rememberlenny

Latency numbers every programmer should know

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

rememberlenny

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

rememberlenny

We want PlanetScale to be the best place to work. But every company says that, and very few deliver. Managers have a role in creating an amazing work experience, but things go awry when the wrong dynamic creeps in.

We have all seen those managers who collect people as “resources” or who control information as a way to gain “power.” In these cultures, people who “can’t” end up leading the charge. This is management mediocrity.

What will make us different? At PlanetScale, we won’t tolerate management mediocrity. We are building a culture where politics get you nowhere and impact gets you far. Managers are here to support people who get things done. They are as accountable to their team as their team is accountable to them.

We evaluate managers on the wellbeing and output of their team, how skillfully they collaborate with and influence others, and how inclusively and transparently they work.

You can expect your manager to:

• Perceive a better version of you and support you in getting there

rememberlenny

THIS WAS ORIGINALLY POSTED ON MY TUMBLR ON FEB 25, 2011. I forgot I had a Tumblr account. I recently logged in (in light of the acquisition by Automattic), found some old posts, and I'm republishing them exactly as they were with zero modifications.

---

CloudFormation: The Big Picture

Amazon announced CloudFormation to the public yesterday, and while the general opinion I could glean from various sources shows that people are excited about this new technology, many are still unsure what it is and how it fits into their current cloud workflow. I feel as though I have a firm grasp on CloudFormation and will attempt to answer some questions here.

Note: I'm definitely not a representative of Amazon in any way, and anything here is simply my educated opinion on the matter.

rememberlenny

stats = Sidekiq::Stats.new

stats.queues

stats.enqueued

stats.processed

stats.failed

rememberlenny

FROM amazonlinux:1
RUN yum -y update
RUN yum -y install make wget tar gcc gcc-c++ file diffutils
RUN wget https://cmake.org/files/v3.7/cmake-3.7.2.tar.gz && \
  tar -xzvf cmake-3.7.2.tar.gz && cd cmake-3.7.2 && \
  ./bootstrap && make && make install
RUN yum -y install cmake autogen automake libtool \
  gzip zip libcurl-devel zlib-static libpng-static xz git \
  boost-static boost-devel \
  bzip2-devel which gd-devel

rememberlenny

A composable pattern for pure state machines with effects

State machines are everywhere in interactive systems, but they're rarely defined clearly and explicitly. Given some big blob of code including implicit state machines, which transitions are possible and under what conditions? What effects take place on what transitions?

There are existing design patterns for state machines, but all the patterns I've seen complect side effects with the structure of the state machine itself. Instances of these patterns are difficult to test without mocking, and they end up with more dependencies. Worse, the classic patterns compose poorly: hierarchical state machines are typically not straightforward extensions. The functional programming world has solutions, but they don't transpose neatly enough to be broadly usable in mainstream languages.

Here I present a composable pattern for pure state machiness with effects,

rememberlenny

counterSlice.js
------------------
import { createSlice, createAsyncThunk } from '@reduxjs/toolkit'
import fakeFetch from './fakeFetch';

export const lazyAdd = createAsyncThunk('counter/lazyAdd',
  async (payload, thunkAPI) => {
    // throw ('error in thunk!')
    const response = await fakeFetch(payload);
    return response;

rememberlenny

const waitFor = (ms) => new Promise(r => setTimeout(r, ms))
const asyncForEach = async (array, callback) => {
  for (let index = 0; index < array.length; index++) {
    await callback(array[index], index, array)
  }
}

const start = async () => {
  await asyncForEach([1, 2, 3], async (num) => {
    await waitFor(50)

rememberlenny

Google Cloud Deploy fix for 8 used quota limit issue 8+ 1 needed

(visually see servers in pic)

** error message **

IN_USE_ADDRESSES | 8 Used Quota | exceeded quotas 8+ needed 1