- How the browser renders the document
- Receives the data (bytes) from the server.
- Parses and converts into tokens (<, TagName, Attribute, AttributeValue, >).
- Turns tokens into nodes.
- Turns nodes into the
DOM
tree.
- Builds
CSSOM
tree from thecss rules
.
#!/bin/bash | |
# make sure you are on latest wsl release (bionic) | |
cat /etc/*release* | |
# add new repositories | |
echo "deb [ arch=amd64,i386 ] https://repo.mongodb.org/apt/ubuntu bionic/mongodb-org/4.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-4.0.list | |
echo "deb [ arch=amd64,i386 ] https://repo.mongodb.org/apt/ubuntu bionic/mongodb-org/4.1 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-4.1.list | |
# try an update (which will fail, but reveal missing keys) |
CertSimple just wrote a blog post arguing ES2017's async/await was the best thing to happen with JavaScript. I wholeheartedly agree.
In short, one of the (few?) good things about JavaScript used to be how well it handled asynchronous requests. This was mostly thanks to its Scheme-inherited implementation of functions and closures. That, though, was also one of its worst faults, because it led to the "callback hell", an seemingly unavoidable pattern that made highly asynchronous JS code almost unreadable. Many solutions attempted to solve that, but most failed. Promises almost did it, but failed too. Finally, async/await is here and, combined with Promises, it solves the problem for good. On this post, I'll explain why that is the case and trace a link between promises, async/await, the do-notation and monads.
First, let's illustrate the 3 styles by implementing
All of the below properties or methods, when requested/called in JavaScript, will trigger the browser to synchronously calculate the style and layout*. This is also called reflow or layout thrashing, and is common performance bottleneck.
Generally, all APIs that synchronously provide layout metrics will trigger forced reflow / layout. Read on for additional cases and details.
elem.offsetLeft
,elem.offsetTop
,elem.offsetWidth
,elem.offsetHeight
,elem.offsetParent
This gist started with a collection of resources I was maintaining on stream data processing — also known as distributed logs, data pipelines, event sourcing, CQRS, and other names.
Over time the set of resources grew quite large and I received some interest in a more guided, opinionated path for learning about stream data processing. So I added the reading list.
Please send me feedback!
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs