AHL Python Hackathon April 2018·
Three weeks ago MAN AHL organised an opensource hackathon at their London office. As part of the Hackathon people should contribute to one of the PyData artifacts they regularly use. To support them in making their first contribution, AHL also coordinated that several core committers of opensource projects were present at the event. I joined in as the representative of the Apache Arrow project.
While it was the first of a kind hackathon for me, it was also the first event for the Apache Arrow project where we had to do preparations for a set of new contributors joining at once. This meant that we had to do some homework in the project. Before the hackathon, the JIRA issues were mostly a mixture of larger topics we planned to work on or small todos that surfaced during a pull request review. For a successful hackathon we needed to define more tasks that were in the 1-2h or the 10h range. This should then allow people to work on at least a small and a medium task at the hackathon.
The event itself was located at AHL’s London office at Riverbank House. We had a nice view over the Thames during the hackathon. The first day started with introductory talks that detailed how the event is structured and why a company like AHL is hosting it. Their CTO explained that while the hackathon will definitely be a boost for their brand in the space of searching for new developers it is also a great opportunity to give something back to the Python ecosystem that they rely upon. (One must also note at this point that they are also the hosts for the PyData London meetup group.) Afterwards each of the core committers presented their open source project and detailed some of the contribution possibilities for the weekend. After a splendid lunch, people distributed to the available projects and then started hacking on their contributions.
Hacking continued for 24 hours with superb meals in between (they even had organized some late night Sushi). People happily made their first small open source contributions and then continued to tackle slightly larger issues. At the end of the hackathon we had final presentations by each of the participating projects about what they have achieved. The most amazing of all was awarded with a small price (some retro consoles): One team worked in a group of four with nearly no sleep to implement Stable distribution in scipy.
During the hackathon, the team Apache Arrow solved various small and medium
issues. It consisted of Kee Chong Tan, Donal
Simmie, Gatis Seja,
Samuel Sinayoko, and Florian
Rathgeber. To get started, we got improved table
column access, column selection in
from_pandas and a better
Arrow. People tripped over some small issues in Apache Arrow while they were
trying to implement their features. I used this to directly
After the small ones passed the code review and CI builds, they were successfully merged to master. Then people progressed to construct Arrow (and Pandas) schemas from DataFrames without converting it fully to an Arrow table first, expose the C++ Builder classes in Python, add zero copy returns for plain NumPy array without Pandas specifics, and improve the usability and documentation of the combination of Arrow and Numba.
Looking back at the hackathon, it was a great event. I have met new contributors and existing users that have different use cases for Apache Arrow than what we use a it for. The hackathon also showed clearly what you need to get new contributors on board: a nice elevator pitch why they should use your project, a decent set of documentation and also a nicely prepared list of possible tasks. Personally, I have also learnt on how to mentor people to do their first contributions and what are the main hurdles to get there. The hackathon was really a win-win situation for the project and the people that came for making their first contributions. They got support and in-person feedback while developing new features whereas the project itself gained a lot by also getting in-person feedback on how hard it to join the group of committers. In the end this resulted for me in a long list of homework on how to improve the experience for people that are interested in making their first contribution to Apache Arrow.
While we should continously update and spread our elevator pitch, we also need to take care to write tutorials for people that are new to the project. For example we have a clear specification of the memory layout but lack a comprehensive guide that does not go into alll detail but outlines the essential features of this memory layout. Furthermore Arrow is a really complicated project. For the Python implementation, you often need to touch Python, Cython and C++ code. We need a small introduction on how and when these three languages are used and where to look at for a certain functionality.
The final thing to make the project more successful for new contributors: list a lot of small tasks somewhere that are easy to implement. While you will build up good relationships with people as long as you have a welcoming and suppotive community, many people will only browse your issues to find a task they could work on. You won’t have the chance to be talk to them and help them make a contribution unless they can find a task they could talk to you about.