Schema.org has a concept of a DataType, things like Text, Number, Date, etc. In JSON-LD, we represent these as strings or numbers, rather than array or object literals. This data could describe the name of a Person, a check-in date and time for a LodgingReservation, a URL of a Corporation, publication date of an Article, etc. As we’ll see, the Schema.org DataType hierarchy is far richer than TypeScript’s type system can accommodate. In this article, we’ll go over the DataType hierarchy and explore how much type checking we can provide.

Last time, we talked about modeling the Schema.org class hierarchy in TypeScript. We ended up with an elegant, recursive solution that treats any type Thing as a "@type"-discriminated union of ThingLeaf and all the direct sub-classes of the type. The next challenge in the journey of building TypeScript typings for the Schema.org vocabulary is modeling Enumerations.

Let’s look at a few examples from the Schema.org website to get a better sense of what Enumerations look like.

Recently, I published schema-dts (npm, GitHub), an open source library that models JSON-LD Schema.org in TypeScript. A big reason I wanted to do this project is because I knew some TypeScript type system features, such as discriminated type unions, powerful type inference, nullability checking, and type intersections, present an opportunity to both model what Schema.org-conformant JSON-LD looks like, while also providing ergonomic completions to the developer.

In a series of posts, I’ll go over some of the Structured Data concepts that lent themselves well to TypeScript’s type system, and those concepts that didn’t. First up: the type hierarchy of JSON-LD Schema.org Schema, and how can be represented in TypeScript.

When testing a codebase in Angular Ivy, I ran into a bunch of test failures I wasn’t seeing before. ExpressionChangedAfterItHasBeenCheckedErrors were being thrown around. In debugging these failures, I found that many of them are the results of side-effects in Observables and Observable pipe operations. I happened to describe these earlier in my piece on Observables, Side-effects, and Subscriptions.

Consider this minimal reproduction:

My previous articles on using AsyncPipe and data refresh patterns in Angular hint at some common anti-patterns dealing with Observables. If there’s any common thread in my advice, it is: delay unpacking an Observable into its scalar types when performing logic you can rewrite as side-effect-free, leaving code with side-effects for subscription callbacks and other downstream logic.

My two earlier articles focused on cases users can benefit from handling more of the object’s lifecycle in its Observable form. In other words, cases where the Observable was being subscribed to and unpacked too soon. Instead, I suggested transforming the Observable using operators like map, switchMap, filter, etc. and taking advantage of the power offered by this form. In the case of Angular, it provides AsyncPipe, which takes the care of the step with side-effects (actually rendering the page) in template code.