Adding upstream version 1.18.0.upstream/1.18.0

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

1 files changed, 122 insertions, 171 deletions

diff --git a/backends/WALKTHROUGH.md b/backends/WALKTHROUGH.md
index 19f4ac0e1..c6461db46 100644
--- a/backends/WALKTHROUGH.md
+++ b/backends/WALKTHROUGH.md
@@ -2,123 +2,102 @@
 
 ## 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.
+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.
+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: 
+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.
+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.
+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.
+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.
 
 ```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.
+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.
+<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.
+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
+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. 
+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. 
+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
@@ -139,39 +118,33 @@ 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.
+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.
+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).
-
-```yml
+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'
@@ -192,84 +165,66 @@ scrape_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 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:
+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.
+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)
+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.
+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](../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.
+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:
+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
 ```
 
@@ -277,26 +232,22 @@ This will get grafana running at ‘<http://localhost:3000/’> Let’s go there
 
 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
+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.
+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.
+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.
 
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