Archive for the 'Server-side' Category

Sharing HTTP API bindings between Swift Vapor and iOS/macOS apps

I’ve been building a macOS/iOS front for my Swift Vapor side project. When I first started I simply duplicated the API HTTP request and response information between the client and server. But, as the project has grown, this has become more tedious. Since both client and server are in Swift, it seems like I should be able to come up with a more scalable solution. Ideally, I’d like to define an API in one place and have both the client and server code use it. So in this post, I will: figure out what’s shareable, wrestle with package managers to share it, and finally integrate the API component into both the server and client.

What can be shared

I first had to determine what parts of the API made sense to share between the client and server. Both would need the same information, but they encode that information in different ways, and those ways might not be compatible. In my thinking there are five pieces of API information: the URL path, HTTP method, query parameters, request body, and response body.

While it would be nice to share the URL path, the client and server encoded them in very different ways. The client used a raw string for the entire path, while the server implicitly encoded the path, one component at a time, in a declarative router. The HTTP method was treated similarly. The client had an enum for the method, while the server encoded the method implicitly in the routing table. So I couldn’t easily share paths and methods. Or, perhaps more accurately, I didn’t feel I could share them without making the client and server code worse.

Query parameters are special in that they are not declared on either side. The server checks for their existence down in the controller code, but there’s no standard declaration or deserialization of them. So currently, I can’t share them. However, I feel like this could be improved somehow using Codable to define all the possible query parameters. I will likely revisit this in the future.

I found that only the request body and the response bodies were used similarly enough on both sides to be sharable. Both declared them as Codable structs, albeit with slightly different names. But I felt like I could consolidate on a naming scheme that would make sense for both sides.

I also briefly thought about putting the sending and receiving of responses into the shareable library. But that didn’t work easily because it would require importing the Vapor framework for the server support, which I didn’t want to do for a Cocoa app. Perhaps more importantly, that part of the code wouldn’t actually be shared; only one side would be using it. So my decision was to only share data structures and not code.

Structure and naming in the shared component

Now that I had decided that I only wanted to share the request and response data structures, I needed to figure out a way to build that. I had a few requirements. First, I needed to minimize name collisions; the library was to be imported into both the client and server apps, which increased the odds of a collision. Second, I needed to name the data structures in a way that wasn’t client or server centric, which was their current state. The names needed to make sense on both the client and server. Thirdly, I needed to name things consistently so they were discoverable. Finally, there were some functional requirements, such as the requests and responses needed to be Codable and Equatable.

First, to solve global name collisions, I nested everything in a namespace. Since Swift doesn’t have real namespaces, I used an enum:

import Foundation

public enum MyAppAPI {


I put the app name in the namespace name because my client app already had a type named API. It also would allow the client to import multiple API bindings in the future without collisions.

As far as structuring the API bindings consistently, I decided to group things by REST resource. As an example, here’s the bindings for the user resource:

import Foundation

public extension MyAppAPI {
    public enum User {
        public struct Envelope<T>: Codable, Equatable where T: Codable, T: Equatable {
            public let user: T

            public init(user: T) {
                self.user = user

        public struct ShowResponse: Codable, Equatable {
            public let id: UUID
            public let email: String

            public init(id: UUID, email: String) {
       = id
       = email

        public struct UpdateRequest: Codable, Equatable {
            public let resetTokens: Bool

            public init(resetTokens: Bool) {
                self.resetTokens = resetTokens

There’s a lot to unpack. First, I put each resource into its own file. Second, I created a “namespace” (i.e. a Swift enum) for each resource inside the main API namespace. Inside the resource namespace, I created the individual requests and resource bodies. My naming convention was REST verb + Request or Response. My hope was this would make them easily discoverable. I also used envelopes in my APIs for clarity, so I declared my envelope for each resource as well.

In doing this work, I ran into a few practical considerations, aka things I had to do to appease Swift. The first thing was making everything public, which was a bit tedious. This had a knock-on effect in that the struct default inits didn’t work because they’re implicitly internal. I had to go through and manually implement the inits so that I could declare them public. This was enough overhead that I briefly reconsidered if having a sharable API component was worth doing. In the end I decided it was worth it, but I really wish there was a way to specify the access level of the implicit init on a struct.

One thing I didn’t attempt to deal with was API versioning. That’s only because I haven’t had to deal with it yet. I suspect I’ll add a namespace under the resource namespace for each non-v1 version, and add the updated request and response bodies there.

How to share

I had a plan of what to share, but I needed to figure out the mechanics of how to share. In most languages, this is done by a package manager, and Swift is no different. In fact, for Swift, there’s at least three package managers that can be chosen: Swift Package Manager (SwiftPM), Carthage, and Cocoapods. Ideally, I would like to use only one for both client and server. Unfortunately, each package manager has its own limitations, which made using only one undesirable.

SwiftPM has Linux support, but Carthage and Cocoapods don’t, meaning SwiftPM is the only viable option for my Swift Vapor app. However, SwiftPM doesn’t currently have the ability to generate Cocoa frameworks or apps, only Swift static libraries or command line apps. Since my API component doesn’t have any resources that require a framework, and Xcode can link against static Swift libraries for Cocoa apps, SwiftPM is a technical possibility. However, there is a decent amount of inconvenience involved with this approach (Googling can turn up tutorials on how). One is that my client apps would need two package managers (SwiftPM and either Carthage or Cocoapods). That’s because outside of my shared API component, all of the other packages my client app needs are only available as Carthage or Cocoapods packages. SwiftPM is supposedly going to get the ability to build Cocoa apps and frameworks sometime in the future, but not today.

In the end, I decided to make my API component both a SwiftPM package and a Carthage package. My Swift Vapor app would use the SwiftPM package, and my Cocoa apps would use the Carthage package. Although this meant a higher up front cost of setting up both, my thinking is it shouldn’t noticeably increase the maintenance burden afterwards. The benefit is each app can use the package manager best suited for its platform, which should make updating the API component easier when changes are made.

Between the two package managers, SwiftPM is stricter, requiring a specific directory hierarchy. Since Carthage uses an Xcode project, it could adapt to any hierarchy. For that reason, I tackled setting the SwiftPM package up first.

Refactoring the Swift Vapor app

In my apps, the server app was the source of truth for API information. It had the full definitions of the request and response bodies, and was the most up-to-date. So my plan was to pull the definitions out of the Vapor app into the API component, publish the component, then have the Vapor app import that component. Later, I could figure out how to refactor the Cocoa client apps.

First I created a SwiftPM package for the API component from the command line, and committed it to a git repo.

> mkdir MyAppAPI
> cd MyAppAPI
> swift package init
> git init
> git commit -am "Initial commit"

Next, I moved over the API request and response definitions from the Vapor app, and modified them to fit the structure and naming conventions I defined earlier. I’d use swift build periodically to make sure everything was valid Swift code from SwiftPM’s perspective. Since I prefer Xcode’s editor to a command line editor, I used swift package generate-xcodeproj to create a project, and then made my changes in Xcode. When I was done, I committed my changes in git.

> git commit -am "Adding API definitions"

To share the package, I needed a remote git repo that both client and server could pull from, plus a version number. Since Bitbucket offers free private repos, I created one there, and added it as the remote origin. (Github recently announced free private repos, too.) Then I tagged my repo with a version, and pushed it to the remote:

> git remote add origin <MyAppAPI bitbucket git URL>
> git tag 0.0.1
> git push origin master --tags

Note that SwiftPM version tags are just the version number, sans any prefix or suffix.

I was now ready to refactor my Vapor app to pull in this shared package and use it instead. First, I modified the Package.swift file to include it as a dependency:

    dependencies: [
        .package(url: "<MyAppAPI bitbucket git URL>", from: "0.0.1"),
    targets: [
        .target(name: "App", dependencies: [

Running vapor update from the command line pulled down the package and updated all my dependencies.

Swift Vapor needs one more thing on the API request and responses before they are useable. Namely, it needs the top-level type of the request and response to conform to the Content protocol that Vapor defines. Fortunately, it’s just an subprotocol of Codable that provides some extra methods, so the requests and responses only need to be declared as conforming. Further, since it’s only the top level types, and I used envelopes to wrap my request and response, for me I only needed to conform the envelopes. I decided to do this all in one file, with the hopes it would be easier to keep up-to-date.

import Foundation
import Vapor
import MyAppAPI

extension MyAppAPI.Session.Envelope: Content {}
extension MyAppAPI.User.Envelope: Content {}

This is a bit tedious, but I didn’t find it overly so. In hindsight, using envelopes saved me a bit of work. If I hadn’t used envelopes each individual request and response would have been the top-level type, and I would have had to conform each to Content.

The last piece was to go through all my controllers and have them import MyAppAPI and use the structs defined there.

In doing this part of the work, I learned a couple of things. First, all the namespacing does make using the shared package a bit verbose. Second, separating out the request and response models from the server helped clarify what was a server model and what was a response payload. I felt this work improved the server codebase.

Refactoring the Cocoa/CocoaTouch app

To get my API package working the Cocoa clients, I needed to make the API package a proper Carthage package, then import that into the Cocoa app. Since Carthage works by building an Xcode project, my first job was to create an Xcode project that built both iOS and macOS targets. This was straight forward, although tedious, since the default Xcode templates create a different folder layout than SwiftPM requires.

First, in order to create a Carthage Xcode project, I deleted the existing Xcode project that the SwiftPM had built. It builds a static Swift library, but Carthage needs an iOS/macOS framework. Second, I needed to commit the Xcode project to git for Carthage, but SwiftPM generates a .gitignore file that excludes Xcode projects. So I removed the *.xcodeproject line from .gitignore. Finally, I created an iOS framework project in Xcode with the same name as my package (e.g. MyAppAPI).

While I now had an Xcode project, it was using the Xcode generated files and not the actual files I wanted. Fixing this took several manual steps. First I closed Xcode, then moved the project file itself to the root folder of the package. I moved the framework header (e.g. MyAppAPI.h) and the Info.plist file into the source folder (e.g. Sources/MyAppAPI). Then I deleted all of the other Xcode generated files. Next I opened the project in Xcode. I removed all of the existing file references that were now broken, and then added all of Swift files, the framework header, and the Info.plist to the project. I had to mark the framework header as public, and then go into the Build Settings to update the path to the Info.plist to be correct. At this point I could build an iOS framework.

The second step was to create a macOS framework. It was similarly messy. First, in Xcode, I created a new target for a macOS framework named MyAppAPI-macOS. I went through and deleted all the files Xcode created in performing that, then pointed the macOS target to all the same files that the iOS target had. The Info.plist and framework header were sharable between iOS and macOS. I did have to update the framework header to import Foundation/Foundation.h instead of UIKit/UIKit.h. In the Build Settings, I had to update the path to the Info.plist. Additionally, I needed the iOS and macOS frameworks to have the same name and bundle identifier so the client code could import using the same name. To do that, I modified the Product Name and the Product Bundle Identifier to manually be the same as the iOS target. Building the macOS target now worked for me.

Xcode also defines a test target and files, but since I was only defining types, I didn’t feel I needed unit tests, so I deleted it.

To verify that Carthage would be able to build my project file, from the root of the package I ran:

> carthage build --no-skip-current

When everything successfully built, I was ready to publish my Carthage package.

Carthage is decentralized like SwiftPM, so to publish my package I just needed to tag a version and the push that to the remote git repository. Unfortunately, Carthage and SwiftPM use two different formats for version tags. Carthage prefixes it’s version tags with “v”, while SwiftPM has no prefix. So I committed all my Carthage changes, tagged both Carthage and SwiftPM, then pushed.

> git commit -am "Adding Carthage support"
> git tag 0.0.2
> git tag "v0.0.2"
> git push origin master --tags

Finally, I could pull the shared API package into my Cocoa apps. That involved adding it as a dependency in Carthage and updating. I added the repo to my Cartfile:

git "<MyAppAPI bitbucket git URL>"

Then updated Carthage from the command line.

> carthage update

Carthage doesn’t modify project files (which I consider a feature), so I went and added the MyAppAPI.framework to both my iOS and macOS client app targets.

Using the shared API package was straight forward in the client apps. It was simply importing the MyAppAPI framework, then using the types. I did end up using some typealiases to rename some of the API responses. This was because the client app’s model was the same as the response model. To me this makes sense, because the server is publishing a REST-ful interface, meaning the response model should be the resource model.

And, with that, I was done!


My side project has both server and client apps, all written in Swift. Because they share a common language, I wanted to leverage that to share HTTP API information between the two. My hope was to reduce redundant definitions that could get out of sync. To accomplish this, I created an API repo that contained the API request and response bodies. The repo was both a valid SwiftPM package and a valid Carthage package at the same time. This allowed both the Swift Vapor server and the Cocoa iOS/macOS apps to include the same shared API package.

Handling periodic tasks in Swift Vapor

In my Swift Vapor side project, there have been a couple of instances where I wanted to run some periodic tasks. My needs were pretty simple: every X amount of time, I wanted to run a closure that would perform some database operations. On top of that, it would be nice to have a tiny bit of infrastructure to make periodic jobs require less boilerplate, have visibility in production (i.e. logging if the job was successful or not), and the ability to verify the jobs in integration tests.

In this post, I’m going to flesh out a periodic job interface, lay out some implementation approaches, talk through what I actually did, and finally how I tested it.

A periodic example

First, I’ll define how I’d like to be able to schedule a periodic task in client code. Here’s an except from my app:

func boot(_ app: Application) throws {
    let jobQueue = try app.make(JobQueue.self)
    jobQueue.schedule(initialDelay: .hours(1),
                      delay: .hours(24),
                      withName: "Accounts.cleanup") { container, connection -> Future<Void> in
        return self.cleanup(with: container, on: connection)

private func cleanup(with container: Container, on connection: DatabaseConnectable) -> Future<Void> {
    return // task that performs clean up

I didn’t need a complicated interface to schedule a periodic job. The initial delay and repeated delay are givens for any periodic task. The name is a bit extra, but it turned out to be quite helpful when testing and logging. Finally, the “job” itself is a closure that’s passed a dependency injection container and a database connection. Those two things are needed for just about any meaningful work in Vapor. Finally, the closure will return a Void promise, which is used by the job infrastructure to know when it is complete, and if it was successful or not.

Although the JobQueue interface is inferable from the example above, here’s exactly how I’ve defined it for my app:

import Foundation
import Vapor

typealias Job = (Container, DatabaseConnectable) -> Future<Void>

protocol JobQueue: Service {
    func schedule(initialDelay: TimeAmount, delay: TimeAmount, withName name: String, _ job: @escaping Job)

I’ve defined it as a protocol because I’ll inject a fake implementation in my integration tests. Since I’m taking advantage of the dependency injection system in Vapor, I conform the protocol to Service. But all I needed was one method on the protocol to actually schedule the periodic job. The TimeAmount is a datatype defined down in the NIO framework.

JobQueue Decisions

Now that I’ve defined the JobQueue interface, I need to figure out how to implement it. I thought of a few options.

One option was to register a timer in my app at the given interval, and run the periodic job closure then and there. The benefit of this is it simple to understand and implement. The downside is it doesn’t scale up or down very well. If my host spins up more than one instance of my app, both instances will try to run this periodic task. Or, if I’m on a hobbyist plan, and my host spins down all my instances because of lack of requests, then my periodic task won’t run at all.

Another option was to find or build something like Ruby’s Resque but for Swift, and use a timer to schedule a background job on Resque-but-for-Swift to perform the periodic job. The benefit would be the job would only be run once no matter how many instances of my app were spun up, plus job persistence. The downside is, as far as my Google searches show, such a thing does not exist, and building it myself would be a significant undertaking.

There were also some options that didn’t fit the interface I gave above, but could be legitimate solutions. First up, I could define a Vapor command that’s invokable from my app’s command line that performs the periodic job. Then, using my host’s infrastructure, manually invoke the command when I think it needs to be run. The downside is it relies on me remembering to run the command. However, it wouldn’t need any internal job infrastructure, and would only run on the instance I wanted. A second option would be to do nothing. If my periodic job didn’t run nothing bad would happen for a very long time. The upside to this is it cost nothing to implement. The downside is there is a timebomb in my app that will one day go off. But that might be fine because the app might not last that long, or be completely redesigned so the periodic job is unnecessary.

In the end, I went with the first solution of registering a timer in my app, and running the closure when it triggered. I decided this for a few reasons. First, I’m lazy and forgetful and won’t remember to run some manual command. Second, my side project probably won’t ever scale beyond one instance. Third, even if it did scale up, the periodic tasks running concurrently wouldn’t hurt anything. Finally, this solution did something (so no timebomb), but was the least investment that did something.

As far as using a timer in my Vapor app, my first attempt was to use a 3rd party Swift library that implemented periodic jobs on top of DispatchQueue. That did seem to work, but it happened outside of Vapor’s EventLoops, which always seemed a little janky to me. Later, I discovered that the NIO framework offered RepeatedTasks on their EventLoops, so I used those instead.

Implementing JobQueue

I had an approach now, I just needed to implement it. First up, building the production implementation of JobQueue.

import Foundation
import Vapor

final class ProductionJobQueue: JobQueue {
    private let eventLoop: EventLoop
    private let container: Container
    private let logger: Logger

    init(eventLoop: EventLoop, container: Container, logger: Logger) {
        self.eventLoop = eventLoop
        self.container = container
        self.logger = logger

The ProductionJobQueue needed a few things in order to function. First, it needs an EventLoop so it can schedule RepeatedTasks. Second, it needs a dependency injection Container so it can get a database connection later to hand to a periodic job. Finally, it takes a Logger because the app logs any periodic job invocation to provide some insight into what’s going on in the deployed app.

The only required method of the JobQueue protocol is the schedule() method:

final class ProductionJobQueue: JobQueue {
    func schedule(initialDelay: TimeAmount, delay: TimeAmount, withName name: String, _ job: @escaping Job) {
        eventLoop.scheduleRepeatedTask(initialDelay: initialDelay, delay: delay) { repeatedTask -> EventLoopFuture<Void> in
            return self.execute(job, withName: name, on: repeatedTask)

This is a thin wrapper around EventLoop.scheduleRepeatedTask(). All the interesting bits of the implementation are in the execute() method which actually calls the job closure.

final class ProductionJobQueue: JobQueue {
    private func execute(_ job: @escaping Job, withName name: String, on repeatedTask: RepeatedTask) -> Future<Void> {
        let startTime = Date()
        return container.withPooledConnection(to: .psql) { connection -> Future<Void> in
            return job(self.container, connection)
        }.map {
            self.logSuccess(for: name, whichStartedAt: startTime)
        }.catch { error in
            self.logFailure(error, for: name, whichStartedAt: startTime)

The execute() method provides most of the value add functionality of the JobQueue. First off, it grabs a database connection for the job, so the job doesn’t have to itself. Once it has a connection, it invokes the job closure with the container and database connection. It waits on the job to complete, then logs the success or failure of the job.

final class ProductionJobQueue: JobQueue {
    private func logSuccess(for name: String, whichStartedAt startTime: Date) {
        let time = Date().timeIntervalSince(startTime).asFormattedMilliseconds"JOB \(name) -> SUCCESS [\(time)]")

    private func logFailure(_ error: Error, for name: String, whichStartedAt startTime: Date) {
        let time = Date().timeIntervalSince(startTime).asFormattedMilliseconds"JOB \(name) -> FAILURE [\(time)]")

The logging of jobs is simple, but provides some necessary transparency. It just logs the name of the job, whether it was a success or failure, and the time it took to complete. Even though I capture the error in the failure case, I don’t log it for now. That’s because it might possibly have sensitive info in it (like database connection info) that I don’t want being saved out to a log. In the future, I might log out the name of the error type, if I discovered I needed more information when debugging production issues.

The asFormattedMilliseconds property above is an extraction of code from my earlier Swift Vapor logging post:

extension TimeInterval {
    private static let intervalFormatter: NumberFormatter = {
        let formatter = NumberFormatter()
        formatter.numberStyle = .decimal
        formatter.maximumFractionDigits = 2
        formatter.multiplier = 1000
        return formatter

    var asFormattedMilliseconds: String {
        return TimeInterval.intervalFormatter.string(for: self).map { $0 + "ms" } ?? "???ms"

Finally, I needed to implement the Service protocol so the class could be used in the dependency injection system.

extension ProductionJobQueue: ServiceType {
    static var serviceSupports: [Any.Type] {
        return [JobQueue.self]

    static func makeService(for worker: Container) throws -> ProductionJobQueue {
        return try ProductionJobQueue(eventLoop: worker.eventLoop, container: worker, logger: worker.make())

As usual, I used ServiceType to conform to the Service protocol. This allowed me to register with only the type, plus have a dependency injection container available when the instance was created. This implementation was a bit different than usual, in that I needed to override the static serviceSupports property to include the JobQueue protocol. This is needed because by default ServiceType only registers the concrete type. By listing JobQueue as supported, client code can ask the container for JobQueue and they’ll get a ProductionJobQueue in the production app. The makeService() method takes full advantage of being given a Container: it passes in the event loop from it, the container itself, and creates a logger from the container.

Finally, ProductionJobQueue is registered as a service from the ProductionConfiguration. At this point, periodic jobs should work in production. But it would be nice to test them.


I spent some time thinking about what and how to test periodic jobs. I knew I wanted integration level testing, and that I’d facilitate that by building a fake implementation of JobQueue. I needed a fake because waiting on timers to fire in a test isn’t a viable option, especially if they’re only firing once a day. Also, for me, integration testing meant I should be able to verify that the periodic job was both registered and it performed what it was supposed to. I considered two possible solutions.

The first idea was to have a fake job queue that individual tests could manipulate the clock on. That is, a test could say “pretend two hours have elapsed” to the fake job queue, and the job queue would go look through the registered jobs and fire the ones that should have fired by then. The upside to this approach is it could be used to validate the initial and repeating delays given at registration were working. The downside is this would require a sophisticated fake that could do time calculations reliably. This would give me less confidence that I was testing production code, as opposed to testing my fake job queue.

The second idea was to require each periodic job to provide a unique name. Then individual tests could ask the fake job queue to invoke a periodic job by that name. The upside is the fake is simple and predictable. The downside is jobs have to have globally unique names, which is a bit of overhead.

I decided on the second option to invoke jobs by name. Because of the logging done in production, the requirement that jobs have globally unique names was already there, so the downside was moot. Additionally, the “upside” of the first option was validating the delay values. But in my case, they were always constants, so there seemed to be little value in validating them. Finally, the ease of implementing the second option was hard to ignore.

This approach turned out to be easy to use in my tests. As an example:

func testRemoveExpiredChallenges() throws {
    // setup

    try app.jobQueue.execute("Accounts.cleanup", container: app.container, connection: connection)

    // validate here

I ended up redacting all the setup and validation code for this test for succinctness, and because all of it was specific to the app and not that interesting. The only part left is invoking the periodic job on the fake job queue, which is done by passing the job name, a Container and a connection to the database. The app seen here is a TestApplication, which I covered in a post about integration testing Swift Vapor.

Because of my selected testing approach, TestJobQueue was easy to implement. Here’s how I conformed it to the JobQueue protocol:

import Foundation
import Vapor

final class TestJobQueue: JobQueue {
    struct Entry {
        let initialDelay: TimeAmount
        let delay: TimeAmount
        let name: String
        let job: Job

    var schedule_wasCalled = false
    var schedule_wasCalled_withEntries = [Entry]()
    func schedule(initialDelay: TimeAmount, delay: TimeAmount, withName name: String, _ job: @escaping Job) {
        schedule_wasCalled = true
        let entry = Entry(initialDelay: initialDelay, delay: delay, name: name, job: job)

The schedule() method is just a recorder. It creates an entry for each scheduled job and stores it in an array. The important bits that are used later are the name and job. I ended up storing the initialDelay and delay so if a test actually wanted to validate them, they could.

The other interesting piece to the TestJobQueue class is the execute() method that tests call when they want to fake a periodic job being triggered.

final class TestJobQueue: JobQueue {
    func execute(_ name: String, container: Container, connection: DatabaseConnectable) throws {
        guard let entry = schedule_wasCalled_withEntries.first(where: { $ == name }) else {

        try entry.job(container, connection).wait()

My implementation is as simple as I could make it. It searches the array of entries looking for the first job with the matching name. If it can’t find a matching job, it silently fails. This is intentional: it mimics what would happen in production if a periodic job wasn’t properly registered. Namely, nothing would happen. It is up to the individual test cases to verify that a job’s side effects are happening. However, execute() does wait on the job to complete before returning, just to make the tests simpler.

The last bit needed for TestJobQueue is registering it with the dependency injection system. I did it differently from the production job queue, however. ProductionJobQueue conformed to ServiceType because it needed access to a dependency injection container to initialize. TestJobQueue doesn’t have the same requirement to instantiate. Further, it would be useful to the tests if the TestJobQueue were exposed to them as that type — so they can access the execute() method. Otherwise they’d have to force cast a JobQueue to a TestJobQueue, and I find that more than a little gross.

For that reason, I modified TestingConfiguration to hold an exact instance:

struct TestingConfiguration: ConfigurationType {
    let jobQueue = TestJobQueue()

    func configure(_ services: inout Services) throws {
        services.register(jobQueue, as: JobQueue.self)

This is the TestingConfiguration that I described in how I do environment configuration for Swift Vapor. Here, it registered a member variable of type TestJobQueue as a JobQueue. By making it publicly available on TestingConfiguration, the TestApplication has easy access to it.

The final piece is exposing the TestJobQueue on TestApplication so it’s convenient for tests to use.

class TestApplication {
    var jobQueue: TestJobQueue {
        return configuration.jobQueue

Since the TestApplication already has a reference to TestingConfiguration, it can return that instance. The test code now works.


My Swift Vapor app had a need to run some clean up jobs periodically. The requirements were simple: it was ok if the job ran on multiple instances or not at all, but it should make a good faith effort to run at least once. I wanted a class that reduced the necessary boilerplate for scheduling a job, while adding logging to the jobs, and being testable. I settled on NIO‘s RepeatedTasks to implement timers to run the periodic tasks in each app instance. For testing, I kept the testing fake simple: the tests could invoke jobs by name, and then validate the side effects.

Building a declarative router for Swift Vapor

As I’ve been using Swift Vapor 3, one of the things that I felt could be better was the way routes were declared. So I built a framework to meet my Vapor routing needs. If you’d like to try it out, go to the Github page and checkout the README. For the rest of this post, I’m going to talk about what my goals were, and highlight parts of the implementation that were interesting to me.

But first, to whet your appetite, here’s a tiny example of declaring routes using my framework:

import Vapor
import RoutingTable

public func routes(_ router: Router) throws {
    let table = RoutingTable(
        .scope("api", middleware: [ApiKeyMiddleware()], children: [
            .resource("users", parameter: User.self, using: UserController.self, children: [
                .resource("sprockets", parameter: Sprocket.self, using: SprocketController.self),
                .resource("widgets", using: WidgetController.self)
            .resource("sessions", using: SessionController.self),
            .post("do_stuff", using: StuffController.doStuff)
    table.register(routes: router)


I had four goals when creating my routing framework:

  1. All routes in one file

    One of the challenges I ran into was determining what all the routes in the app were and how they fit together. It is possible to print out all the routes by running a command line tool, but that didn’t help me with finding where the associated code was.

    I also attempted to take advantage of RouteCollections at one point in order to make my routes() method less verbose. It did improve the verbosity, but at the expense of all the routes in one place. Ideally, I’d like to have my cake and eat it, too: all routes in one file, but expressed concisely.

  2. Hierarchical declaration

    Routes are hierarchical by nature, and I’d like to declare them that way. By that, I mean building a tree data type that is hierarchical in Swift syntax when declared, as opposed to calling a series of methods that build up a tree at runtime.

    I see a couple of benefits from making the route declaration hierarchical. First, it’s easier for me to see how the endpoints fit together or relate to one another. I can see the hierarchy in the code syntax itself, instead of parsing method calls to build up the hierarchy in my head. Second, it can reduce boilerplate code by inheriting configuration from the parent to the child.

  3. Re-usability of controllers

    By re-usability of controllers, I mean a controller can be used in more than one place in the routing hierarchy. For example, maybe a controller implements managing sprockets. It could be exposed in one place in the routing hierarchy for normal users, but also in a different place for admin users. Part of making this useful would be allowing the routing declaration to specify which controller endpoints are available at each place in the hierarchy. e.g. the admin sub-hierarchy should allow the DELETEing of sprockets, but the normal user’s sub-hierarchy shouldn’t.

    Being re-usable implies controllers don’t know where they are in the route hierarchy. To me, this makes sense because of my iOS/macOS background. In that context, view controllers don’t know where they appear in the app. Instead, app architecture relies on Coordinators (or a similar pattern) to know when and where to display the view controllers. Because of the separation of concerns, view controllers can be re-used in multiple places of the app. I think of API/web controllers in the same way.

  4. Use higher level concepts

    In my experience, few things reduce boilerplate code and increase readability like the introduction of higher level concepts. In the case of routing, I’m thinking about scopes and resources.

    Scopes add the ability to group routes logically, so that the code maintainer knows they fit together to accomplish a common goal. It also means routes in a scope can inherit the same path prefix and/or middleware. Some examples of scopes could be an API scope or an admin user scope.

    Resources allow me to describe REST-like resources in a succinct way. Although resource declaration can be succinct, the code maintainer can infer a lot about it. That’s because REST-like resources are known to support certain subpaths and HTTP methods that behave in specific ways. So although fewer things are declared about a resource, more is known about it than if I had declared each route individually.

The Results

Based on these goals, I came up with an idealized version of what I wanted route declaration to look like:

scope "api", [ApiMiddleware.self] {
    resource "users", parameter: User.self, using: UserController.self {
        resource "sprockets", using: SprocketsController.self

What I like about the above is it has just enough punctuation and keywords to make it readable, but no more than that is unnecessary. It also makes use of curly braces to denote hierarchy. When I’m reading Swift code, curly braces make my brain think parent/child relationship in a way other punctuation doesn’t. It also seems to help make Xcode do sane things with indentation.

Here I’m going to admit that much of my inspiration for what routing could be came from Elixir’s Phoenix framework, and its routing library. I feel like its route declaration is very close to my ideal. In addition, it supports more features, including the ability to generate full URLs from a named resource.

Unfortunately, I couldn’t achieve my ideal in Swift. The example I gave above isn’t valid Swift, nor was there a way to extend Swift to support it. A lot of Phoenix’s elegance and power comes from Elixir’s hygienic macros, which Swift doesn’t have.

Instead, here’s the closest I could come in Swift:

.scope("api", middleware: [ApiKeyMiddleware()], children: [
    .resource("users", parameter: User.self, using: UserController.self, children: [
        .resource("sprockets", parameter: Sprocket.self, using: SprocketController.self)

It has a lot more punctuation and keywords than is really necessary to convey what I want. It also uses square brackets for arrays to denote hierarchy, which are a bit clumsy especially when used in Xcode. But given Swift’s limitations, I feel like it comes pretty close.

Interesting Implementation Bits

When implementing my declarative router I ran into some implementation hurdles that I thought were interesting enough to write down.

Verbosity reduction

One of my stated goals was to reduce the verbosity needed to declare all my routes. Some of the reduction came for free just by using higher level concepts like scopes and resources, and making the declarations hierarchical so common configuration could be shared. But all that space savings could be undone if I messed up the declaration API. I paid particular attention to leveraging Swift’s type inference and default parameters.

I realized early on that I needed a tree that was homogenous in type, but polymorphic in behavior. There are three types that could appear in a routing declaration: a scope, a resource, and a raw endpoint (i.e. a GET, PUT, PATCH, POST, or DELETE). Each of those has its own properties, and handles registering routes differently. Of those, both scopes and resources could have children, which could themselves be scopes, resources, or raw endpoints. That left me with a couple of options.

The first option that I considered was using an enum to represent the three types (scope, resource, and raw endpoint). Since Swift enums can have associated data, each value could contain all their necessary properties. However, enums had a couple of problems. First, they don’t allow default values on construction. Which means each declaration would have to specify all values even if they weren’t used. Second, eventually each enum value would have to register all the routes it represented, and since each enum type had to do that differently, there would be a giant switch statement somewhere. That didn’t seem elegant to me, so I abandoned that approach.

The second option was to declare a common protocol (e.g. Routable) and have scope, resource, and raw endpoint types conform to that protocol. Then I had scopes and resources declare their children to be of type Routable so type homogenous trees could be built. That turned out to mostly work. The problem I ran into was the raw endpoints were more verbose than I wanted. For example:

Scope("api", middleware: [ApiMiddleware()], children: [
    RawEndpoint(.post, "do_stuff", using: StuffController.doStuff)

I felt having the typename RawEndpoint in the declaration was unnecessary and uninteresting. The important bit was the HTTP method, but that was obscured by the typename. My next thought was use the HTTP method name as the typename (e.g. Post, Get, etc). This worked, but at a cost. First, it meant I had five different types that all did the same thing, except for one parameter. Second, the HTTP method names are common words and had to exist at the top level scope. This made me worried about typename collisions.

I tried to fix those problems by adding static methods to my protocol as a shorthand way to create the raw endpoint type like so:

extension Routable {
    static func get<T>(_ path: String..., using controller: T) -> Routable {
        return RawEndpoint(.get, path, using: controller)

However, when I tried to use the static methods in a declaration:

Scope("api", middleware: [ApiMiddleware()], children: [
    .post("do_stuff", using: StuffController.doStuff) // ERROR

Swift complained, seemingly because it couldn’t infer the type because the static methods were on a protocol. I could have specified the full type name to the method, but I felt that would have made the declaration too verbose. But I thought I was close to something that would work. I just needed the type inference to work on the static methods.

That lead me to the final option that I actually used. My hunch was I needed use a concrete type rather than a protocol in my declaration API. That would allow me to use static methods in the declaration and Swift’s type inference would work. To put it another way, I could make this work:

Scope("api", middleware: [ApiMiddleware()], children: [
    .post("do_stuff", using: StuffController.doStuff)

If children was declared to be an array of a concrete (i.e. non-protocol) type, and if post() were a static method on that concrete type.

The challenge now was I needed two seemingly opposed concepts. I needed each declaration item (i.e. scope, resource, raw endpoint) to be polymorphic since they each should act differently based on their type. I had achieved that via making them conform to a common protocol. However, in order to make Swift type inference happy, I needed a concrete type.

So I used type erasure, kind of. I wrapped the protocol Routable in a struct called AnyRoutable. It works like a type erasure datatype in that it implements the Routable protocol by calling the methods on the Routable instance it contains. This gave me a single concrete type while still allowing polymorphism.

To make this work, I essentially made AnyRoutable a facade to the rest of the framework. Every node in the routing tree would be declared as an AnyRoutable, which could, internal to the framework, wrap the correct declaration item type. To make building an entire tree from one type possible, I added static methods on AnyRoutable that created each declaration type: scope, resource, and each of the HTTP methods. For example, something like:

struct AnyRoutable {
    static func get<T>(_ path: String..., using controller: T) -> AnyRoutable {
        return AnyRoutable(RawEndpoint(.get, path, using: controller))

The trick was I had the static methods deal only in AnyRoutable; children were declared as them, and the return types were AnyRoutable. Since all parameters and return types were a concrete type, Swift could easily infer the type, and the static methods could be called without them. Implementation wise, the static methods simply created the appropriate Routable subtype, then wrapped it in AnyRoutable. This had the added bonus of only needing to make AnyRoutable public in the framework. The Routable implementations for resource, scopes, and endpoints stayed hidden.

Although it took me a while to reach the final implementation, the pattern seems generally useful. It allows polymorphism in the implementation, while only exposing one concrete type to client code, which means type inference can be used. I suspect there might be other options for solving this problem. For example, I never tried a class hierarchy and I wonder if that could be made to work. However, I’m pretty happy with AnyRoutable since I got a facade pattern out of it as well.

Resource reflection

After designing the API interface, the next most difficult thing was figuring out how to implement controllers that managed REST-like resources. In a nutshell, if a router declares a resource, my framework should be able to look at the implementing controller and determine what verbs (i.e. index, show, update, create, delete, new, or edit) it supports, and automatically declare those routes. I wanted this to require as little boilerplate as possible. As a bonus, I wanted to determine the verb support at compile time.

I quickly realized that there would have to be some overhead no matter what, just because of the limitations of Swift. Swift has no way of asking “does this type implement this method” outside of having that type declare conformance to a protocol that requires the method. It doesn’t have a fully dynamic and reflective runtime like Objective-C, nor a structural type system. So I accepted that resource controllers would have to declare conformance to a protocol for each of the verbs it supported.

But even accepting that limitation, I still wanted to determine which verbs were available at compile time. Since the controller declared conformance to a protocol, and a generic method could require parameter conformance to a protocol, for a long time I held out hope this this was possible. For example, I could do this:

func indexRoutes<ControllerType: ResourceIndexable>(_ controller: ControllerType) -> [Route] {
    // generate route for index verb

func indexRoutes<ControllerType>(_ controller: ControllerType) -> [Route] {
    return []

class MyController: ResourceIndexable {

let controller = MyController()
let routes = indexRoutes(controller) // calls the correct method

The issue I ran into was trying to put all the verb routes together. The method that put all the routes together would have to know about all the resource verb protocols it conformed too. That’s because if the method didn’t require conformance to a verb protocol, Swift would act like it didn’t conform to that protocol regardless of it actually did or not. So for this to work, I would have to implement a generic method for every combination of the seven verb protocols that could occur. That seemed excessive to me. In the end, I simply had the code query at runtime which protocols the controller implemented.

This part was interesting to me because it seems like it should be solvable problem. However, in its current state, Swift doesn’t appear able to overcome it. I do wonder if a hygienic macro system would make it feasible.

Dependency injection woes

The next two implementation struggles relate to the implementation details of Vapor itself, and not something inherent to the issue of building a declarative router. But they still contained valuable learnings.

At a base level, the router connects routes with a controller that will handle requests to that route. To make the route declaration as concise as possible, I only required the type of the controller, as opposed to a live instance that might have be constructed and/or configured. I decided I would instantiate the controller later, when registering the route, using Vapor’s dependency injection system. The problem was when it came to register the route, Vapor’s dependency injection system was in the wrong state.

Vapor’s dependency injection effectively has two phases. The first phase is the setup phase, where it registers types it calls “services” which can be instantiated later, in the second phase. In the second phase, a “container” exists, and can be used to instantiate any of the service types registered in the first phase. When the router is being initialized, the system is in the setup phase, and can’t instantiate any of the controller types because there’s no container.

I considered using my own custom protocol on controllers that allowed them to be constructed without a dependency injection container. However, after trying that out, it seemed surprising in that everything else uses Vapor’s DI system. Plus my custom protocol would be more restrictive; it wouldn’t be able to allow custom init parameters to the controller (unless all controllers needed them), nor would it offer access to the DI system to allow construction of said parameters.

In the end, I was able to defer controller allocation until the first route was actually executed. By then, containers were available. Further, Vapor’s DI system took care of caching the controller for me.

This problem was interesting because I’ve run into it a few times in Vapor. I need to be on the lookout for different patterns to work around not having a DI container available when I need it.

Testing difficulties

The final issue I ran into was trying to unit test my framework. Since it’s implemented in Swift, my go to method is to define all dependencies as protocols, then for my tests to create fakes I can manipulate and observe.

Unfortunately, most of the Vapor types I had to interact with weren’t easily fakeable. The big one I needed to fake was Vapor’s Router protocol. Being a protocol, I thought it would be a slam dunk. Unfortunately, all the interesting methods on Router were in protocol extensions, meaning my testing fakes could not override those methods.

What I ended up doing was defining my own protocol for the methods on Router that I used, then using that everywhere in my code. Since the methods were declared in the protocol, they were overridable by my testing fakes. This allowed me to do the unit testing I wanted. That only left the issue of how to use Vapor’s Router in the non-testing version of the framework.

Indirection solves a lot of problems, this being another example. I declared a concrete type that conformed to my router protocol that my framework could use. I then had it wrap a Vapor Router, and implement the protocol by calling the corresponding methods on Router. Then, at the top level object, I hid the wrapping of Router behind a helper method, so it looked like my framework dealt with Routers natively.

The lesson I take from this is testing when relying on 3rd party frameworks is hard. Unit testing with UIKit and AppKit types is no different. Also, defining my own protocol and wrapping a 3rd party protocol in a concrete type to conform to it seems like a repeatable strategy.


When using Swift Vapor’s routing API, I discovered I wanted a few more things than it offered. Namely, I wanted all routes declared in one file, a hierarchal declaration, re-usability of controllers, and higher level concepts like scopes and resources. In building a framework to support these concepts, I ran into a few implementation concerns. The first was learning to design an API in such a way to reduce verbosity. The others were trying to determining protocol conformance at compile time, working around two-stage dependency injection limitations, and trying to test my code while not having fakeable protocols from 3rd party frameworks.