ids have an additional, unique, identifier, so if a server receives parallel requests within the same client span, the traces are recorded separately: each will start their own trace-server. In practice this should not happen, since clients should use a separate span for each remote call, however this protects against unconformant clients.


For the latest pre-release (and unstable) code, add github repo to your mix dependencies:

def deps do
  [{:tapper, github: "Financial-Times/tapper"}]

For release versions, the package can be installed by adding tapper to your list of dependencies in mix.exs:

def deps do
  [{:tapper, "~> 0.6"}]

Under Elixir 1.4+ the :tapper application will be auto-discovered from your dependencies, so there is no need to add :tapper to your application's extra_applications etc.


Tapper looks for the following application configuration settings under the :tapper key:

attribute type description
system_id String.t This application's id; used for service_name in default endpoint host used in annotations.
ip tuple This application's principle IPV4 or IPV6 address, as 4- or 8-tuple of ints; defaults to IP of first non-loopback interface, or {} if none.
port integer This application's principle service port, for endpoint port in annotations; defaults to 0
reporter atom | {atom, any} | function/1 Module implementing Tapper.Reporter.Api [1], or function of arity 1 to use for reporting spans; defaults to Tapper.Reporter.Console.

All keys support the Phoenix-style {:system, var} format, to allow lookup from shell environment variables, e.g. {:system, "PORT"} to read PORT environment variable[2].

[1] If the reporter is given as {module, arg} it is expected to specify an OTP server to be started under Tapper's main supervisor.
[2] Tapper uses the DeferredConfig library to resolve all configuration under the :tapper key, so see its documention for more resolution options.

Zipkin Reporter

The Zipkin reporter (Tapper.Reporter.Zipkin) has its own configuration:

attribute description
collector_url full URL of Zipkin server api for receiving spans
client_opts additional options for HTTPoison client, see HTTPoison.Base.request/5

e.g. in config.exs (or prod.exs etc.)

config :tapper,
    system_id: "my-application",
    reporter: Tapper.Reporter.Zipkin

config :tapper, Tapper.Reporter.Zipkin,
    collector_url: "http://localhost:9411/api/v1/spans"

Other Reporters

Module Description
Tapper.Reporter.AsyncReporter collects spans before sending them to another reporter
Tapper.Reporter.Console just logs JSON spans
Tapper.Reporter.Null reports and logs nothing

Custom Reporters

You can implement your own reporter module by implementing the Tapper.Reporter.Api behaviour.

This defines a function ingest/1 that receives spans in the form of Tapper.Protocol.Span structs, with timestamps and durations in microseconds. For JSON serialization, see Tapper.Encoder.Json which encodes to a format compatible with Zipkin server.

The configuration's reporter property is usually either an atom specifying a simple module, or a supervisor-child-style {module, args} tuple specifying an OTP server to be started under Tapper's main supervisor. Additionally, it may be a 1-argument function which is useful for testing.


Tapper adds a trace_id key to the Logger metadata on Tapper.start/1 or Tapper.join/6, so if you want this in your logs, configure your logger formatter/backend to output this key, e.g.

config :logger,
  format: "[$level] $metadata$message\n",
  metadata: [:trace_id],

Will output something like:

[info] trace_id=b1db8e59c0f02152130c3fbb317d57fb  Something to log home about

Note that trace_id metadata is added regardless of whether the trace is sampled, so when you propagate the trace context for unsampled traces, you can still at least see the trace id in the logs, and track it across your system, which may be useful!

Erlang and Time

It is recommended that you run the Erlang VM in multi-time-warp mode for greater timing accuracy. This is achieved by setting the +C multi_time_warp command line option, e.g. by using the ERL_FLAGS environment var or erl_opts in your Distillary release.

The default time mode (no_time_warp) works well enough, but may introduce an error of up to 1% in time stamp and time duration measurements, due to the way it keeps the Erlang monotonic clock in sync with the system clock.

Why 'Tapper'?

Dapper (Dutch - original Google paper) - Brave (English - Java client library) - Tapper (Swedish - Elixir client library)

Because Erlang, Ericsson 🇸🇪.

RIP Joe Armstrong - a glorious exception to the rule that you should never meet your heros.