Today the first Hadoop Get Together Berlin 2012 took place - David got the event hosted by and at Axel Springer
who kindly also paid for the (soon to be published) videos. Thanks also to the unbelievable Machine company
for the tasty buffet after the meetup. Another thanks to Open Source Press for donating three of their Hadoop books
Today's selection was quite diverse: The event started with a presentation by Markus Andrezak
who gave an overview of Kanban and how it helped him change the development workflow over at eBay/mobile
. Being well suited for environments that require flexibility Kanban is well suited to decrease risk associated with any single release by bringing the number of features released down to an absolute minimum. At Mobile his team got release cycles down to once a day. More than ten times a day however aren't unheard of as well. The general goal for him was to reduce the risk associated with releases by reducing the number of features released per release, reducing the number of moving parts in one release and as a result reducing the number of potential sources for problems: If anything goes wrong, rolling back is no issue - nor is narrowing down on the potential sources of bugs in the changed software that were introduced in that particular release.
This development and output focused part of the process is complemented by an input focused Kanban cycle for product design: Products are going from idea to vision to a more detailed backlog to development and finally live the same as issues in development itself move from Todo to in progress, under review and done.
With both cycles the main goal is to keep the number of items in progress as low as possible. This will result in more focus for each developer and greatly reduce overhead: Don't do more than one or two things at a time. Only catch: Most companies are focused on keeping development busy at all times - their goal is to reach 100% utilization. This however is in no way correlated to actual efficiency: By having 100% utilization there is not way you can deal with problems along the way, there is no buffer. Instead the idea should be to concentrate on a constant flow of released and live features instead.
Now what is the link of all that to Hadoop? (Hint: No, this is no pun on the project's slow release cycle.) The process of Kanban allows for frequent releases, it allows for frequent feedback. This enables a model of development that starts out from a model of your business case (no matter how coarse that may be), start building some code, measure your performance with that code based on actual usage data and adjust the model accordingly. Kanban lets you iterate very quickly on that loop getting you ahead of competitors eventually. In terms of technology one strong tool in their toolbox to really do data analytics on their incoming data is to use Hadoop and scale up analysing business data.
In the second talk Martin Scholl started out by drawing a comparison from music vs. printed music sheets to the actual performance of musicians in a concert: The former represents static, factual data. The latter represents a process that may be recorded, but may not by copied itself as it lives by the interactions with the audience. The same holds true for social networks: Their current state and the way you look at them is deeply influenced by your way of interacting with the system in realtime.
So in addition to data storage solutions for static data, he argues, we also need a way to process streaming data in an efficient and fault tolerant way. The system he uses for that purpose is Storm
that was open-sourced by Twitter late last year. Built on top of zeroMQ it allows for flexible and fault tolerant messaging. Example applications mentioned are event analysis (filtering, aggregation, counting, monitoring), parallel distributed rpc based on message passing.
Two concrete examples include setting up a live A/B testing environment that is dynamically reconfigurable based on it's input as well as event handling in a social network environment where interactions might trigger messages being sent by mail and instant message but also trigger updates in a recommendation model.
In the last talk Fabian Hüske from TU Berlin introduced Stratosphere
- an EU founded research project that is working on an extended computational model on top of HDFS that provides more flexibility and better performance. Being developed before the rise of Apache Hadoop YARN
unfortunately essentially what they did was to re-implement the whole map/reduce computational layer and put their system into that. Would be interesting to see how a port to YARN performs and what sort of advantages it gives in production.
Looking forward to seeing you all in June for Berlin Buzzwords
- make sure to submit your presentation soon, call for presentations won't be extended this year.