Posts By: Will Keleher

AsyncLocalStorage Makes the Commons Legible

When multiple teams share a resource like a code-base, a database, or an analytics pipeline, that resource can often suffer from the Tragedy of the Commons. Well intentioned engineers can add slow code, not prioritize important improvements because those improvements aren't aligned with team incentives, and just generally not leave the codebase or database better than they found it.

ClassDojo has a monolithic backend web-server API, but it and its backing databases often suffered from hard to diagnose performance problems because engineers would accidentally add code with poorly indexed queries, with database fanouts, or that blocked the event loop, and they wouldn't realize that that code was causing any problems. We intentionally made our Model layer request-agnostic, which helps us create better tests & simpler code, but it meant that we didn't have a way of instrumenting database & performance cost by request until Node 14 started supporting AsyncLocalStorage.

AsyncLocalStorage "creates stores that stay coherent through asynchronous operations." This lets us reference a consistent context over the course of a request to get information about the route, store information about how many database requests that request has triggered, count how many documents we've fetched, and dramatically increase our visibility into what every request is doing. Because every single one of our routes is tied to a product area, and each of our product areas is owned by a team, we were suddenly able to give each team insight into any performance problems they'd inadvertently added to the codebase. None of our engineers and teams ever wanted to cause performance problems, but the lack of pre-AsyncLocalStorage system legibility meant that these issues were difficult to track down, and teams always had higher priority work than tracking down unattributed performance problems.

When we set up our AsyncLocalStorage per-request instrumentation system, we found some crazy things:

  • 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 information to return to clients
  • and plenty of other routes that weren't doing things nearly this egregious, but were still bad enough that we wanted to fix them

Once we had this instrumentation, it was incredibly simple for the right team to find and fix these issues. We'd looked into some of these & similar issues before, but finding & fixing them was incredibly time-consuming. Now, we have a single log that triggers an alert and tells you everything you need to know to track down the problem.

What did we track?

  1. Whenever we query one of our databases, we now include a comment with the query with the route name that triggered the request. When we go through our database's slow query logs, this comment shows us where the query actually came from, and lets us accurately attribute our database load. This makes our database optimization work so much simpler because many of our query patterns could be caused by many different routes!
  2. We also track:
  • the number of database queries made per database and per table-model over the course of a request
  • the number of items returned over the course of a request per database and per table-model
  • the total duration spent querying per-database and per table-model

Whenever we exceed safe thresholds for any of these metrics over the course of a request, we log out the details and send an alert to the appropriate team. We also send all of this data to our data warehouse, Redshift, so we can run more detailed queries about what each route is doing.

  1. We track overall event-loop-lag per container, and whenever event-loop-lag crosses 1 second (which is incredibly high!), we log out the same AsyncLocalStorage based request-cost details for all recent requests. This always points to situations where we've fetched a lot of data, and are then doing something computationally complex with it.

Monoliths need Transparency

It's hard to overestimate how much using AsyncLocalStorage like this has helped easily improve our backend web-server's performance. One of the major drawbacks to a monolith like ours can be that teams aren't able to easily isolate their code's performance from that of the rest of the monolith. Having insight into what is actually happening on a granular level is what's going to allow us to continue having an easy time scaling our monolith for a long time.

When the ClassDojo engineering team started using TypeScript on our backend, I was incredibly frustrated. It felt like I needed to constantly work around the type system, and it felt like the types weren't providing any value at all. And, they weren't providing any value to me: I had fundamental misconceptions about how TypeScript worked, and my attempts to work around the Type system by using lots of any and as created some truly abominable TypeScript code.

Type Inference

Whenever you create a variable using let, const, or var, and don't provide a type, TypeScript will infer the type: this means it will use its best guess for the type. These best guesses are pretty good, but they aren't magic!

If you use let or var, TypeScript will assume that it can re-assign to any similar value. If you write let method = "GET";, TypeScript will infer that method is a string, and will let you later reassign method: method = "KonMari". If instead you use const, const method = "GET", TypeScript will infer that method is of type "GET". This means when you use let, you'll often need to use a type. let method: HTTPMethod = "GET" will allow only type-safe reassignment.

When TypeScript infers types for Objects and Arrays, it will similarly give its best guess. In JavaScript, objects are mutable: if you set up a variable like const request = { method: "GET" }, it'll assume that the type that you want is { method: string } to let you update the method field. A type of { method: string } won't be usable by something that wants { method: HTTPMethod }, so you need to either explicitly tell it the type (const request: { method: HTTPMethod } = { method: "GET" }, or tell TypeScript that it's safe to infer a stricter type (const request = { method: "GET" as const }).

Here's a TypeScript playground that explores type inference in a bit more depth, and the type inference part of the handbook is great if you want a detailed introduction.

Why isn't it narrowing?

One of the things that I found most frustrating about TypeScript was writing a clause that felt like it should have narrowed the type of a variable and not have it actually narrow. I'd often use as to bypass the type system entirely and force something to narrow, but overriding TypeScript like this is a real anti-pattern. When I finally learned about type narrowing, and type guards, TypeScript became so much more pleasant to use.

One of the very first things I learned about JavaScript was that you should never use in with Objects because in doesn't differentiate between an object's properties and those of its prototype. In TypeScript, in is crucial to use for type narrowing. If you have a function that takes a type like Error | (Error & { type: "NotFound", table: string }) | (Error & { type: "NotAllowed", reason: string }), you can't write if (!err.type) return because TypeScript doesn't know whether err has that field or not. Instead, you need to write if (!("type" in err)) return and TypeScript won't error, and will correctly narrow the type.

One related confusion was why I couldn't use if statements to narrow a type. I'd try to write code like the following:

Don't do this!

1type NotFound = Error & { NotFound: true; table: string };
2type ServerError = Error & { ServerError: true; message: string };
3type ClientError = Error & { ClientError: true; status: number };
4
5function getResponse(err: Error | NotFound | ServerError | ClientError) {
6 if ((err as ClientError).ClientError) {
7 return { status: (err as ClientError).status, message: "client error" };
8 }
9 if ((err as NotFound).NotFound) {
10 const notFoundError = err as NotFound;
11 return {
12 status: 404,
13 message: `not found in ${notFoundError.table}`;
14 }
15 }
16
17 return {
18 status: 500,
19 message: (err as ServerError).message || "server error",
20 }
21}

TypeScript can automatically discriminate a union type if the members of a union all share a field. This can lead to much simpler, type-safe, and easier to work with code!

1type NotFound = Error & { type: "NotFound", table: string };
2type ServerError = Error & { type: "ServerError", message: string; };
3type ClientError = Error & { type: "ClientError", status: number; };
4
5function getResponse(err: Error | NotFound | ServerError | ClientError) {
6 // doing this first lets TypeScript discriminate using the `type` property
7 if (!("type" in err)) {
8 return {
9 status: 500,
10 message: "server error",
11 }
12 }
13
14 if (err.type === "ClientError") {
15 // err now has type ClientError, so we can use status!
16 return { status: err.status, message: "client error" };
17 }
18
19 if (err.type === "NotFound") {
20 return {
21 status: 404,
22 message: `not found in ${err.table}`
23 }
24 }
25
26 // it even narrows this to ServerError!
27 // although it may be wiser to have an explicit `if` statement and assert that every case is handled
28 return {
29 status: 500,
30 message: err.message,
31 }
32}

Finally, if there are more complex types that you need to narrow, writing a custom type guard function is always an option! This is a function that returns a boolean that lets TypeScript narrow a type whenever you call it!

1// `method isHTTPMethod` tells callers that any string that you've checked with this function is an HTTPMethod
2function isHTTPMethod (method: string): method is HTTPMethod {
3 return ["GET", "PUT", "POST", "DELETE"].includes(method);
4}

Here's a TypeScript playground where you can see how this works.

"Brands" are required for Nominal Typing

TypeScript uses "structural typing", which means that only the shape of something matters, and the name of the type is completely irrelevant. If something looks like a duck, it's a duck as far as TypeScript is concerned. This is surprising if you're coming from a "nominal typing" background where two types with the same shapes can't be assigned to each other.

1// this is valid TypeScript
2type EmailAddress = string;
3type Url = string;
4const emailAddress: EmailAddress = "myEmail@classdojo.com";
5const url: Url = emailAddress;

If you want nominal-style types for a string or number, you need to use "Brands" to create "impossible" types.

1type EmailAddress = string & { __impossible_property: "EmailAddress" };
2type URL = string & { __impossible_property: "Url" };
3const emailAddress = "myEmail@classdojo.com" as EmailAddress; // we need to use `as` here because the EmailAddress type isn't actually possible to create in JS
4const url: URL= emailAddress; // this now errors the way we'd want

(The above isn't how you'd actually want to write this code: I'm writing it in a way that hopefully makes it a bit more clear what's going on. If you'd like to see a more realistic example, take a look at this playground)

TypeScript isn't just "JS with types"

TypeScript is a great language, but just treating it as "JS with types" is a surefire recipe for frustration. It takes time to learn the language, and I wish I'd spent more of that time up-front reading through the docs: it'd have saved a ton of pain. The TypeScript handbook is great, and there are tons of great resources online to help understand TypeScript better. There's still a ton I don't know about TypeScript, but finally learning about inference, type narrowing, and structural typing has made developing with TypeScript so much nicer.

A decent number of programming tasks boil down to manipulating text and files:

  • automating code migrations
  • exporting & transforming data
  • exploring logs
  • looking for patterns in a codebase

Knowing the basics of shell tools that are designed to manipulate text & files can make these tasks simple, and the interactive pipe-based programming environment of the shell makes figuring out the right command fun. It makes sense that tons of engineers evangelize learning tools like sed, awk, ag/rg, find, cut, head, tail, xargs, jq, and all of the other amazing shell tools that are designed for dealing with streams of text and files.

That said, while shell tools are useful, it is always possible to accomplish the same tasks with whatever programming language you regularly use! As long as you have the ability to easily manipulate text and files in a script, you'll find that it can make entire classes of tasks easy to accomplish. If you aren't comfortable manipulating text and files like this, you won't even notice the potential opportunities you have to make your own job easier, and to provide important business value.

At ClassDojo, we have people who use Bash, Go, and NodeJS for these scripts. All of these languages work well! Some of the high-value text & file manipulation scripts we've written have done things like:

  • automatically rewrite require statements in our tests to allow converting our tests to Typescript (Bash)
  • do a large code migration to allow targeted detection of changes to fixtures & indexes (NodeJS)
  • set up a MongoDB export job (NodeJS)
  • set up product-areas for all of our HTTP routes to allow better team ownership over errors (Bash)
  • set up fast local database restores (NodeJS and Bash)
  • parallelize tests (Go)
  • parallelize & cache lint steps (Bash)

Shell scripting can be difficult to get into, but it doesn't mean that you can't start getting the same value out of whatever language you regularly use. Getting comfortable with text manipulation, file manipulation, and regular expressions is incredibly useful no matter what tool you choose to get there.

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