The education system is based on a model that is does not go along with current times. It could be improved by taking ideas from the world of open source. Students go to school to learn from what the teacher has to offer. But everything is done in a master-slave model: the students expect the teacher to push the information to them.
Tag Archives: LinuxCon EU 2013
In the last couple of years, we realized that data is cheap. This has enabled global surveillance on everyone. The solution is opensource.
This is a Q&A session as usual when Linus appears on stage.
SPDX is a format for exchanging information about licenses and copyrights of a “component” (loosely defined). The main goal is to avoid doing work multiple times: once a component has been vetted, it shouldn’t be redone all the time. Continue reading
Webkit after the Blink fork (April ’13): substantial amount of code was removed (the Chrome-specific stuff); number of contributors and numbers of commits has dropped to about half.
A comparison of open source and commercial static analysis tools (Jenkins case study) – Zack Samachora (Coverity)
This talk discusses Coverity Scan, which does static analysis of open source projects, and compares the results it has on Jenkins (as an example of an open source project) with the results of clang and a few others.
How can we scale the legal work when projects get bigger?
Linux Trace Toolkit is an old tool, this talk shows how to use it for monitoring rather than debugging/profiling.
Tools exist for debugging/profiling individual parts of an application, but this talk is about debugging/profiling the entire application, which may consist of multiple executables or multiple machines.
Average 6000 tweets per second, but peaks up to a record of 140000 tps.
Originally: ruby on rails and mysql. Added caching but it’s a band-aid. Monolithic design made engineering difficult. Adding extra machines is not scalable. Performance optimization made code less readable.
Vision: 10x improvement, isolate failure,
Ruby vm was consuming a lot of resources. Two new features were using jvm and doing great, so migrated there.
Decompose the monolith.
Each team took a different approach to concurrency and failure. So move this to a library: Finagle. Abstracts all the distributed system stuff.
Zipkin: tracing for Finagle.
Partitioning of the data center:learned from Google how to do this more dynamically. Using cgroups to isolate resources. Mesos
Twitter embraced open source from day one: it has good quality and you can learn from your peers.
Incremental change always wins.
View the data center as the computer, not a single server.