Exporters

Process and export your telemetry data

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将遥测数据发送到 OpenTelemetry Collector，以确保其被正确导出。在生产环境中使用 Collector 是最佳实践。若要可视化你的遥测数据，可将其导出到后端系统，例如 Jaeger、Zipkin、 Prometheus，或某个特定厂商的后端。

可用的导出器

镜像仓库中包含一份 Python 可用导出器的列表。

在所有导出器中，OpenTelemetry 协议 (OTLP) 导出器是以 OpenTelemetry 数据模型为基础设计的，能够无信息丢失地输出 OTel 数据。此外，许多处理遥测数据的工具都支持 OTLP （例如 Prometheus、Jaeger 和大多数厂商），在你需要时为你提供高度的灵活性。若要了解更多关于 OTLP 的信息，请参阅 OTLP 规范。

本页面介绍了主要的 OpenTelemetry Python 导出器以及如何进行配置。

注意

如果你使用了零代码自动插桩，你可以参考配置指南来了解如何设置导出器。

OTLP

Collector 设置

注意

如果你已经配置好 OTLP Collector 或后端，可以跳过此部分，直接设置应用的 OTLP 导出器依赖。

为测试和验证你的 OTLP 导出器，你可以运行一个 Docker 容器形式的 Collector，将遥测数据直接输出到控制台。

在一个空目录下创建名为 collector-config.yaml 的文件，并添加以下内容：

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]

然后运行以下命令，在 Docker 容器中启动 Collector：

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

现在，这个 Collector 已能通过 OTLP 接收遥测数据。之后你可能需要配置 Collector，将遥测数据发送到你的可观测性后端。

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)

Note

There are temporality presets for each instrumentation kind. These presets can be set with the environment variable OTEL_EXPORTER_METRICS_TEMPORALITY_PREFERENCE, for example:

export OTEL_EXPORTER_METRICS_TEMPORALITY_PREFERENCE="DELTA"

The default value for OTEL_EXPORTER_METRICS_TEMPORALITY_PREFERENCE is "CUMULATIVE".

The available values and their corresponding settings for this environment variable are:

CUMULATIVE
- Counter: CUMULATIVE
- UpDownCounter: CUMULATIVE
- Histogram: CUMULATIVE
- ObservableCounter: CUMULATIVE
- ObservableUpDownCounter: CUMULATIVE
- ObservableGauge: CUMULATIVE
DELTA
- Counter: DELTA
- UpDownCounter: CUMULATIVE
- Histogram: DELTA
- ObservableCounter: DELTA
- ObservableUpDownCounter: CUMULATIVE
- ObservableGauge: CUMULATIVE
LOWMEMORY
- Counter: DELTA
- UpDownCounter: CUMULATIVE
- Histogram: DELTA
- ObservableCounter: CUMULATIVE
- ObservableUpDownCounter: CUMULATIVE
- ObservableGauge: CUMULATIVE

Setting OTEL_EXPORTER_METRICS_TEMPORALITY_PREFERENCE to any other value than CUMULATIVE, DELTA or LOWMEMORY will log a warning and set this environment variable to CUMULATIVE.

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

注意

如果你已经设置了 Prometheus 或兼容 Prometheus 的后端，可以跳过本节，直接为你的应用设置 Prometheus 或者 OTLP exporter 依赖。

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

注意

当使用 Prometheus 的 OTLP 接收器（Reciever）时，确保在应用中设置 OTLP 端点为 http://localhost:9090/api/v1/otlp。

并非所有的 Docker 环境都支持 host.docker.internal。在某些情况下，你可能需要将 host.docker.internal 替换为 localhost 或你机器的 IP 地址。

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

提示

如果你已经设置了 Zipkin 或兼容 Zipkin 的后端，可以跳过本节并直接为你的应用设置 Zipkin exporter 依赖。

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"))

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