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diff --git a/backends/WALKTHROUGH.md b/backends/WALKTHROUGH.md new file mode 100644 index 0000000..0c330ee --- /dev/null +++ b/backends/WALKTHROUGH.md @@ -0,0 +1,294 @@ +# 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: + +``` +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. + +``` +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/#installation), 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. + +``` +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. `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. `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. + +``` +curl -L 'https://github.com/prometheus/prometheus/releases/download/v1.7.1/prometheus-1.7.1.linux-amd64.tar.gz' -o /tmp/prometheus.tar.gz + +mkdir /opt/prometheus + +tar -xf /tmp/prometheus.tar.gz -C /opt/prometheus/ --strip-components 1 +``` + +This should get prometheus installed into the container. Let’s test that we can run +prometheus and connect to it’s web interface. This will look similar to what +follows: + +``` +[root@prometheus prometheus]# /opt/prometheus/prometheus +INFO[0000] Starting prometheus (version=1.7.1, branch=master, revision=3afb3fffa3a29c3de865e1172fb740442e9d0133) + source="main.go:88" +INFO[0000] Build context (go=go1.8.3, user=root@0aa1b7fc430d, date=20170612-11:44:05) source="main.go:89" +INFO[0000] Host details (Linux 4.9.36-moby #1 SMP Wed Jul 12 15:29:07 UTC 2017 x86_64 prometheus (none)) source="main.go:90" +INFO[0000] Loading configuration file prometheus.yml source="main.go:252" +INFO[0000] Loading series map and head chunks... source="storage.go:428" +INFO[0000] 0 series loaded. source="storage.go:439" +INFO[0000] Starting target manager... source="targetmanager.go:63" +INFO[0000] Listening on :9090 source="web.go:259" +``` + +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. + +``` +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). + +```yml +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: + +``` +[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 homogeneus 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. + +``` +# 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](../backends/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: + +``` +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. + +[]() |