I always hated this expression and I don’t fit in the companies who use it to describe their corporate culture, but I never realized why until I read this Wikipedia entry:
“Fun and action are the rule here, and employees take few risks, all with quick feedback; to succeed, the culture encourages them to maintain a high level of relatively low-risk activity.”
(page 108, Corporate Cultures book by Deal and Kennedy)
Can we use whitebox monitoring data to forecast cascading failures in distributed software?
It is winter in Switzerland, holiday time is when you dream interesting ideas while drinking Glühwein and reckless people who ski off-track get buried alive or cause giant avalanches.
What does an avalanche have to do with software? If you’re a novice, running your extra-lean startup “technical infrastructure” on a feeble single server, it is that post gone viral that turned a fledging success into a pile of fuming ashes. If you’re a seasoned guy, maybe a DevOps or an SRE, it’s that rapid-fire and sneaky Query of Death that commits software genocide, killing your nodes faster than they can recover.
Avalanches sound scary, but zoom out, repeat and something magical happens. Order from chaos appears.
It is time to rethink how we build compilers to make software more secure.
Symbolic execution is a way to analyze software behavior. It is used to explore a large set of parallel program paths at once and can lead to exploit that trigger vulnerabilities deep into the bowel of a piece of code.
I recently discovered Neil Gunther’s work on computer performance analysis and in particular his “Universal Scalability Law”, which links the relative capacity of a system:
to two term:
- a contention cost due to queuing effects caused by limited resources.
- a coherency cost to maintain shared state.
Using it, you can get the ideal number of workers for a given job:
I have yet to see how useful it could be in my real world: that is large scale, distributed systems.
I will baptize February the Month of the Long Tail. Discussions about the impact of fat distributions have been high on my radar lately.
Heroku has hit Hacker News‘ top 10 multiple times in the last few days. RapGenius has been unhappy about the performance of their Rails application and posted an in-depth analysis of performance issues on Heroku’s Bamboo and Cedar stacks.
RapGenius unearthed issues in Heroku’s stacks that made it scale badly. Heroku posted a post-mortem a few days later and promised some fixes.
There are some interesting lessons to learn from this tale.
It is common knowledge that singles, particularly bachelors, live in complete chaos and their dwellings remember troll caves rather than human houses. Once they get married, their place magically change into perfect —or at least passable— residences. (Well, until they have kids.)
I want to challenge this discriminating notion about singles. Demonstrating, with a little math, that it’s not a problematic male trait but just a consequence of the situation.
By the way, it’s not just males that are weird when they live alone: