OpenCensus Erlang library

Version: 0.5.0

Erlang stats collection and distributed tracing framework

Using with Rebar3 project

Add as dependency to rebar.config:

{deps, [opencensus]}.

Or to use the latest from git master branch:

{deps, [{opencensus, {git, "https://github.com/census-instrumentation/opencensus-erlang.git", {branch, "master"}}}]}.

Span data is stored and manipulated in an ETS table. Span context must be tracked in order to create child span's, propagate span context across process or node boundaries and for the library to which span data to manipulate in the context it is called.

opencensus provides two methods for tracking this context, the process dictionary and a variable holding a ctx record.

With ocp the span context is tracked in the current process`s process dictionary.

some_fun() ->
  ocp:with_child_span(<<"some_fun/0">>,
                      fun() ->
                          ... body ..
                      end).

More details on working with spans can be found here and in the modules documentation for ocp, oc_trace and oc_span.

Propagating Span Context

Opencensus comes with two forms of span context encoding for sending over the wire. oc_span_ctx_header encodes a span context suitable for transfering as an HTTP header and oc_span_ctx_binary will encode and decode a binary form used in GRPC and other binary protocols.

For example, creating the header for sending with an HTTP client might look like:

EncodedSpanCtx = oc_span_ctx_header:encode(ocp:current_span_ctx()),
Headers = [{oc_span_ctx_header:field_name(), EncodedSpanCtx}],

Samplers

oc_sampler_never: Never enable a new trace, but keeps a trace enabled if its propagated context is enabled.

oc_sampler_always: Enable every new trace for sampling.

oc_sampler_probability: Takes a probability, default 0.5, that any new trace will be sampled.

Reporters

Google Cloud Trace: Support for v1 in master, v2 and grpc coming soon;

Prometheus: Exports spans as Prometheus metrics.

Logging

OTP-21 includes a new logging framework. When a context is created with a span (for example ocp:with_child_span/1 or oc_trace:with_child_span/2) opencensus will update the current process's logger metadata to include the trace_id, span_id and trace_options with the latest ids under the key span_ctx, trace_options will be 1 if the trace is enabled. To use these with the default formatter you can create a custom template that includes them if they exist like so:

{logger_formatter,
        #{template => [time, " ", pid, " ",
                       {[span_ctx, trace_id], ["trace_id=", [span_ctx, trace_id], " "], []},
                       {[span_ctx, span_id], ["span_id=", [span_ctx, span_id], " "], []},
                       {[span_ctx, trace_options], ["trace_options=", [span_ctx, trace_options], " "], []},
                       msg, "\n"]}}

Stats

OpenCensus stats collection happens in two stages:

Definition of measures and recording of data points
Definition of views and aggregation of the recorded data

Defining Measures

oc_stat_measure:new('opencensus.io/http/server/server_latency', "Time between first byte of "
                    "request headers read to last byte of response sent, or terminal error.",
                    milli_seconds).

Recording

Measurements are data points associated with a measure. ocp:record implicitly tags the set of Measurements with the tags from the pdict context:

ocp:record('opencensus.io/http/server/server_latency', ServerLatency),

Views

Views are how Neasures are aggregated. You can think of them as queries over the set of recorded data points (measurements).

Views have two parts: the tags to group by and the aggregation type used.

Currently four types of aggregations are supported:

oc_stat_aggregation_count: Count aggregation is used to count the number of times a sample was recorded.
oc_stat_aggregation_distribution: Distribution aggregation is used to provide a histogram of the values of the samples.
oc_stat_aggregation_sum: Sum aggregation is used to sum up all sample values.
oc_stat_aggregation_latest: Saves only the last datapoint.

Here we create a view with the distribution aggregation over our measure:

oc_stat_view:subscribe(#{name => "opencensus.io/http/server/server_latency",
                         description => "Latency distribution of HTTP requests",
                         tags => [http_server_method, http_server_path],
                         measure => 'opencensus.io/http/server/server_blatency',
                         aggregation => {oc_stat_aggregation_distribution, 
                                         [{buckets, [0, 100, 300, 650, 800, 1000]}]}})

Stat Exporters

Prometheus: Exports stat views as Prometheus metrics. Simply register as a collector:

prometheus_registry:register_collector(oc_stat_exporter_prometheus)

Development

$ rebar3 compile

Running tests:

$ rebar3 ct

Updating OpenCensus standard protobuf encoder and decoder

Language independent interface types for Census are found in the opencensus-proto repo. The opencensus Erlang app provides functionality for converting from the apps internal representation to the standard protobuf interface. Below are the steps to update the Erlang module and header for encoding and decoding the protobufs:

$ git clone https://github.com/census-instrumentation/opencensus-proto priv/opencensus-proto
$ rebar3 protobuf compile

OpenCensus

Modules