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diff --git a/exporting/WALKTHROUGH.md b/exporting/WALKTHROUGH.md new file mode 100644 index 000000000..ac1712916 --- /dev/null +++ b/exporting/WALKTHROUGH.md @@ -0,0 +1,259 @@ +<!-- +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. + + + +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. + + + +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) + + + +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. + + + +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 + + + +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. + + + +## 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. + +[](<>) |