Exporters

Send telemetry to the OpenTelemetry Collector to make sure it’s exported correctly. Using the Collector in production environments is a best practice. To visualize your telemetry, export it to a backend such as Jaeger, Zipkin, Prometheus, or a vendor-specific backend.

Available exporters

The registry contains a list of exporters for Python.

Among exporters, OpenTelemetry Protocol (OTLP) exporters are designed with the OpenTelemetry data model in mind, emitting OTel data without any loss of information. Furthermore, many tools that operate on telemetry data support OTLP (such as Prometheus, Jaeger, and most vendors), providing you with a high degree of flexibility when you need it. To learn more about OTLP, see OTLP Specification.

This page covers the main OpenTelemetry Python exporters and how to set them up.

OTLP

Collector Setup

To try out and verify your OTLP exporters, you can run the collector in a docker container that writes telemetry directly to the console.

In an empty directory, create a file called collector-config.yaml with the following content:

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
exporters:
  debug:
    verbosity: detailed
service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [debug]
    metrics:
      receivers: [otlp]
      exporters: [debug]
    logs:
      receivers: [otlp]
      exporters: [debug]

Now run the collector in a docker container:

docker run -p 4317:4317 -p 4318:4318 --rm -v $(pwd)/collector-config.yaml:/etc/otelcol/config.yaml otel/opentelemetry-collector

This collector is now able to accept telemetry via OTLP. Later you may want to configure the collector to send your telemetry to your observability backend.

Dependencies

If you want to send telemetry data to an OTLP endpoint (like the OpenTelemetry Collector, Jaeger or Prometheus), you can choose between two different protocols to transport your data:

• HTTP/protobuf
• gRPC

Start by installing the respective exporter packages as a dependency for your project:

• HTTP/Proto
• gRPC

pip install opentelemetry-exporter-otlp-proto-http

pip install opentelemetry-exporter-otlp-proto-grpc

Usage

Next, configure the exporter to point at an OTLP endpoint in your code.

• HTTP/Proto
• gRPC

from opentelemetry.sdk.resources import SERVICE_NAME, Resource

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

from opentelemetry import metrics
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader

# Service name is required for most backends
resource = Resource.create(attributes={
    SERVICE_NAME: "your-service-name"
})

tracerProvider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="<traces-endpoint>/v1/traces"))
tracerProvider.add_span_processor(processor)
trace.set_tracer_provider(tracerProvider)

reader = PeriodicExportingMetricReader(
    OTLPMetricExporter(endpoint="<traces-endpoint>/v1/metrics")
)
meterProvider = MeterProvider(resource=resource, metric_readers=[reader])
metrics.set_meter_provider(meterProvider)

from opentelemetry.sdk.resources import SERVICE_NAME, Resource

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

from opentelemetry import metrics
from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader

# Service name is required for most backends
resource = Resource.create(attributes={
    SERVICE_NAME: "your-service-name"
})

tracerProvider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(OTLPSpanExporter(endpoint="your-endpoint-here"))
tracerProvider.add_span_processor(processor)
trace.set_tracer_provider(tracerProvider)

reader = PeriodicExportingMetricReader(
    OTLPMetricExporter(endpoint="localhost:5555")
)
meterProvider = MeterProvider(resource=resource, metric_readers=[reader])
metrics.set_meter_provider(meterProvider)

Console

To debug your instrumentation or see the values locally in development, you can use exporters writing telemetry data to the console (stdout).

The ConsoleSpanExporter and ConsoleMetricExporter are included in the opentelemetry-sdk package.

from opentelemetry.sdk.resources import SERVICE_NAME, Resource

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter

from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader, ConsoleMetricExporter

# Service name is required for most backends,
# and although it's not necessary for console export,
# it's good to set service name anyways.
resource = Resource.create(attributes={
    SERVICE_NAME: "your-service-name"
})

tracerProvider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(ConsoleSpanExporter())
tracerProvider.add_span_processor(processor)
trace.set_tracer_provider(tracerProvider)

reader = PeriodicExportingMetricReader(ConsoleMetricExporter())
meterProvider = MeterProvider(resource=resource, metric_readers=[reader])
metrics.set_meter_provider(meterProvider)

export OTEL_EXPORTER_METRICS_TEMPORALITY_PREFERENCE="DELTA"

Jaeger

Backend Setup

Jaeger natively supports OTLP to receive trace data. You can run Jaeger in a docker container with the UI accessible on port 16686 and OTLP enabled on ports 4317 and 4318:

docker run --rm \
  -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
  -p 16686:16686 \
  -p 4317:4317 \
  -p 4318:4318 \
  -p 9411:9411 \
  jaegertracing/all-in-one:latest

Usage

Now following the instruction to setup the OTLP exporters.

Prometheus

To send your metric data to Prometheus, you can either enable Prometheus’ OTLP Receiver and use the OTLP exporter or you can use the Prometheus exporter, a MetricReader that starts an HTTP server that collects metrics and serialize to Prometheus text format on request.

Backend Setup

You can run Prometheus in a docker container, accessible on port 9090 by following these instructions:

Create a file called prometheus.yml with the following content:

scrape_configs:
  - job_name: dice-service
    scrape_interval: 5s
    static_configs:
      - targets: [host.docker.internal:9464]

Run Prometheus in a docker container with the UI accessible on port 9090:

docker run --rm -v ${PWD}/prometheus.yml:/prometheus/prometheus.yml -p 9090:9090 prom/prometheus --enable-feature=otlp-write-receive

Dependencies

Install the exporter package as a dependency for your application:

pip install opentelemetry-exporter-prometheus

Update your OpenTelemetry configuration to use the exporter and to send data to your Prometheus backend:

from prometheus_client import start_http_server

from opentelemetry import metrics
from opentelemetry.exporter.prometheus import PrometheusMetricReader
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.resources import SERVICE_NAME, Resource

# Service name is required for most backends
resource = Resource.create(attributes={
    SERVICE_NAME: "your-service-name"
})

# Start Prometheus client
start_http_server(port=9464, addr="localhost")
# Initialize PrometheusMetricReader which pulls metrics from the SDK
# on-demand to respond to scrape requests
reader = PrometheusMetricReader()
provider = MeterProvider(resource=resource, metric_readers=[reader])
metrics.set_meter_provider(provider)

With the above you can access your metrics at http://localhost:9464/metrics. Prometheus or an OpenTelemetry Collector with the Prometheus receiver can scrape the metrics from this endpoint.

Zipkin

Backend Setup

You can run Zipkin on in a Docker container by executing the following command:

docker run --rm -d -p 9411:9411 --name zipkin openzipkin/zipkin

Dependencies

To send your trace data to Zipkin, you can choose between two different protocols to transport your data:

• HTTP/protobuf
• Thrift

Install the exporter package as a dependency for your application:

• HTTP/Proto
• Thrift

pip install opentelemetry-exporter-zipkin-proto-http

pip install opentelemetry-exporter-zipkin-json

Update your OpenTelemetry configuration to use the exporter and to send data to your Zipkin backend:

• HTTP/Proto
• Thrift

from opentelemetry import trace
from opentelemetry.exporter.zipkin.proto.http import ZipkinExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.resources import SERVICE_NAME, Resource

resource = Resource.create(attributes={
    SERVICE_NAME: "your-service-name"
})

zipkin_exporter = ZipkinExporter(endpoint="http://localhost:9411/api/v2/spans")

provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(zipkin_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

from opentelemetry import trace
from opentelemetry.exporter.zipkin.json import ZipkinExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.sdk.resources import SERVICE_NAME, Resource

resource = Resource.create(attributes={
    SERVICE_NAME: "your-service-name"
})

zipkin_exporter = ZipkinExporter(endpoint="http://localhost:9411/api/v2/spans")

provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(zipkin_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

Custom exporters

Finally, you can also write your own exporter. For more information, see the SpanExporter Interface in the API documentation.

Batching span and log records

The OpenTelemetry SDK provides a set of default span and log record processors, that allow you to either emit spans one-by-on (“simple”) or batched. Using batching is recommended, but if you do not want to batch your spans or log records, you can use a simple processor instead as follows:

from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

processor = SimpleSpanProcessor(OTLPSpanExporter(endpoint="your-endpoint-here"))