The presentation slides for the talk can be found here.
After the talk there will be chance to have a play with Spark yourself. Firstly you need to download the Spark binaries and unpack them to a folder on your local machine.
I also have a few USB keys with the binaries on, so wave your hand if the network is slow.
If you want to run an interactive PySpark interpreter then run the following command inside the spark directory:
The master flag lets you specify the master node of the cluster, however the local option allows you to run this on your machine with the specified number of worker threads. Be careful not to set this number two high (certainly no higher than the number of CPU cores you have available) or you will freeze your machine.
If you want to be able to import packages not in the standard library (or numpy) the you can include the .egg, .zip or .py files as an argument. You can then use them as a standard import:
You can write your scripts and submit them to spark using the spark-submit script in the bin folder. This works similarly to the pySpark shell:
Remember to add the following import to your script:
The spark-submit script will handle the dependencies for this automatically. For your scripts you also need to form a spark context object yourself (unlike for the pySpark shell where this is provided for you):
Supplying the master address in the script is optional, if you do you don’t need to use the
--master flag in the spark-submit call.
The easiest way to get up and running with Spark is to try the word count example. You can run this via the shell or copy and paste the code into a script and use spark-submit:
Obviously the code above doesn’t deal with punctuation etc and only gives you a word count. The power of Python can then be used to do cool things with this “big” data!
An easy way to see Spark Streaming in action is to extend the word count example to count incoming words to a port on your machine:
To provide input to the streaming job, you need to first run a Netcat server:
and then run the spark script using spark-submit. Now type away to your hearts content in your Netcat terminal and see the word counts pop up on the spark terminal.
Obliviously this basic script can be extended to do all kinds of interesting things!
As part of my PhD I was generously given a brand new Microsoft Surface Pro 3. Unfortunately, as I have been an Ubuntu user for the better part of a decade, using Windows again was a less than optimal experience. Having read many blog posts about the Surface 3 hardware not being supported by the current Ubuntu kernel I resolved to wait and battle on with Windows 8.1. However, after a sever bout of yak shaving I managed to get the latest 15.04 beta 2 working (mostly).
Before you attempt this with your own shiney Surface Pro 3 (SP3) I highly, strongly, emphatically recommend that you grab a 8Gb USB thumb drive and make a Windows recovery disk (via Search > “Create a recovery drive”). The SP3 does have its own recovery partition, however to get the dual boot working you are going to be messing with partition tables and it is a really good idea to have a stand alone recovery option in case everything goes to hell. Also, as always, back up your personal files. They should be safe with the repartitioning, but you never know. You have been warned!
I set up the dual boot by following this very informative, and well illustrated, blog post from David Elner. However, there were a few key differences.
So after all of the above I have working Ubuntu install on my SP3:
So that’s it. Other users have reported fun and games with Windows overwriting grub and have resorted to putting their boot partition on an MicroSD card. I haven’t had this problem yet but it is early days. In the near future I will attempt to run the latest mainline kernel builds (3.19 and 4.0) to see if they get the touchpad working, but at the moment I am very happy with the outcome and my SP3 is now a viable work computer (even if it is still a bit of a toasterfridge)
As part of my MRes I am using Microsoft’s Azure cloud platform. I was pleasantly surprised to see that Microsoft provide Linux support for Azure and SDK’s for a lot of programming languages. However I hit a few bumps getting the command line tools installed due to some quirks with Ubuntu.
The easiest way to get the Azure CLI is to use the Node.js package manager
$ sudo apt-get install nodejs $ sudo apt-get install npm
This should all work fine and then you can simply install the Azure CLI using the command below:
$ npm install azure-cli -g
Now on other Linux distros this may work fine. However on Ubuntu 14.04 there was an issue due to the fact that Azure CLI calls Node.js using the
node command and on Ubuntu it is called
nodejs. As a result you get the error below when you call
/usr/bin/env: node: No such file or directory
To fix this, use the command below to tell Ubuntu that when any program calls
node it really means
$ sudo update-alternatives --install /usr/bin/node nodejs /usr/bin/nodejs 100
You should now be able to call
azure and all its functions!