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distributed-tracing-with-apm-workshop

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Distributed Tracing with APM Workshop

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# Distributed Tracing with APM Workshop

This is a follow up repo to a [2018 Dash APM Workshop](https://github.com/burningion/dash-apm-workshop), incorporating feedback from the event.

Specifically, this adds:

* Starting with Automatic Instrumentation, and a more complex example program
* More live traffic, to see Trace Search (announced at DASH)
* Debugging a bigger, more complex system, to showcase a more real world use case
* More Datadog UI usage
* More relevant examples and names for traces
* More realistic errors 
* ... and should also work in Windows.

If you've stumbled upon it and have feedback, or have something you'd  like to see, feel free to create an issue.

**This workshop also exists in [Kubernetes](https://github.com/burningion/distributed-tracing-with-apm-workshop/tree/k8s-autodiscovery) on another branch.**

You can view a live, interactive version of this repo on Katacoda [here](https://www.katacoda.com/burningion/scenarios/distributed-tracing-datadog).

If you're interested in learning more about using APM in kubernetes, this repo is also used in the [Introduction to Monitoring Kubernetes](https://learn.datadoghq.com/enrol/index.php?id=14) course live on Datadog's Learning system.

# Before the Workshop / Prerequisites

The workshop is scheduled for an hour and a half. In this short time frame, having prerequisites installed is crucial to maximize everyone's hands on time.

Please ensure you accomplish at least steps 1 and 2 before the event, so everyone has a chance to complete the event.

1. Install [Docker CE](https://store.docker.com/search?type=edition&offering=community), allowing us to use Docker and Docker Compose. Please note you will be required to create an account in order to download Docker CE.

2. Pull Docker images to your local machine *before the event*, so we don't all overwhelm the WiFi.

You can do this by cloning this repo, and running a `docker-compose up` in the top level directory. This will pull the images and libraries to your local computer. This will take a few minutes.

When it finishes, you'll be able to load http://localhost:5000, and see some example data. Refresh until you see 3 pumps listed. This means the downstream microservice containers have come up too.

3. (Optional) The workshop itself is written in a [Jupyter notebook](http://jupyter.org/), allowing you to mix and edit content locally.

If you want to be able to add notes and save them locally, you'll want to install Jupyter, and then open the `Distributed Tracing Workshop.ipynb` by doing a `jupyter-notebook` in the top level repo directory.

This is completely optional, as Github has a built in viewer for Jupyter notebooks. You can view the repo's notebook [here](https://github.com/burningion/distributed-tracing-with-apm-workshop/blob/master/Distributed%20Tracing%20Workshop.ipynb).

# Running the Application

![Water Sensor App](https://github.com/burningion/distributed-tracing-with-apm-workshop/raw/master/images/dashboard.png)

You'll need to have a Datadog account with APM and logging enabled. A free trial should work to play with.

```bash
$ POSTGRES_USER=postgres POSTGRES_PASSWORD=<pg password> DD_API_KEY=<api key> docker-compose up
```

You can open the web app at `http://localhost:5000`, create some pumps, and look at your Datadog traces to see the distributed traces.

The frontend of the app is a React node app using [Material UI](https://material-ui.com/). It lives in the `single-page-frontend` folder. You can start it up for development with a:

```bash
$ npm install
$ npm start
```

It should connect to the running frontend API container, allowing for easier development. When you're finished making changes, you can do a `npm build`, and then copy the javascript from the `build` subdirectory into the Flask frontend app.

# Ideas for Live Debugging via Tracing

These are some ideas, that have yet to be implemented:

* A bad deploy that triggers a problem, breaking parts of API
* Introducing latency in a service in the middle of the request lifecycle
* Introducing a traffic spike / poison payload downstream
* Introducing Chaos changes

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