jonathan-kosgei

I have been an aggressive Kubernetes evangelist over the last few years. It has been the hammer with which I have approached almost all my deployments, and the one tool I have mentioned (shoved down clients throats) in almost all my foremost communications with clients, and it was my go to choice when I was mocking my first startup (saharacluster.com).

A few weeks ago Docker 1.13 was released and I was tasked with replicating a client's Kubernetes deployment on Swarm, more specifically testing running compose on Swarm.

And it was a dream!

All our apps were already dockerised and all I had to do was make a few modificatons to an existing compose file that I had used for testing before prior said deployment on Kubernetes.

And, with the ease with which I was able to expose our endpoints, manage volumes, handle networking, deploy and tear down the setup. I in all honesty see no reason to not use Swarm. No mission-critical feature, or incredibly convenient really nice to have feature in Kubernetes that I'm go

antirez

Redis 4.2 roadmap

1. Redis Cluster

• Speed up key -> hashslot association. Now makes RBB loading 4x slower when there are many small keys.
• Better multi data center story
• redis-trib C coded and moved into redis-cli
• Backup / Restore of Cluster
• Non blocking MIGRATE (also consider not using 2X memory)
• Faster resharding
• Bug fixing and stress testing to bring it to next level of maturity

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

daicham

image: java:8-jdk

stages:
  - build
  - test
  - deploy

before_script:
#  - echo `pwd` # debug
#  - echo "$CI_BUILD_NAME, $CI_BUILD_REF_NAME $CI_BUILD_STAGE" # debug

antirez

Random weighted element with Redis sorted sets

Imagine you have elements A, B and C with weights 1, 2 and 3. You compute the sum of the weights, which is 1+2+3 = 6

At this point you add all the elements into a sorted set using this algorithm:

SUM = ELEMENTS.TOTAL_WEIGHT // 6 in this case.
SCORE = 0

bmhatfield

# In order for gpg to find gpg-agent, gpg-agent must be running, and there must be an env
# variable pointing GPG to the gpg-agent socket. This little script, which must be sourced
# in your shell's init script (ie, .bash_profile, .zshrc, whatever), will either start
# gpg-agent or set up the GPG_AGENT_INFO variable if it's already running.

# Add the following to your shell init to set up gpg-agent automatically for every shell
if [ -f ~/.gnupg/.gpg-agent-info ] && [ -n "$(pgrep gpg-agent)" ]; then
    source ~/.gnupg/.gpg-agent-info
    export GPG_AGENT_INFO
else

Juraci

#/bin/bash

red=`tput setaf 1`
green=`tput setaf 2`
yellow=`tput setaf 3`
upstream=$1
branch=$2

# checks if there is a remote repo called upstream
check_upstream() {

mariozig

# Assume we are in your home directory
cd ~/

# Clone the repo from GitLab using the `--mirror` option
$ git clone --mirror git@your-gitlab-site.com:mario/my-repo.git

# Change into newly created repo directory
$ cd ~/my-repo.git

# Push to GitHub using the `--mirror` option.  The `--no-verify` option skips any hooks. 

hellerbarde

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