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Ori Zohar 2021-02-15 12:32:10 -08:00
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daprdocs/content/en/concepts/observability-concept.md
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@ -12,17 +12,17 @@ When building an applications, understanding how the system is behaving is an im
While some data points about an application can be gathered from the underlying infrastructure (e.g. memory consumption, CPU usage), other meaningful information must be collected from an "application aware" layer - one that can show how an important series of calls is executed across microservices. This usually means a developer must add some code to instrument an application for this purpose. Often, instrumentation code is simply meant to send collected data such as traces and metrics to an external monitoring tool or service that can help store, visualize and analyze all this information. Having to maintain this code, which is not part of the core logic of the application, is another burden on the developer - sometimes requiring understanding monitoring tools APIs, using additional SDKs etc. This instrumentation may also add to the portability challenges of an application which may require different instrumentation depending on where the application is deployed (e.g. different cloud providers offer different monitoring solutions and an on-prem deployment might require an on-prem solution).
## Observability for your application with Dapr
When building an application which is leveraging Dapr building blocks to perform service-to-service calls and pub/sub messaging, Dapr offers an advantage in respect to [distributed tracing]({{<ref tracing>}}) - Because this inter-service communication flows through the Dapr sidecar, the sidecar is in a unique position to offload the burden of application level instrumentation.
When building an application which is leveraging Dapr building blocks to perform service-to-service calls and pub/sub messaging, Dapr offers an advantage in respect to [distributed tracing]({{<ref tracing>}}) - Because this inter-service communication goes through the Dapr sidecar, the sidecar is in a unique position to offload the burden of application level instrumentation.
### Distributed tracing
Dapr can be [configured to emit tracing data]({{<ref setup-tracing.md>}}), and because Dapr does so using widely adopted protocols such as the [Zipkin](https://zipkin.io) protocol, it can be easily integrated with multiple [monitoring backends]({{<ref supported-tracing-backends>}}).
<img src="/images/observability-tracing.png" width=1000 alt="Description of image">
<img src="/images/observability-tracing.png" width=1000 alt="Distributed tracing with Dapr">
### OpenTelemetry collector
Dapr can also be configured to work with the [OpenTelemetry Collector]({{<ref open-telemetry-collector>}}) which offers even more compatibility with external monitoring tools.
<img src="/images/observability-opentelemetry-collector.png" width=1000>
<img src="/images/observability-opentelemetry-collector.png" width=1000 alt="Distributed tracing via OpenTelemetry collector">
### Tracing context
Dapr uses [W3C tracing]({{<ref w3c-tracing>}}) specification for tracing context and can generate and propagate the context header itself or propagate user provided context headers.
@ -30,7 +30,7 @@ Dapr uses [W3C tracing]({{<ref w3c-tracing>}}) specification for tracing context
## Observability for the Dapr sidecar and system services
Like for other parts of your system, you will want to be able to observe Dapr itself and collect metrics and logs emitted by the Dapr sidecar that runs along each microservice as well as the Dapr related services in your environment such as the control plane services that are deployed for a Dapr enabled Kubernetes cluster.
<img src="/images/observability-sidecar.png" width=1000>
<img src="/images/observability-sidecar.png" width=1000 alt="Dapr sidecar metrics, logs and health checks">
### Logging
Dapr generates [logs]({{<ref "logs.md">}}) to provide visibility into sidecar operation and to help users identify issues and perform debugging. Logs events contain warning, error, info, and debug messages produced by Dapr system services. Dapr can also be configured to send logs to collectors such as [Fluentd]({{< ref fluentd.md >}}) and [Azure Monitor]({{< ref azure-monitor.md >}}) so they can be easily searched, analyzed and provide insights.