This document provides explanation, context, and examples for a proposal for the new code generation for iOS.
Example code in this document is used only to illustrate the concepts being discussed, as is not comprehensive. Actual generated objects may have additional properties, functions, or nested types to support all functionality. For an examples of an entire generated operation, see the Example Generated Output in the repository.
While the generated models in version 1.0 look much different than the current generated code, under the hood, they function relatively similarly. Though there are a few important functional differences, they are still structs backed by a dictionary of keys and values. Consuming your response data looks very similar to the previous version. Fragments and Type Cases are still accessed as nested objects. The most noticeable difference is that the generated code will be a fraction of its previous size and should be much easier to read and understand!
The most important functional differences are:
The generated response objects are now immutable. This allows for the generated code to be much more compact. Previously, fields on the generated models could be mutated, however this was not used for mutating objects server-side. Response objects could be mutated and then saved to the local cache to make manual cache mutations.
The ability to mutate the local cache will implemented by using mutable fields on generated schema types in a follow-up to this RFC. This will be implemented prior to the 1.0 release.
Local cache mutations using response objects had a number of limitations:
- Cache data could only be mutated in the scope of a defined operation.
- Validation of mutated data was weak.
- In certain edge cases, data that would be invalid according to the schema could be inserted in the local cache. This could cause failures when reading cached data. In the worst case scenario, this could result in runtime crashes.
- Data that had not yet been fetched from the server was difficult to insert in the local cache.
- This was especially problematic when wanting to add values for Non-null fields on partially fetched objects.
In addition to generating the immutable operation response data models, the new codegen generates "Schema Types". Schema Types represent the backing types defined on the GraphQL schema itself. These objects provide metadata that is used by the Apollo Client under the hood to understand the relationships between the types in your generated operation response models. These generated Schema Types will also be expanded prior to the 1.0 release to include fields that allow for local cache mutations. For more information, see Schema Type Generation.
In previous versions of the Code Generation tool, this functionality was exposed via the mergeInFieldsFromFragmentSpreads option, which defaulted to false. Merging in fragment fields provides for easier consumption of generated models, but it also increases the size of the generated code. Because the size of the generated code is being dramatically reduced with the new Code Generation tooling, we have opted to always merge in fragment fields. If generated code size becomes a concern with the new Code Generation, adding an option to disable fragment merging may be considered in the future. For an example of this see Merging Fragment Fields Into Parent SelectionSet
For normalization of cache data, a mechanism for providing unique cache keys for entities is necessary. In the previous version of Apollo, this was configured via a single cacheKeyForObject closure that could be set on the ApolloClient. In version 1.0, this configuration will move to extensions on the Schema Types. For more information, see Cache Key Resolution
The previous Apollo versions used double optionals (??) to represent null vs nil input values. This was unclear to most users and make reading and reasoning about your code difficult in many situations. The new version provides a custom enum for this cases named GraphQLNullable. For more information, see Nullable Arguments - GraphQLNullable
We are excited to say that the 1.0 release of Apollo iOS will support code generation for projects that use multiple modules! There will be multiple options for generating your model objects:
- Single Target
- Single Location
- All files will be generated into a folder that is included in your application target.
- Co-located Models
- Generated operation objects will be located relative to the defining
.graphqlfile. - Schema types will be generated in a single folder.
- Generated operation objects will be located relative to the defining
- Single Location
- Modular (Built-in support for SPM & Cocoapods)
- Single Location
- All files will be generated into a folder that can be included as it's own module.
- Co-located Models
- Generated operation objects will be located relative to the defining
.graphqlfile. - Schema types and shared fragments will be generated into a folder that can be included as it's own module.
- Generated operation objects will be located relative to the defining
- Single Location
The primary limitation with multi-module support is that code generation must be run on your entire project at one time. You will not be able to run code generation for modules individually at this time. More information about how to generate models for multi-module projects will be coming prior to the 1.0 release.
The logic for generating, validating, and executing selections for Type Cases has changed significantly. While this change is entirely under the hood – it should rarely, if ever, affect the consumer – because the generated code and the way the executor parses Type Cases functionally deviates from it's previous behavior, it is included here.
Type Cases previously included all of the selections that would be selected if the underlying __typename of the returned object matched the Type Case. For interfaces, the same TypeCase could be used for multiple different __typename values.
In the new generated models, type cases are generated to only select the additional fields that should be selected if the underlying __typename matches that type. The executor can now handling selecting multiple different Type Cases for the same object, if it matches multiple Type Cases (ie. A concrete type and an interface). This simplifies the execution logic; reduces the amount of generated code necessary; and makes the generated objects easier to understand.
For more information see TypeCase Selections.
There are a number of reasons to build a new Codegen tool. There are limitations of the current Codegen, as well as improvements and features that can be added with a new Codegen tool that are difficult to address with the current tooling.
The current Codegen tooling is written in Typescript, and supports multiple languages. This code base is not maintained and is in a messy state currently. Making changes to this tooling is difficult because of the state of the code and because it must maintain compatibility with generating code for other languages. Additionally, we believe that a Codegen tool written primarily in Swift opens up more opportunity for the community to make contributions in the future.
The current Codegen tooling CLI runs as a node package. This requires iOS developers to include an NPM project. This is not ideal, as it adds a lot of cruft to our user’s projects. Since many iOS engineers are not familiar with NPM, the installation and usage of it creates additional hurdles to getting a project started and maintaining projects that iOS engineers struggle with. We have pulled out the GraphQL compiler to work without NPM, and by wrapping it in a Swift library, we can remove the NPM dependency.
This Codegen proposal uses a dictionary of values that are passed around and accessed each time a property is accessed, this has some run time implications as they must be retrieved from the dictionary each time they are accessed. This is similar to the way the current Codegen works.
An alternative approach may map field data onto stored properties on the response object once and only once during parsing, which is typically done on a background thread. However, this approach would require a lot of data duplication and would increase the complexity and size of the generated code considerably.
The size of the generated code for large or complex queries can rapidly become very large under the current CodeGen. This is something we would like to improve upon. Though we understand that there is only so much we can do to reduce code size while accurately reflecting all the data and handling edge cases.
The current Codegen generates objects that are often difficult to parse and understand for developers looking at the generated code. While we recognize that the functionality required and edge cases that must be accounted for cause complexity to be inevitable, we hope that the new Codegen can reduce the complexity. This may or may not be possible, and complexity will likely have trade-offs with functionality, generated code size, and other goals.
The size of the compiled binary when using our generated data objects must also be considered. Alternatives have been proposed that use classes for models rather than structs to reduce the size of the compiled binary. While classes can reduce binary size, they incurs an additional runtime cost when consumed.
This proposal opts for using lightweight structs that only store a single property in memory — a pointer to their data dictionary. By restricting the size of our structs to a single pointer, we are able to achieve the benefits of structs without incurring the majority of the overhead they create. See Memory Management and Performance of Value Types for more information.
We have had customer concerns with the compilation time of large generated queries under the current Codegen. While we recognize that large queries will always add some noticeable time to compilation, it is a goal of this project to minimize the build time impact of our generated data objects.
Easy wins in this aspect of performance can be gained by explicitly providing types where they could be inferred. Other compilation time improvements should be considered and any trade-offs with other goals weighed.
Because response data from a query may be considerably large, it is important to consider the memory usage of our generated data objects. This proposal utilizes multiple mechanisms for minimizing memory usage.
Because the underlying storage of the current Codegen objects is a dictionary, it is already heap allocated and shared. Accessing fields through different structs (like fragments) does not lead to additional copies of the data. This proposal maintains this functionality.
One of the primary use cases for fragments is for dependency inversion and component reuse. A fragment of data can be used with a UI component or other object, irrespective of the GraphQL operation the data comes from or the other fields that were fetched in addition to the fragments fields.
While the ideal way to provide this functionality is to generate fragments as protocols, which the generated data objects conform to, this does not work. This is due to lack of Swift language support for covariant protocol conformance and protocols with associated types as concrete properties. For more information on this see: Appendix A: Why Fragment Protocols Don’t Work
This proposal aims to provide a simple way to construct fragment objects from the generated data objects.
Data received in the response of a GraphQL Operation must be validated to ensure that all required fields exist and all objects are valid. The response data objects should return data that is guaranteed to be valid. While the current Codegen does data validation appropriately, it is important to note that this is a required goal of any proposed replacement as well.
Our generated objects should be easy to use by the consumer in order to be useful. This involves the structure of the data; the manner in which type cases and fragments are accessed; usage of enums and unions; providing strict type safety and nullability; and enabling code completion for all fields (including merged fields).
Because we cannot understand or determine the optimal structure for each user’s individual use cases, it is likely that many users will use adapters to map our generated data objects onto their own models/view models. This is especially true for users who want to store data in a separate cache, such as CoreData or Realm. The generated data objects must be structured in a way that provides an easy way to access the data for mapping onto the user’s custom model types.
While we do not expect the generated data objects to be appropriate for every use case, it is our aim to maximize the instances in which custom models/view models are not necessary. This requires type safety, nullability, code completion, and merged fields to work in an intuitive manner.
The generated code, as well as the implementation of the code generation tooling, should be architected in a flexible manner that allows for additional features additions to be implemented as easily as possible.
For all examples in this document, we will use the following schema:
type Query {
allAnimals: [Animal!]!
allPets: [Pet!]!
classroomPets: [ClassroomPet!]!
}
interface Animal {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
}
interface Pet {
humanName: String
favoriteToy: String!
owner: Human
}
interface HousePet implements Animal & Pet {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
humanName: String
favoriteToy: String!
owner: Human
bestFriend: Pet
rival: Pet
livesWith: ClassroomPet
}
interface WarmBlooded implements Animal {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
bodyTemperature: Int!
laysEggs: Boolean!
}
type Height {
relativeSize: RelativeSize!
centimeters: Int!
meters: Int!
feet: Int!
inches: Int!
yards: Int!
}
type Human implements Animal & WarmBlooded {
firstName: String!
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
bodyTemperature: Int!
laysEggs: Boolean!
}
type Cat implements Animal & Pet & WarmBlooded {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
humanName: String
favoriteToy: String!
owner: Human
bodyTemperature: Int!
laysEggs: Boolean!
isJellicle: Boolean!
}
type Dog implements Animal & Pet & HousePet & WarmBlooded {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
humanName: String
favoriteToy: String!
owner: Human
bodyTemperature: Int!
laysEggs: Boolean!
bestFriend: HousePet
rival: Cat
livesWith: Bird
}
type Bird implements Animal & Pet & WarmBlooded {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
humanName: String
favoriteToy: String!
owner: Human
bodyTemperature: Int!
laysEggs: Boolean!
wingspan: Int!
}
type Fish implements Animal & Pet {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
humanName: String
favoriteToy: String!
owner: Human
}
type Rat implements Animal & Pet {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
humanName: String
favoriteToy: String!
owner: Human
}
type Crocodile implements Animal {
species: String!
height: Height!
predators: [Animal!]!
skinCovering: SkinCovering
age: Int!
}
type PetRock implements Pet {
humanName: String
favoriteToy: String!
owner: Human
}
union ClassroomPet = Cat | Bird | Rat | PetRock
enum RelativeSize {
LARGE
AVERAGE
SMALL
}
enum SkinCovering {
FUR
HAIR
FEATHERS
SCALES
}In order to fulfill all of the stated goals of this project, the following approach is proposed for the structure of the Codegen:
We will refer to each individual object fetched in a GraphQL response as an “entity”. An entity defines a single type (object, interface, or union) that has fields on it that can be fetched.
A SelectionSet defines a set of fields that have been selected to be visible for access on a given entity. The SelectionSet determines the shape of the generated response data objects for a given operation.
Given the query:
query {
allAnimals {
species
... on Pet {
humanName
}
}
}Each animal in the list of allAnimals is a single entity. Each of those entities has a concrete type (Cat, Fish, Bird, etc.). For each animal entity, we define a group of SelectionSets that exposes the species field and, if the entity is a Pet, the humanName field.
Each generated data object conforms to a SelectionSet protocol, which defines some universal behaviors. Type cases, fragments, and root types all conform to this protocol. For reference see SelectionSet.swift.
The generated data objects are structs that have a single stored property. The stored property is to another struct named ResponseDict, which has a single stored constant property of type [String: Any].
Often times the same data can be represented by different generated types. For example, when checking a type condition or accessing a fragment on an entity. By using structs with a single dictionary pointer, we are able to reference the same underlying data, while providing different accessors for fields at different scopes.
This allows us to store all the fetched data for an entity one time, rather than duplicating data in memory. The structs allow for hyper-performant conversions, as they are stack allocated at compile time and just increment a pointer to the single storage dictionary reference.
Accessors to the fields that a generated object has are implemented as computed properties that access the dictionary storage.
Let’s start with a simple example to illustrate what the Fields object looks like:
Query Input:
query {
allAnimals {
species
height {
feet
}
}
}Generated Output: (Animal Object)
struct Animal: SelectionSet, HasFragments {
let data: ResponseDict
var species: String { data["species"] }
var height: Height { data["height"] }
struct Height: SelectionSet {
let data: ResponseDict
var feet: Int { data["feet"] }
}
}In this simple example, the Animal object has a nested Height object. Each conforms to SelectionSet and each has a single stored property let data: ResponseDict. The ResponseDict is a struct that wraps the dictionary storage, and provides custom subscript accessors for casting/transforming the underlying data to the correct types. For more information and implementation details, see: ResponseDict.swift
GraphQL execution is the process in which the Apollo iOS client converts raw data — either from a network response or the local cache — into a SelectionSet. The execution process determines which fields should be “selected”; maps the data for those fields; decodes raw data to the correct types for the fields; validates that all fields have valid data; and returns SelectionSet objects that are guaranteed to be valid.
A field that is “selected” is mapped from the raw data onto the SelectionSet to be accessed using a generated field accessor. If data exists in the cache or on a raw network response for a field, but the field is not “selected” the resulting SelectionSet will not include that data after execution.
Because SelectionSet field access uses unsafe force casting under the hood, it is necessary that a SelectionSet is only ever created via the execution process. A SelectionSet that is initialized manually cannot be guaranteed to contain all the expected data for its field accessors, and as such, could cause crashes at run time. SelectionSets returned from GraphQL execution are guaranteed to be safe.
By default, GraphQLOperation field variables; fields on InputObjects; and field arguments are nullable. For a nullable argument, the value can be provided as a value, a null value, or omitted entirely. In GraphQL, omitting an argument and passing a null value have semantically different meanings. While often, they may be identical, it is up to the implementation of the server to interpret these values. For example, a null value for an argument on a mutation may indicate that a field on the object should be set to null, while omitting the argument indicates that the field should retain it's current value -- or be set to a default value.
Because of the semantic difference between null and ommitted arguments, we have introduced GraphQLNullable. GraphQLNullable is a generic enum that acts very similarly to Optional, but it differentiates between a nil value (the .none case), and a null value (the .null case). Values are still wrapped using the .some(value) case as in Optional.
The previous Apollo versions used double optionals (??) to represent null vs nil. This was unclear to most users and make reading and reasoning about your code difficult in many situations. GraphQLNullable makes your intentions clear and explicit when dealing with nullable input values.
For more information and implementation details, see: GraphQLNullable.swift
An overview of the format of all generated object types.
In addition to generating SelectionSets for your GraphQLOperations, types will be generated for each type (object, interface, or union) that is used in any operations across your entire application. These types will include all the fields that may be fetched by any operation used and can include other type metadata.
The schema types have a number of functions.
- Include metadata that allows the
GraphQLExecutorand runtime type checking onTypeCases to operate. - Can be extended to provide cache key computation for types to configure the normalized cache.
- Used for interacting with the cache for manual read/write functionality.
- Used to create mock objects for generated
SelectionSets to be used in unit tests.
These schema types can be included directly in your application target, or be generated into a separate shared library that can be used across modules in your application.
Schema types are implemented as class objects, not structs. They will use reference type semantics and are mutable within a cache transaction.
For each concrete type declared in your schema and referenced by any generated operation, an Object subclass is generated. Each Object type contains a static var __metadata containing a struct that provides a list of the interfaces implemented by the concrete type.
public final class Dog: Object {
override public class var __typename: String { "Dog" }
// MARK: - Metadata
override public class var __metadata: Metadata { _metadata }
private static let _metadata: Metadata = Metadata(
implements: [Animal.self, Pet.self, WarmBlooded.self, HousePet.self]
)
}For each interface type declared in your schema and referenced by any generated operation, an Interface subclass is generated. Interfaces wrap an underlying Object type and ensure that only objects of types that they are only initialized with a wrapped object of a type that implements the interface.
public final class Pet: Interface {}For each union type declared in your schema and referenced by any generated operation, a UnionType enum is generated. UnionType enums have cases representing each possible type in the union. Each case has an associated value of the Object type represented by that case. UnionType enums are referenced as fields by being wrapped in a Union<UnionType> enum that provides access to the underlying UnionType and unknown cases. See Handling Unknown Types for more information. UnionType enums contain a static let possibleTypes property that provides a list of the concrete Object types contained in the union.
public enum ClassroomPet: UnionType, Equatable {
case Cat(Cat)
case Bird(Bird)
case Rat(Rat)
case PetRock(PetRock)
public init?(_ object: Object) {
switch object {
case let ent as Cat: self = .Cat(ent)
case let ent as Bird: self = .Bird(ent)
case let ent as Rat: self = .Rat(ent)
case let ent as PetRock: self = .PetRock(ent)
default: return nil
}
}
public var object: Object {
switch self {
case let .Cat(object as Object), let .Bird(object as Object), let .Rat(object as Object), let .PetRock(object as Object):
return object
}
}
static public let possibleTypes: [Object.Type] =
[AnimalKingdomAPI.Cat.self, AnimalKingdomAPI.Bird.self, AnimalKingdomAPI.Rat.self, AnimalKingdomAPI.PetRock.self]
}A SchemaConfiguration object will also be generated for your schema. This object will have a function that maps the Object types in your schema to their __typename string. This allows the execution to convert data (from a network response from the cache) to the correct Object type at runtime.
For an example of generated schema metadata see AnimalKindgomAPI/Schema.swift.
Enums will be generated for each enum type in the schema that is used in any of the operations defined in your application. These enums will conform to a simple EnumType protocol. When used as the type for a field on a SelectionSet, these enums will be wrapped in the generic GraphQLEnum. Unlike the previous code generation engine, the new code generation will respect the capitalization of the enum cases from the schema.
enum RelativeSize: String, EnumType {
case LARGE
case AVERAGE
case SMALL
}struct Animal: SelectionSet {
// ...
var size: GraphQLEnum<RelativeSize> { data["size"] }
}GraphQLEnum wraps your generated EnumTypes and provides the __unknown case with an associated value of a raw string. This is necessary for clients to provide forward-compatibility with new enum cases added to a schema in the future. GraphQLEnum has pattern matching and Equatable conformance implemented that allows you to consume it as if it were your underlying EnumType in most cases.
Examples:
let size: GraphQLEnum<RelativeSize> = .init(.SMALL)
size == .SMALL // trueWhen using switch, you must provide a case for the unknown value.
switch size {
case .SMALL: break
case .AVERAGE: break
case .LARGE: break
case .__unknown(_): break
default: break
}Because pattern matching is being used to match against the underling EnumType cases, you must also provide a default case.
To ensure exhaustive switch cases without a default case your generated cases can be wrapped in .case().
switch size {
case .case(.SMALL): break
case .case(.AVERAGE): break
case .case(.LARGE): break
case .__unknown(_): break
}If you want to ignore the unknown case, you can access the .value field, which returns an optional value of the wrapped type. If the type is an unknown case .value will be nil.
switch size.value {
case .SMALL: break
case .AVERAGE: break
case .LARGE: break
default: break
// or
case .none: break
}See GraphQLEnum.swift for implementation details.
Input objects will be generated for each input type in the schema that is used in an argument for any of the operations defined in your application. Input objects are structs that are backed by a InputDict struct that stores the values for the fields on the input object in a dictionary. This allows for InputObjects to be treated as values types but use copy-on-write semantics under the hood.
Nullable fields on input objects are represented using GraphQLNullable to allow for both null and nil values.
Following the Input Coercion rules from the GraphQL spec, the server defined default value for a field will be used when passing nil. Nullable fields on input objects are represented using GraphQLNullable to allow for both null and nil values. For non-nullable fields, if the schema provides a default value, the field will be represented as an optional to allow for nil to be passed.
Examples:
Nullable field with no default value:
input MyInput {
size: RelativeSize
}struct MyInput: InputObject {
public private(set) var dict: InputDict
init(size: GraphQLNullable<RelativeSize> = nil) {
dict = InputDict(["size": size])
}
var size: GraphQLNullable<RelativeSize> {
get { dict["size"] }
set { dict["size"] = newValue }
}
}Nullable field with a default value:
input MyInput {
size: RelativeSize = SMALL
}struct MyInput: InputObject {
public private(set) var dict: InputDict
init(size: GraphQLNullable<RelativeSize>) { ... }
/// If `.none`, defaults to server-provided default value (.SMALL)
var size: GraphQLNullable<RelativeSize> { ... }
}Non-nullable field with no default value:
input MyInput {
size: RelativeSize!
}struct MyInput: InputObject {
public private(set) var dict: InputDict
init(size: RelativeSize) { ... }
var size: RelativeSize { ... }
}Non-nullable field with a default value:
input MyInput {
size: RelativeSize! = SMALL
}struct MyInput: InputObject {
public private(set) var dict: InputDict
init(size: RelativeSize?) { ... }
/// If `nil`, defaults to server-provided default value (.SMALL)
var size: RelativeSize? { ... }
}Note that we are not generating these fields with the provided default values. This is to account for default values that may change on the schema in the future. See Generate Default Parameter Values for
InputObjectDefault Values for more discussion.
A GraphQLOperation is generated for each operation defined in your application. GraphQLOperations can be queries (GraphQLQuery), mutations (GraphQLMutation), or subscriptions (GraphQLSubscription).
Each generated operation will conform to the GraphQLOperation protocol defined in GraphQLOperation.swift.
Simple Operation - Example:
class AnimalQuery: GraphQLQuery {
let operationName: String = "AnimalQuery"
let document: DocumentType = .notPersisted(definition: .init(
"""
query AnimalQuery {
allAnimals {
species
}
}
""")
init() {}
struct Data: SelectionSet {
// ...
}
}For an operation that takes input arguments, the initializer will be generated with parameters for each argument. Arguments can be scalar types, GraphQLEnums, or InputObjects. During execution, these arguments will be used as the operation's variables, which are then used as the values for arguments on SelectionSet fields matching the variables name.
Operation With Scalar Argument - Example:
class AnimalQuery: GraphQLQuery {
let operationName: String = "AnimalQuery"
let document: DocumentType = .notPersisted(definition: .init(
"""
query AnimalQuery($count: Int!) {
allAnimals {
predators(first: $count) {
species
}
}
}
""")
var count: Int
init(count: Int) {
self.count = count
}
var variables: Variables? { ["count": count] }
struct Data: SelectionSet {
// ...
struct Animal: SelectionSet {
static var selections: [Selection] {[
.field("predators", [Predator.self], arguments: ["first": .variable("count")])
]}
}
}
}In this example, the value of the count property is passed into the variables for the variable with the key "count". The Selection for the field "predators", the argument "first" has a value of .variable("count"). During execution, the predators field will be evaluated with the argument from the operation's "count" variable.
For nullable arguments, the code generator will wrap the argument value in a GraphQLNullable. The executor will evaluate the GraphQLNullable to format the operation variables correctly. See GraphQLNullable for more information.
Operation With Nullable Scalar Argument - Example:
class AnimalQuery: GraphQLQuery {
let operationName: String = "AnimalQuery"
let document: DocumentType = .notPersisted(definition: .init(
"""
query AnimalQuery($count: Int) {
allAnimals {
predators(first: $count) {
species
}
}
}
""")
var count: GraphQLNullable<Int>
init(count: GraphQLNullable<Int>) {
self.count = count
}
var variables: Variables? { ["count": count] }
// ...
}Each SelectionSet has metadata properties that provide the Apollo library the ability to check for valid type conversions at runtime.
__typename is a computed property on each concrete instance of a SelectionSet that defines the concrete type of the underlying entity that the SelectionSet represents. This is a String representation that is fetched from the server using the __typename metadata field in the query. All queried selection sets will automatically include the __typename field.
__objectType is a computed property that provides a strongly typed wrapper for __typename. It converts the __typename string into a concrete case of the Object enum from the schema.
__parentType is a static property on each SelectionSet type that defines the known type that the SelectionSet is being fetched on. The __parentType may be an Object, Interface, or Union. This property is represented by the ParentType enum. __parentType is generated for each SelectionSet, not computed at runtime.
Indicates the fields that should be “selected” during GraphQL execution. See Selection Generation for more information.
To illustrate the difference between these properties, we will use an example. Given the query:
query {
allAnimals {
species
}
}The allAnimals field has a type of Animal, which is an interface. Each concrete instance of the Animal struct could have a different concrete type (Cat, Fish, Bird, etc.)
The __typename field, provided by the server would provide the actual concrete type for each entity as a String.
The __objectType property would convert this into a strongly typed Object from the generated schema types. This property will have different values for each concrete Animal object.
The __parentType for all of these entities would still be the same — the Animal interface.
Each field selected in a SelectionSet's selections can be accessed via a generated field accessor. Generated field accessors provide type-safe access to the values for fields that are selected on the SelectionSet. These field accessors access the data on the underlying ResponseDict, which holds the data for the SelectionSet. The data is then cast to the correct type and any transformations needed are applied under the hood. Because the GraphQL execution validates response data before mapping it onto generated SelectionSets, the data is guarunteed to exist and be the correct type.
When the species field on an Animal is selected, the following field accessor is generated:
var species: String { data["species"] }The ResponseDict accesses the field's value and force casts it to a String, which will always be safe.
Fragments are used in GraphQL operations primarily for two reasons:
- Sharing common
SelectionSets across multiple operations - Querying fields on a more specific type than the current parent type
When sharing a common SelectionSet across multiple operations, a fragment can be used. This can reduce the size of your operation definition files. Additionally, and often more importantly, it allows you to reuse generated SelectionSet data objects across multiple operations. This can enable your code to consume fragments of an operation’s response data irrespective of the operation executed. Using fragments this way acts as a form of abstraction similar to protocols. It has often been proposed that fragments should be represented as generated protocols, however due to implementation details of the Swift language, this approach has serious limitations. See Appendix A: Why Fragment Protocols Don’t Work for more information.
Instead of protocols, fragments are generated as concrete SelectionSet structs that any SelectionSet that contains the fragment can then be converted to.
Fragment Definition:
fragment AnimalDetails on Animal {
species
}Query Input:
query AllAnimalSpecies {
allAnimals {
...AnimalDetails
}
}Generated Output:
AnimalDetails.Swift
struct AnimalDetails: SelectionSet, Fragment {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Animal.self) }
let data: ResponseDict
var species: String { data["species"] }
}AllAnimalSpeciesQuery.swift (Animal Object)
struct Animal: SelectionSet, HasFragments {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Animal.self) }
let data: ResponseDict
var species: String { data["species"] }
struct Fragments: ResponseObject {
let data: ResponseDict
var animalDetails: AnimalDetails { _toFragment() }
}
}The query’s Animal struct conforms to HasFragments, which is a protocol that exposes a fragments property that exposes the nested Fragments struct. The fragments a SelectionSet contains are exposed in this Fragments struct via computed properties that utilize a _toFragment() helper function. This allows you to access the AnimalDetails fragment via myAnimal.fragments.animalDetails.
In the above example you may note that the species field is accessible directly on the Animal object without having to access the AnimalDetails fragment first. This is because fields from fragments that have the same __parentType as the enclosing SelectionSet are automatically merged into the enclosing SelectionSet.
Inline fragments are fragments that are unnamed and defined within an individual operation. These fragments cannot be shared, and as such, individual fragment SelectionSets are not generated. Inline fragments are used strictly for handling “Type Cases“.
When using a fragment to fetch fields on a more specific interface or type than the SelectionSet’s __parentType, we create a new SelectionSet for the more specific type. We refer to these more specific SelectionSets as “Type Cases”.
Note: A Type Case can be defined using either an inline fragment or an independent, named fragment.
For example, an inline fragment ... on Pet { humanName } would generate an AsPet object nested inside of the enclosing entity’s SelectionSet.
A Type Case is always represented as an optional property on the enclosing entity, as the enclosing entity may or may not be of a type that matches the fragment’s type.
Let’s take a look at an example of this:
Query Input:
query {
allAnimals {
species
... on Pet {
humanName
}
}
}Generated Output: (Animal Object)
struct Animal: RootSelectionSet {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Animal.self) }
let data: ResponseDict
var asPet: AsPet? { _asType() }
var species: String { data["species"] }
struct AsPet: TypeCase {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Pet.self) }
let data: ResponseDict
var species: String { data["species"] }
var humanName: String? { data["humanName"] }
}
}The computed property for asPet uses an internal function _asType(), which is defined in an extension on SelectionSet. This function checks the concrete __objectType against the __parentType of the Type Case to see if the entity can be converted to the SelectionSet of the TypeCase. An AsPet struct will only be returned if the underlying entity for the Animal is a type that conforms to the Pet Interface, otherwise asPet will return nil.
Similarly to merging in fragment fields, fields from a parent and any sibling TypeCases that match the __parentType of a TypeCase are merged in as well. In the above example the species field that is selected by the Animal SelectionSet is merged into the child AsPet TypeCase. The AsPet represents the same entity as the Animal, and because we know that the species field will exist for the entity, it is merged in. Since the field will already be selected and will exist in the underlying ResponseDict, the child SelectionSet does not need to duplicate the Selection for the field. Only a duplicated field accessor needs to be generated. For more explanation of how the Selections for TypeCases work, see TypeCase Selections.
Additionally, since any fields from other TypeCases defined on the parent SelectionSet that match the type of a TypeCase are guaranteed to exist, they are also merged in. This makes it much easier to consume the data on a generated TypeCase.
Expanding on the above example, we can see how sibling TypeCase selections can be merged.
Query Input:
query {
allAnimals {
species
... on Pet {
humanName
}
... on Cat {
isJellicle
}
}
}Generated Output: (Animal Object)
struct Animal: RootSelectionSet {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Animal.self) }
let data: ResponseDict
var species: String { data["species"] }
var asPet: AsPet? { _asType() }
var asCat: AsCat? { _asType() }
struct AsPet: TypeCase {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Pet.self) }
let data: ResponseDict
var species: String { data["species"] }
var humanName: String? { data["humanName"] }
}
struct AsCat: TypeCase {
static var __parentType: ParentType { .Object(AnimalKingdomAPI.Cat.self) }
let data: ResponseDict
var species: String { data["species"] }
var humanName: String? { data["humanName"] }
var isJellicle: Bool { data["isJellicle"] }
}
}The AsCat TypeCase is on the __parentType “Cat" and the "Cat" object type implements the “Pet" Interface. Given this information the code generation engine can deduce that, any AsCat will also have the humanName field selected by the AsPet TypeCase. This field gets merged in and the AsCat has a field accessor for it.
Union types are generated just like any other SelectionSet. Because a union has no knowledge of the underlying type or the selections available, a union SelectionSet will not generally include any field accessors itself. Rather, a union will only provide access to its child TypeCasess.
Example:
query {
classroomPets {
... on Pet {
humanName
}
... on Bird {
wingspan
}
}
}struct ClassroomPet: RootSelectionSet {
static var __parentType: ParentType { .Union(AnimalKingdomAPI.ClassroomPet.self) }
var asPet: AsPet? { _asType() }
var asBird: AsBird? { _asType() }
struct AsPet: TypeCase {
static var __parentType: ParentType { .Interface(AnimalKingdomAPI.Pet.self) }
var humanName: String? { data["humanName"] }
}
struct AsBird: TypeCase {
static var __parentType: ParentType { .Object(AnimalKingdomAPI.Bird.self) }
var humanName: String? { data["humanName"] }
var wingspan: Int { data["wingspan"] }
}
}Each SelectionSet includes an array of Selections that indicate what fields should be “selected” during execution.
A parent SelectionSet will conditionally include its children’s selections as nested selections. The GraphQLExecutor determines if child selections should be included.
While merged fields will be generated as field accessors on children, the selections array for each SelectionSet does not merge in selections from parents, children, or siblings. The selections array will closely mirror the query operation definition that the generated objects are based upon.
Selection is an enum with cases representing different types of selections. A simple field selection is represented as a Field, but nested selections that are conditionally included are represented by additional types.
The Selection.field(Field) case represents a specific field that should be selected. It contains a Field struct that includes the field name; the field alias if it exists; any arguments the field takes, and the field’s type.
Example:
query {
allAnimals {
species
}
}struct Animal: RootSelectionSet {
static var selections: [Selection] {[
.field("species", String.self)
]}
}If a field takes arguments, the arguments will be generated on the field’s Selection. Arguments are represented as a dictionary of argument names and their values. An argument’s value is represented as an InputValue and can be a scalar value, a list of other InputValues, a generated input type, or a variable. Variable arguments have their values provided when an instance of an operation is created.
Scalar Value Argument Example:
query {
allAnimals {
predators(first: 3)
}
}struct Animal: RootSelectionSet {
static var selections: [Selection] {[
.field("predators", [Predator].self, arguments: ["first": 3])
]}
}Variable Argument Example:
query($count: Int) {
allAnimals {
predators(first: $count)
}
}
class Query: GraphQLQuery {
var count: Int
init(count: Int) { ... }
struct Animal: RootSelectionSet {
static var selections: [Selection] {[
.field("predators", [Predator].self, arguments: ["first": .variable("count")])
]}
}
}One or more Selections may be conditionally included based on a @skip or @include directive. These Selections provide a variable name for a variable of type Boolean on the operation that will determine if the Selections are included.
Single Field Example:
query($skipSpecies: Boolean) {
allAnimals {
species @skip(if: $skipSpecies)
}
}class Query: GraphQLQuery {
var skipSpecies: Bool
init(skipSpecies: Bool) { ... }
struct Animal: RootSelectionSet {
static var selections: [Selection] {[
.skip(if: "skipSpecies", .field("species", String.self))
]}
}
}Multiple Fields Example:
query($includeDetails: Boolean) {
allAnimals {
species
@include(if: $includeDetails) {
height {
meters
}
skinCovering
}
}
}class Query: GraphQLQuery {
var includeDetails: Bool
init(includeDetails: Bool) { ... }
struct Animal: RootSelectionSet {
static var selections: [Selection] {[
.field("species", String.self)
.include(if: "includeDetails", [
.field("height", Height.self),
.field("skinCovering", GraphQLEnum<SkinCovering>.self),
]),
]}
struct Height: RootSelectionSet {
static var selections: [Selection] {[
.field("meters", Int.self)
]}
}
}
}Fragments included by a SelectionSet reference the Fragment SelectionSet and automatically include all the fragment’s selections.
Example:
query {
allAnimals {
...AnimalDetails
}
}
fragment AnimalDetails on Animal {
height {
meters
}
skinCovering
}struct AnimalDetails: RootSelectionSet, Fragment {
static var selections: [Selection] {[
.field("height", Height.self)
.field("skinCovering", GraphQLEnum<SkinCovering>.self),
]}
struct Height: RootSelectionSet {
static var selections: [Selection] {[
.field("meters", Int.self)
]}
}
}
class Query: GraphQLQuery {
struct Data: RootSelectionSet {
static var selections: [Selection] {[
.field("allAnimals", [Animal].self),
]}
struct Animal: RootSelectionSet {
static var selections: [Selection] {[
.fragment(AnimalDetails.self)
]}
}
}
}When a SelectionSet has a nested type case, the type case’s selections are only included if the __typename of the object matches a type that is compatible with the TypeCase’s __parentType. This is determined at runtime by the GraphQLExecutor during the process of executing the selections on each SelectionSet. While the selections for each TypeCase are not duplicated, field accessors for fields merged from the parent and other TypeCases will be generated on each TypeCase struct. This is described in Merging TypeCase Fields Into Children and Siblings.
The selections for a TypeCase are included if:
- If the
__parentTypeis anObjecttype:- If the runtime type of the data object is equal to the object type.
- If the
__parentTypeis anInterfacetype:- If the runtime type of the data object is a type that implements the interface.
- If the
__parentTypeis aUniontype:- If the runtime type of the data object is an object type in the union’s possible types.
Inline TypeCase Example:
query {
allAnimals {
species
... on Pet {
humanName
}
... on Bird {
wingpsan
}
}
}struct Animal: RootSelectionSet {
static var __parentType: ParentType { .Interface(.Animal) }
static var selections: [Selection] {[
.field("species", String.self),
.typeCase(AsPet.self),
.typeCase(AsBird.self),
]}
var species: String { data["species" ]}
var asPet: AsPet? { _asType() }
var asBird: AsBird? { _asType() }
struct AsPet: TypeCase {
static var __parentType: ParentType { .Interface(.Pet) }
static var selections: [Selection] {[
.field("humanName", String?.self),
]}
var species: String { data["species" ]}
var humanName: String? { data["humanName" ]}
}
struct AsBird: TypeCase {
static var __parentType: ParentType { .Interface(.Pet) }
static var selections: [Selection] {[
.field("wingspan", Int.self),
]}
var species: String { data["species" ]}
var humanName: String? { data["humanName" ]}
var wingspan: Int { data["wingspan" ]}
}
}Named Fragment TypeCase Example: Field and type case accessors omitted for brevity.
query {
allAnimals {
species
...PetDetails
}
}
fragment PetDetails on Pet {
humanName
}struct PetDetails: RootSelectionSet, Fragment {
static var __parentType: ParentType { .Interface(.Pet) }
static var selections: [Selection] {[
.field("humanName", String?.self),
]}
}
struct Animal: RootSelectionSet {
static var __parentType: ParentType { .Interface(.Animal) }
static var selections: [Selection] {[
.field("species", String.self),
.typeCase(AsPet.self),
]}
struct AsPet: TypeCase {
static var __parentType: ParentType { .Interface(.Pet) }
static var selections: [Selection] {[
.fragment(PetDetails.self),
]}
}
}A SelectionSet that represents the root selections on its __parentType is a RootSelectionSet. Nested selection sets for TypeCases are not RootSelectionSets.
While a TypeCase only provides the additional selections that should be selected for its specific type, a RootSelectionSet guarantees that all fields for itself and its nested type cases are selected. When considering a specific TypeCase, all fields will be selected either by the root selection set, a fragment spread, the type case itself, or another compatible TypeCase on the root selection set.
For this reason, only a RootSelectionSet can be executed by a GraphQLExecutor. Executing a non-root SelectionSet would result in fields from its parent RootSelectionSet not being collected into the ResponseDict for the SelectionSet's data.
Types that are added to your schema server side after the code generation has run could be returned in a response from the server, but will not have a generated Object type object that recognizes them. These types are unknown to the client-side type system. Because all data about these types is not known, certain functionality will be limited on unknown types. The RootSelectionSet fields will be selected properly for unknown types, but any child TypeCase will not be present on unknown types, as we are unable to know if the unknown type matches a TypeCase or not.
Each generated object can provide a function for computing it's cache key by conforming to the CacheKeyProvider protocol. Extensions can be created manually to provide conformance to this protocol on object types.
extension Cat: CacheKeyProvider {
static func cacheKey(for data: JSONObject) -> String? {
guard let humanName = data["humanName"] as? String,
let species = data["species"] as? String else {
return nil
}
return humanName + "_" + species
}
}This function will be called whenever a cache key needs to be computed for a JSON response with a __typename matching the typename for a Cat object. (This mapping uses the generated mapper function on the SchemaConfiguration.)
If nil is returned, the object will be treated as if it does not have a unique cache key and will cached without normalization.
When reading/writing data to the cache, the
__typenamewill always be prepended to the returned cache key. It does not need to be included in the value returned by yourCacheKeyProvider. This means that cache keys only need to be guaranteed to be unique across objects of the same type.
Multiple types that compute their cache keys in the same way can share their cache key provider function via protocol composition.
protocol PetCacheKeyProvider: CacheKeyProvider { }
extension PetCacheKeyProvider {
static func cacheKey(for data: JSONObject) -> String? {
guard let humanName = data["humanName"] as? String,
let species = data["species"] as? String else {
return nil
}
return humanName + "_" + species
}
}
extension Cat: PetCacheKeyProvider {}
extension Dog: PetCacheKeyProvider {}
extension Fish: PetCacheKeyProvider {}In the future, we hope to provide mechanisms to have
CacheKeyProviderimplementations automatically generated based on client-side directives that can be added as extensions to your graphql schema directly.
If you would like to automatically provide cache key computation for unknown types (types that are added to your schema after code generation), you can extend your generated SchemaConfiguration to conform to the SchemaUnknownTypeCacheKeyProvider protocol.
extension AnimalKindgomAPI: SchemaUnknownTypeCacheKeyProvider {
static func cacheKeyForUnknownType(withTypename typename: String, data: JSONObject) -> String? {
guard let id = data["id"] as? String else {
return nil
}
return id
}
}Consider the following fragment and queries.
fragment HeightInMeters on Animal {
height {
meters
}
}
query AnimalHeight {
allAnimals {
...HeightInMeters
height {
feet
yards
}
}
}
query AnimalMeters {
allAnimals {
...HeightInMeters
}
}If we generated protocols for the HeightInMeters fragment, it would look like this:
protocol HeightInMeters {
associatedtype Height: HeightInMeters_Height
var height: Height { get }
}
protocol HeightInMeters_Height {
var meters: Int { get }
}The generated queries ResponseData objects would be: (generated code simplified for example)
// AnimalHeightQuery.Data.Animal
struct Animal: RootSelectionSet, HeightInMeters {
struct Height: RootSelectionSet, HeightInMeters_Height {
let meters: Int
let feet: Int
let yards: Int
}
let height: Height { data["height"] }
}
// AnimalMetersQuery.Data.Animal
struct Animal: RootSelectionSet, HeightInMeters {
final class Height: RootSelectionSet, HeightInMeters_Height {
let meters: Int
}
let height: Height { data["height"] }
}Then you could not reference the fragment as a concrete type for re-use (such as in a UI component).
class AnimalMetersLabelView {
let animalHeight: HeightInMeters // Compiler Error:
// "Protocol with associatedtype cannot be
// referenced as concrete property type."
}This gets even more complicated (and broken) when you nest fragments inside of each other.
While SE-309 aims to make working with existential types easier, it does not solve this problem. The error will only be moved from when you reference the HeightInMeters protocol, to when you attempt access its height field.
Here we want to generate the Pet & WarmBlooded types, but we also want to generate an additional composed type that is both a Pet & Warmblooded. We do that by explicitly copying the referenced fragment into a nested field on the Pet TypeCase. The idea here is that you are able to configure your response objects to provide data in the shape you want. Even if certain selections – or entire type cases – are redundant, you can provide them to ensure that your generated models provide fields in the way you want to consume them in your application.
Query Input:
query {
allAnimals {
species
... on Pet {
...PetDetails
... on WarmBlooded {
...WarmBloodedDetails
}
}
...WarmBloodedDetails
}
}
fragment PetDetails on Pet {
humanName
favoriteToy
}
fragment WarmBloodedDetails on WarmBlooded {
bodyTemperature
}Output:
public struct Animal: RootSelectionSet: HasFragments {
var species: String { data["species"] }
var asPet: AsPet? { _asType() }
var asWarmBlooded: AsWarmBlooded? { _asType() }
/// Animal.AsPet
struct AsPet: TypeCase, HasFragments {
var species: String { data["species"] }
var humanName: String? { data["humanName"] }
var favoriteToy: String { data["favoriteToy"] }
var asWarmBlooded: AsWarmBlooded? { _asType() }
struct Fragments: ResponseObject {
var PetDetails: PetDetails { _toFragment() }
}
/// Animal.AsPet.AsWarmBlooded
struct AsWarmBlooded: TypeCase, HasFragments {
var species: String { data["species"] }
var humanName: String? { data["humanName"] }
var favoriteToy: String { data["favoriteToy"] }
var bodyTemperature: Int { data["bodyTemperature"] }
struct Fragments: ResponseObject {
var warmBloodedDetails: WarmBloodedDetails { _toFragment() }
}
}
}
/// Animal.AsWarmBlooded
struct AsWarmBlooded: TypeCase, HasFragments {
var species: String { data["species"] }
var bodyTemperature: Int { data["bodyTemperature"] }
struct Fragments: ResponseObject {
var warmBloodedDetails: WarmBloodedDetails { _toFragment() }
}
}
}Previous proposals for iOS code generation have implemented Codable on generated model object. This functionality has been discussed by the community frequently. However, this proposal does not include Codable conformance. While Codable has become a commonly used standard in iOS development, we do not believe it adds value to our generated objects.
Under the hood, all the JSON from a response must be parsed and validated through the GraphQLExecutor before being mapped onto the generated SelectionSet models. It cannot be automatically decoded onto Codable objects using the JSONDecoder. We could explore creating a custom decoder that uses the GraphQLExecutor, but adding this additional layer of abstraction would only add more complexity to the internal execution process and likely negatively impact performance without providing any new user-facing functionality.
We also see little value in Codable conformance for encoding the objects after the data has been executed and mapped onto them. The ideal way to persist GraphQL data is in the NormalizedCache that provided by the Apollo Client. The NormalizedCache relies on the GraphQLExecutor for reading and writing cache data, so Codable doesn't provide us any value internally.
Storing GraphQL data outside of the NormalizedCache is generally discouraged. While we won't prevent users from doing so, it is not officially supported by the Apollo iOS Client. We are looking into features to make the NormalizedCache more feature rich and performant in future versions. Investing in Codable support provides no value to users that are using the NormalizedCache, and as such is outside the scope of this project at the current time.
For fields with default values provided by the schema, we have decided to generate the fields as optional, but not include the default values in the generated code.
input MyInput {
size: RelativeSize = SMALL
}struct MyInput: InputObject {
public private(set) var dict: InputDict
init(size: GraphQLNullable<RelativeSize>) { ... }
/// If `nil`, defaults to server-provided default value (.SMALL)
var size: GraphQLNullable<RelativeSize> { ... }
}An alternative approach is to provide the default value as a generated default argument.
struct MyInput: InputObject {
public private(set) var dict: InputDict
init(size: GraphQLNullable<RelativeSize> = .some(.SMALL)) { ... }
/// If `nil`, defaults to server-provided default value (.SMALL)
var size: GraphQLNullable<RelativeSize> { ... }
}This however does not account for the fact that future changes to the default value of a field on an input type in a schema are considered to be backwards compatible. By generating the default value, we create a client that explicitly sends the value that was the default value when the type was generated – not necessarily the current default value of the server. In this case, the user could still explicitly pass nil to the initializer to indicate the intention to use the current default value as resolved by the server. However this is unclear at the call site and does not fall inline with the intentions of input coercion in the GraphQL Spec.
For this reason, we have opted to not generate default values.
@designatedNerd’s initial proposal includes enums with associated values for subtypes.
These SubType enums work for concrete types but not interfaces (because a type could conform to multiple interfaces). We don't plan on generating all the concrete types as data structures unless they are specifically enumerated (... on Pet)
It is undecided if we should implement these or not. They are only valuable in the specific scenario where you have inline fragments for multiple concrete types.
Given this query:
query {
allAnimals {
... on Bird {
wingspan
}
... on Cat {
bodyTemperature
}
}
}Without the Subtypes enum:
struct Animal: RootSelectionSet {
var asBird: AsBird? { _asType() }
var asCat: Cat? { _asType() }
struct AsBird: TypeCase { ... }
struct AsCat: TypeCase { ... }
}With the Subtypes enum:
struct Animal: RootSelectionSet {
var asBird: AsBird? { _asType() }
var asCat: Cat? { _asType() }
enum Subtype {
case bird(AsBird)
case cat(AsCat)
case _other(Animal)
}
var subtype: Subtype {
switch __objectType {
case is Bird.self: return .bird(AsBird(data: data))
case is Cat.Type: return .cat(AsCat(data: data))
default: return ._other(self)
}
}
struct AsBird: TypeCase { ... }
struct AsCat: TypeCase { ... }
}Possible Options:
- Don't implement the subtypes enum at all
- Use a directive
@generateSubTypeEnum(or some other name) to inform us that the subtypes enum should be generated (if the directive is not present default is option #1.)query { allAnimals @generateSubTypeEnum { ... on Bird { wingspan } ... on Cat { bodyTemperature } } }
- Implement logic so that if your query has one or more fragments on a concrete type, then we generate the subtypes (generate the enum with only 1 case +
_other) - Implement logic so that if your query has more than one fragment on a concrete type, then we generate the subtypes
Looking towards the future, the 1.0 implementation of the code generation engine opens the door to many possible future improvements to Apollo iOS. Here are some of the most highly considered additions that may come in future versions.
Under this proposal, computation of cache keys must be implemented manually using the process described in Cache Key Resolution. In the future, we hope to add a keyFields client-side directive that can be added to your project as extensions to the types on your GraphQL schema. This would allow us to generate the CacheKeyProviders for you.
In order to cast new concrete types to type conditions, we would need to know the metadata about what interfaces the types implement. We could possibly use a schema introspection query to fetch additional types added to the schema after code generation. Some information about these types may also be able to be assumed based on the response data returned from the server, indicating if a specific unknown type matches with some certain type cases.
Similar to the proposal for subtype enums for Type Cases, subtypes enum could be genera