One aspect of ClassDojo’s engineering culture that tends to surprise people is that we don’t have dedicated QA engineers or testers. We use a variety of strategies to keep our end-to-end feature development fast, continuous, and high quality. These strategies include a strong culture of writing automated tests, along with regular playtesting sessions.

What do we do instead of QA?

At ClassDojo, we prioritize truly continuous deployment of our backend monolith, which means removing as many manual blocking steps as we can, and we’ve built systems to do so safely. Engineers have end-to-end ownership of features, and we rely heavily on writing automated unit and integration tests to ensure that we’re not pushing code that causes unexpected behavior or errors. Our canary containers help automatically roll back any commits that cause errors, and we monitor our logs carefully.

However, as thorough as we try to be, automated tests and monitoring can’t catch everything. To supplement our automated systems, we hold regular playtesting sessions to serve as manual testing as well as context sharing.

What is playtesting?

Playtesting is simply manually testing the products and features the team has built. You may have also heard of the term “dogfooding,” which refers to actually using your product as a real user. Some folks on the team are able to dogfood as legitimate parent users since their kids’ teachers use ClassDojo. However, since the majority of us are not teachers in a classroom nor parents connected to a real classroom, we do intentional playtesting sessions instead.

How do we run a playtesting session?

Who should join?

Individual teams might playtest new features right before shipping, in which case the whole team should join. We also schedule sessions that are open to anyone at the company so teams can share their recent features and solicit feedback from the wider group. For the best insight and context sharing, we encourage including folks from various functions, such as engineers, designers, PMs, marketers, and customer success agents. Playtesters don’t need to prepare anything ahead of time, or even have any knowledge of the flows. They just need to have the right type of device if it’s a mobile feature that’s only released on one platform.

The process

The first step is scheduling a time for the group of testers to get together and play through certain flows. Usually 30-45 minutes is sufficient, depending on how large or complex the feature is. Ahead of time, the product owner should prepare a set of loose guidelines for how to run through the flows, including any necessary setup steps such as installing an alpha build or turning on certain feature flags.

At the beginning of the session, the facilitator or product owner gives a brief overview of the flows and setup, then all the playtesters simply go through the flows independently on their own devices and accounts. Doing this synchronously instead of async lets us quickly identify whether an issue is widespread or a particular edge case, and answer any questions on the spot. We typically use an Asana board where playtesters can add cards for anything that comes up — not just bug reports but also general product feedback and points of confusion.

We reserve 5-10 minutes at the end of the session to go through the cards and make sure the issues are clear. From there, the product owner and team can prioritize them at their next prioritization meeting.

What are the benefits?

Our playtesting often finds bugs in obscure edge cases not covered by automated tests. With an app and user network as complex as ours, it’s nearly impossible to cover all use cases with test fixtures, so it helps to have a variety of people testing on their own real-world accounts.

Playtesting is one of our best methods for cross-team and cross-functional context sharing. Screenshots, product specs, and demos can only go so far in conveying what a new feature really involves. Having teammates actually get their hands on the features is a great way to share what’s being built. It’s also valuable to get fresh perspectives from folks who perhaps hadn’t explored that product area before. It’s like having an in-house focus group to give feedback.

If you have ideas on how we can improve our playtesting or manual testing strategies, reach out! We’d love to hear from you.

One of the main engineering backbones here are ClassDojo is our CI/CD pipeline. By having a pipeline that automates tests, migrations, rolling out to production, and so much more, we allow the engineering team to focus on building great products. No managing a release train or babysitting a deployment!

In this episode, I chat with Dom and Gregg about what CI/CD means to us, how we do it, and what steps you can take to start your own journey to continuous deployment.

In the dark times before AsyncLocalStorage, it could be hard to tell why a request would occasionally time out. Were there multiple relatively slow queries somewhere in that route's code? Was another request on the same container saturating a database connection pool? Did another request block the event loop? It was possible to use tracing, profiling, and logs to track down problems like these, but it could be tricky; setting up per route metrics using AsyncLocalStorage makes it a ton easier!

When ClassDojo set up our AsyncLocalStorage-based per-route instrumentation we found things like:

• a route that occasionally made 30,000+ database requests because it was fanning out over a large list of items
• another route that blocked the event-loop for 15-20 seconds a few times a day, and caused timeouts for any other requests that our server was handling at the same time
• a third route that was occasionally fetching 500,000+ items to render simple counts to return to clients

I wrote about this a bit more in AsyncLocalStorage Makes the Commons Legible. If you're not familiar with ClassDojo, it's a parent-teacher communication platform. Our monolithic NodeJS web-server backend API normally serves ~10,000 requests per second.

I'd like to go through some of the details of how we set up this per-request instrumentation. For this post, we'll be starting with a relatively standard NodeJS web-server with pre-router middleware, and a router that finds an appropriate route to handle a request. It should look something like this:

app.use(({ req, res }, next) => {
  const start = Date.now();
  onFinished(res, () => afterResponse(req, res, start));
  next();
});
app.use(rateLimitingMiddleware);
app.use(bodyParserMiddleware);
app.use(allOfTheRestOfOurMiddlware);

app.use(router);
app.use(notFoundMiddleware);

To add instrumentation to this setup, we'll want to do the following:

1. Create a per-request async store in our first middleware
2. Store details about the database request caused by our request in our request's async store
3. Send the request's database request details to our data lake.
4. If any of the database request details violate our per-request limits, we log it as a server-error so that a team can see it & take action

Starting our pre-request async store

In a NodeJS web server, each middleware calls the next, so if we start an async local storage context in our very first middleware, every subsequent middleware should have access to the same storage context. (I had a lot of trouble understanding why this worked, so I wrote up a simplified gist that hopefully demonstrates what's going on.)

import { AsyncLocalStorage } from "async_hooks";

export const requestTrackingAsyncLocalStore = new AsyncLocalStorage();

// requestTrackingAsyncLocalStoreMiddleware wraps the downstream koa middlewares inside an async local storage context
export function requestTrackingAsyncLocalStoreMiddleware({ req, res }, next) {
  const store = {
    requestCost,
    req,
    res,
  };
  // running the next middleware in the chain in the context of this 'run' makes sure that all calls
  // to getStore() in the scope of this requests are bound to the correct store instance
  return requestTrackingAsyncLocalStore.run(store, next);
}

// add this to the router! (this would be in a different file)
app.use(requestTrackingAsyncLocalStoreMiddleware);
app.use(rateLimitingMiddleware);
app.use(....);

Store details about request about behavior in our pre-request async store

Now that we have a pre-request async local store, we can grab it and start using it! We'll want to learn:

1. How many database requests do we make over the course of an HTTP request? Are we running into the N+1 query problem on any of our routes?
2. How long do those database requests take in total? Requests that take a long time can indicate spots where we're doing a lot of expensive work.
3. How many documents are these requests returning? If we're processing 10,000s of documents in NodeJS, that can slow down a server quite a bit, and we may want to move that work to our database instead.

export function increment(type: "request_count" | "duration" | "document_count", table: string, n: number = 1) {
  const store = requestTrackingAsyncLocalStore.getStore();
   // we'll probably want to track this to see if we're losing async context over the course of a request
  if (!store) return;
  _.set(store, ["requestCost", type], _.get(store, ["requestCost", type], 0) + n);
  _.set(store, ["requestCost", "byTable", table, type], _.get(store, ["requestCost", "byTable", table,], 0) + n);
}

If we add code that wraps our database client's request, it should hopefully be easy to add these increment calls at an appropriate point.

Handle the request report

Once we have this request report, we can do whatever we'd like with it! At ClassDojo, we log a server-error whenever a route is doing anything particularly egregious: that way, we get quick feedback when we've made a mistake. We also use a firehose to send this data to redshift (our data lake) so that we can easily query it. Either way, this is something that we can do after we're done sending our response to the client:

app.use(requestTrackingAsyncLocalStoreMiddleware);
app.use(({ req, res }, next) => {
  // this use of new AsyncResource will preserve the async context
  res.on("finished", new AsyncResource("requestTrackingLogging").bind(() => {
      const store = requestTrackingAsyncLocalStore.getStore();
      if (!store) throw new Error(`Something has gone awry with our async tracking!`);
      if (isEgregiouslyBad(store.requestCost)) logOutBadRequest(store);
      requestCostFirehose.write(store);
  }))
  next();
});

Tracking down places where we lose async context

While the async store might feel like magic, it's not, and sommon common situations will cause you to lose async context:

1. using callbacks rather than promises. In those situations, you'll need to create an AsyncResource to bind the current async context

setTimeout(new AsyncResource("timeout").bind(() => doRequestTrackingThings()), 1);
redisClient.get("key", new AsyncResource("timeout").bind(() => doRequestTrackingThings()))

2. Some promise libraries might not support async-hooks. Bluebird does, but requires setting asyncHooks to true: Bluebird.config({ asyncHooks: true });.

It may take a bit of work to track down and fix all of the places where you're losing async context. Setting up your increment calls to log out details about those situations can help!

export function increment(type: "request_count" | "duration" | "document_count", table: string, n: number = 1) {
  const store = requestTrackingAsyncLocalStore.getStore();
  if (!store) {
    logServerError(`We lack async context for a call to increment ${type} ${table} by ${n}`, new Error().stack);
    return;
  }
  ...
}

Increased Observability is great!

Putting effort into increasing the observability of a system can make that system much easier to manage. For a NodeJS web-server, we've found a lot of benefits in using AsyncLocalStorage to improve per-request visibility: it has let us improve latency on a few routes, reduced our event-loop blocking, and given us a better view of opportunities to improve performance.