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Send Data with the Honeycomb Distribution for Python

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Honeycomb provides the Honeycomb OpenTelemetry Distribution for Python to help you instrument your applications and send telemetry data to Honeycomb as quickly and easily as possible. Under the hood, the Honeycomb Distribution uses OpenTelemetry for Python, so advanced users or those who have already instrumented their applications with OpenTelemetry do not need to use this Distribution.

The Honeycomb Distribution reads variables you provide and translates them to variables understood by the upstream OpenTelemetry SDK. For example, the Honeycomb Distribution automatically configures exporters to send telemetry data to Honeycomb’s API (if you are using our US instance, api.honeycomb.io, or if you are using our EU instance, api.eu1.honeycomb.io). If you want to send data to Honeycomb using OpenTelemetry without the Honeycomb Distribution, you will need to configure your implementation to match variables expected by OpenTelemetry.

In this guide, we explain how to set up automatic and custom, or manual, instrumentation for a service written in Python. If you prefer learning by example, we provide several examples of applications configured to send OpenTelemetry data to Honeycomb using the Honeycomb OpenTelemetry Distribution for Python.

Before You Begin 

Before you can set up automatic instrumentation for your Python application, you will need to do a few things.

Prepare Your Development Environment 

To complete the required steps, you will need:

• A working Python environment with Python 3.7 or higher
• An application written in Python

Get Your Honeycomb API Key 

To send data to Honeycomb, you’ll need to sign up for a free Honeycomb account and create a Honeycomb Ingest API Key. To get started, you can create a key that you expect to swap out when you deploy to production. Name it something helpful, perhaps noting that it’s a getting started key. Make note of your API key; for security reasons, you will not be able to see the key again, and you will need it later!

If you want to use an API key you previously stored in a secure location, you can also look up details for Honeycomb API Keys any time in your Environment Settings, and use them to retrieve keys from your storage location.

Add Automatic Instrumentation 

Automatic instrumentation is provided by an agent, a command line tool opentelemetry-instrument that is used to run your application. Add additional instrumentation to your application manually using the included OpenTelemetry Python SDK.

Acquire Dependencies 

Configure 

export HONEYCOMB_API_ENDPOINT="api.honeycomb.io:443" # US instance
#export HONEYCOMB_API_ENDPOINT="api.eu1.honeycomb.io:443" # EU instance
export OTEL_SERVICE_NAME="your-service-name"
export HONEYCOMB_API_KEY="your-api-key"

export OTEL_SERVICE_NAME="your-service-name"
export HONEYCOMB_API_KEY="your-api-key"

export HONEYCOMB_TRACES_DATASET=my-traces

Advanced Configuration 

Explore all configuration options for the Honeycomb Distribution:

Run 

opentelemetry-instrument python YOUR_APPLICATION_NAME.py

poetry run opentelemetry-instrument python YOUR_APPLICATION_NAME.py

Add Custom Instrumentation 

Automatic instrumentation is the easiest way to get started with instrumenting your code. To get additional insight into your system, you should also add custom, or manual, instrumentation where appropriate.

To learn more about custom, or manual, instrumentation, visit the comprehensive set of topics covered by Manual Instrumentation for Python in OpenTelemetry’s documentation.

Acquire Dependencies 

To start adding custom instrumentation, ensure that the opentelemetry-api package exists as a direct dependency in your project. This package provides access to the high-level instrumentation APIs, which gives the ability to retrieve the current span to enrich with additional attributes, to create new spans, and to generate metrics.

python -m pip install opentelemetry-api

poetry add opentelemetry-api

Add Attributes to Spans 

Adding context to a currently executing span in a trace can be useful. For example, you may have an application or service that handles users, and you want to associate the user with the span when querying your dataset in Honeycomb. To do this, get the current span from the context and set an attribute with the user ID:

from opentelemetry import trace

# ...

span = trace.get_current_span()
span.set_attribute("user.id", user.id())

Acquire a Tracer 

To create spans, you need to acquire a Tracer.

from opentelemetry import trace

tracer = trace.get_tracer("tracer.name.here")

When you create a Tracer, OpenTelemetry requires you to give it a name as a string. This string is the only required parameter.

When traces are sent to Honeycomb, the name of the Tracer is turned into the library.name field, which can be used to show all spans created from a particular tracer.

In general, pick a name that matches the appropriate scope for your traces. If you have one tracer for each service, then use the service name. If you have multiple tracers that live in different “layers” of your application, then use the name that corresponds to that “layer”.

The library.name field is also used with traces created from instrumentation libraries.

Create Spans 

Automatic instrumentation can show the shape of requests to your system, but only you know the really important parts. To get the full picture of what’s happening, you should add custom instrumentation and create some custom spans. To do this, create or re-use a Tracer instance and start a span.

from opentelemetry import trace

tracer = trace.get_tracer("tracer.name.here")
with tracer.start_as_current_span("some-long-running-handler"):
    # prepare to auth

    with tracer.start_as_current_span("what-it-takes-to-authorize-handling"):
        # do the auth

    # do the authorized long running handling of cool stuff

Creating Spans Around Methods 

You can use a decorator to wrap the execution of a method with a span. The span will be automatically closed once the method has completed.

from opentelemetry import trace

tracer = trace.get_tracer("tracer.name.here")

# ...

@tracer.start_as_current_span("do_work")
def do_work():
    # do some work ...

Add Multi-Span Attributes 

Sometimes you want to add the same attribute to many spans within the same trace. This attribute may include variables calculated during your program, or other useful values for correlation or debugging purposes.

To add this attribute, leverage the OpenTelemetry concept of baggage. Baggage allows you to add a key with a value as an attribute to every subsequent child span of the current application context. In Python, baggage is configured as part of the OpenTelemetry trace context. Modifications to the trace context must be both attached and detached when no longer used to make sure context state is disposed of correctly.

The following example adds a user ID attribute to multiple spans in a trace.

from opentelemetry import baggage
from opentelemetry.context import attach, detach

...

token = attach(
    baggage.set_baggage("user.id", user.id())
)
with tracer.start_as_current_span(name="hello"):
    span.set_attribute("message", "hello world!")
detach(token)

Note: Any Baggage attributes that you set in your application will be attached to outgoing network requests as a header. If your service communicates to a third party API, do NOT put sensitive information in the Baggage attributes.

Sampling 

To control how many spans are being sent to Honeycomb, you can configure the OpenTelemetry SDK to sample the data it generates. You can configure sampling with environment variables. The following example sets a sample rate of 2, meaning 50% of traces are sampled and sent to Honeycomb:

export SAMPLE_RATE="2"

If you have multiple services that communicate with each other, it is important that they have the same sampling configuration. Otherwise, each service might make a different sampling decision, resulting in incomplete or broken traces. You can sample using a standalone proxy as an alternative, like Honeycomb Refinery, or when you have more robust sampling needs.

Distributed Trace Propagation 

When a service calls another service, you want to ensure that the relevant trace information is propagated from one service to the other. This allows Honeycomb to connect the two services in a trace.

Distributed tracing enables you to trace and visualize interactions between multiple instrumented services. For example, your users may interact with a front-end API service, which talks to two internal APIs to fulfill their request. In order to have traces connect spans for all these services, it is necessary to propagate trace context between these services, usually by using an HTTP header.

Both the sending and receiving service must use the same propagation format, and both services must be configured to send data to the same Honeycomb environment.

Visualizing Traces Locally 

Honeycomb’s OpenTelemetry Distribution for Python can create a link to a trace visualization in the Honeycomb UI for local traces. Local visualizations enable a faster feedback cycle when adding, modifying, or verifying instrumentation.

To enable local visualizations:

1. Set the HONEYCOMB_ENABLE_LOCAL_VISUALIZATIONS environment variable to true:

   export HONEYCOMB_ENABLE_LOCAL_VISUALIZATIONS=true
   

2. Run your application:

   opentelemetry-instrument python myapp.py
   

   The output displays the name of the root span and a link to Honeycomb that shows its trace. For example:

   Trace for <root-span-name>
   Honeycomb link: <link to Honeycomb trace>
   

3. Select the link to view the trace in detail within the Honeycomb UI.

Using HTTP/protobuf instead of gRPC 

By default, the Honeycomb OpenTelemetry SDK uses the gRPC protocol to send telemetry data. To use HTTP instead of gRPC, set the export protocol for the OpenTelemetry SDK using the following environment variable:

export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf

Troubleshooting 

To explore common issues when sending data, visit Common Issues with Sending Data in Honeycomb.

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