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  notify:
    if: always()
    name: Notify
    needs:
      - job1
      - job2
      - job11
      - job3
      - job4
    runs-on: ubuntu-latest

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{
  "level": "info",
  "ts": 1589785322.1196852,
  "caller": "logger/logger.go:36",
  "msg": "logger initialized",
  "context": {
    "request": {
      "id": "1234567",
      "source": "sourcevalue",
      "rpcservice": "servicev",

amitmahbubani

#!/usr/bin/env bash
# This script is meant to build and compile every protocolbuffer for each
# service declared in this repository (as defined by sub-directories).
# It compiles using docker containers based on Namely's protoc image
# seen here: https://github.com/namely/docker-protoc

set -e

REPOPATH=${REPOPATH-/opt/protolangs}
CURRENT_BRANCH=${CIRCLE_BRANCH-"branch-not-available"}

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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.

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function mapValues(obj, fn) {
  return Object.keys(obj).reduce((result, key) => {
    result[key] = fn(obj[key], key);
    return result;
  }, {});
}

function pick(obj, fn) {
  return Object.keys(obj).reduce((result, key) => {
    if (fn(obj[key])) {