# IPLD Schemas Feature Summary

# Constructs for Describing Common Data Forms

IPLD Schemas present basics that are well-understood in many programming ecosystems (sum types, product types, some specific recursive types):

Typed maps


Typed lists


Typedef'd primitives

type Foo int


type FooBar struct {
  f Foo
  b Bar

Discriminated unions (several styles matching those commonly found in the wild)

type union FooOrBar {
  Foo "f"
  Bar "b"
} representation keyed


type FBB enum {
  | "Foo"
  | "Bar"
  | "Baz"

A simple syntax for 'maybe' data elements

type Foo struct {
  f nullable Bar

type Bar [nullable Foo]

A clear syntax for non-required fields

type Foo struct {
  f optional Bar

Hooks for transparently transforming data with programmatic logic

advanced ROT13
type EncryptedString string representation advanced ROT13

# Mapped to the Data Model

All of this maps over the IPLD Data Model:

  • Every type has a representation. e.g. Maps are represented as Maps, Structs are also represented as Maps.
  • Representations can specify a mapping for serialized field names, which can be useful in cases such as requiring serialization compactness but preferring verbosity in the schema for descriptive purposes.
  • Representations are customizable, including mapping a type onto a different representation kind entirely. e.g. a Struct can be declared to have a List representation (making it shorter to serialize, if less self-describing); or a Struct can be represented by simple string patterns.

Some things can go even further than the basic representations. "Advanced data layouts" define types that can act as already-familiar data kinds such as Maps or Lists or Bytes (byte arrays), while using a fully pluggable system to transform the content into other forms for representation. A common use of such a feature is splitting data into fully separate chunks for storage, allowing:

  • Very large byte arrays stored across many distinct blocks, presenting as a single iterable Bytes kind.
  • Sharding abstractions for large data sets presented as Map or List kinds.
  • Transparent wiring of at-rest encryption presenting as the unencrypted form of encrypted data.

All the same tools that work over the IPLD Data Model also work atop Schemas:

  • Traversals
  • Pathing
  • Selectors
  • etc.

All of the generic IPLD algorithms can work over a Schema node in exactly the same way as over nodes in the plain Data Model. This allows novel behaviors such as pathing through a Struct by field names even if the Struct representation is a list.