This Week I Learned
Continued with the Getting Started with Docker Swarm Mode Pluralsight courseStarted reading the book The Annotated Turing: A Guided Tour Through Alan Turing's Historic Paper on Computability and the Turing Machine by Charles Petzold
This Week I Tweeted
Huawei is accused of attempting to copycat a T-Mobile robot, and the charges read like a comical spy movieThe US on Monday charged the Chinese phone giant Huawei with trying to steal trade secrets from T-Mobile, among other crimes.
One Justice Department indictment includes internal emails between Huawei's US and Chinese employees who prosecutors said were trying to copy a T-Mobile device-testing robot.
The emails read like a comical spy movie, with one set of employees trying to avoid wrongdoing and another engineer getting caught putting part of the robot into his bag.
Huawei said that it hasn't violated any US laws and that it already settled with T-Mobile in a civil lawsuit.
What the heck is going on here?
How Do You Count Every Solar Panel in the U.S.? Machine Learning and a Billion Satellite Images
The DeepSolar Project, developed by engineers and computer scientists at Stanford University, is a machine learning framework that analyzes a dataset of satellite images in order to identify the size and location of installed solar panels.
To accurately count the panels, the DeepSolar team used a machine learning algorithm to analyze more than a billion high-resolution satellite images. The algorithm identified what the team believes to be almost every solar power installation across the contiguous 48 states.
The DeepSolar analysis reached a total of 1.47 million solar installations in the U.S., a much higher number than either of the two most commonly cited estimates.
“We can use recent advances in machine learning to know where all these assets are, which has been a huge question, and generate insights about where the grid is going and how we can help get it to a more beneficial place,” said Ram Rajagopal, associate professor of civil and environmental engineering, who supervised the project with Arun Majumdar, professor of mechanical engineering.
This is cool..but do they really need to use a billion images.. can't they use a subset and come pretty close to the real answer as well?
40x faster hash joiner with vectorized execution
For the past four months, I’ve been working with the incredible SQL Execution team at Cockroach Labs as a backend engineering intern to develop the first prototype of a batched, column-at-a-time execution engine. During this time, I implemented a column-at-a-time hash join operator that outperformed CockroachDB’s existing row-at-a-time hash join by 40x. In this blog post, I’ll be going over the philosophy, challenges, and motivation behind implementing a column-at-a-time SQL operator in general, as well as some specifics about hash join itself.
In CockroachDB, we use the term “vectorized execution” as a short hand for the batched, column-at-a-time data processing that is discussed throughout this post.
I love it when you see drastic speed improvements like this, I remember one time when we upgraded some hardware to use SSDs and more RAM.. a reporting query that took a minute now finished in less than a second.. I thought the results were wrong because it finished too fast lol
Some cool stuff you might enjoy
Kodak Premium Puzzle Presents: The World's Largest Puzzle 51,300 Pieces 27 Wonders from Around The World 28.5 Foot x 6.25 Foot Jigsaw Puzzle
That is 8.68 meters wide for the metric people....aka 1 big white shark length
I don't always listen to classical music.. but when I do.. I make sure it's played on an electric guitar, enjoy this piece of Ludwig van Beethoven - Moonlight Sonata ( 3rd Movement ) by Tina S
