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Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

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+<!--
+title: "Exporting to Netdata, Prometheus, Grafana stack"
+description: "Using Netdata in conjunction with Prometheus and Grafana."
+custom_edit_url: https://github.com/netdata/netdata/edit/master/exporting/WALKTHROUGH.md
+sidebar_label: Netdata, Prometheus, Grafana stack
+-->
+
+# Netdata, Prometheus, Grafana stack
+
+## Intro
+
+In this article I will walk you through the basics of getting Netdata, Prometheus and Grafana all working together and
+monitoring your application servers. This article will be using docker on your local workstation. We will be working
+with docker in an ad-hoc way, launching containers that run `/bin/bash` and attaching a TTY to them. I use docker here
+in a purely academic fashion and do not condone running Netdata in a container. I pick this method so individuals
+without cloud accounts or access to VMs can try this out and for it's speed of deployment.
+
+## Why Netdata, Prometheus, and Grafana
+
+Some time ago I was introduced to Netdata by a coworker. We were attempting to troubleshoot python code which seemed to
+be bottlenecked. I was instantly impressed by the amount of metrics Netdata exposes to you. I quickly added Netdata to
+my set of go-to tools when troubleshooting systems performance.
+
+Some time ago, even later, I was introduced to Prometheus. Prometheus is a monitoring application which flips the normal
+architecture around and polls rest endpoints for its metrics. This architectural change greatly simplifies and decreases
+the time necessary to begin monitoring your applications. Compared to current monitoring solutions the time spent on
+designing the infrastructure is greatly reduced. Running a single Prometheus server per application becomes feasible
+with the help of Grafana.
+
+Grafana has been the go to graphing tool for… some time now. It's awesome, anyone that has used it knows it's awesome.
+We can point Grafana at Prometheus and use Prometheus as a data source. This allows a pretty simple overall monitoring
+architecture: Install Netdata on your application servers, point Prometheus at Netdata, and then point Grafana at
+Prometheus.
+
+I'm omitting an important ingredient in this stack in order to keep this tutorial simple and that is service discovery.
+My personal preference is to use Consul. Prometheus can plug into consul and automatically begin to scrape new hosts
+that register a Netdata client with Consul.
+
+At the end of this tutorial you will understand how each technology fits together to create a modern monitoring stack.
+This stack will offer you visibility into your application and systems performance.
+
+## Getting Started - Netdata
+
+To begin let's create our container which we will install Netdata on. We need to run a container, forward the necessary
+port that Netdata listens on, and attach a tty so we can interact with the bash shell on the container. But before we do
+this we want name resolution between the two containers to work. In order to accomplish this we will create a
+user-defined network and attach both containers to this network. The first command we should run is: 
+
+```sh
+docker network create --driver bridge netdata-tutorial
+```
+
+With this user-defined network created we can now launch our container we will install Netdata on and point it to this
+network.
+
+```sh
+docker run -it --name netdata --hostname netdata --network=netdata-tutorial -p 19999:19999  centos:latest '/bin/bash'
+```
+
+This command creates an interactive tty session (`-it`), gives the container both a name in relation to the docker
+daemon and a hostname (this is so you know what container is which when working in the shells and docker maps hostname
+resolution to this container), forwards the local port 19999 to the container's port 19999 (`-p 19999:19999`), sets the
+command to run (`/bin/bash`) and then chooses the base container images (`centos:latest`). After running this you should
+be sitting inside the shell of the container.
+
+After we have entered the shell we can install Netdata. This process could not be easier. If you take a look at [this
+link](/packaging/installer/README.md), the Netdata devs give us several one-liners to install Netdata. I have not had
+any issues with these one liners and their bootstrapping scripts so far (If you guys run into anything do share). Run
+the following command in your container.
+
+```sh
+bash <(curl -Ss https://my-netdata.io/kickstart.sh) --dont-wait
+```
+
+After the install completes you should be able to hit the Netdata dashboard at <http://localhost:19999/> (replace
+localhost if you're doing this on a VM or have the docker container hosted on a machine not on your local system). If
+this is your first time using Netdata I suggest you take a look around. The amount of time I've spent digging through
+`/proc` and calculating my own metrics has been greatly reduced by this tool. Take it all in.
+
+Next I want to draw your attention to a particular endpoint. Navigate to
+<http://localhost:19999/api/v1/allmetrics?format=prometheus&help=yes> In your browser. This is the endpoint which
+publishes all the metrics in a format which Prometheus understands. Let's take a look at one of these metrics.
+`netdata_system_cpu_percentage_average{chart="system.cpu",family="cpu",dimension="system"} 0.0831255 1501271696000` This
+metric is representing several things which I will go in more details in the section on Prometheus. For now understand
+that this metric: `netdata_system_cpu_percentage_average` has several labels: (`chart`, `family`, `dimension`). This
+corresponds with the first cpu chart you see on the Netdata dashboard.
+
+![](https://github.com/ldelossa/NetdataTutorial/raw/master/Screen%20Shot%202017-07-28%20at%204.00.45%20PM.png)
+
+This CHART is called `system.cpu`, The FAMILY is `cpu`, and the DIMENSION we are observing is `system`. You can begin to
+draw links between the charts in Netdata to the Prometheus metrics format in this manner.
+
+## Prometheus
+
+We will be installing Prometheus in a container for purpose of demonstration. While Prometheus does have an official
+container I would like to walk through the install process and setup on a fresh container. This will allow anyone
+reading to migrate this tutorial to a VM or Server of any sort.
+
+Let's start another container in the same fashion as we did the Netdata container. 
+
+```sh
+docker run -it --name prometheus --hostname prometheus
+--network=netdata-tutorial -p 9090:9090  centos:latest '/bin/bash'
+``` 
+
+This should drop you into a shell once again. Once there quickly install your favorite editor as we will be editing
+files later in this tutorial. 
+
+```sh
+yum install vim -y
+```
+
+Prometheus provides a tarball of their latest stable versions [here](https://prometheus.io/download/).
+
+Let's download the latest version and install into your container.
+
+```sh
+cd /tmp && curl -s https://api.github.com/repos/prometheus/prometheus/releases/latest \
+| grep "browser_download_url.*linux-amd64.tar.gz" \
+| cut -d '"' -f 4 \
+| wget -qi -
+
+mkdir /opt/prometheus
+
+sudo tar -xvf /tmp/prometheus-*linux-amd64.tar.gz -C /opt/prometheus --strip=1
+```
+
+This should get Prometheus installed into the container. Let's test that we can run Prometheus and connect to it's web
+interface.
+
+```sh
+/opt/prometheus/prometheus
+```
+
+Now attempt to go to <http://localhost:9090/>. You should be presented with the Prometheus homepage. This is a good
+point to talk about Prometheus's data model which can be viewed here: <https://prometheus.io/docs/concepts/data_model/>
+As explained we have two key elements in Prometheus metrics. We have the _metric_ and its _labels_. Labels allow for
+granularity between metrics. Let's use our previous example to further explain.
+
+```conf
+netdata_system_cpu_percentage_average{chart="system.cpu",family="cpu",dimension="system"} 0.0831255 1501271696000
+```
+
+Here our metric is `netdata_system_cpu_percentage_average` and our labels are `chart`, `family`, and `dimension`. The
+last two values constitute the actual metric value for the metric type (gauge, counter, etc…). We can begin graphing
+system metrics with this information, but first we need to hook up Prometheus to poll Netdata stats.
+
+Let's move our attention to Prometheus's configuration. Prometheus gets it config from the file located (in our example)
+at `/opt/prometheus/prometheus.yml`. I won't spend an extensive amount of time going over the configuration values
+documented here: <https://prometheus.io/docs/operating/configuration/>. We will be adding a new job under the
+`scrape_configs`. Let's make the `scrape_configs` section look like this (we can use the DNS name Netdata due to the
+custom user-defined network we created in docker beforehand).
+
+```yaml
+scrape_configs:
+  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
+  - job_name: 'prometheus'
+
+    # metrics_path defaults to '/metrics'
+    # scheme defaults to 'http'.
+
+    static_configs:
+      - targets: ['localhost:9090']
+
+  - job_name: 'netdata'
+
+    metrics_path: /api/v1/allmetrics
+    params:
+      format: [ prometheus ]
+
+    static_configs:
+      - targets: ['netdata:19999']
+```
+
+Let's start Prometheus once again by running `/opt/prometheus/prometheus`. If we now navigate to Prometheus at
+<http://localhost:9090/targets> we should see our target being successfully scraped. If we now go back to the
+Prometheus's homepage and begin to type `netdata\_` Prometheus should auto complete metrics it is now scraping.
+
+![](https://github.com/ldelossa/NetdataTutorial/raw/master/Screen%20Shot%202017-07-28%20at%205.13.43%20PM.png)
+
+Let's now start exploring how we can graph some metrics. Back in our NetData container lets get the CPU spinning with a
+pointless busy loop. On the shell do the following:
+
+```sh
+[root@netdata /]# while true; do echo "HOT HOT HOT CPU"; done
+```
+
+Our NetData cpu graph should be showing some activity. Let's represent this in Prometheus. In order to do this let's
+keep our metrics page open for reference: <http://localhost:19999/api/v1/allmetrics?format=prometheus&help=yes>. We are
+setting out to graph the data in the CPU chart so let's search for `system.cpu` in the metrics page above. We come
+across a section of metrics with the first comments  `# COMMENT homogeneous chart "system.cpu", context "system.cpu",
+family "cpu", units "percentage"` followed by the metrics. This is a good start now let us drill down to the specific
+metric we would like to graph.
+
+```conf
+# COMMENT
+netdata_system_cpu_percentage_average: dimension "system", value is percentage, gauge, dt 1501275951 to 1501275951 inclusive
+netdata_system_cpu_percentage_average{chart="system.cpu",family="cpu",dimension="system"} 0.0000000 1501275951000
+```
+
+Here we learn that the metric name we care about is `netdata_system_cpu_percentage_average` so throw this into
+Prometheus and see what we get. We should see something similar to this (I shut off my busy loop)
+
+![](https://github.com/ldelossa/NetdataTutorial/raw/master/Screen%20Shot%202017-07-28%20at%205.47.53%20PM.png)
+
+This is a good step toward what we want. Also make note that Prometheus will tag on an `instance` label for us which
+corresponds to our statically defined job in the configuration file. This allows us to tailor our queries to specific
+instances. Now we need to isolate the dimension we want in our query. To do this let us refine the query slightly. Let's
+query the dimension also. Place this into our query text box.
+`netdata_system_cpu_percentage_average{dimension="system"}` We now wind up with the following graph.
+
+![](https://github.com/ldelossa/NetdataTutorial/raw/master/Screen%20Shot%202017-07-28%20at%205.54.40%20PM.png)
+
+Awesome, this is exactly what we wanted. If you haven't caught on yet we can emulate entire charts from NetData by using
+the `chart` dimension. If you'd like you can combine the `chart` and `instance` dimension to create per-instance charts.
+Let's give this a try: `netdata_system_cpu_percentage_average{chart="system.cpu", instance="netdata:19999"}`
+
+This is the basics of using Prometheus to query NetData. I'd advise everyone at this point to read [this
+page](/exporting/prometheus/#using-netdata-with-prometheus). The key point here is that NetData can export metrics from
+its internal DB or can send metrics _as-collected_ by specifying the `source=as-collected` URL parameter like so.
+<http://localhost:19999/api/v1/allmetrics?format=prometheus&help=yes&types=yes&source=as-collected> If you choose to use
+this method you will need to use Prometheus's set of functions here: <https://prometheus.io/docs/querying/functions/> to
+obtain useful metrics as you are now dealing with raw counters from the system. For example you will have to use the
+`irate()` function over a counter to get that metric's rate per second. If your graphing needs are met by using the
+metrics returned by NetData's internal database (not specifying any source= URL parameter) then use that. If you find
+limitations then consider re-writing your queries using the raw data and using Prometheus functions to get the desired
+chart.
+
+## Grafana
+
+Finally we make it to grafana. This is the easiest part in my opinion. This time we will actually run the official
+grafana docker container as all configuration we need to do is done via the GUI. Let's run the following command:
+
+```sh
+docker run -i -p 3000:3000 --network=netdata-tutorial grafana/grafana
+```
+
+This will get grafana running at <http://localhost:3000/>. Let's go there and
+login using the credentials Admin:Admin.
+
+The first thing we want to do is click "Add data source". Let's make it look like the following screenshot
+
+![](https://github.com/ldelossa/NetdataTutorial/raw/master/Screen%20Shot%202017-07-28%20at%206.36.55%20PM.png)
+
+With this completed let's graph! Create a new Dashboard by clicking on the top left Grafana Icon and create a new graph
+in that dashboard. Fill in the query like we did above and save.
+
+![](https://github.com/ldelossa/NetdataTutorial/raw/master/Screen%20Shot%202017-07-28%20at%206.39.38%20PM.png)
+
+## Conclusion
+
+There you have it, a complete systems monitoring stack which is very easy to deploy. From here I would begin to
+understand how Prometheus and a service discovery mechanism such as Consul can play together nicely. My current prod
+deployments automatically register Netdata services into Consul and Prometheus automatically begins to scrape them. Once
+achieved you do not have to think about the monitoring system until Prometheus cannot keep up with your scale. Once this
+happens there are options presented in the Prometheus documentation for solving this. Hope this was helpful, happy
+monitoring.
+
+[![analytics](https://www.google-analytics.com/collect?v=1&aip=1&t=pageview&_s=1&ds=github&dr=https%3A%2F%2Fgithub.com%2Fnetdata%2Fnetdata&dl=https%3A%2F%2Fmy-netdata.io%2Fgithub%2Fexporting%2FWALKTHROUGH&_u=MAC~&cid=5792dfd7-8dc4-476b-af31-da2fdb9f93d2&tid=UA-64295674-3)](<>)