For those times you don’t want to eval…

I wanted to make some advanced logic available, easily configurable via a database, in a couple apps that I’ve been working on recently.

Honestly, I could have just stored the code in the database and eval’d it — but no, I don’t want to take the risk of arbitrarily executing code. I could have done some gymnastics like running it in a network-less container with defined inputs and outputs. I could have made the configuration more capable. What I decided to do in the end, though, was to write an extremely compact domain-specific language (DSL).

To write this DSL, I chose a Lisp-style syntax due to its dead-simple parsing. The basic idea is to parse the string for tokens, generate an abstract syntax tree (AST), then just recurse through the AST and run whatever code is required.

In one example, I wanted to have an extendible SQL WHERE clause, with a very limited set of operators — AND, OR, LIKE, =.

(and (like (attr "") (str "%Keita%")) (= (attr " (str "Tokyo")))

This example will generate the SQL WHERE clause:

(( LIKE '%Keita%') AND ( = 'Tokyo'))

Here’s pseudocode for how I write the parser / interpreter for this:

  "and": (left, right) => { return f"(({left}) AND ({right}))" }
  "or":  (left, right) => { return f"(({left}) OR ({right}))" }
  "like":(left, right) => { return f"(({left}) LIKE ({right}))" }
  "=": (left, right) => { return f"(({left}) = ({right}))" }
  "str": (str) => { return escape_sql(str) }
  "attr": (str) => { return escape_sql_for_attr_name(str) }

def parse_ast(code):
  # parse the "code" string into nested arrays:
  # "(1 (2 3))" becomes ["1", ["2", "3"]]

def execute_node(ast):
  cmd = ast[0]
  argv = ast[1:]
  resolved_argv = [ execute_node(x) for x in argv ]
  return COMMANDS[cmd](*resolved_argv)

def execute(code):
  ast = parse_ast(code)

As you can see, this is a very simple example that takes the DSL and transforms it in to a SQL string. If you wanted to do parameterized queries, you might return a tuple with the string as the first element and a map of parameters for the second, for example.

The ability to map the language so closely to the AST, and being able to evaluate the AST just by recursion, makes this implementation simple and easy to write, easy to extend, and easy to embed in existing applications. While I probably won’t be switching to writing Common Lisp full time (for practical reasons), I definitely do get the appeal of the language itself.

This tool isn’t something I use all the time. It’s probably something that should be used very sparingly, and in specific circumstances. That said, it’s a good tool in my toolbox for those times for when I want to have on-the-fly customizable logic without the security concerns of using eval, or the complexity of creating a sandboxed environment for potentially unsafe code.

Last note: while this solution may be more secure than eval, it is definitely not 100% secure. In the simple example above, we do escape strings so SQL injection shouldn’t be a problem, but it doesn’t check if the column defined by the attr function is valid, or if the user is allowed to query information based on that column or not (although something like that would be possible). I would not use something like this to process completely untrusted input.

AWS English

My Brief Thoughts on the AWS Kinesis Outage

There have been multiple analyses about the recent (2020/11/25) outage of AWS Kinesis and its cascading failure mode, taking a chunk of AWS services with it — including seemingly unrelated Cognito — due to dependencies hidden to the user. If you haven’t read the official postmortem statement by AWS yet, go read it now.

There are an infinite amount of arguments that can made about cascading failure; I’m not here to talk about that today. I’m here to talk about a time a few years ago I was evaluating a few systems to do event logging. Naturally, Kinesis was in consideration, and our team interviewed an AWS Solution Architect about potential design patterns we could implement, what problems they would solve, what hiccups we may encounter on the way, et cetera.

At the time, I didn’t think much of it, but in hindsight it should have been a red flag.

ME: “So, what happens when Kinesis goes down? What kind of recovery processes do you think we need to have in place?”

SA: “Don’t worry about that, Kinesis doesn’t go down.”

The reason I didn’t think much of it at that time was that our workload would have been relatively trivial for Kinesis, and I mentally translated the reply to “don’t worry about Kinesis going down for this particular use case”.

We decided to not go with Kinesis for other reasons (I believe we went with Fluentd).

Maybe my mental translation was correct? Maybe this SA had this image of Kinesis as a system that was impervious to fault? Maybe it was representative of larger problem inside AWS of people who overestimated the reliability of Kinesis? I don’t know. This is just a single data point — it’s not even that strong. A vague memory from “a few years ago”? I’d immediately dismiss it if I heard it.

The point of this post is not to disparage this SA, nor to disparage the Kinesis system as a whole (it is extremely reliable!), but to serve as a reminder (mostly to myself) that one should be immediately suspicious when someone says “never” or “always”.


Venturing in to the realm of Hackintosh-ing

Like you, I’ve been finding myself working from home more often than not. These days, I probably go to an office once a month. I have a 16 inch MacBook Pro, but using it in clamshell mode, all the time, connected to a 4K monitor was… not ideal. It would often thermally throttle way down (often, it would be really sluggish — I wondered, how fast is this running? 800MHz. 90C. Fans at 100%.)

The 16″ MacBook Pro was a great machine for when I’d go to an office 3-5 days a week, but it just doesn’t make sense when it’s essentially used as a desktop.

This is where I thought, why don’t I just get a desktop Mac, then? That gives me a few choices: iMac, iMac Pro, Mac Pro, Mac Mini.

iMac (Pro): Pretty good performance, but a little expensive for my target price. Also, I had just gotten a new 4K monitor, and I didn’t want to get rid of it so soon.

Mac Pro: Way too expensive.

Mac Mini: Probably pretty similar in performance and thermal characteristics to my MacBook Pro.

I didn’t want to spend too much on it — especially with the Apple Silicon Macs coming out, meaning Intel support is on its way out, limiting the longevity of whatever I’d be buying. I’ll probably get a 2nd or 3rd generation Apple Silicon Mac, so what I wanted is something high performance that makes sense to bridge the gap of a couple years.

This led me down the rabbit hole of building a Hackintosh. My target price was “less than $2,000” (the price also is due to tax reasons — I thought it would be more fun to build a PC than it would be to calculate depreciation over 4 years). r/hackintosh on Reddit was a huge help — it was an invaluable resource when picking parts. I would look through everyone’s success stories and pick similar (or identical) parts. Here’s my entry (from when it had Catalina — I have since upgraded it to Big Sur).

Using some parts from my previous PC, the new parts came out to about $1,500. Not bad for a 10-core i9-10900k with 32GB RAM and a 1TB SSD.

I’ve never done water cooling before, and it sounded pretty cool, so I got an all-in-one unit that was really easy to install. Building a custom water cooling loop sounds fun, but really time and tool intensive — not something I have a lot of here in Tokyo. Maybe next time.

All in all, doing this Hackintosh was a fun little project. There are a lot of excellent resources that hold your hand through the whole process. If you’re interested in trying it out for yourself, I recommend reading the OpenCore Install Guide and looking through the aforementioned Reddit community.

When I started out to write this blog post, I installed Catalina 10.15.7 with OpenCore 0.6.2. Since then, I’ve upgraded to Big Sur 11.0.1 with OpenCore 0.6.3. I’ve only done one major upgrade, but it was relatively smooth.

One word of warning, though: Hackintosh-ing is definitely not for the faint of heart, or someone who is not prepared to spend hours debugging some issue that involves reading a bunch of white text on a black background. I would not recommend this to anyone who doesn’t like fiddling with computers. I had a bunch of small, strange issues (Ethernet instability, hardware video decoding, to name a couple) that required multiple reboots and trial-and-error with the config.plist configuration file.

Thanks for reading! If you have any questions, don’t hesitate to leave a comment or send me a Tweet.

AWS English WordPress

WordPress on AWS Lambda (EFS Edition)

I previously wrote a post about running WordPress on AWS Lambda, but it was before EFS support was announced (EFS is a managed network file system AWS provides). Being able to use EFS completely changes the way WordPress works in Lambda (for the better!), so I felt it warranted a new blog post.

In addition, this time I’m using Terraform instead of SAM. This matches the existing infrastructure-as-code setup I use when I deploy infrastructure for clients. Here’s the Terraform module (source code).


It works. It’s OK. Check it out, it’s running here. It’s not the best, but it isn’t bad, either. The biggest performance bottleneck is the EFS filesystem, and there’s no getting around that. PHP is serving static assets bundled with WordPress as well, which adds to some latency (in this configuration, CloudFront is caching most of these files, however). Tuning opcache to cache files in memory longer helped a lot.

Because EFS is synchronized across all the instances of Lambda, online updates, installs, and uploads work as expected.

What You’ll Need

In this setup, Lambda is only used for running PHP — installing the initial WordPress files is done on an EC2 instance that has the EFS volume mounted. This is a list of what you’ll need.

  1. An AWS account.
  2. A VPC with Internet access through a NAT gateway or instance (comparison). This is important because EFS connectivity requires Lambda to be set up in a VPC, but it won’t have Internet access by default.
  3. Terraform (the module uses v0.12 syntax, so you’ll need to use v0.12.)
  4. A MySQL database (I’m using MySQL on RDS using the smallest instance available)
  5. An EC2 instance to perform the initial setup and install of WordPress.

For a list of the resources that Terraform will provision, take a look at the Resources page here.


These steps assume you’re running this Terraform module standalone — if you want to run it in the context of an existing Terraform setup, prepare to adjust accordingly.

If you’re following this step-by-step, be sure to choose the us-west-2 region. Lambda Layer that I’m using for this is only published in the us-west-2 region. I’m working on getting the layer published in other regions, but in the meantime, use my fork of the php-lambda-layer to create your own in the region of your choosing.

1. Start the EC2 instance.

(If it isn’t already running)

I’m using a t3a.nano instance. Install the amazon-efs-utils package to get ready for mounting the EFS volume.

Also, while you’re in the console, note down the ID of a Security Group that allows access to RDS and the IDs of the private subnets to launch Lambda in.

2. Get Terraform up and running.
$ git clone
$ cd ./terraform-aws-wordpress-on-lambda-efs

Create a file called, and put the following contents in to it:

# An array of the Security Group IDs you listed in step 1.
security_group_ids = ["sg-XXX"]

# An array of the Subnet IDs you listed in step 1.
subnet_ids = ["subnet-XXX", "subnet-XXX", "subnet-XXX"]

If you want to use a custom domain name (instead of the default randomly-generated CloudFront domain name), set the acm_certificate_arn and domain_name variables as well.

Now, you’re ready to create the resources.

$ terraform apply

If you’re asked for your AWS credentials, Ctrl-C and try setting the authentication information via environment variables. I manage a lot of AWS accounts, so I use the AWS_PROFILE environment variable.

Terraform will ask you if you want to go ahead with the apply or not — look over the changes (the initial apply should not have any modifications or deletions), then respond yes.

When the apply has finished, you should see some outputs. If you don’t (or you already closed the window), you can always run terraform output. Keep this window open, you’ll need it in the next step.

3. Mount EFS on the EC2 instance.

First, we need to give the EC2 instance access to the EFS filesystem. Terraform created a security group for us (it’s in the efs_security_group_id output), so attach that to your EC2 instance.

Log in to your EC2 server, then mount the EFS filesystem (replace fs-XXXXX with the value of the efs_file_system_id output):

$ sudo -s
# mkdir /mnt/efs
# mount -t efs fs-XXXXX:/ /mnt/efs

If you’re having trouble mounting the filesystem, double check the security groups and take a look at the User Guide.

4. Install WordPress.

Now that the filesystem is mounted, we can finally proceed to install WordPress. Terraform automatically created a directory in the EFS filesystem (/mnt/efs/roots/wp-lambda-$RANDOM_STRING), so cd to there first. Download the latest version of WordPress, then extract the files there.

Now, you can go ahead with the famous five-minute install like you would with any other WordPress site! If you didn’t set a custom domain name, your site should be accessible at the domain name outputted at cloudfront_distribution_domain_name. If you did set a custom domain, then set a CNAME or alias to the CloudFront distribution domain name, then you should be able to access the site there.

Where to go from here

Here are some ideas for performance improvements that I haven’t tried, but should have some potential.

  • Upload files to S3 instead of WordPress. I use this plugin by Human Made: humanmade/S3-Uploads.
  • Experiment with adjusting the opcache settings in src/php.ini.
  • Use a lightweight nginx server to serve static assets from EFS to CloudFront.
  • Experiment with setting Cache-Control headers in handler.php for static files.


There are a couple hard limits imposed by AWS due to the technical limitations of the infrastructure.

Here are some other limitations that you’ll have to keep in mind.

  • No FTP / SSH access — you’ll need to manage an EC2 instance if you need command line or direct file access.
  • All the considerations of accessing a connection-oriented database from Lambda. You can try using Aurora Serverless if you run in to connection problems. RDS Proxy may also be able to provide you with a solution.


Thanks for reading! If you have any questions or comments, please don’t hesitate to leave a comment or send me a tweet.


Habits I’ll be keeping after COVID-19

During the COVID-19 pandemic, schools and daycares have been closed, so my family decided to use this as an opportunity to make some habits to make sure we can get through this period with minimal interruptions to life and work. Here are some habits that have worked so well for us that we’re planning on keeping them, even after the kids go back to school / daycare.

Keeping a schedule

We use a schedule to make sure the time we eat, sleep, and do activities are at regular times every day. At first, I thought this would be a good tool to let the kids know when we were working and when we could play together, but it’s proven to be a useful tool to both adults and children. I’ve written a blog post in Japanese about this as well.

Cleaning the house every day

This is related to the schedule, but we clean up the house every day at 5 PM, start the Roomba, take a bath, then get ready for dinner. Before, we would clean up whenever we felt like it, and as you can imagine, the room got pretty messy. We’d probably vacuum once a week or so. Now, the house stays clean and it’s much less stressful.

Taking the trash out

No, this isn’t a euphemism. Before, we would let the trash (especially recyclables like glass bottles, cans, etc) stock up before bringing it to the trash room, but now that we’re a little more flexible on time, we can take it down to the trash room immediately.

Code Snippets English

A quick shortcut to open a Ruby gem in VS Code

While working on a Ruby project, I often find myself referring to the code of various libraries when it’s easier than looking up the documentation. For this, I used to use code (bundle show GEM_NAME), but recently I’ve been getting this warning:

[DEPRECATED] use `bundle info $GEM_NAME` instead of `bundle show $GEM_NAME`

Okay, that’s fine, but bundle info returns a bunch of stuff that would confuse VS Code:

> bundle info devise
  * devise (4.7.1)
	Summary: Flexible authentication solution for Rails with Warden
	Path: /Users/keita/.asdf/installs/ruby/2.7.0/lib/ruby/gems/2.7.0/gems/devise-4.7.1

Luckily there’s bundle info $GEM_NAME --path. code (bundle info devise --path) is kind of long to type out every time, though, so I decided to make an alias.

I use the Fish shell, so the code here is written for that shell. Adapt it to your shell as required. You’ll also need the VS Code terminal integration installed for this to work.

function bundlecode
  if test -e ./Gemfile
    code (bundle info $argv[1] --path)
    set_color -o red
    echo "Couldn't find `Gemfile`. Try again in a directory with a `Gemfile`."
    set_color normal


> bundlecode devise
# VS Code opens!

How I use Git

I’ve been using Git at work for around 10 years now. I started using Git with a GUI (Tower — back when I was eligible for the student discount!), but now I use the CLI for everything except complicated diffs and merges, where I use Kaleidoscope.

A question I get asked by my coworkers often is: “how in the world do you manage using Git without a GUI?”. This blog post is supposed to answer this question.

First, I use the Fish shell. It fits with the way I think. A lot of you probably use bash or zsh, that’s fine, there is lots of documentation on how to integrate Git with those shells. This is the relevant part of .config/fish/

set __fish_git_prompt_show_informative_status 'yes'
set __fish_git_prompt_color_branch magenta
set __fish_git_prompt_color_cleanstate green
set __fish_git_prompt_color_stagedstate red
set __fish_git_prompt_color_invalidstate red
set __fish_git_prompt_color_untrackedfiles cyan
set __fish_git_prompt_color_dirtystate blue

function fish_prompt
  # ... 
  set_color normal
  printf ' %s' (prompt_pwd)
  printf '%s' (__fish_git_prompt)
  printf ' > '

I’ve omitted the irrelevant portions (status checking, # prompt when root, etc. If you want to see the full file, I’ve posted it as a gist.

On a clean working directory (that is, no changed files that haven’t been committed to the repository), this looks like this:

When updating some files, it will change to something like this:

This prompt doesn’t change in real time, so changes from other terminals won’t automatically change this prompt. I have a habit of tapping the “return” key to update the prompt.

To commit these changes:

The commands I use most often:

  • git add (if you only want to add a portion of a file, git add -p is your friend) / git commit
  • git push / git pull (git pull --rebase for feature branches being shared with other devs)
  • git diff @ — show all changes, staged or not, between the working directory and the latest commit of the branch you are on (@ is a alias for HEAD)
  • git diff --cached — show only changes that are being staged for the next commit
  • git status
  • git difftool / git mergetool (this will open Kaleidoscope)

This was obviously a very cursory, high-level look at how I use Git, but I hope it was useful. It’s been a long time since I’ve used a Git GUI full time, but whenever I do use one (for example, when helping a coworker), it feels clunky compared to using the CLI (that’s not saying I don’t have my complaints about the CLI — that’s another blog post 😇).

If you have any more questions, leave a comment or contact me on Twitter, and I’ll update this post with the answers.

English Tools Useful Utilities

“Logging in” to AWS ECS Fargate

I’m a big fan of AWS ECS Fargate. I’ve written in the past about managing ECS clusters, and with Fargate — all of that work disappears and is managed by AWS instead. I like to refer to this as quasi-serverless. Sorta-serverless? Almost-serverless? I’m open to better suggestions. 😂

There are a few limitations of running in Fargate, and this blog post will focus on working around one limitation: there’s easy way to get an interactive command line shell within a running Fargate container.

The way I’m going to establish an interactive session inside Fargate is similar to how CircleCI or Heroku does this: start a SSH server in the container. This requires two components: the SSH server itself, which will be running in Fargate, and a tool to automate launching the SSH server. Most of this blog post will be about the tool to automate launching the server, called ecs-fargate-login.

If you want to skip to the code, I’ve made it available on GitHub using the MIT license, so feel free to use it as you wish.

How it works

This is what ecs-fargate-login does for you, in order:

  1. Generate a temporary SSH key pair.
  2. Use the ECS API to start a one-time task, setting the public key as an environment variable.
    • When the SSH server boots, it reads this environment variable and adds it to the list of authorized keys.
  3. Poll the ECS API for the IP address of the running task. ecs-fargate-login supports both public and private IPs.
  4. Start the ssh command and connect to the server.

When the SSH session finishes, ecs-fargate-login will make sure the ECS task is stopping.

Use cases

Most of my clients use Rails, and Rails provides an interactive REPL (read-eval-print loop) within the Rails environment. This REPL is useful for running one-off commands like creating new users or fixing some data in the database, checking and/or clearing cache items, to mention a few common tasks. Rails developers are accustomed to using the REPL, so while not entirely necessary (in the past, I usually recommended fixing data using direct database access or with one-time scripts in the application repository), it is a nice-to-have feature.

In conclusion

I don’t use this tool daily, but probably a few times a week. A few clients of mine use it as well, and they’re generally happy with how it works. However, if you have any recommendations about how it could be improved, or how the way the tool itself is architected could be improved, I’m always open to discussion. This was my first serious attempt at writing Golang code, so there are probably quite a few beginner mistakes in the code, but it should work as expected.

AWS English

Hosting a Single Page Application with an API with CloudFront and S3

I’ve written about how to host a single page application (SPA) on AWS using CloudFront and S3 before, using the CloudFront “rewrite not found errors as a 200 response with index.html” trick.

Recently, working on a few serverless apps, I’ve realized that this trick, while quick, isn’t perfect. The specific case where it broke down was when the API is configured as a behavior on CloudFront (I usually scope the API to /api on the same domain as the frontend, so CORS and OPTIONS requests aren’t necessary). If the API returned a 404 Not Found response, CloudFront would rewrite it to 200 OK index.html, and the front-end application would get confused. Unfortunately, CloudFront doesn’t support customized error responses per behavior, so the only way to fix this was to use [email protected] instead.

Here’s the code for the Lambda function:

'use strict'

const path = require('path')

exports.handler = (evt, context, cb) => {
  const { request } = evt.Records[0].cf

  const uriParts = request.uri.split("/")

  if (
    // Root resource with a file extension.
      uriParts.length === 2 && path.extname(uriParts[1]) !== ""
    ) ||
    // Anything inside the "static" directory.
    uriParts[1] === "static"
  ) {
    // serve the original request to S3
  } else {
    // change the request to index.html
    request.uri = '/index.html'

  cb(null, request)

This code assumes all requests to a root request with a file extension, or anything in the /static/ directory is a static file that should be served from S3. All other requests will be rewritten to index.html. These are the defaults for create-react-app, but you’ll probably need to change them to meet your requirements. (Remember, [email protected] functions need to be created in us-east-1)

Attach this Lambda function to the CloudFront behavior responsible for serving from the S3 origin as origin-request, and you should be good to go. Don’t forget to remove the 404-to-200 rewrite.

AWS English WordPress

Serverless WordPress on AWS Lambda

Update 2020/07/29: AWS recently announced EFS support for Lambda, which makes running WordPress in Lambda easier, with fewer limitations. Here’s the new article about how to run WordPress in Lambda using EFS.

There are a few ways to run WordPress “serverless” on AWS. I’m going to talk about running WordPress on Lambda for this article. If you’re interested in how you can run WordPress serverless-ly on Fargate, I’m working on a post about that too.

Keep in mind that while it is possible to do this, it’s not for everyone. It’s probably not for me. Probably not for you. Use at your own risk!

Before we start, there is a core feature of Lambda that make running WordPress in Lambda quite troublesome: Read-only file system. WordPress expects a writable, persistent, local file system. We’ll be using the S3 Uploads plugin by Human Made to handle media uploads. However, core and plugin updates will not work. There’s no workaround for this, so to install / update files, we’ll need to make a new Lambda deployment.

So: let’s go! First, you’ll want to clone my boilerplate repository. I’ve prepared a WordPress installation and a simple glue script to actually boot WordPress.

$ git clone

My plan of attack is: run WordPress in the Lambda function using a PHP custom runtime, make uploads work with S3 instead of the local filesystem, and wire up the database. In the repository above, I’ve configured static assets to be served from S3 as well.

Now, let’s prepare the database. Lambda has two networking modes: public and VPC mode. In public mode, the Lambda has default access to the public internet, but nothing else. In VPC mode, the Lambda is booted inside the VPC, and doesn’t have public internet access by default. Because WordPress requires public internet access we have to either run it in public mode, or run it in VPC mode and prepare a NAT gateway (about $30 to $50 a month, depending on the region). If Lambda runs in public mode, the database must also be publicly accessible — something that is frowned upon from a security standpoint. You should choose the option that fits your risk and price profile. In my case, I’m going with the NAT gateway route.

Now we’ve got the messy stuff out of the way, we’ll have to assemble the Lambda runtime. AWS has an article on their blog detailing how to make a PHP custom runtime, but Stackery provides a batteries-included PHP layer. It includes everything you need to make a PHP application that assumes it’s running in a traditional server environment run in AWS Lambda.

# Replace "km-wordpress-on-lambda-deployment-201906" with something that makes sense for you. It's globally unique, so copying and pasting this will result in an error.
# Make sure you're in the same region as your database!

$ DEPLOY_BUCKET="km-wordpress-on-lambda-deployment-201906"
$ aws s3 mb "s3://$DEPLOY_BUCKET"
$ cd <the directory you cloned the GitHub repository to>

Now, it’s time to install WordPress! We’ll add the WordPress files to the deployment package. As usual, copy wp-config-example.php to wp-config.php. Enter your database details. If you have a hostname that you’re going to use with CloudFront, enter it now. If not, you’ll have to wait until after the CloudFront distribution is created, then try again.

Now, let’s deploy. This will create a new CloudFront distribution and S3 bucket for public assets, so maybe it’s a good time to make a cup of coffee. If you haven’t installed the SAM CLI, do that before the next block.

$ sam package --template-file template.yaml --output-template-file serverless-output.yaml --s3-bucket "$DEPLOY_BUCKET"
$ sam deploy --template-file serverless-output.yaml --stack-name wordpress-on-lambda --capabilities CAPABILITY_IAM
$ aws s3 sync ./src/php s3://deploy-bucket-XXXXX --exclude "*.php" --exclude "*.ini"

I’ll be using the default CloudFront domain for this demo. If you’re going to be using your own domain, you need to modify the template.yaml file to add the an alias to the CloudFront distribution. Use the following command to show the CloudFront domain name.

$ aws cloudformation describe-stacks --stack-name wordpress-on-lambda | jq '.Stacks[0].Outputs'

OK! Now, you should be able to access the CloudFront URL, and you’ll get redirected to the friendly WordPress installer! If you’ve set up your wp-config.php correctly, the installation should go smoothly.

The site I set up for this post is available here:

Lessons Learned

This is for almost no-one. I think the only valid use case (in this current form) for running WordPress in AWS Lambda is a site that gets periodic, unpredictable spikes of intense traffic — a use case where Lambda’s scalability and price model pays off. This is also a use case where, presumably, the benefits of the scalability trumps the inconvenience of not being able to use the online updaters and installers (also, I’m assuming the database will be able to keep up with the load).

However, if updating and installing themes or plugins could be managed outside of the Lambda environment (say, with wp-cli), with deployments automated… Then, it may be a little more applicable to a larger audience.

If you’re looking for a cheap solution to host your personal blog (like me!), you might just want to bite the bullet and check out any of the hosted WordPress solutions out there.

If you liked this post, or you’d like to provide some input, please do so in the comments. My favorite AWS service is Lambda, and I like pushing it a bit, so look forward to similar posts in the future. If you find bugs in the boilerplate, or you can make improvements, please open an issue or PR!

Miscellaneous Tidbits

  • Aurora Serverless sounds like it would be the best match for this setup. It probably is. Just keep in mind that Aurora Serverless doesn’t support publicly accessible clusters. To use it, you’ll need to go the Lambda-in-VPC, NAT gateway route.
  • Regarding public / private access and NAT gateways, if you’re like me and believe in the future of IPv6 and think that you can just use an egress-only internet gateway – you’re wrong! Lambda doesn’t seem to support IPv6 at this time.
  • You can actually use a NAT instance if the NAT gateway is overkill. However, I would recommend using the NAT gateway if you can. It comes with automatic scalability and redundancy, so you don’t have to babysit your NAT instance. (If you need more than one NAT instance, use the gateway. Seriously.)
  • At time of writing, my patches to php-lambda-layer haven’t been merged yet, so you can use my patched version (the boilerplate repository has this applied already).
  • If you’re really going all-in, consider using an Application Load Balancer rather than API Gateway to save money. API Gateway has zero fixed costs, but there is a point where ALB will become cheaper than API Gateway.
  • Doing some crude calculations, you should be able to handle an average of a few hundred users per day under the perpetual free tier. Your highest bill may be data transfer to the user.