n the morning of May 12, LinkedIn, the networking site devoted to making professionals “more productive and successful,” emailed scores of my contacts and told them I’m a professional racist. It was one of those updates that LinkedIn regularly sends its users, algorithmically assembled missives about their connections’ appearances in the media. This one had the innocent-sounding subject, “News About William Johnson,” but once my connections clicked in, they saw a small photo of my grinning face, right above the headline “Trump put white nationalist on list of delegates.” [.....] It turns out that when LinkedIn sends these update emails, people actually read them. So I was getting upset. Not only am I not a Nazi, I’m a Jewish socialist with family members who were imprisoned in concentration camps during World War II. Why was LinkedIn trolling me?

[LinkedIn] are proud to announce today that we are open sourcing Dr. Elephant, a powerful tool that helps users of Hadoop and Spark understand, analyze, and improve the performance of their flows.

'Here's a story for you.
I'm not a party to any of this. I've done nothing wrong, I've never been suspected of doing anything wrong, and I don't know anyone who has done anything wrong. I don't even mean that in the sense of "I pissed off the wrong people but technically haven't been charged." I mean that I am a vanilla, average, 9-5 working man of no interest to anybody. My geographical location is an accident of my birth. Even still, I wasn't accidentally born in a high-conflict area, and my government is not at war. I'm a sysadmin at a legitimate ISP and my job is to keep the internet up and running smoothly.
This agency has stalked me in my personal life, undermined my ability to trust my friends attempting to connect with me on LinkedIn, and infected my family's computer. They did this because they wanted to bypass legal channels and spy on a customer who pays for services from my employer. Wait, no, they wanted the ability to potentially spy on future customers. Actually, that is still not accurate - they wanted to spy on everybody in case there was a potentially bad person interacting with a customer.
After seeing their complete disregard for anybody else, their immense resources, and their extremely sophisticated exploits and backdoors - knowing they will stop at nothing, and knowing that I was personally targeted - I'll be damned if I can ever trust any electronic device I own ever again.
You all rationalize this by telling me that it "isn't surprising", and that I don't live in the [USA,UK] and therefore I have no rights.
I just have one question.
Are you people even human?'

Rest.li is a REST+JSON framework for building robust, scalable service architectures using dynamic discovery and simple asynchronous APIs. Rest.li fills a niche for building RESTful service architectures at scale, offering a developer workflow for defining data and REST APIs that promotes uniform interfaces, consistent data modeling, type-safety, and compatibility checked API evolution.

Do not read in to this article too much, especially for trying to understand how the Linux VM or the kernel works.  The authors misread the "global spinlock on the zone" source code and the interpretation in the article is dead wrong.

With SSD, we find that garbage collection will become a very significant bottleneck, especially for systems which have little control over the storage layer and rely on Java memory management. Big heapsizes make the cost of garbage collection expensive, especially the single threaded CMS Initial mark. We believe that data systems must revisit their caching strategies with SSDs. In this regard, SSD has provided an efficient solution for handling fragmentation and moving towards predictable multitenancy.

This paper introduces MonitorRank, an algorithm that can reduce the time, domain knowledge, and human effort required to find the root causes of anomalies in such service-oriented architectures. In the event of an anomaly, MonitorRank provides a ranked order list of possible root causes for monitoring teams to investigate. MonitorRank uses the historical and current time-series metrics of each sensor as its input, along with the call graph generated between sensors to build an unsupervised model for ranking. Experiments on real production outage data from LinkedIn, one of the largest online social networks, shows a 26% to 51% improvement in mean
average precision in finding root causes compared to baseline and current state-of-the-art methods.