david-a-perez

// time:   [1.8843 µs 1.8897 µs 1.8955 µs]
pub fn original(input: &[u8]) -> Option<usize> {
    let mut idx = 0;
    'outer: while idx + 13 < input.len() {
        let mut state = 0;
        for (next_idx, byte) in input[idx..idx + 14].iter().enumerate().rev() {
            let bit_idx = byte % 32;
            if state & (1 << bit_idx) != 0 {
                idx += next_idx + 1;
                continue 'outer;

ptarjan

Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD