mirror of https://github.com/dapr/docs.git
66 lines
3.0 KiB
Markdown
66 lines
3.0 KiB
Markdown
# Distributed Tracing
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Dapr uses OpenTelemetry (previously known as OpenCensus) for distributed traces and metrics collection. OpenTelemetry supports various backends including [Azure Monitor](https://azure.microsoft.com/en-us/services/monitor/), [Datadog](https://www.datadoghq.com), [Instana](https://www.instana.com), [Jaeger](https://www.jaegertracing.io/), [SignalFX](https://www.signalfx.com/), [Stackdriver](https://cloud.google.com/stackdriver), [Zipkin](https://zipkin.io) and others.
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## Contents
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- [Tracing Design](#tracing-design)
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- [Correlation ID](#correlation-id)
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- [Configuration](#configuration)
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- [References](#references)
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## Tracing design
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Dapr adds a HTTP/gRPC middleware to the Dapr sidecar. The middleware intercepts all Dapr and application traffic and automatically injects correlation IDs to trace distributed transactions. This design has several benefits:
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* No need for code instrumentation. All traffic is automatically traced (with configurable tracing levels).
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* Consistent tracing behavior across microservices. Tracing is configured and managed on Dapr sidecar so that it remains consistent across services made by different teams and potentially written in different programming languages.
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* Configurable and extensible. By leveraging OpenTelemetry, Dapr tracing can be configured to work with popular tracing backends, including custom backends a customer may have.
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* OpenTelemetry exporters are defined as first-class Dapr components. You can define and enable multiple exporters at the same time.
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## Correlation ID
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For HTTP requests, Dapr injects a **X-Correlation-ID** header to requests. For gRPC calls, Dapr inserts a **X-Correlation-ID** as a field of a **header** metadata. When a request arrives without an correlation ID, Dapr creates a new one. Otherwise, it passes the correlation ID along the call chain.
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## Configuration
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Dapr tracing is configured by a configuration file (in local mode) or a Kubernetes configuration object (in Kubernetes mode). For example, the following configuration object enables distributed tracing:
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```yaml
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apiVersion: dapr.io/v1alpha1
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kind: Configuration
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metadata:
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name: tracing
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spec:
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tracing:
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enabled: true
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expandParams: true
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includeBody: true
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```
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Please see the [References](#references) section for more details on how to configure tracing on local environment and Kubernetes environment.
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Dapr supports pluggable exporters, defined by configuration files (in local mode) or a Kubernetes custom resource object (in Kubernetes mode). For example, the following manifest defines a Zipkin exporter:
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```yaml
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apiVersion: dapr.io/v1alpha1
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kind: Component
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metadata:
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name: zipkin
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spec:
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type: exporters.zipkin
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metadata:
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- name: enabled
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value: "true"
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- name: exporterAddress
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value: "http://zipkin.default.svc.cluster.local:9411/api/v2/spans"
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```
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## References
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* [How-To: Set up Distributed Tracing with Azure Monitor](../../howto/diagnose-with-tracing/azure-monitor.md)
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* [How-To: Set Up Distributed Tracing with Zipkin](../../howto/diagnose-with-tracing/zipkin.md)
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