dapr
/
quickstarts
mirror of https://github.com/dapr/quickstarts.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Update README.md

This commit is contained in:
Mark Fussell 2020-04-06 13:45:09 -07:00 committed by GitHub
parent 468a0043fa
commit 1cb23d3bb2
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 20 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

40
8.observability/README.md
View File

@ -1,16 +1,16 @@
# Observability with Dapr
This sample will explore the [observability](https://github.com/dapr/docs/blob/master/concepts/observability/README.md) capabilities of Dapr. Observability includes metric collection, tracing, logging and health checks. In this sample you'll be enabling [distributed tracing](https://github.com/dapr/docs/tree/master/concepts/observability/traces.md) to an application without changing any application code or creating a dependency on any specific tracing system. Since Dapr uses [OpenTelemetry](https://opentelemetry.io/), a variety of systems can be used (here you'll be using [Zipkin](https://zipkin.io/)).
This sample explores the [observability](https://github.com/dapr/docs/blob/master/concepts/observability/README.md) capabilities of Dapr. Observability includes metric collection, tracing, logging and health checks. In this sample you'll be enabling [distributed tracing](https://github.com/dapr/docs/tree/master/concepts/observability/traces.md) on an application without changing any application code or creating a dependency on any specific tracing system. Since Dapr uses [OpenTelemetry](https://opentelemetry.io/), a variety of observability tools can be used to view and capture the traces. In this sampple you'll be using [Zipkin](https://zipkin.io/).
In this sample you will:
In this sample you will;
- Deploy Zipkin and configure it as a tracing provider for Dapr.
- Instrument an application and deploy it
- Troubleshoot a performance issue in a distributed application.
- Deploy Zipkin and configure it as a tracing provider for Dapr in Kubernetes.
- Instrument an application for tracing and then deploy it.
- Troubleshoot a performance issue.
## Prerequisites
This sample builds on top of the [distributed calculator](../3.distributed-calculator/README.md) sample and so requires a Dapr installation on a Kubernetes cluster as well as a state store. If you have not done so for previous samples:
This sample builds on the [distributed calculator](../3.distributed-calculator/README.md) sample and requires Dapr to be installed on a Kubernetes cluster along with state store. It is suggested to go through the distributed calculator sample before this one, which sets you up ready for this sample. If you have not done this then;
1. Clone this repo using `git clone https://github.com/dapr/samples.git`
@ -19,13 +19,13 @@ This sample builds on top of the [distributed calculator](../3.distributed-calcu
## Configure Dapr tracing in the cluster
Review *./deploy/tracing.yaml*:
Review the Dapr configuration file *./deploy/tracing.yaml* below:
```yaml
apiVersion: dapr.io/v1alpha1
kind: Configuration
metadata:
name: tracing
name: appconfig
spec:
tracing:
enabled: true
@ -33,19 +33,19 @@ spec:
includeBody: true
```
This configuration definition will enable Dapr to do tracing. Deploy the configuration by running:
This configuration file enables Dapr tracing. Deploy the configuration by running:
`kubectl apply -f ./deploy/tracing.yaml`
## Deploy Zipkin to the cluster and set it as the tracing provider
In this sample Zipkin is used as the underlying tracing system. Examine [*./deploy/zipkin.yaml*](./deploy/zipkin.yaml) and see how it includes three sections:
In this sample Zipkin is used for tracing. Examine [*./deploy/zipkin.yaml*](./deploy/zipkin.yaml) and see how it includes three sections:
1. A **Deployment** for Zipkin using the *openzipkin/zipkin* docker image.
2. A **Service** which will expose Zipkin internally as a ClusterIP in Kubernetes.
3. A **Component** that defines Zipkin as the tracing provider for Dapr.
Deploy to your cluster by running:
Deploy Zipkin to your cluster by running:
`kubectl apply -f ./deploy/zipkin.yaml`
@ -57,29 +57,29 @@ On your browser go to [http://localhost:9411](http://localhost:9411). You should
## Instrument the application for tracing and deploy it
To instrument a service for tracing with Dapr, no code changes are required - Dapr handles all of the tracing using the Dapr side-car. All that is needed is to add the Dapr annotation for tracing in the deployment yaml along with the other Dapr annotations. The annotation looks like this:
To instrument a service for tracing with Dapr, no code changes are required, Dapr handles all of the tracing using the Dapr side-car. All that is needed is to add the Dapr annotation for the configuration (which enables tracing) in the applicaton deployment yaml along with the other Dapr annotations. The configuration annotation looks like this:
```yaml
...
annotations:
...
dapr.io/config: "tracing"
dapr.io/config: "appconfig"
...
```
For this sample, no action is needed since tracing is already enabled for every service in the distributed calculator app. You can find the annotation in each one of the calculator yaml files - For example review the yaml file for the calculator front end service [here](https://github.com/dapr/samples/blob/master/3.distributed-calculator/deploy/react-calculator.yaml#L36).
For this sample, a configuration has already been enabled for every service in the distributed calculator app. You can find the annotation in each one of the calculator yaml files. For example review the yaml file for the calculator front end service [here](https://github.com/dapr/samples/blob/master/3.distributed-calculator/deploy/react-calculator.yaml#L36).
Note you did not introduce any dependency on Zipkin into the calculator app code or deployment yaml files.
Note you did not introduce any dependency on Zipkin into the calculator app code or deployment yaml files. The Zipkin Dapr component is configured to read tracing events and write these to an exporter.
Now deploy the distributed calculator application to your cluster following the instructions found in sample [3.distributed-calculator](https://github.com/dapr/samples/blob/master/3.distributed-calculator/README.md) and have the application running on a Kubernetes cluster. See that you can browse to the calculator UI via a browser.
Now deploy the distributed calculator application to your cluster following the instructions found in sample [3.distributed-calculator](https://github.com/dapr/samples/blob/master/3.distributed-calculator/README.md). Then browse to the calculator UI.
> **Note:** If the distributed calculator is already running on your cluster you will need to restart it for the tracing enablement to take effect. You can do so by running:
> **Note:** If the distributed calculator is already running on your cluster you will need to restart it for the tracing to take effect. You can do so by running:
> `kubectl rollout restart deployment addapp calculator-front-end divideapp multiplyapp subtractapp`
## Discover and troubleshoot a performance issue using Zipkin
To show how observability can help discover and troubleshoot issues on a distributed application, you'll update one of the services in the calculator app. This updated version will simulate a performance degradation in the multiply operation of the calculator that you will then investigate using the traces emitted by the Dapr sidecar. Run the following to apply a new version of the python-multiplier service:
To show how observability can help discover and troubleshoot issues on a distributed application, you'll update one of the services in the calculator app. This updated version simulates a performance degradation in the multiply operation of the calculator that you can then investigate using the traces emitted by the Dapr sidecar. Run the following to apply a new version of the python-multiplier service:
`kubectl apply -f ./deploy/python-multiplier.yaml`
@ -103,11 +103,11 @@ Now look for any performance issues by filtering on any requests that have taken
![Zipkin](./img/zipkin-3.png)
You can quickly see that the multiply method invocation has been unusually slow (took over 1 second). Since the problem may be either at the calculator-frontend service or the python-multiplier service you can dig further by clicking on the entry:
You can quickly see that the multiply method invocation is unusually slow (takes over 1 second). Since the problem may be either at the calculator-frontend service or the python-multiplier service you can dig further by clicking on the entry:
![Zipkin](./img/zipkin-4.png)
Now you can see which specific call was delayed (via the `data` field. Here it's the 12 * 2 operation) and confirm that it is the multiplier service which you have updated that is causing the slowdown (find the code for the slow multiplier under the python directory).
Now you can see which specific call was delayed via the `data` field (here it's the 12 * 2 operation) and confirm that it is the multiplier service which you updated that is causing the slowdown (You can find the code for the slow multiplier under the python directory).
## Clean up