Graph Databases, a little connected tour

EuroPython talk by: Francisco Fernández Castaño
The talk was classified as novice and so it was – very basic graph database ideas.
Castaño started by presenting the general idea of graph databases and showing some use cases.
One of the most known use cases – social media data, friends-of-friends.
Then he presented Neo4j which is graph database written in Java but originally was written in Python. Neo4j is known to be very scalable and to support ACID transactions. Another nice property about Neo4j is the ability to extend the give rest API but your own needs.
The next part of talked focused on the cypher language – which is the way to query the Neo4j database. Neo4j have some nice UI properties and I really missed some reference or example for that on this talk.
Last but not least tip given in the talk, you can try Neo4j without having to install it on your machine – (free sand box of up to 1k nodes and 10k edges).

The Shogun Machine Learning Toolbox

EuroPython talk by: Heiko Strathmann,
Strathmann presented the Shogun toolbox. Shogun is a Machine Learning toolbox which implement most of the common supervised and unsupervised algorithms. Shogun is implemented in C++ and interacts with Python as well as with Java, Ruby, Matlab, Lau, etc. Shogun is meant to run on single machine and no distributed.
Shogun is an open source project started on 2004. It has 8 core developers + 20 contributors and it is fairly active. It also has a collebaration with Google summer of code – 29 projects so far, 8 on going.
The C++ maybe the most significant advantage of Shogun over SciPy. It is simply faster and enables more efficient memory allocation and run time optimization and well as multi-language support . Therefore enabling training and classifying data based on larger number of samples and higher dimension.
The binding \ interfaces to other languages is done using –

Building, bug tracing and deployment is done using –

Summarizing – Specially when going larger Shogun seems like a good alternative to SciPy (competition makes both products better). Shogun site offers some tutorials include notebooks and demos.

Log everything with logstash and elasticsearch

EuroPython talk by: Peter Hoffmann@peterhoffmann

A talk by Peter Hoffmann from Blue Yonder which is one of the sponsors of the conference.


Another very good talk by an experienced speaker. However the name  is kind of misleading. Yes – logstash and elasticsearch were mentioned however to main concept of the talk was really logging chain and centralized logging while logstash and elasticsearch are two tools along this chain and there are some alternatives in every step of this chain.

This talk gave me a lot to think about the logging on the company aspect and how should they run, monitor, etc. Also there is some tension to solve \ define about what is logged and what error message \ outputs an app should provide (“logging best practices”).

The video is already available in the EuroPython site and I hope that the slides would be available too soon.

Full Stack Python

EuroPython talk by: Matt Makai, @mattmakai 

I expected this talk to be full with buzzwords and it was… but in a good sense. 

Makai is the builder of Full Stack Python site as such he spoke about what you need from the moment you have an idea to a python web app until you deploy including all the essential steps – wsgi server, hosting, logging etc.
Every layer in the site include relevant link and tutorials. Good starting point for a python web-app developer.
The talk was interesting due to Makai enthusiasm to teach and to share his knowledge (and of course to promote his site and that’s legit as well) and his professional knowledge. There are really few people the can speak so fluently for 25 minutes.

5 interesting things (18/7/2014)

A\B Testing – No need to say that A\B is a very hot buzz word in the industry. A\B tests were used long ago in psychology but today there are much more accessible and easy to set. The following series of posts (one is still not publish) describe 5 simple guide lines for A\B testing.

Shellify – decorator which turns Python models into shell. I don’t see an everyday usage to it, maybe on developing but it has a high coolness factor which is important as well.

Code review without your glasses – I like this post because in my humble opinion it is very creative, beyond the box. It is also a good reminder about about what to notice in code review.

Side kick – the last link reminded me of rubber duck debugging and on the way I found this –

Deployment academy – a series of posts by Rainforest in their blog. This time a post about “zero downtime database migrations”. The post is simple and easy to understand and answer a common issues in the life of a developer. Of course, practices and specific problems differ from one organization to another but the core ideas are as is.

Awesome *

Are we going back?

In the past week I have bumped into two github repositories such as awesome-python and awesome-sysadmin. Both repositories do a great job and compose and interesting list \ index of relevant tools.

However, this made me feel that we are going back. Those indices reminded me the pre-search-engines days or shell I say the BME days (before modern era ;). Specifically this reminded me of Alta Vista (but also other indices sites – do you recall Lycos?) where all the links where indexed under some category and sub categories and one should have dig in those categories to find what he looked for.

Aren’t the search engines today strong enough to answer the query “python machine learning package”?. Is human indexing really our resort? I don’t really think so. I believe in the power of the human behind the machine and their ability to build a good enough searching engines. Those indices can be very useful but I would rather have them built automatically and not manually.