Wednesday, May 31, 2017

Bit Shifting for a Shard ID in Ruby

As our database grew, we had to take a serious look at how can we could split it up by our clients, as most of them wanted to have their own data separate from the others anyway. A few months ago I found a great article from Pinterest, that describes how they sharded their MySQL database.

A sharded entity needs a UUID to uniquely identify the record across all shards. Most of the programming languages can generate a UUID easily, however, what was amazing to me, was that Pinterest generated its own unique ids by encapsulating three distinct numbers in one. Wait, what??! Read that article, it’s definitely worth your time.

While Pinterest encapsulated three numbers into one, we only needed two, a client_id and an entity_id. Our client_id would be a much smaller number than our entity_id, we wanted to reserve more bits for the latter.

It turns out, Ruby has many friendlier tools to deal with binary operations. Let's look at them!

What is the binary representation of the integer number 123?


Out of the 7 bits, you'll see that the 3rd one is turned off, all the others are turned on, giving us 64 + 32 + 16 + 8 + 2 +1 = 123. How can we get this binary representation in Ruby? It's super easy, just use the to_s(2) method to do it.

pry(main)> 123.to_s(2)

This is the exact same string representation as the one in the image above, where the third bit is turned off and represented with a zero.

I'd like to keep the client_id on the left side, but I'd like to reserve bits on the right side. For the sake of simplicity, I will keep this as a small number. Let's add 5 bits to the right-hand side of these bits by using the bitwise left shift operator.

pry(main)> (123 << 5).to_s(2)
=> "111101100000"

The original number, 123, is still represented on the left side, but 5 "0"s were added to the right-hand side. You get the numeric representation of this by leaving out the to_s(2) call at the end:

pry(main)> 123 << 5
=> 3936

This number can be converted back to binary:

pry(main)> 3936.to_s(2)
=> "111101100000"

On the left side I have the binary representation of 123, but how about those bits on the right side? What are those representing? Right now, those bits are all turned off, they will give you 0 ("00000".to_i(2) => 0). How can I store the number 3 on the right side? The bits should look like this:


The binary "|" will turn the two rightmost bits on:

pry(main)> (123 MM 5 | 3).to_s(2)
=> "111101100011"

Again, leaving out the to_s(2) will provide the number representation:

pry(main)> (123 << 5 | 3)
=> 3939

The storing part will work, but how can we get our two numbers back from this one combined number? Well, we have to split the bits and convert the binary representation to an integer.

Five bits were used on the right side to store our second number. We need to chop those off to get the number stored on the left side. The bitwise right shift (>>) will do just that:

pry(main)> (3939 >> 5).to_s(2)
=> "1111011"

The string "1111011" is our original 123 in a binary string format. We can convert that to an integer by using the to_i(2) String method:

pry(main)> (3939 >> 5).to_s(2).to_i(2)
=> 123

I right shifted the original number, 3939, converted it to a binary string and converted that to an Integer.

There are more efficient ways to do this by using a binary "&" (3939 >> 5) & 0b1111111 => 123 with the max value the bits can represent. That's what the Pinterest article had, but I found using the Ruby conversion methods a bit more friendly to those of us who are not dealing with binary data on a daily basis.

We have the number on the left side, but what about the number on the right side? When we convert the number representation (3939) to a binary string, we know that the five characters on the right side will represent the bits of our other number. Ruby String’s last(x) will do just that:

pry(main)> (3939 >> 0).to_s(2).last(5)
=> "00011"

Converting this binary String to Integer should be similar to what we've done before:

pry(main)> (3939 >> 0).to_s(2).last(5).to_i(2)
=> 3

Using the binary "&" with the max number the bits can store will do this conversion in one step: (3939 >> 0) & 0b11111 => 3. As a side note, the binary number can be represented as a hexadecimal value: (3939 >> 0) & 0x1F => 3. This is a lot shorter than a series of ones and zeros.

There is a limit of how large the numbers can be as you have a limit of bits to store those. The max number can be determined by flipping the available bits on. For an 7 bit number it's 64 + 32 + 16 + 8 + 4 + 2 + 1 = 127 or 2**x-1, where x is the number of bits. In our case it is 2**7-1 = 127.

We ended up using a 64-bit Integer for our shard_id, which is a BIGINT in MySQL. We store client_id in 22 bits giving us the maximum of 2**22-1 = 4_194_304 and 40 bits for the entity_id with 2**40-1 = 1_099_511_627_775 max value. The remaining two bits are "worth in gold, we can expand the number or store a third (albeit small) number in it.

Tuesday, April 25, 2017

Fireside Chat

When I saw a retweet from Jason Fried about available tickets to a fireside chat with him at Basecamp, I jumped on it. I figured if I can kill two birds with one stone, - meeting him in person and seeing their offices - it's a no-brainer. Company Culture was the topic of the conversation led by Aimee Groth, who visited Chicago to publicize her new book, Kingdom of Happiness about Zappos' culture.

Basecamp HQ

Basecamp HQ is as cool as you think it is. Very few desks, a couple of meeting rooms. It reminded me more of a train terminal with its large windows and limited furnishing than a real office. The office is centered around an auditorium, which is an effective PR and educational platform for the company.

I enjoyed looking at the walls covered with postcards from employees all over the world, but I especially liked David's H-1B approval notice from the USCIS from 2005. I laughed out loud when I noticed it, as I had to go through similar hassle myself, but mine is safely guarded with my documents at home.

Basecamp works in six weeks work schedule. Whatever the team can get down in six weeks, they will deliver it. The scope can change, but the six weeks schedule is hard set. This timeframe helps them delivering functionality, and since the company is working remotely, it worked out well for them.

They don't have managers who only manage people or projects, the teams are led by team leads. These team leads are developers as well, shipping code on a daily basis. Jason and his team realized that managers who do not code, grow away from the work. According to him, "professional (full time) managers forget to do the work".
At one point they've tried rotating team leads, but that did not work out, as the continuity was lost. I could see that: "I see this problem, but I won't deal with it, I'll leave it for the next person, who will take over." Basecamp is looking for people who are self-managed, however, Jason emphasized multiple times: "people like to be led". It's important to "rally the folks by clear goals and purpose".

Jason also talked about the Jeff Bezos investment in the company, which meant a small ownership stake in Basecamp. They did not need the money to survive, David and Jason felt having a person like Mr. Bezos is mutually beneficial to both parties. "Who would not like to have Jeff Bezos as an advisor in his or her company?!" They have not talked to Jeff Bezos for a while, but if they wanted to, they could just reach out to his secretary, set up a meeting, and Jeff would fly to Chicago for a meeting or dinner with them.

The best advice from Bezos - and according to Jason, this was worth the entire dividend they have paid for his investment - was: "invest in the things in your business, that won't change". Don't chase the shiny new things, stick to what will not change. For them, it's Basecamp. The company had 4-5 products that they sold a couple of years ago to focus on their main product, which is Basecamp.

Jason went into details why other products were divested (like HighRise, Backpack, Campfire). Maintaining the web, Android and iOS versions of their products resulted in 15 different projects. That led to insufficient focus for each platform for each product with the employees they had at the time. They could - of course - have hired other developers, but they intentionally wanted to stay small. They did not want to get richer, be the next billionaire, they were just as happy with what they had. This sounds strange, almost naive in the era of bloated startups that are bleeding money chasing to be the next Facebook.

I enjoyed the Q&A at the very end. Some interesting questions came up about the startup community in Chicago, about VCs in general. Jason kindly offered to stay as long as everybody's questions were answered. Really a courteous offer, considering it was after 8 pm on a Friday night.

Oh, yes, and one more thing: Basecamp has 130,000 paying customers. It's a remarkable achievement by a company that has never taken VC money, was profitable from the get-go, and created an exciting app in the "not-so-exciting" domain of project management.

Tuesday, March 28, 2017


As I was exploring how to make Golang even faster on AWS Lambda, I found a project that promised sub-millisecond execution time compared to my (already pretty good) ~60 millisecond. It used Python execution that ran the Go code in-process in contrast to my attempt, where I had to spawn a new process and execute the lambda code there. Very clever, no wonder that solution did not have the 60-millisecond penalty for running that code. However, in order to build the sample code for this AWS Lambda I had to use Docker.

I've heard about Docker years ago, understood what it's used for at a very high level, however, I have never really given it a serious try. I figured it was time. Boy, I was in for some pleasant surprise!

The project AWS Lambda Go from Eawsy used Docker to containerize their build environment on my laptop. What does that mean? Imagine having a build server running on your computer in seconds, where the version of the Go compiler, the Python environment is set by the author of the Dockerfile. I'd use a little build engine that takes in my code, runs its magic and a zip file comes out that I can run on Lambda. What?!

I wrote all these different tutorials about running MRI Ruby on AWS Lambda or interacting with a Postgres DB with Clojure and I had to set up all the prereqs in plain text: "you have to have Postgres running, and Clojure, and MRI Ruby". I provided all the different Makefile scripts to follow the examples. However, with Docker, I'd just provide a Dockerfile that sets up the environment in the future.

I believe containers are big and will be even bigger very soon.

I see more and more applications where the code describes the behavior and the container descriptor describes the environment.

They live side by side, clearly stating what virtual components the software needs to execute. Engineers can run the software with those containers locally, and the software can be deployed to the cloud with those images pre-built, with tight control over its execution context.

There are many resources to learn Docker. I started with reading the Docker in Action book and went further by reading the Docker in Practice book.

I created a Docker templates repository, where I collected ideas for different recipes. Do I need a Ruby worker with Redis and Postgres backend? I'll just run docker compose up with this docker_compose.yml file and I have an environment, where everything from the OS to the version of Redis and Postgres is predefined. If it works on my machine, it will work on yours, too.

There are many things I like about Docker as compared to Vagrant or other virtual machine solutions. The biggest thing for me is the low power Docker containers would need. Vagrant images would reserve 2 of your 4 cores and 8GB memory when Docker will only take from the host as much as it needs. If it's 32MB, that's it, if it's 1GB, it will take that much.

Docker is the future, and you will see more and more code repos with a Dockerfile in it.

Wednesday, February 8, 2017


The first time I heard about Golang was a few years back, when the great guys at Brad's Deals, our next door office neighbor organized and hosted the local Go meetup there. Then IO.js and Node.js war broke out and TJ Holowaychuck shifted from Node.js to Golang announcing the move in an open letter to the community.
I did not think much of the language, as its reputation was far from the beauty of a real functional language.

Fast forward a couple of years and I am giving Ruby a serious try on AWS Lambda. Ruby works there, however, it needs enough memory and 3000 ms (3 seconds) to do anything. We have to invoke some of them millions of times in a month and when we calculated the cost for it, the bill gets fairly large quickly.

I created a simple AWS Lambda with Ruby just to print the words "Hello, World!" with 128 MB memory. It took 5339 ms to execute it.

Ruby Hello World on AWS Lambda

Then one night I wrote a tiny Go program:

package main

import "fmt"

func main() {
  fmt.Println("Hello, World!")

I cross compiled (since I am working on OSX) with the command GOOS=linux GOARCH=amd64 go build to Linux, packaged it up with a Node.JS executor and ran it. I couldn't believe my eyes, it took only 68 ms to get the string "Hello, World!" back. 68 ms! And it was on a 128 MB memory instance. It was beautiful!

Go Hello World on AWS Lambda

Ruby would need four times the memory and it would still execute ~10 times slower than Go. That was the moment when I got hooked.

Go is a simple language. I am not saying it's easy to learn, it's subjective: it depends on your background, your experience. But it's far from the beauty of Haskell or Clojure. However, the team I am working with would have no trouble switching between Go and Ruby multiple times a day.

What kind of a language today does not have map or reduce functions?! Especially when functions are first-class citizens in the language. It turns out, I can write my own map function if I need to:

package collections

import (

func fmap(f func(int) string, numbers []int) []string {
  items := make([]string, len(numbers))

  for i, item := range numbers {
    items[i] = f(item)

  return items

func TestFMap(t *testing.T) {
  numbers := []int{1, 2, 3}
  result := fmap(func(item int) string { return strconv.Itoa(item) }, numbers)
  assert.Equal(t, []string{"1", "2", "3"}, result)

Writing map with recursion would be more elegant, but it's not as performant as using a slice with defined length that does not have to grow during the operation.


Go was created by some very smart people at Google, I wanted to understand their decision to keep a language this pure.
Google has a large amount of code in C and C++, however, those languages are far from modern concepts, like parallel execution and web programming to name a few. Those languages were created in the 60s and 80s, well before the era of multi-core processors and the Internet. Compiling a massive codebase in C++ can easily take hour(s), and while they were waiting for compilation, the idea of a fast compiling, simple, modern language idea was born. Go does not aim to be shiny and nice, no, its designers kept it:

  • to be simple and easy to learn
  • to compile fast
  • to run fast
  • to make parallel processing easy

Google hires massive number of fresh CS graduates each year with some C++ and Java programming experience, these engineers can feel right at home with Go, where the syntax and concept is similar to those languages.


Go comes with many built-in tools, like code formatting and benchmarking to name the few. In fact I set up Vim Go that leverages many of those tools for me. I can run, test code with only a couple of keystrokes.

Let's see how performant the procedure I wrote above is. But before I do that I'll introduce another function where the slice's length is not pre-determined at the beginning of the operation, this way it has to auto-scale internally.

func fmapAutoScale(f func(int) string, numbers []int) []string {
  // Initialize a slice with default length, it will auto-scale
  var items []string

  for _, item := range numbers {
    items = append(items, f(item))

  return items

The function is doing the same as fmap, similar test should verify the logic.

I added two benchmark tests to cover these functions:

// Run benchmark with this command
// go test -v fmap_test.go -run="none" -benchtime="3s" -bench="BenchmarkFmap"
// -benchmem
func BenchmarkFmap(b *testing.B) {

  numbers := []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
  for i := 0; i < b.N; i++ {
    fmap(func(item int) string { return strconv.Itoa(item)  }, numbers)

func BenchmarkFmapAutoScale(b *testing.B) {

  numbers := []int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
  for i := 0; i < b.N; i++ {
    fmapAutoScale(func(item int) string { return strconv.Itoa(item)  },

When I ran the benchmark tests, this is the result I received:

 % go test -v fmap_test.go -run="none" -benchtime="3s" -bench="BenchmarkFmap"
 BenchmarkFmap-4   ‡ 10000000 | 485 ns/op | 172 B/op | 11 allocs/op
 BenchmarkFmapAS-4 ‡ 5000000  | 851 ns/op | 508 B/op | 15 allocs/op
 ok   command-line-arguments  10.476s

The first function, where I set the slice size to the exact size is more performant than the second one, where I just initialize the slice and let it autoscale. The ns/op displays the execution length per operation in nanoseconds. The B/op output describes the bytes it uses per operation. The last column describes how many memory allocations it uses per operation. The difference is insignificant, but you can see how this can become very useful as you try writing performant code.


Go is getting popular. In fact, very popular. It was TIOBE's "Language of the Year" gaining 2.16% in one year. I am sure you'll be seeing articles about Go more and more. Check it out if you haven't done it yet, as the chance of finding a project or job that uses Go is increasing every day.

Sunday, November 13, 2016

Recursion Done Right - Haskell Influenced Recursion in Ruby

Learning Haskell has influenced the way I think about code. I wrote about currying before in various languages, but Haskell taught me a bit about how to do recursion properly.

Although fast paced, I really like the examples in the book Learn you a Little Haskell for Great Good. As one chapter talks about recursion and higher order functions, I was amazed by the simplicity of the code that lets you do basic list operations.

Here is how one could find the maximum of a list:

maximum' :: (Ord a) => [a] -> a
maximum' [] = error "maximum of an empty list"
maximum' (x:xs) = max x (maximum' xs)

There is already a maximum function in Haskell's core library, this example just shows you what you need to do to implement it yourself.

I am not going into details about the type declaration, but there are a couple of points I'd like to talk about. The pattern matching in the second line checks for the case, where the collection is an empty array. When that happens, an exception is thrown. The last line does pattern matching as well, it grabs the head and the tail of the list and saves it into the x and xs variables. Then it uses the max functions to figure out which number is greater: x or the recurred result of maximum' with the tail of the list. This is a prime example of declarative code, its simplicity is striking and the fact that I don’t have to know how max works with the recursion makes it a joy to read.

Let’s look at another example. Here is how you could implement map in Haskell yourself:

map' :: (a -> b) -> [a] -> [b]
map' f [] = []
map' f (x:xs) = f x : map' f xs

Similarly, the edge-case is handled first. The last line has the logic, function f is applied to the head of the list, the result is concatenated with the recurred result of map' of f function and the tail of the list.

All right, let’s see how we could express this logic in Ruby.

Here is my first attempt:

module Collections
  def self.maximum(collection)
    head = collection.first
    tail = collection[1..-1]

    return 0 unless tail
    max head, (maximum tail)

  def self.max(a, b)
    a > b ? a : b
  private_class_method :max

RSpec.describe 'Recursion done right' do
   context 'maximum' do
     it 'returns an empty array as max of empty list' do
       expect(Collections.maximum([])).to eq(0)

    it 'returns the maximum of a list' do
      expect(Collections.maximum([1,3,2])).to eq(3)

I did not find a max method in Ruby, I added that as a private class method. This is still pretty easy to read, but a bit more verbose than what I'd like it to be. I wanted to find the (x:xs) head-tail (car-cdr for you LiSP folks) equivalent in Ruby, I knew that will be key to make it a more succinct solution. This is it: (head, *tail) = collection. I also had to change the guard to quit from the recursion to look for an empty array, as the splat operator will provide that.

Here is my revised solution:

module Collections
  def self.maximum(collection)
    (head, *tail) = collection

    return 0 if tail.empty?
    max head, (maximum tail)

This is better, but the destructuring can take place in the argument:

module Collections
  def self.maximum((head, *tail))
    return 0 if tail.empty?
    max head, (maximum tail)

This is pretty darn close to the solution in Haskell.
Now let’s look at the map function.

These specs describe the behavior:

context 'map' do
  it 'mapping [] with (*3) gives []' do
    expect(>(x){ x*3 }, [])).to be_empty
  it 'mapping [1,2,3] with (*3) gives [1,6,9]' do
    expect(>(x){ x*3 }, [1,2,3])).to eq([3,6,9])

My implementation of map takes a lambda with one argument, which multiplies that one argument by three, and the second argument is the collection of items the map function will operate on.

This is my implementation for it:

module Collections
  def, (head, *tail))
    return [] unless head

    [f.(head)] + map(f, tail)

The key to make it concise is the destructuring the collection argument into head and tail. The guard statement makes sure the recursion will quit once there is no item in the head. The bulk of the logic is the last line of the method: the lambda is applied to the head, it's converted into an array and that value is concatenated with the result of the recurred result of the lambda and the rest of the collection.

In our case, the following calculation takes place:

map (*3) [1,2,3]
[(3*1)] + map (*3) [2,3]
[(3*1)] + [(3*2)] + map (*3) [3]
[(3*1)] + [(3*2)] + [(3*3)]
[3] + [6] + [9]

Haskell takes pride in how easily it implements the quicksort algorithm. Let’s see how it’s done there:

quicksort :: (Ord a) => [a] -> [a]
quicksort [] = []
quicksort (x:xs) =
    let smallerSorted = quicksort [a | a <- xs, a <= x]
        biggerSorted = quicksort [a | a <- xs, a > x]
    in  smallerSorted ++ [x] ++ biggerSorted

I don’t blame you if this seems to be a bit more cryptic than you wanted to be. It takes a little practice to read what is really going on here. I'll explain it, as it will help our own Ruby implementation. The first line is the type declaration, ignore that for now. The second line is the guard, sorting an empty array will give you back an empty array. The meat of the logic begins on the third line. The collection argument is destructured into head and tail, just like I've been doing in the examples above. Based on the head value, we are filtering the elements into smaller-equal, and bigger parts. We do all this recursively until the list is exhausted. Right before the result is returned, the three items, the smaller sorted, the head value and the bigger sorted elements are combined into one collection.

Let’s see how this is done in Ruby. Here are the specs I prepared to prove the logic:

context 'quicksort' do
  it 'returns an empty list for empty list' do
    expect(Collections.quicksort([])).to eq([])
  it 'sorts a list of items' do
    expect(Collections.quicksort([2,5,3])).to eq([2,3,5])

Here is how I'd like the code to be:

def self.quicksort((head, *tail))
  return [] unless head

  smaller_sorted = quicksort(Collections.filter(->(x) { x <= head }, tail))
  bigger_sorted = quicksort(Collections.filter(->(x) { x > head }, tail))
  smaller_sorted + [head] + bigger_sorted

This logic is very close to the Haskell example, but unfortunately, I don't have the filter function just yet. (Ruby standard library offers the select method on enumerables, but let's keep these examples free from all that.) filter takes a lambda as its predicate function, and a collection it needs to operate on. This spec proves out our logic:

context 'filter' do
  specify 'filter (>2) [] returns an empty list' do
    expect(Collections.filter(->(x){ x > 2 }, [])).to be_empty
  specify 'filter (>2) [1,3,5] returns [3,5]' do
    expect(Collections.filter(->(x){ x > 2 }, [1,3,5])).to eq([3,5])

And the implementation is similar what you've seen before:

def self.filter(f, (head, *tail))
  return [] unless head

  if f.(head)
    [head] + filter(f, tail)
    filter(f, tail)

And now, when you run the entire spec, the quicksort implementation just magically works.

specs executed

Studying Haskell taught me a few things about recursion. The head and tail concept is essential to make the code simple and neat. Without that it would have been a lot more noisier. Whenever I used recursion before, I always felt I needed an accumulator. I wanted something I could jump to and investigate when something went wrong. I would have written the filter function like this before:

def self.filter(f, (head, *tail), accumulator=[])
  return accumulator unless head

  accumulator << head if f.(head)

  filter(f, tail, accumulator)

Although this works, adding the accumulator with a default argument to the list just makes this code a lot noisier, but I do like not having conditional branches in it, it's just easier to reason about this code.

You can review the examples in this gist.

Based on what you read here, try implementing replicate, take, reverse, repeat and zip functions yourself. In case you need directions, check out this gist to see how I did it.

Friday, October 21, 2016

Vim Katas

It was about 5 years ago when I watched Jim Weirich solving the Roman Numeral kata at an SCNA conference in Chicago. I was amazed by how he mastered his editor of choice: Emacs. His fingers were flying on the keyboard and he wrangled the code I have rarely seen anybody before that.


I started using Vim in 2008 or 2009, but I never really invested the time to be good at it. I read the man pages, I went through Vim tutor, but I never really picked up or started using most of the advanced features.

I remember how great I felt when I reset my Caps Lock key to function as Ctrl key. The power of hitting <Ctrl+c> with my pinky and index finger just to trigger <Esc> without reaching up to the top left on the keyboard made me feel I've just found kryptonite.

I've had the book Practical Vim for some time, but I never really practiced the examples in it. I looked at them here and there, tried them out, but I've never made a habit of practicing those daily. Then one day I got sick and tired of my inabilities, I started a new markdown document where I jotted down the first exercise and the project of Vim Katas was born. Every time I commuted to work, I started with the first one and practiced all of them. Once I got to the end of it, I read the book further and added new exercises to it.

I might have covered 60% of the book by now, but when I bump into a repeatable task and I leverage a macro for it, it always puts a smile on my face.

Using Vim reminds me of learning to play a musical instrument. It takes time and effort to be good at it, and the keystrokes have to be at your fingertips. Once you slow down and think about it, the effectiveness of the text-based editor fades away, you would be more efficient by using visual editor instead (like Sublime Text, IntelliJ or Visual Studio).

Vim (and I am sure many other tools) has this huge wall that people have to climb first to appreciate it. Clone that repo, open the first markdown file in Vim, and start practicing!

Monday, June 6, 2016

Using Ruby with ActiveRecord in AWS Lambda

In my previous blog post I described how you can run MRI Ruby on AWS Lambda. In this article I'll guide you through adding gems to the project: first faker, and then the mysql2 gem with activerecord, and finally we will have the Ruby code talk to an RDS instance, all this through an AWS Lambda.

I recorded all my changes in this project, feel free to jump in where you want, you'll find commit points after each section.

1. Add faker to the project

You can pick up the changes from the previous blog post here. Let's start by editing our Ruby app, add a Gemfile to it in the hello_ruby directory:

source ''

gem 'faker'

Run BUNDLE_IGNORE_CONFIG=1 bundle install --path vendor in that directory. The --path vendor argument is important, as we have to package all the files in the vendor directory. Make sure the BUNDLED WITH part of your Gemfile.lock is not there as that can cause you some pain when you deploy your code to AWS Lambda.

Edit the lib/hello.rb file like this:

#!/usr/bin/env ruby

require 'faker'

puts "Hello - '#{}' from Ruby!"

We required the faker gem and used it to generate a fake name. Run the app in the terminal with bundle exec ruby lib/hello.rb command.

Hello - 'Jamar Gutmann II' from Ruby!

You will get a different name between the single quotes, but that's the point, faker generates a random name for us.

Commit point

2. Use faker with Traveling Ruby

The run target in the Makefile will have to copy all vendorized gems, plus it needs to configure the app to run with the correct bundler settings. This step is heavily influenced by how Traveling Ruby packages gems for deployment, please review their tutorial as reference.

Add a bundler_config template file to the resources directory with this content:

BUNDLE_WITHOUT: development:test

Change the resources/ file to set the Gemfile’s location:

set -e

# Figure out where this script is located.
SELFDIR="`dirname \"$0\"`"
SELFDIR="`cd \"$SELFDIR\" && pwd`"

# Tell Bundler where the Gemfile and gems are.
export BUNDLE_GEMFILE="$SELFDIR/lib/vendor/Gemfile"

# Run the actual app using the bundled Ruby interpreter, with Bundler activated.
exec "$SELFDIR/lib/ruby/bin/ruby" -rbundler/setup "$SELFDIR/lib/app/hello.rb"

Modify the Makefile's run target with the following changes:


run: ## Runs the code locally
    @echo 'Run the app locally'
    @echo '-------------------'
    @rm -fr $(OSXDIR)
    @mkdir -p $(OSXDIR)/lib/ruby
    @tar -xzf resources/traveling-ruby-20150715-2.2.2-osx.tar.gz -C $(OSXDIR)/lib/ruby
    @mkdir $(OSXDIR)/lib/app
    @cp hello_ruby/lib/hello.rb $(OSXDIR)/lib/app/hello.rb
    @cp -pR hello_ruby/vendor $(OSXDIR)/lib/
    @rm -f $(OSXDIR)/lib/vendor/*/*/cache/*
    @mkdir -p $(OSXDIR)/lib/vendor/.bundle
    @cp resources/bundler-config $(OSXDIR)/lib/vendor/.bundle/config
    @cp hello_ruby/Gemfile $(OSXDIR)/lib/vendor/
    @cp hello_ruby/Gemfile.lock $(OSXDIR)/lib/vendor/
    @cp resources/ $(OSXDIR)/hello
    @chmod +x $(OSXDIR)/hello
    @cd $(OSXDIR) && ./hello


Run the target with make run and you should see something similar to this in the terminal:

$: make run
Run the app locally
Hello - 'Kelly Huel' from Ruby!

We've just run the app with Traveling Ruby's Ruby interpreter, and we used the faker gem's functionality as well!

Commit point

3. Deploy the app with faker to AWS Lambda

In order to run your app in AWS Lambda, you only need to change the package target in your Makefile, everything else, the delete, create, invoke targets should remain the same. Change the file like this:


package: ## Package the code for AWS Lambda
    @echo 'Package the app for deploy'
    @echo '--------------------------'
    @rm -fr $(LAMBDADIR)
    @rm -fr deploy
    @mkdir -p $(LAMBDADIR)/lib/ruby
    @tar -xzf resources/traveling-ruby-20150715-2.2.2-linux-x86_64.tar.gz -C $(LAMBDADIR)/lib/ruby
    @mkdir $(LAMBDADIR)/lib/app
    @cp hello_ruby/lib/hello.rb $(LAMBDADIR)/lib/app/hello.rb
    @cp -pR hello_ruby/vendor $(LAMBDADIR)/lib/
    @rm -f $(LAMBDADIR)/lib/vendor/*/*/cache/*
    @mkdir -p $(LAMBDADIR)/lib/vendor/.bundle
    @cp resources/bundler-config $(LAMBDADIR)/lib/vendor/.bundle/config
    @cp hello_ruby/Gemfile $(LAMBDADIR)/lib/vendor/
    @cp hello_ruby/Gemfile.lock $(LAMBDADIR)/lib/vendor/
    @cp resources/ $(LAMBDADIR)/hello
    @chmod +x $(LAMBDADIR)/hello
    @cp resources/index.js $(LAMBDADIR)/
    @cd $(LAMBDADIR) && zip -r hello index.js lib/
    @mkdir deploy
    cd $(LAMBDADIR) && mv ../deploy/
    @echo '... Done.


The added rows are very similar to the ones we had to add to run the app locally with Traveling Ruby. Delete the lambda function and recreate it by using the Makefile. When you invoke it, your should see something like this:

START RequestId: 3f6ae8f5-23c1-11e6-9acc-0f50ffa39e9b Version: $LATEST
    Hello - 'Mrs. Lelah Bradtke' from Ruby!

END RequestId: 3f6ae8f5-23c1-11e6-9acc-0f50ffa39e9b
REPORT RequestId: 3f6ae8f5-23c1-11e6-9acc-0f50ffa39e9b
       Duration: 3425.01 ms
       Billed Duration: 3500 ms
       Memory Size: 512 MB
       Max Memory Used: 65 MB

The Hello - 'xyz' from Ruby! string contains the Faker gem generated name. You can also invoke the Lambda function through the AWS Management Console, you should see something similar to this in the Log output section:


Commit point

4. Publish a newer version to AWS Lambda

Dropping and recreating the Lambda function works, but it's not the most effective solution. AWS allows you to update your function which we'll do with this new target in the Makefile:


publish: package ## Deploys the latest version to AWS
        aws lambda update-function-code \
                --function-name HelloFromRuby \
                --zip-file fileb://./deploy/


This target will let you update the function code. It also calls the package target to make sure your latest changes will be deployed to AWS.

Commit point

5. Create a new RDS database with one table

Add this script to your Makefile, it will create a minimal RDS instance for you, you can drop that instance, connect to the DB and drop/create the database with some seed data in it.



create-rds-instance: ## Creates an RDS MySQL DB instance
    aws rds create-db-instance \
        --db-instance-identifier MyInstance01 \
        --db-instance-class db.t1.micro \
        --engine mysql \
        --allocated-storage 10 \
        --master-username master \
        --master-user-password $(DBPASSWD)

delete-rds-instance: ## Deletes an RDS MySQL DB instance
    aws rds delete-db-instance \
        --db-instance-identifier MyInstance01 \

db-connect: ## Connects to the RDS instance
    mysql --user=master --password=$(DBPASSWD) --host

create-db: ## Creates a DB with a table and records
    @echo "Dropping  and creating database"
    @echo "-------------------------------"
    @mysql -u master --password='$(DBPASSWD)' --host -e "DROP DATABASE IF EXISTS $(DBNAME)" > /dev/null 2>&1
    @mysql -u master --password='$(DBPASSWD)' --host -e "CREATE DATABASE $(DBNAME)" > /dev/null 2>&1
    @mysql -u master --password='$(DBPASSWD)' --host $(DBNAME) < resources/schema.sql > /dev/null 2>&1
    @mysql -u master --password='$(DBPASSWD)' --host $(DBNAME) < resources/seed.sql > /dev/null 2>&1
    @echo "... Done"


Create the RDS instance first, AWS will need some time to initialize it. Allow incoming connections to it by adjusting the "Inbound" traffic through your own IP under your Security Group:


You can connect to the RDS instance through the mysql console using the db-connect target. You'll need to adjust the hostname to yours. Once that works out, use the create-db target to create a DB with a table and add two records to it. If all goes well, this is what you should see when you query the users table in the MySQL console:

mysql> SELECT * FROM users;
| id | login  | email            | firstname | lastname |
|  1 | jsmith | | John      | Smith    |
|  2 | bjones | | Bob       | Jones    |
2 rows in set (0.04 sec)

Commit point

6. Connect to MySQL with Rails' ActiveRecord

Add the mysql2 and active_record gems to the Ruby app's Gemfile:

gem 'activerecord'
gem 'mysql2', '0.3.18'

We need to use the 0.3.18 version of the mysql2 gem, as that comes packaged with Traveling Ruby. Run bundle install to get the new gems via Bundler.

Modify the lib/hello.rb file to have this:

#!/usr/bin/env ruby

require 'faker'
require 'active_record'

  :adapter  => "mysql2",
  :host     => "", # use your instance name
  :username => "master",
  :password => "Kew2401Sd",
  :database => "awslambdaruby"

class User < ActiveRecord::Base

puts "Number of users: #{User.count}"
puts "First user: #{User.first.firstname} #{User.first.lastname}"
puts "Hello - '#{}' from Ruby!"

You need to adjust the :host value to your RDS instance host name as mine won't work for you. You'll know that everything is set up properly when you see this in the terminal:

$: bundle exec ruby lib/hello.rb
Number of users: 2
First user: John Smith
Hello - 'Miss Darrick Powlowski' from Ruby!

Commit point

7. Use Traveling Ruby’s packaged mysql gem

You need to download the Traveling Ruby packaged mysql2 gem from their S3 bucket. Let’s put it into our resources directory.

Modify the package target like this:


package: ## Packages the code for AWS Lambda
    @echo 'Package the app for deploy'
    @echo '--------------------------'
    @rm -fr $(LAMBDADIR)
    @rm -fr deploy
    @mkdir -p $(LAMBDADIR)/lib/ruby
    @tar -xzf resources/traveling-ruby-20150715-2.2.2-linux-x86_64.tar.gz -C $(LAMBDADIR)/lib/ruby
    @mkdir $(LAMBDADIR)/lib/app
    @cp hello_ruby/lib/hello.rb $(LAMBDADIR)/lib/app/hello.rb
    @cp -pR hello_ruby/vendor $(LAMBDADIR)/lib/
    @rm -fr $(LAMBDADIR)/lib/vendor/ruby/2.2.0/extensions
    @tar -xzf resources/mysql2-0.3.18-linux.tar.gz -C $(LAMBDADIR)/lib/vendor/ruby/
    @rm -f $(LAMBDADIR)/lib/vendor/*/*/cache/*
    @mkdir -p $(LAMBDADIR)/lib/vendor/.bundle
    @cp resources/bundler-config $(LAMBDADIR)/lib/vendor/.bundle/config
    @cp hello_ruby/Gemfile $(LAMBDADIR)/lib/vendor/
    @cp hello_ruby/Gemfile.lock $(LAMBDADIR)/lib/vendor/
    @cp resources/ $(LAMBDADIR)/hello
    @chmod +x $(LAMBDADIR)/hello
    @cp resources/index.js $(LAMBDADIR)/
    @cd $(LAMBDADIR) && zip -r hello index.js lib/ > /dev/null
    @mkdir deploy
    @cd $(LAMBDADIR) && mv ../deploy/
    @echo '... Done.'


We need to replace the content of the 2.0.0/extensions directory with the Traveling Ruby's Linux version, as the one copied there is OSX specific.

AWS Lambda has an IP address other than your IP. In order to make it easy for you now, (and don't do this anywhere else), I'd suggest making your AWS Instance available without IP restriction. Do this only temporarily, to test things out, remove this Inbound rule once you've seen your Lamba working. You can specify the VPC your Lambda has access to, but the topic of AWS Lambda security would need another blog post just in itself.

This is how I opened up my RDS instance for any IP out there:


If everything is configured properly, you should see something like this in your terminal when you call the Lambda function with the make invoke command:

% make invoke
rm -fr tmp && mkdir tmp
aws lambda invoke \
        --invocation-type RequestResponse \
        --function-name HelloFromRuby \
        --log-type Tail \
        --region us-east-1 \
        --payload '{"name":"John Adam Smith"}' \
        tmp/outfile.txt \
        | jq -r '.LogResult' | base64 -D
START RequestId: 8444ede9-26d8-11e6-954c-fbf57aab89fb Version: $LATEST
2016-05-31T02:36:50.587Z        8444ede9-26d8-11e6-954c-fbf57aab89fb
Number of users: 2
First user: John Smith
Hello - 'Jeanne Hansen' from Ruby!

END RequestId: 8444ede9-26d8-11e6-954c-fbf57aab89fb
REPORT RequestId: 8444ede9-26d8-11e6-954c-fbf57aab89fb
Duration: 5072.62 ms
Billed Duration: 5100 ms
Memory Size: 512 MB
Max Memory Used: 53 MB

Sweet! The Ruby code in this AWS Lambda function reports back 2 users and correctly displays the first record.

Commit point

Being able to use MRI Ruby with gems opens up a ton possibilities for us (and I hope for you as well). AWS Lambdas are neat little workers that can scale up and down very well. It's much easier to launch a 1000 AWS Lambdas at the same time than running Ruby processes with resque or sidekiq on worker boxes.