Exporters

Process and export your telemetry data

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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:

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

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.

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)

Jaeger

后端设置

Jaeger 原生支持 OTLP，用于接收链路（trace）数据。你可以通过运行一个 Docker 容器来启动 Jaeger，其 UI 默认在端口 16686 上可访问，并在端口 4317 和 4318 上启用 OTLP：

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

使用方法

现在，按照说明设置 OTLP exporters。

Prometheus

要将你的指标（metrics）数据发送到 Prometheus， 你可以选择 启用 Prometheus 的 OTLP 接收器 并且使用 OTLP exporter，或者使用 Prometheus exporter，这是一种 MetricReader， 他启动一个 HTTP 服务器，根据请求收集指标并将数据序列化为 Prometheus 文本格式。

后端设置

你可以按照以下步骤在 Docker 容器中运行 Prometheus，并通过端口 9090 访问：

创建一个名为 prometheus.yml 的文件，并将以下内容写入文件：

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

使用以下命令在 Docker 容器中运行 Prometheus，UI 可通过端口 9090 访问：

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

后端设置

你可以通过执行以下命令，在 Docker 容器中运行 Zipkin：

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:

Install the exporter package as a dependency for your application:

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:

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)

自定义导出器（Exporter）

最后，你还可以编写自己的导出器。有关更多信息，请参见 API 文档中的 SpanExporter 接口.

批量处理 Span 和日志记录

OpenTelemetry SDK 提供了一组默认的 span 和日志记录处理器，允许你选择按单条（simple）或按批量（batch）方式导出一个或多个 span。推荐使用批量处理，但如果你不想批量处理 span 或日志记录，可以使用 simple 处理器，方法如下：

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

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

Last modified June 24, 2025: Bump prettier from 3.5.3 to 3.6.0 (#7170) (0930994d)