Tag: Computer Science

Why Learning to Code is So Damn Hard

Erik Trautman of the Viking Code School Blog did an outstanding job explaining “Why Learning to Code is So Damn Hard“.  It’s a rather long read, but it’s easy to get, and it explains the different stages extremely well.

coding_is_hard_combined_chart

He looks both at the variability of confidence as one gets competence, the scarcity of resources for each stage, and the scope of knowledge.  Just to have it here for a quick reference, here are the stages:

  1. The Hand Holding Honeymoon is the joy-filled romp through highly polished resources teaching you things that seem tricky but are totally do-able with their intensive support. You will primarily learn basic syntax but feel great about your accomplishments.
  2. The Cliff of Confusion is the painful realization that it’s a lot harder when the hand-holding ends and it feels like you can’t actually do anything on your own yet. Your primary challenges are constant debugging and not quite knowing how to ask the right questions as you fight your way towards any kind of momentum.
  3. The Desert of Despair is the long and lonely journey through a pathless landscape where every new direction seems correct but you’re frequently going in circles and you’re starving for the resources to get you through it. Beware the “Mirages of Mania”, like sirens of the desert, which will lead you astray.
  4. The Upswing of Awesome is when you’ve finally found a path through the desert and pulled together an understanding of how to build applications. But your code is still siloed and brittle like a house of cards. You gain confidence because your sites appear to run, you’ve mastered a few useful patterns, and your friends think your interfaces are cool but you’re terrified to look under the hood and you ultimately don’t know how to get to “production ready” code. How do you bridge the gap to a real job?

I’ll also borrow the resource density chart, which is very handy:

coding_is_hard_resource_density

 

Not only the article explains why learning to code is so hard, but it also suggests how to make it easier, depending at which stage you are.

I think this is one of the best articles on professional development in software development that I’ve seen ever.  Do read the whole thing.  It’s that good!

PEG.js – Parser Generator for JavaScript

PEG.js – Parser Generator for JavaScript.

PEG.js is a simple parser generator for JavaScript that produces fast parsers with excellent error reporting. You can use it to process complex data or computer languages and build transformers, interpreters, compilers and other tools easily.

Scaling the Facebook data warehouse to 300 PB

Scaling the Facebook data warehouse to 300 PB

At Facebook, we have unique storage scalability challenges when it comes to our data warehouse. Our warehouse stores upwards of 300 PB of Hive data, with an incoming daily rate of about 600 TB. In the last year, the warehouse has seen a 3x growth in the amount of data stored. Given this growth trajectory, storage efficiency is and will continue to be a focus for our warehouse infrastructure.

The anternet

Stanford researchers discover the ‘anternet’

Transmission Control Protocol, or TCP, is an algorithm that manages data congestion on the Internet, and as such was integral in allowing the early web to scale up from a few dozen nodes to the billions in use today. Here’s how it works: As a source, A, transfers a file to a destination, B, the file is broken into numbered packets. When B receives each packet, it sends an acknowledgment, or an ack, to A, that the packet arrived.

This feedback loop allows TCP to run congestion avoidance: If acks return at a slower rate than the data was sent out, that indicates that there is little bandwidth available, and the source throttles data transmission down accordingly. If acks return quickly, the source boosts its transmission speed. The process determines how much bandwidth is available and throttles data transmission accordingly.

It turns out that harvester ants (Pogonomyrmex barbatus) behave nearly the same way when searching for food. Gordon has found that the rate at which harvester ants – which forage for seeds as individuals – leave the nest to search for food corresponds to food availability.

A forager won’t return to the nest until it finds food. If seeds are plentiful, foragers return faster, and more ants leave the nest to forage. If, however, ants begin returning empty handed, the search is slowed, and perhaps called off.

Prabhakar wrote an ant algorithm to predict foraging behavior depending on the amount of food – i.e., bandwidth – available. Gordon’s experiments manipulate the rate of forager return. Working with Stanford student Katie Dektar, they found that the TCP-influenced algorithm almost exactly matched the ant behavior found in Gordon’s experiments.