When the ClassDojo engineering team was in the office, we loved our information radiators: we had multiple huge monitors showing broken jenkins builds, alerts, and important performance statistics. They worked amazingly well for helping us keep our CI/CD pipelines fast & unblocked, helped us keep the site up & fast, and helped us build an engineering culture that prioritized the things we showed on the info radiators. They worked well while the whole team was in the office, but when we went fully remote, our initial attempt of moving that same information into a slack channel failed completely, and we had to find a different way to get the same value.

Most teams have an #engineering-bots channel of some sort: it's a channel that quickly becomes full of alerts & broken builds, and that everyone quickly learns to ignore. For most of these things, knowing that something was broken isn't particularly interesting: we want to know what the current state of the world is, and that's impossible to glean from a slack channel (unless everyone on the team has inhuman discipline around claiming & updating these alerts).

We had, and still have, an #engineering-bots channel that has 100s of messages in it per day. As far as I know, every engineer on the team has that channel muted because the signal to noise ratio in it is far too low. This meant that we occasionally had alerts that we completely missed because they quickly scrolled out of view in the channel, and that we'd have important builds that'd stay broken for weeks. This made any fixes to builds expensive, allowed some small production issues to stay broken, and slowed down our teams.

After about a year of frustration, we decided that we needed to figure out a way to give people a way to set up in-home info-radiators. We had a few requirements for a remote-work info-radiator:

1. It needed to be configurable: teams needed a way to see only their broken builds & the alerts that they cared about. Most of the time, the info-radiator shouldn't show anything at all!
2. It needed be on an external display: not everyone had an office setup with enough monitor real-estate to support a page and keep it open
3. It needed to display broken builds from multiple Jenkins locations, broken builds from GitHub Actions, and triggered alerts from Datadog and Pagerduty on a single display

We set up a script that fetches data from Jenkins, Github Actions, Datadog, Pagerduty, and Prowler, transforms that data into an easily consumable JSON file, and finally uploads that file to S3. We then have a simple progressive web app that we installed on small, cheap Android displays that fetches that JSON file regularly, filters it for the builds that each person cares about, and renders them nicely.

These remote info-radiators have made it much simpler to stay on top of alerts & broken builds, and have sped us up as an engineering organization. There's been a lot written about how valuable info-radiators can be for a team, but I never appreciated their value until we didn't have them, and the work we put into making sure we had remote ones has already more than paid for itself.

ClassDojo occasionally has a few containers get into bad states that they're not able to recover from. This normally happens when a connection for a database gets into a bad state -- we've seen this with Redis, MySQL, MongoDB, and RabbitMQ connections. We do our best to fix these problems, but we also want to make it so that our containers have a chance of recovering on their own without manual intervention. We don't want to wake people up at night if we don't need to! Our main strategy to make that happen is having our containers decide whether they should try restarting themselves.

The algorithm we use for this is straightforward: every ten seconds, the container checks if it's seen an excessive number of errors. If it has, it tries to claim a token from our shutdown bucket. If it's able to claim a token, it starts reporting that it's down to our load balancer and container manager (in this case, nomad). Our container manager will take care of shutting down the container and bringing up a new one.

On every container, we keep a record of how many errors we've seen over the past minute. Here's a simplified version of what we're doing:

1let recentErrorTimes: number[] = [];
2function serverError(...args: things[]) {
3  recentErrorTimes.push(Date.now());
4}
5
6export function getPastMinuteErrorCount () {
7  return recentErrorTimes.count((t) => t >= Date.now() - 60_000);
8}

Check out ERROR, WARN, and INFO aren't actionable logging levels for some more details on ClassDojo's approach to logging and counting errors.

After tracking our errors, we can then check whether we've seen an excessive number of errors on an interval. If we've seen an excessive number of errors we'll use a leaky token bucket to decide whether or not we should shut down. Having a leaky token bucket for deciding whether or not we should try to shut down the container is essential: if we don't have that, a widespread issue that's impacting all of our containers would cause ALL of our containers to shut down and we'd bring the entire site down. We only want to cull a container when we're sure that we're leaving enough other containers to handle the load. For us, that means we're comfortable letting up to 10 containers shut themselves down without any manual intervention. After that point, something is going wrong and we'll want an engineer in the loop.

1let isUp = true;
2const EXCESSIVE_ERROR_COUNT = 5;
3const delay = (ms: Number) => new Promise((resolve) => setTimeout(resolve, ms));
4
5export async function check () {
6  if (!isUp) return;
7  if (getPastMinuteErrorCount() >= EXCESSIVE_ERROR_COUNT && await canHaveShutdownToken()) {
8    isUp = false;
9    return;
10  }
11
12  await delay(10_000);
13  check();
14}
15
16export function getIsUp () {
17  return isUp;
18}

At this point, we can use getIsUp to start reporting that we're down to our load balancer and to our container manager. We'll go through our regular graceful server shutdown logic and when our container manager brings up a new container, starting from scratch should make us likely to avoid whatever issue caused the problem in the first place.

1router.get("/api/haproxy", () => {
2  if (getIsUp()) return 200;
3  return 400;
4});

We use redis for our leaky token bucket. If something goes wrong with the connection to Redis, our culling algorithm won't work and we're OK with that. We don't need our algorithm to be perfect -- we just want it to be good enough to increase the chance that a container is able to recover from a problem on its own.

For our leaky token bucket, we decided to do the bare minimum: we wanted to have something simple to understand and test. For our use case, it's OK to have the leaky token bucket fully refill every ten minutes.

1/**
2 * returns errorWatcher:0, errorWatcher:1,... errorWatcher:5
3 * based on the current minute past the hour
4 */
5export function makeKey(now: Date) {
6  const minutes = Math.floor(now.getMinutes() / 10);
7  return `errorWatcher:${minutes}`;
8}
9
10const TEN_MINUTES_IN_SECONDS = 10 * 60;
11const BUCKET_CAPACITY = 10;
12export async function canHaveShutdownToken(now = new Date()): Promise<boolean> {
13  const key = makeKey(now);
14  const multi = client.multi();
15  multi.incr(key);
16  multi.expire(key, TEN_MINUTES_IN_SECONDS);
17  try {
18    const results = await multi.execAsync<[number, number]>();
19    return results[0] <= BUCKET_CAPACITY;
20  } catch (err) {
21    // if we fail here, we want to know about it
22    // but we don't want our error watcher to cause more errors
23    sampleLog("errorWatcher.token_fetch_error", err);
24    return false;
25  }
26}

See Even better rate-limiting for a description of how to set up a leaky token bucket that incorporates data from the previous time period to avoid sharp discontinuities between time periods.

Our container culling code has been running in production for several months now, and has been working quite well! Over the past two weeks, it successfully shut down 14 containers that weren't going to be able to recover on their own and saved a few engineers from needing to do any manual interventions. The one drawback has been that it makes it easier to ignore some of these issues causing these containers to get into these bad states to begin with, but it's a tradeoff we're happy to make.

Automated and semi-automated code migrations using shell text manipulation tools are great! Turning a migration task that might take multiple days or weeks of engineering effort into one that you can accomplish in a few minutes can be a huge win. I'm not remotely an expert at these migrations, but I thought it'd still be useful to write up the patterns that I use consistently.

Use ag, rg, or git grep to list files

Before anything else, you need to edit the right files! If you don't have a way of finding your codebase's files, you might accidentally edit random cache files, package files, editor files, or other dependencies. Editing those files is a good way to end up throwing away a codebase and cloning it from scratch again.

I normally use ag -l . to list files because ag, the Silver Searcher, is set up to respect .gitignore already. A simple find and replace might look like ag -l . | xargs gsed -i 's|bad pattern|replacement|'. It'd be simpler to do that replacement with your editor, but the ag -l . | xargs gsed -i pattern is one that you can expand on in a larger script.

Pause for user input: not all migrations are fully automatable

A lot of migrations can't actually be fully automated. In those cases, it can be worth building a miniature tool to make editing faster (and more fun!).

1# spaces in file names will kill this for loop
2# thankfully, I've never worked in a code base where people put spaces in filenames
3for file in $(ag -l bad_pattern); do
4  echo "how should we replace bad_pattern in ${file}? Here's context:"
5  ag -C 3 bad_pattern "${file}"
6  echo ""
7  read good_pattern
8  # quoting in sed commands is tricky!
9  # using `${var}` rather than $var avoids potential problems here
10  gsed -i "s|bad_pattern|${good_pattern}|" "${file}"
11done

You can expand this pattern to look for a number and choose an appropriate option, but just having something that speeds up going through files makes life better!

Handle relative import paths with for loops

I've often needed to add a new import statement with a relative path to files as part of a migration, and every time I've been surprised that my editor hasn't been able to help me out more: what am I missing? I normally use a for loop and increase both the max-depth of files I'm looking at and the number of ../ on the path:

1dots="."
2import_path="/file/path"
3for ((depth=0; depth<5; depth++)); do
4 dots="$dots/..";
5 for file in $(ag -l --depth $depth | grep .ts); do
6   if ! grep $import_path $file; then
7     gsed -i "1i import '${dots}${import_path}';" $file;
8   fi
9 done
10done

Rely on your code formatter

Not needing to worry about code formatting is AMAZING. If your codebase is set up with a code formatter (like prettier or gofmt), it allows you to make changes without worrying about whitespace and then let the code formatter fix things later. It may even make sense to intentionally remove white-space from a pattern in order to make a replacement simpler to write!

Use the right tool for the job

1. Some code migrations require a tool that looks at the AST rather than the text in a code file and transforms that AST. These tools are more powerful & flexible than shell tools, but they require a bit more effort to get working. In NodeJS, there's jscodeshift and codemods. I don't know what's available for other languages.
2. Your editor & language might support advanced migrations. If it does, learning how to do those migrations with your editor will likely be more effective than using these techniques or may prove a useful complement to these techniques.
3. Bash tools like sed, awk, grep, and cut are designed to deal with text and files. Code is text and files! Other tools work, but they might not be designed to deal with files and streams of text.
4. Shell tools are great, but a tool you know well and are excited about using is better than a tool you don't want to learn! Whatever programming language you're most comfortable with should have ways of dealing with and changing files and text. Having some way of manipulating text & files is important!. There are even tools like rb or nq (I wrote this one!) that let you use the Ruby or NodeJS syntax you're familiar with on the command line in a script you're writing.

Use sed: it's designed for this

sed is the streaming text editor, and it's the perfect tool for many code migrations. A surprising number of code migrations boil down to replacing a code pattern that happens on a single line with a different code pattern: sed makes that easy. Here are a few notes:

1. If you're on a mac, you'll want to download a modern version of sed. I use gnu-sed: brew install gnu-sed
2. use | (or anything else!) as your delimiter rather than /. sed takes the first character after the command as the delimiter, and / will show up in things that you want to replace pretty often! Writing gsed 's|/path/file.js|/path/file.ts|' is nicer than gsed 's/\/path\/file.js/\/path\/file.ts/'.
3. In gsed, the --null-data (-z) option separates lines by NUL characters which lets you easily match and edit multiline patterns. If you use this, don't forget to use the g flag at the end to get all matches: everything in a file will be on the same 'line' for sed.
4. When referring to shell variables, use ${VAR_NAME} rather than $VAR_NAME. This will simplify using them in sed commands.
5. Use -E (or -r with gsed) for extended regular expressions and use capture groups in your regular expressions. git grep -l pattern | xargs gsed -Ei 's|pat(tern)|\1s are birds|g'

("perl pie" (perl -pi -e) can be another good tool for finding and replacing patterns! It's just not one I know.)

Many migrations might take multiple steps

When you're migrating code, don't worry about migrating everything at once. If you can break down the problem into a few different commands, those individual commands can be simple to write: you might first replace a function call with a different one and then update import statements to require the new function that you added.

When you write a regular expression in a find-and-replace, you can sometimes get false positives. Rather than trying to update your regular expression to skip the false positives, I often find it simpler to write a regular expression to replace those false positives with a temporary pattern, update the remaining matches, and then replace the temporary pattern.

With all of this, you'll need to rely on git (or another version control system). It's really easy to make mistakes! If you don't have an easy way to undo mistakes, you'll be sad.

Automate ALL the code migrations!

Manipulating text & files like this is a skill, and it's one that takes some practice to learn. Even if it's much slower to automate a code change, spending the time to automate it will help you build the skills to automate larger, more complex, and more valuable code migrations. I remember spending over an hour trying to figure out how to automate changing a pattern that was only in 10 spots in our codebase. It would have taken 5 minutes to do manually, but I'm glad I spent 10x the time doing it the slow way with shell tools because that experience made me capable of tackling more complex migrations that wouldn't be feasible to do manually.