Installing Grafana on Raspberry Pi 2 (2015-10-24)

Collecting time series data like stock market or home automation data and storing it in InfluxDB is easy using the new Go client library of InfluxDB.

The next step is displaying the data like temperatures as continuously updated graphs.

Grafana is a nice, widely used web interface displaying data as graphs. It works well with InfluxDB but also with other backends like ElasticSearch.

In a previous post I documented how InfluxDB can be installed on the Raspberry Pi. This post summarizes my experience at installing Grafana on the Pi. Also that tool is working reliable since a while on the Pi. It comes with a web-server so you can display the graphs on any device.

For installing Grafana on the Raspberry Pi 2 it needs to be built from scratch (I couldn't find it in a repository for ARM). It appears to consist of Go and node.js. Hence you need to have the Go compiler, git, and nodes.js installed.

Building the Go backend:

$ go get github.com/grafana/grafana
$ cd $GOPATH/src/github.com/grafana/grafana
$ go run build.go setup 
$ $GOPATH/bin/godep restore && go run build.go build

Building the frontend:

 $ sudo aptitude install npm
 $ sudo aptitude install node
 $ sudo npm config set registry http://registry.npmjs.org/
 $ sudo npm install tap

Finally I found myself in the dependency hell of node.js - the Go part was just so simple. IIRC to get grunt installed I needed to update npm as well as install n.

 $ sudo npm install npm -g
 $ sudo npm install n -g
 $ sudo npm install grunt -g

Finally the frontend can be build (you need to be in the grafana directory):

$ sudo npm install

At the beginning it resulted in version compatibility issues of some libraries. What helped is re-installing the required versions of them with sudo npm install tool@version -g and then running the npm install again.

Finally run:

$ grunt --force

If all that was successful Grafana can be started:

$ ./bin/grafana-server 

Then you can login now on port 3000 create your user and organization, adding your InfluxDB data sources, and finally your graphs based on the data you are constantly adding to InfluxDB.

Really a cool thing and a pretty useful tool for displaying all kinds of measurements you are doing.

Installing Influx DB on Raspberry Pi 2 (2015-10-18)

InfluxDB is a simple time series database written entirely in Go without external dependencies. This makes it attractive to have it running on a Raspberry 24/7 to collect home automation or stock market data or anything else which is waiting for being stored locally when the laptop is closed. I installed it a while ago and it is running well. Below my notes probably useful also for others (and also for me later on). I hope it is complete.

For Influx exists pre-built packages but unfortunately none for the ARM architecture required for the Raspberry.

Since it is written in Go and the Go compiler supports ARM it can be compiled from scratch.

First we need the latest and greatest Go compiler on the Raspi (currently 1.5.1) which needs to be compiled first itself. You can cross-compile it but having a recent Go compiler on the Pi is certainly useful. Since Go is now entirely written in Go (1.5) we need to bootstrap the compilation of Go.

Over in Dave Chaneys blog there is a recipe which works well also for 1.5.1. It takes an hour for compilation, but on the old Raspberry it took the whole night.

Having the current go version and the Go paths setup you should see something like that:

$ go version
go version go1.5.1 linux/arm

The next step is cloning the repository of InfluxDB and changing to its directory:

$ go get github.com/influxdb/influxdb
$ cd go/src/github.com/influxdb/influxdb

Changing to a stable tag of the latest version (git tag --list) can also not harm because we want to have a stable system.

$ git checkout v0.9.4.1

Finally the dependencies have to be resolved and then it can be build (being in the influxdb source directory.

$ go get -u -f -t ./...
$ go build ./...
$ go install ./...

For creating a complete package the gvm tool needs to be installed. That handles Go versions on the command line. Then the package.sh script could be used. But for my purposes I skip that.

For running InfluxDB we are going to use a user named influxdb and giving it a password:

$ sudo adduser influxdb

Now we let's install the InfluxDB binary to a global location.

$ sudo mkdir /opt/influxdb
$ sudo cp /home/daniel/go/bin/influxd /opt/influxdb/
$ sudo cp /home/daniel/go/bin/influx /opt/influxdb/
$ sudo chown -R influxdb /opt/influxdb
$ sudo chgrp -R influxdb /opt/influxdb

Also other directories needs to have the rights for influxdb user.

$ sudo mkdir -p /etc/opt/influxdb
$ sudo chown -R influxdb /etc/opt/influxdb
$ sudo chgrp -R influxdb /etc/opt/influxdb

$ sudo mkdir -p /var/run/influxdb
$ sudo chown -R influxdb /var/run/influxdb
$ sudo chgrp -R influxdb /var/run/influxdb

$ sudo touch /etc/default/influxdb
$ sudo chown -R influxdb /etc/default/influxdb
$ sudo chgrp -R influxdb /etc/default/influxdb

As influxdb user the config needs to be created:

$ su influxdb
$ /opt/influxdb/influxd config > /etc/default/influxdb

For getting the daemon booted automatically we need to install the rc script as root.

# cp <...>/src/github.com/influxdb/influxdb/scripts/init.sh /etc/initd/influx

Then we add it to the runlevels (in /etc/initd).

# insserv influx

Starting the service...

# service influx start
Starting the process influxdb [ OK ]
influxdb process was started [ OK ]

Now it is time to test if it is running:

# service influx status
influxdb Process is running [ OK ]

You can also connect now with your browser to port 8083.

Using GPUs with #golang (2014-05-07)

A nice presentation about how to use the Go programming language (#golang) with CUDA (from FOSDEM 2014): Scientific GPU Computing with Google's Go Language

Go 1.1 Released (2013-05-06)

The new version keeps compatiblity and adds several language and library changes. Some of them are method values, terminating statements, the size of int on lx-amd64 (now 64 bit), as well as heap size changes for large applications. The complete list is here.

Nvidia's Hyper Q Helps Applications to Share One GPU (2013-04-30)

This interesting article describes how the Hyper-Q scheduler from Nvidia is used in order to run multiple MPI processes concurrently on one GPU. Using the alarm state (in order to exclude fully loaded hosts/GPUs for new jobs in the Grid Engine job list/queue) and load values/load thresholds from the GPU and the CPUs Grid Engine should be able to handle this scenario well.