Inflight Magazine no. 11
The 11th issue of the Wing Inflight Magazine.
The 11th issue of the Wing Inflight Magazine.
Hello Wingnuts!
We recently updated the project's roadmap to share more details about our vision for the project. As the Wing language and toolchain has an ambitious goal, we're hoping this gives you a better idea of what to expect in the coming months.
We want to stabilize as many of the items below as possible and we're eagerly interested in you feedback and collaboration, either through GitHub or our Discord server.
We want to provide a robust CLI for people to compile, test, and perform other essential functions with their Wing code.
We want the CLI to be easy to install, update, and set up on a variety of systems.
Dependency management should be simple and insulated from problems specific to individual Node package managers (e.g npm, pnpm, yarn).
The Wing toolchain makes it easy to create and publish Wing libraries (winglibs) with automatically generated API docs, and it doesn't require existing knowledge of node package managers.
Wing Platforms allow you to specify a layer of infrastructure customizations that apply to all application code written in Wing. It should be simple to create new Wing platforms or extend existing platforms.
With Wing Platforms it's possible to specify both multi-cloud abstractions in Wing as well as the actual platform (for example, the implementation of cloud.Bucket) in Wing.
Wing lets you easily write tests that run both locally and on the cloud.
Wing test runner can be customized per platform.
Tests in Wing can be run in a variety of means -- via the CLI, via the Wing Console, and in CI/CD.
The design of the test system should make it easy for developers to write reproducible (or deterministic) tests, and also provide facilities for debugging.
Wing's syntax and type system is robust, well documented, and easy for developers to learn.
Developers coming from other mainstream languages with C-like syntax (Java, C++, TypeScript) should feel right at home.
Most Wing code is statically typed in order to support automatic permissions.
Wing should be able to interoperate with a vast majority of TypeScript libraries.
It should be straightforward to import libraries that are available on npm and automatically have corresponding Wing types generated for them based on the TypeScript type information.
The language also has mechanisms for more advanced users to use custom JavaScript code in Wing.
We want Wing to have friendly, easy to understand error messages that point users towards how to fix their problems.
Wing has a built-in language server that gives users a first-class editing and refactoring experience in their IDEs.
Wing provides a batteries-included experience for performing common programming tasks like working with data structures, file systems, calculations, random number generation, HTTP requests, and other common needs.
Wing also has a ecosystem of Wing libraries ("winglibs") that make it easy to write cloud applications by providing easy-to-use abstractions over popular cloud resources.
This includes a []cloud
module](/docs/api/category/cloud) that is an opinionated set of resources for writing applications on the most popular public clouds.
The cloud primitives are designed to be cloud-agnostic (we aren't biased towards a specific cloud provider).
These cloud primitives all can be run to a high degree of fidelity and performance with the local simulator.
Not all winglibs may be fully stable when the language reaches 1.0.
We want to provide a first class local development experience for Wing that makes it easy and fast to test your applications locally.
It gives you observability into your running application and interact live with different components.
It gives you a clearer picture of your infrastructure graph and how preflight and inflight code are related.
It complements the experience of writing code in a dedicated editor.
We want it to be easy for people to get exposed to Wing code and have ways to try applications without having to install Wing locally.
The Wing docs should provide content appealing to different kinds of developers trying to acquire different kinds of information at different stages -- from tutorials to references to how-to-guides (documentation quadrants)
Wing docs need to have content for both the personas of developers writing their own applications and platform engineers aiming to provide simpler abstractions and tools for their teams.
We want to provide hundreds of examples and code snippets to make it easy to learn the syntax of the language and easy to see how to solve common use cases.
If you have any questions, would like to contribute feel free to reach out to us and join us on our mission to make cloud development easier for everyone.
- The Wing Team
The 10th issue of the Wing Inflight Magazine.
Fig 1: The “Blue Zone” Application Ported to Cloud with Wing
Directly porting software applications to the cloud often results in inefficient and hard-to-maintain code. However, using the new cloud-oriented programming language Wing in combination with Hexagonal Architecture has proven to be a winning combination. This approach strikes the right balance between cost, performance, flexibility, and security.
In this series, I will share my experiences migrating various applications from mainstream programming languages to Winglang. My first experience implementing the Hexagonal Architecture in Wing was reported in the article "Hello, Winglang Hexagon!”. While it was enough to acquire confidence in this combination, it was built on an oversimplified "Hello, World" greeting service, and as such lacked some essential ingredients and was insufficient to prove the ability of such an approach to work at scale.
In Part One, I focus on porting the “Blue Zone” application, featured in the recently published book “Hexagonal Architecture Explained”, from Java to Wing. The “Blue Zone” application brings in a substantial code base, still not too huge to dive into unmanageable complexity, yet representative of a large class of applications. Also, the fact that it was originally written in mainstream Java brings an interesting case study of creating a cloud-native variant of such applications.
This report also serves as a tribute to Juan Manuel Garrido de Paz, the book's co-author, who sadly passed away in April 2024.
Before we proceed, let's recap the fundamentals of the Hexagonal Architecture pattern.
Refer to Chapter Two of the “Hexagonal Architecture Explained” book for a detailed and formal pattern description. Here, I will bring an abridged recap of the main sense of the pattern in my own words.
The Hexagonal Architecture pattern suggests a simple yet practical approach to separate concerns in software. Why is the separation of concerns important? Because the software code base quickly grows even for a modest in terms of delivered value application. There are too many things to take care of. Preserving cognitive control requires a high-level organization in groups or categories. To confront this challenge the Hexagonal Architecture pattern suggests splitting all elements involved in a particular software application into five distinct categories and dealing with each one separately:
“Hexagonal Architecture” has served well as a hook to the pattern. It’s easy to remember and generates conversation. However, in this book we want to be correct: The name of the pattern is “Ports & Adapters”, because there really are ports, and there really are adapters, and your architecture will show them.
External Actors that communicate with or are communicated by the Application. These could be human end users, electronic devices, or other Applications. The original pattern suggests further separation into Primary (or Driving) Actors - those who initiate an interaction with the Application, and Secondary (or Driven) Actors - those with whom the Application initiates communication.
Ports - a fancy name for formal specification of Interfaces the Primary Actors could use (aka Driving Ports) or Secondary Actors need to implement (aka Driven Ports) to communicate with the Application. In addition to the formal specification of the interface verbs (e.g. BuyParkingTicket
) Ports also provide detailed specifications of data structures that are exchanged through these interfaces.
Adapters fill the gaps between External Actors and Ports. As the name suggests, Adapters are not supposed to perform any meaningful computations, but rather basically convert data from/to formats the Actors understand to/from data ****the Application understands.
Configurator pulls everything together by connecting External Actors to the Application through Ports using corresponding Adapters. Depending on the architectural decisions made and price/performance/flexibility requirements these decisions were trying to address, a specific Configuration can be produced statically before the Application deployment or dynamically during the Application run.
Contrary to popular belief, the pattern does not imply that one category, e.g. Application, is more important than others, nor does it suggest ultimately that one should be larger while others smaller. Without Ports and Adapters, no Application could be practically used. Relative sizes are often determined by non-functional requirements such as scalability, performance, cost, availability, and security.
The pattern suggests reducing complexity and risk by focusing on one problem at a time, temporally ignoring other aspects. It also suggests a practical way to ensure the existence of multiple configurations of the same computation each one addressing some specific needs be it test automation or operation in different environments.
The picture below from “Hexagonal Architecture Explained” book nicely summarizes all main elements of the pattern:
Fig 2: The Hexagonal Architecture Patterns in a Nutshell
From the application README
:
BlueZone allows car drivers to pay remotely for parking cars at regulated zones in a city, instead of paying with coins using parking meters.
I chose this application for two primary reasons. First, it was recommended by the “Hexagonal Architecture Explained” book as a canonical example. Second, it was originally developed in Java. I was curious to see what is involved in porting a non-trivial Java application to the cloud using the Wing programming language.
The “Hexagonal Architecture Explained” book provides reasonable recommendations in Chapter 4.9, “What is development sequence?”. It makes sense to start with “Test-to-Test” and proceed further. However, I did what most software engineers normally do— starting with translating the Java code to Wing. Within a couple of part-time days, I reached a stage where I had something working locally in Wing with all external interfaces simulated.
While technically it worked, the resulting code was far too big relative to the size of the application, hard to understand even for me, aesthetically unappealing, and completely non-Wingish. Then, I embarked on a two-week refactoring cycle, looking for the most idiomatic expression of the core pattern ideas adapted to the Wing language and cloud environment specifics.
What comes next is different from how I worked. It was a long series of chaotic back-and-forth movements with large portions of code produced, evaluated, and scrapped. This usually happens in software development when dealing with unfamiliar technology and domains.
Finally, I’ve come up with something that hopefully could be gradually codified into a more structured and systematic process so that it will be less painful and more productive the next time. Therefore, I will present my findings in the conceptually desirable sequence to be used next time, rather than how it happened in reality.
To be more accurate, the best and most cost-effective way is to start with a series of acceptance tests for the system's architecturally essential use cases. Chapter 5.1 of the “Hexagonal Architecture Explained” book, titled “How does this relate to use cases?”, elaborates on the deep connection between use case modeling and Hexagonal Architecture. It’s worth reading carefully.
Even the previous statement wasn’t 100% accurate. We are supposed to start with identifying Primary External Actors and their most characteristic ways of interacting with the system. In the case of the “Blue Zone” application, there are two Primary External Actors:
For the Car Drive actor, her primary use case would be “Buy Ticket”; for the Parking Inspector, his primary use case would be “Check Car”. By elaborating on these use cases’ implementation we will identify Secondary External Actors and the rest of the elements.
The preliminary use case model resulting from this analysis is presented below:
Fig 3: “Blues Zone” Application Use Case Mode
Notice that the diagram above contains only one Secondary Actor - the Payment Service and does not include any internal Secondary Actors such as a database. While these technology elements will eventually be isolated from the Application by corresponding Driven Ports they do not represent any Use Case External Actor, at least in traditional interpretation of Use Case Actors.
Specifying use case acceptance criteria before starting the development is a very effective technique to ensure system stability while performing internal restructurings. In the case of the “Blue Zone” application, the use case acceptance tests were specified in Gherkin language using the Cucumber for Java framework.
Currently, a Cucumber framework for Wing does not exist for an obvious reason - it’s a very young language. While an official Cucumber for JavaScript does exist, and there is a TypeScript Cucumber Tutorial I decided to postpone the investigation of this technology and try to reproduce a couple of tests directly in Wing.
Surprisingly, it was possible and worked fairly well, at least for my purposes. Here is an example of the Buy Ticket use case happy path acceptance test specified completely in Wing:
bring "../src" as src;
bring "./steps" as steps;
/*
Use Case: Buy Ticket
AS
a car driver
I WANT TO
a) obtain a list of available rates
b) submit a "buy a ticket" request with the selected rate
SO THAT
I can park the car without being fined
*/
let _configurator = new src.Configurator("BuyTicketFeatureTest");
let _testFixture = _configurator.getForAdministering();
let _systemUnderTest = _configurator.getForParkingCars();
let _ = new steps.BuyTicketTestSteps(_testFixture, _systemUnderTest);
test "Buy ticket for 2 hours; no error" {
/* Given */
["name", "eurosPerHour"],
["Blue", "0.80"],
["Green", "0.85"],
["Orange", "0.75"]
]);
_.next_ticket_code_is("1234567890");
_.current_datetime_is("2024/01/02 17:00");
_.no_error_occurs_while_paying();
/* When */
_.I_do_a_get_available_rates_request();
/* Then */
_.I_should_obtain_these_rates([
["name", "eurosPerHour"],
["Blue", "0.80"],
["Green", "0.85"],
["Orange", "0.75"]
]);
/* When */
_.I_submit_this_buy_ticket_request([
["carPlate", "rateName", "euros", "card"],
["6989GPJ", "Green", "1.70", "1234567890123456-123-062027"]
]);
/* Then */
_.this_pay_request_should_have_been_done([
["euros", "card"],
["1.70", "1234567890123456-123-062027"]
]);
/* And */
_.this_ticket_should_be_returned([
["ticketCode", "carPlate", "rateName", "startingDateTime", "endingDateTime", "price"],
["1234567890", "6989GPJ", "Green", "2024/01/02 17:00", "2024/01/02 19:00", "1.70"]
]);
/* And */
_.the_buy_ticket_response_should_be_the_ticket_stored_with_code("1234567890");
}
While it’s not a truly human-readable text, it’s close enough and not hard to understand. There are quite a few things to unpack here. Let’s proceed with them one by one.
The test above assumes a particular project folder structure and reflects the Wing module and import conventions, which states
It's also possible to import a directory as a module. The module will contain all public types defined in the directory's files. If the directory has subdirectories, they will be available under the corresponding names.
From the first two lines, we can conclude that the project has two main folders: src
where all source code is located, and test
where all tests are located. Further, there is a test\steps
subfolder where individual test step implementations are kept.
The next three lines allocate a preflight Configurator
object and extract from it two pointers:
_testFixture
pointing to a preflight class responsible for the test setup_systemUnderTest
which points to a Primary Port Interface intended for Car Drivers.Within the “Buy ticket for 2 hours; no errors”, we allocate an inflight BuyTicketTestSteps
object responsible for implementing individual steps. Conventionally, this object gets an almost invisible name underscore, which improves the overall test readability. This is a common technique for developing a Domain-Specific Language (DSL) embedded in a general-purpose host language.
It’s important to stress, that while it did not happen in my case, it’s fully conceivable to start the project with a simple src
and test\steps
folder structure and a simple test setup to drive other architectural decisions.
Of course, with no steps implemented, the test will not even pass compilation. To make progress, we need to look inside the BuyTicketTestSteps
class.
The test steps class for the Buy Ticket Use Case is presented below:
bring expect;
bring "./Parser.w" as parse;
bring "./TestStepsBase.w" as base;
bring "../../src/application/ports" as ports;
pub class BuyTicketTestSteps extends base.TestStepsBase {
_systemUnderTest: ports.ForParkingCars;
inflight var _currentAvailableRates: Set<ports.Rate>;
inflight var _currentBoughtTicket: ports.Ticket?;
new(
testFixture: ports.ForAdministering,
systemUnderTest: ports.ForParkingCars
) {
super(testFixture);
this._systemUnderTest = systemUnderTest;
}
inflight new() {
this._currentBoughtTicket = nil;
this._currentAvailableRates = Set<ports.Rate>[];
}
pub inflight the_existing_rates_in_the_repository_are(
sRates: Array<Array<str>>
): void {
this.testFixture.initializeRates(parse.Rates(sRates).toArray());
}
pub inflight next_ticket_code_is(ticketCode: str): void {
this.testFixture.changeNextTicketCode(ticketCode);
}
pub inflight no_error_occurs_while_paying(): void {
this.testFixture.setPaymentError(ports.PaymentError.NONE);
}
pub inflight I_do_a_get_available_rates_request(): void {
this._currentAvailableRates = this._systemUnderTest.getAvailableRates();
}
pub inflight I_should_obtain_these_rates(sRates: Array<Array<str>>): void {
let expected = parse.Rates(sRates);
expect.equal(this._currentAvailableRates, expected);
}
pub inflight I_submit_this_buy_ticket_request(sRequest: Array<Array<str>>): void {
let request = parse.BuyRequest(sRequest);
this.setCurrentThrownException(nil);
this._currentBoughtTicket = nil;
try {
this._currentBoughtTicket = this._systemUnderTest.buyTicket(request);
} catch err {
this.setCurrentThrownException(err);
}
}
pub inflight this_ticket_should_be_returned(sTicket: Array<Array<str>>): void {
let sTicketFull = Array<Array<str>>[
sTicket.at(0).concat(["paymentId"]),
sTicket.at(1).concat([this.testFixture.getLastPayResponse()])
];
let expected = parse.Ticket(sTicketFull);
expect.equal(this._currentBoughtTicket, expected);
}
pub inflight this_pay_request_should_have_been_done(sRequest: Array<Array<str>>): void {
let expected = parse.PayRequest(sRequest);
let actual = this.testFixture.getLastPayRequest();
expect.equal(actual, expected);
}
pub inflight the_buy_ticket_response_should_be_the_ticket_stored_with_code(code: str): void {
let actual = this.testFixture.getStoredTicket(code);
expect.equal(actual, this._currentBoughtTicket);
}
pub inflight an_error_occurs_while_paying(error: str): void {
this.testFixture.setPaymentError(parse.PaymentError(error));
}
pub inflight a_PayErrorException_with_the_error_code_that_occurred_should_have_been_thrown(code: str): void {
//TODO: make it more specific
let err = this.getCurrentThrownException()!;
log(err);
expect.ok(err.contains(code));
}
pub inflight no_ticket_with_code_should_have_been_stored(code: str): void {
try {
this.testFixture.getStoredTicket(code);
expect.ok(false, "Should never get there");
} catch err {
expect.ok(err.contains("KeyError"));
}
}
}
This class is straightforward: it parses the input data, uniformly presented as Array<Array<str>>
, into application-specific data structures, sends them to either testFixture
or _systemUnderTest
objects, keeps intermediate results, and compares expected vs actual results where appropriate.
The only specifics to pay attention to are the proper handling of preflight and inflight definitions. I’m grateful to Cristian Pallares, who helped me to make it right.
We have three additional elements with clearly delineated responsibilities:
Let’s take a closer look at each one.
The source code of the Parser module is presented below:
bring structx;
bring datetimex;
bring "../../src/application/ports" as ports;
pub class Util {
pub inflight static Rates(sRates: Array<Array<str>>): Set<ports.Rate> {
return unsafeCast(
structx.fromFieldArray(
sRates,
ports.Rate.schema()
)
);
}
pub inflight static BuyRequest(
sRequest: Array<Array<str>>
): ports.BuyTicketRequest {
let requestSet: Set<ports.BuyTicketRequest> = unsafeCast(
structx.fromFieldArray(
sRequest,
ports.BuyTicketRequest.schema()
)
);
return requestSet.toArray().at(0);
}
pub inflight static Tickets(
sTickets: Array<Array<str>>
): Set<ports.Ticket> {
return unsafeCast(
structx.fromFieldArray(
sTickets,
ports.Ticket.schema(),
datetimex.DatetimeFormat.YYYYMMDD_HHMM
)
);
}
pub inflight static Ticket(sTicket: Array<Array<str>>): ports.Ticket {
return Util.Tickets(sTicket).toArray().at(0);
}
pub inflight static PayRequest(
sRequest: Array<Array<str>>
): ports.PayRequest {
let requestSet: Set<ports.PayRequest> = unsafeCast(
structx.fromFieldArray(
sRequest,
ports.PayRequest.schema()
)
);
return requestSet.toArray().at(0);
}
pub inflight static CheckCarRequest(
sRequest: Array<Array<str>>
): ports.CheckCarRequest {
let requestSet: Set<ports.CheckCarRequest> = unsafeCast(
structx.fromFieldArray(
sRequest,
ports.CheckCarRequest.schema()
)
);
return requestSet.toArray().at(0);
}
pub inflight static CheckCarResult(
sResult: Array<Array<str>>
): ports.CheckCarResult {
let resultSet: Set<ports.CheckCarResult> = unsafeCast(
structx.fromFieldArray(
sResult, ports.CheckCarResult.schema()
)
);
return resultSet.toArray().at(0);
}
pub inflight static DateTime(dateTime: str): std.Datetime {
return datetimex.parse(
dateTime,
datetimex.DatetimeFormat.YYYYMMDD_HHMM
);
}
pub inflight static PaymentError(error: str): ports.PaymentError {
return Map<ports.PaymentError>{
"NONE" => ports.PaymentError.NONE,
"GENERIC_ERROR" => ports.PaymentError.GENERIC_ERROR,
"CARD_DECLINED" => ports.PaymentError.CARD_DECLINED
}.get(error);
}
}
This class, while not sophisticated from the algorithmic point of view, reflects some important architectural decisions with far-reaching consequences.
First, it announces a dependency on the system Ports located in the src\application\ports
folder. Chapter 4.8 of the “Hexagonal Architecture Explained” book, titled “Where do I put my files?”, makes a clear statement:
The folder structure is not covered by the pattern, nor is it the same in all languages. Some languages (Java), require interface definitions. Some (Python, Ruby) don't. And some, such as Smalltalk, don't even have the concept of files!
It warns, however, that “we’ve observed that folder structures that don't match the intentions of the pattern end up causing damage”. For strongly typed languages like Java, it recommends keeping specifications of Driving and Driven Ports in separate folders.
I started with such a structure, but very soon realized that it just enlarges the size of the code and prevents it from taking full advantage of the Wing module and import conventions. Based on this I decided to keep all Ports in one dedicated folder. Considering the current size of the application, this decision looks justified.
Second, it exploits an undocumented Wing module and import feature that makes all public static inflight methods of a class named Util
directly accessible by the client modules, which improves the code readability.
Third, it uses two Wing Standard Library extensions, datetimex
, and structx
developed to compensate for some features I needed. These extensions were part of my “In Search for Winglang Middleware” project endor.w
, I reported about here, here, and here.
Justification for these extensions will be clarified when we look at the core architectural decision about representing the Port Interfaces and Data.
Traditional strongly typed Object-Oriented languages like Java advocate encapsulating all domain elements as objects. If I followed this advice, the Ticket
object would look something like this:
pub inflight class Ticket {
pub ticketCode: str;
pub carPlate: str;
pub rateName: str;
pub startingDateTime: std.Datetime;
pub endingDateTime: std.Datetime;
pub price: num;
pub paymentId: str;
new (ticketCode: str, ...) {
this.ticketCode = ticketCode;
...
}
pub toJson(): Json {
return Json {
ticketCode = this.ticketCode,
...
}
pub static fromJson(data: Json): Ticket {
return new Ticket(
data.get("ticketCode").asStr(),
...
);
}
pub toFieldArray(): Array<str> {
return [
this.ticketCode,
...
];
}
pub static fromFieldArray(records: Array<Array<str>>): Set<Ticket> {
let result = new MutSet<Ticket>[];
for record in records {
result.add(new Ticket(
record.at(0),
...
);
}
return result.copy();
}
} such
Such an approach introduces 6 extra lines of code per data field for initialization and conversation plus some fixed overhead of method definition. This creates a significant boilerplate overhead.
Mainstream languages like Java and Python try alleviating this pain with various meta-programming automation tools, such as decorators, abstract base classes, or meta-classes.
In Wing, all this proved to be sub-optimal and unnecessary, provided minor adjustments were made to the Wing Standard Library.
Here is how the Ticket
data structure can be defined:
pub struct Ticket { //Data structure representing objects
//with the data of a parking ticket:
ticketCode: str; //Unique identifier of the ticket;
//It is a 10-digit number with leading zeros
//if necessary
carPlate: str; //Plate of the car that has been parked
rateName: str; //Rate name of the zone where
//the car is parked at
startingDateTime: std.Datetime; //When the parking period begins
endingDateTime: std.Datetime; //When the parking period expires
price: num; //Amount of euros paid for the ticket
paymentId: str; //Unique identifier of the payment
//made to get the ticket.
}
In Wing, structures are immutable by default, and that eliminates a lot of access control problems.
Without any change, the Wing Standard Library will support out-of-the-box Json.stringify(ticket)
serialization to Json
string and Ticket.fromJson(data)
de-serialization. That’s not enough for the following reasons:
Ticket
objects to Json
rather than a Json string
Json
serialization and de-serialization functions need to handle the std.Datetime
fields correctly. Currently the Json.stringify()
will convert any std.Datetime
to an ISO string, but Ticket.fromJson()
will fail.std.Datetime
. For example, the “Blue Zone” application uses the YYYYMM HH:MM format.All these additional needs were addressed in two Trusted Wing Libraries: datetimex
and struct
. While the implementation was not trivial and required a good understanding of how Wing and TypeScript interoperability works, it was doable with reasonable effort. Hopefully, these extensions can be included in future versions of the Wing Standard Library.
The special, unsafeCast
function helped to overcome the Wing strong type checking limitations. To provide better support for actual vs expected comparison in tests, I decided that fromFieldArray(...)
will return Set<...>
objects. Occasionally it required toArray()
conversion, but I found this affordable.
Now, let’s take a look at the main _systemUnderTest
object.
Following the “Hexagonal Architecture Explained” book recommendations, port naming adopts the ForActorName convention. Here is how it is defined for the ParkingCar External Actor:
pub struct BuyTicketRequest { //Input data needed for buying a ticket
//to park a car:
carPlate: str; //Plate of the car that has been parked
rateName: str; //Rate name of the zone where the car is parked at
euros: num; //Euros amount to be paid
card: str; //Card used for paying, in the format 'n-c-mmyyyy', where
// 'n' is the card number (16 digits)
// 'c' is the verification code (3 digits),
// 'mmyyyy' is the expiration month and year (6 digits)
}
/**
* DRIVING PORT (Provided Interface)
*/
pub inflight interface ForParkingCars {
/**
* @return A set with the existing rates for parking a car in regulated
* zones of the city.
* If no rates exist, an empty set is returned.
*/
getAvailableRates(): Set<rate.Rate>;
/**
* Pay for a ticket to park a car at a zone regulated by a rate,
* and save the ticket in the repository.
* The validity period of the ticket begins at the current date-time,
* and its duration is calculated in minutes by applying the rate,
* based on the amount of euros paid.
* @param request Input data needed for buying a ticket.
* @see BuyTicketRequest
* @return A ticket valid for parking the car at a zone regulated by the rate,
* paying the euros amount using the card.
* The ticket holds a reference to the identifier of the payment
* that was made.
* @throws BuyTicketRequestException
* If any input data in the request is not valid.
* @throws PayErrorException
* If any error occurred while paying.
*/
buyTicket (request: BuyTicketRequest): ticket.Ticket;
}
As with Ticket
and Rate
objects, the BuyTicketRequest
object is defined as a plain Wing struct
relying on the automatic conversion infrastructure described above.
The ForParkingCars
is defined as the Wing interface
. Unlike the original “Blue Zone” implementation, this one does not include BuyTicketRequest
validation in the port specification. This was done on purpose.
While strong object encapsulation would encourage including the validate()
method in the BuyTicketRequest
class, with open immutable data structures like the ones adopted here, it could be done where it belongs - in the use case implementation. On the other hand, including the request validation logic in port specification brings in too many implementation details, too early.
This one is used for providing testFixture
functionality, and while it is long, it is also completely straightforward:
bring "./Rate.w" as rate;
bring "./Ticket.w" as ticket;
bring "./ForPaying.w" as forPaying;
/**
* DRIVING PORT (Provided Interface)
* For doing administration tasks like initializing, load data in the repositories,
* configuring the services used by the app, etc.
* Typically, it is used by:
* - Tests (driving actors) for setting up the test-fixture (driven actors).
* - The start-up for initializing the app.
*/
pub inflight interface ForAdministering {
/**
* Load the given rates into the data repository,
* deleting previously existing rates if any.
*/
initializeRates(newRates: Array<rate.Rate>): void;
/**
* Load the given tickets into the data repository,
* deleting previously existing tickets if any.
*/
initializeTickets(newTickets: Array<ticket.Ticket>): void;
/**
* Make the given ticket code the next to be returned when asking for it.
*/
changeNextTicketCode(newNextTicketCode: str): void;
/**
* Return the ticket stored in the repository with the given code
*/
getStoredTicket(ticketCode: str): ticket.Ticket;
/**
* Return the last request done to the "pay" method
*/
getLastPayRequest(): forPaying.PayRequest;
/**
* Return the last response returned by the "pay" method.
* It is an identifier of the payment made.
*/
getLastPayResponse(): str;
/**
* Make the probability of a payment error the "percentage" given as a parameter
*/
setPaymentError(errorCode: forPaying.PaymentError): void;
/**
* Return the code of the error that occurred when running the "pay" method
*/
getPaymentError(): forPaying.PaymentError;
/**
* Set the given date-time as the current date-time
*/
changeCurrentDateTime(newCurrentDateTime: std.Datetime): void;
}
Now, we need to dive one level deeper and look at the application logic implementation.
bring "../../application/ports" as ports;
bring "../../application/usecases" as usecases;
pub class ForParkingCarsBackend impl ports.ForParkingCars {
_buyTicket: usecases.BuyTicket;
_getAvailableRates: usecases.GetAvailableRates;
new(
dataRepository: ports.ForStoringData,
paymentService: ports.ForPaying,
dateTimeService: ports.ForObtainingDateTime
) {
this._buyTicket = new usecases.BuyTicket(dataRepository, paymentService, dateTimeService);
this._getAvailableRates = new usecases.GetAvailableRates(dataRepository);
}
pub inflight getAvailableRates(): Set<ports.Rate> {
return this._getAvailableRates.apply();
}
pub inflight buyTicket(request: ports.BuyTicketRequest): ports.Ticket {
return this._buyTicket.apply(request);
}
}
This class resides in the src/outside/backend
folder and provides an implementation of the ports.ForParkingCars
interface that is suitable for a direct function call. As we can see, it assumes two additional Secondary Ports: ports.ForStoringData
and ports.ForObtainingTime
and delegates actual implementation to two Use Case implementations: BuyTicket
and GetAvailableRates
. The BuyTicket
Use Case implementation is where the core system logic resides, so let’s look at it.
bring math;
bring datetimex;
bring exception;
bring "../ports" as ports;
bring "./Verifier.w" as validate;
pub class BuyTicket {
_dataRepository: ports.ForStoringData;
_paymentService: ports.ForPaying;
_dateTimeService: ports.ForObtainingDateTime;
new(
dataRepository: ports.ForStoringData,
paymentService: ports.ForPaying,
dateTimeService: ports.ForObtainingDateTime
) {
this._dataRepository = dataRepository;
this._paymentService = paymentService;
this._dateTimeService = dateTimeService;
}
pub inflight apply(request: ports.BuyTicketRequest): ports.Ticket {
let currentDateTime = this._dateTimeService.getCurrentDateTime();
this._validateRequest(request, currentDateTime);
let paymentId = this._paymentService.pay(
euros: request.euros,
card: request.card
);
let ticket = this._buildTicket(request, paymentId, currentDateTime);
this._dataRepository.saveTicket(ticket);
return ticket;
}
inflight _validateRequest(request: ports.BuyTicketRequest, currentDateTime: std.Datetime): void {
let requestErrors = validate.BuyTicketRequest(request, currentDateTime);
if requestErrors.length > 0 {
throw exception.ValueError(
"Buy ticket request is not valid",
requestErrors
);
}
}
inflight _buildTicket(
request: ports.BuyTicketRequest,
paymentId: str,
currentDateTime: std.Datetime
): ports.Ticket {
let ticketCode = this._dataRepository.nextTicketCode();
let rate = this._dataRepository.getRateByName(request.rateName);
let endingDateTime = BuyTicket._calculateEndingDateTime(
currentDateTime,
request.euros,
rate.eurosPerHour
);
return ports.Ticket {
ticketCode: ticketCode,
carPlate: request.carPlate,
rateName: request.rateName,
startingDateTime: currentDateTime,
endingDateTime: endingDateTime,
price: request.euros,
paymentId: paymentId
};
}
/**
* minutes = (euros * minutesPerHour) / eurosPerHour
* endingDateTime = startingDateTime + minutes
*/
static inflight _calculateEndingDateTime(
startingDateTime: std.Datetime,
euros: num,
eurosPerHour: num
): std.Datetime {
let MINUTES_PER_HOUR = 60;
let minutes = math.round((MINUTES_PER_HOUR * euros) / eurosPerHour);
return datetimex.plus(startingDateTime, duration.fromMinutes(minutes));
}
}
The “Buy Ticket” Use Case implementation class resides within the src/application/usescases
folder. It returns an inflight function responsible for executing the Use Case logic:
Ticket
Ticket
recordTicket
record in the databaseThe main reason for implementing Use Cases as inflight functions is that all Wing event handlers are inflight functions. While direct function calls are useful for local testing, they will typically be HTTP REST or GraphQL API calls in a real deployment.
The actual validation of the BuyTicketRequest
is delegated to an auxiliary Util
class within the Verifier.w
module. The main reason is that individual field validation might be very detailed and involve many low-level specifics, contributing little to the overall use case logic understanding.
Following the “Hexagonal Architecture Explained” book recommendations, this is implemented within a Configurator
class as follows:
bring util;
bring endor;
bring "./outside" as outside;
bring "./application/ports" as ports;
enum ApiType {
DIRECT_CALL,
HTTP_REST
}
enum ProgramType {
UNKNOWN,
TEST,
SERVICE
}
pub class Configurator impl outside.BlueZoneApiFactory {
_apiFactory: outside.BlueZoneApiFactory;
new(name: str) {
let mockService = new outside.mock.MockDataRepository();
let programType = this._getProgramType(name);
let mode = this._getMode(programType);
let apiType = this._getApiType(programType, mode);
this._apiFactory = this._getApiFactory(
name,
mode,
apiType,
mockService,
mockService,
mockService
);
}
_getProgramType(name: str): ProgramType { //TODO: migrate to endor??
if name.endsWith("Test") {
return ProgramType.TEST;
} elif name.endsWith("Service") || name.endsWith("Application") {
return ProgramType.SERVICE;
} elif std.Node.of(this).app.isTestEnvironment {
return ProgramType.TEST;
}
return ProgramType.UNKNOWN;
}
_getMode(programType: ProgramType): endor.Mode {
if let mode = util.tryEnv("MODE") {
return Map<endor.Mode>{ //TODO Migrate this function to endor
"DEV" => endor.Mode.DEV,
"TEST" => endor.Mode.TEST,
"STAGE" => endor.Mode.STAGE,
"PROD" => endor.Mode.PROD
}.get(mode);
} elif programType == ProgramType.TEST {
return endor.Mode.TEST;
} elif programType == ProgramType.SERVICE {
return endor.Mode.STAGE;
}
return endor.Mode.DEV;
}
_getApiType(
programType: ProgramType,
mode: endor.Mode,
): ApiType {
if let apiType = util.tryEnv("API_TYPE") {
return Map<ApiType>{
"DIRECT_CALL" => ApiType.DIRECT_CALL,
"HTTP_REST" => ApiType.HTTP_REST
}.get(apiType);
} elif programType == ProgramType.SERVICE {
return ApiType.HTTP_REST;
}
let target = util.env("WING_TARGET");
if target.contains("sim") {
return ApiType.DIRECT_CALL;
}
return ApiType.HTTP_REST;
}
_getApiFactory(
name: str,
mode: endor.Mode,
apiType: ApiType,
dataService: ports.ForStoringData,
paymentService: ports.ForPaying,
dateTimeService: ports.ForObtainingDateTime
): outside.BlueZoneApiFactory {
let directCall = new outside.DirectCallApiFactory(
dataService,
paymentService,
dateTimeService
);
if apiType == ApiType.DIRECT_CALL {
return directCall;
} elif apiType == ApiType.HTTP_REST {
return new outside.HttpRestApiFactory(
name,
mode,
directCall
);
}
}
pub getForAdministering(): ports.ForAdministering {
return this._apiFactory.getForAdministering();
}
pub getForParkingCars(): ports.ForParkingCars {
return this._apiFactory.getForParkingCars();
}
pub getForIssuingFines(): ports.ForIssuingFines {
return this._apiFactory.getForIssuingFines();
}
}
This is an experimental, still not final, implementation, but it could be extended to address the production deployment needs. It adopts a static system configuration by exploiting the Wing preflight machinery.
In this implementation, a special MockDataStore
object implements all three Secondary Ports: data service, paying service, and date-time service. It does not have to be this way and was created to save time during the scaffolding development.
The main responsibility of the Configuratior
class is to determine which type of API should be used:
The actual API creation is delegated to corresponding ApiFactory
classes.
What is remarkable about such an implementation is that the same test suite is used for all configurations, except for real HTML-based UI mode. The latter could also be achieved but would require some HTML test drivers like Selenium.
It is the first time I have achieved such a level of code reuse. As a result, I run local direct call configuration most of the time, especially when I perform code structure refactoring, with full confidence that it will run in a remote test and production environment without a change. This proves that the Wing cloud-oriented programming language and Hexagonal Architecture is truly a winning combination.
Including the full source code of every module would increase this article's size too much. Access to the GitHub repository for this project is available on demand.
Instead, I will present the overall folder structure, two UML class diagrams, and a cloud resources diagram reflecting the main program elements and their relationships.
├── src
│ ├── application
│ │ ├── ports
│ │ │ ├── ForAdministering.w
│ │ │ ├── ForIssuingFines.w
│ │ │ ├── ForObtainingDateTime.w
│ │ │ ├── ForParkingCars.w
│ │ │ ├── ForPaying.w
│ │ │ ├── ForStoringData.w
│ │ │ ├── Rate.w
│ │ │ └── Ticket.w
│ │ ├── usecases
│ │ │ ├── BuyTicket.w
│ │ │ ├── CheckCar.w
│ │ │ ├── GetAvailableRates.w
│ │ │ └── Veryfier.w
│ ├── outside
│ │ ├── backend
│ │ │ ├── ForAdministeringBackend.w
│ │ │ ├── ForIssuingFinesBackend.w
│ │ │ └── ForParkingCarsBackend.w
│ │ ├── http
│ │ │ ├── html
│ │ │ │ ├── _htmlForParkingCarsFormatter.ts
│ │ │ │ └── htmlForParkingCarsFormatter.w
│ │ │ ├── json
│ │ │ │ ├── jsonForIssuingFinesFormatter.w
│ │ │ │ └── jsonForParkingCarsFormatter.w
│ │ │ ├── ForIssuingFinesClient.w
│ │ │ ├── ForIssuingFinesController.w
│ │ │ ├── ForParkingCarsClient.w
│ │ │ ├── ForParkingCarsController.w
│ │ │ └── middleware.w
│ │ ├── mock
│ │ │ └── MockDataRepository.w
│ │ ├── ApiFactory.w
│ │ ├── BlueZoneAplication.main.w
│ │ ├── DirectCallApiFactory.w
│ │ └── HttpRestApiFactory.w
│ └── Configurator.w
├── test
│ ├── steps
│ │ ├── BuyTicketTestSteps.w
│ │ ├── CheckCarTestSteps.w
│ │ ├── Parser.w
│ │ └── TestStepsBase.w
│ ├── usecase.BuyTicketTest.w
│ └── usecase.CheckCarTest.w
├── .gitignore
├── LICENSE
├── Makefile
├── README.md
├── package-lock.json
├── package.json
└── tsconfig.json
Fig 4: Folder Structure
Application logic-wise the project is small. Yet, it is already sizable to pose enough challenges for cognitive control over its structure. The current version attempts to strike a reasonable balance between multiple criteria:
While computing all sets of desirable metrics is beyond the scope of this publication, one back-of-envelope calculation could be performed here and now manually: the percentage of files under the application
and outside
folders, including intermediate folders (let’s call “value”) and the total number of files and folders (let’s call it “stuff”). In the current version, the numbers are:
Total: 55
src/application: 16
src/: 41
Files: 43
Strict Value to Stuff Ratio: 16*100/55 = 29.09%
Extended Value to Stuff Ratio: (15+19)*100/42 = 74.55%
Is it big or little? Good or Bad? It’s hard to say at the moment. The initial impression is that the numbers are healthy, yet coming up with more founded conclusions needs additional research and experimentation. A real production system will require a significantly larger number of tests.
From the cognitive load perspective, 43 files is a large number exceeding the famous 7 +/- 2 limit of human communication channels and short memory. It requires some organization. In the current version, the maximal number of files at one level is 8 - within the limit.
The presented hierarchical diagram only partially reflects the real graph picture - cross-file dependencies resulting from the bring
statement are not visible. Also, the __node_files__
folder reflecting external dependencies and having an impact on resulting package size is omitted as well.
In short, without additional investment in tooling and methodology of metrics, the picture is only partial.
We still can formulate some desirable direction: we prefer to deal as much as possible with assets generating the direct value and as little as possible with supporting stuff required to make it work. Ideally, a healthy Value to Stuff ratio would come from language and library support. Automatic code generation, including that performed by Generative AI, would reduce the typing but not the overall cognitive load.
Depicting all “Blue Zone” application elements in a single UML Class Diagram would be impractical. Among other things, UML does not support directly separate representation of preflight and inflight elements. We can visualize separately the most important parts of the system. For example, here is a UML Class Diagram for the application
part:
Fig 5: `src/application` Class Diagram
DO NOT PUBLISH
@startuml
left to right direction
hide members
struct Ticket
struct Rate
struct BuyTicketRequest
interface ForDrivingCars <<primary>>
ForDrivingCars ..> BuyTicketRequest
ForDrivingCars ..> Ticket
interface ForStoringData
ForStoringData ..> Rate
ForStoringData ..> Ticket
struct PayRequest
enum PaymentError
interface ForPaying
ForPaying ..> PayRequest
ForPaying ..> PaymentError
interface ForObtainingDateTime
class BuyTicket <<function>>
class Veryfier
Veryfier ..> BuyTicketRequest
BuyTicket --> Veryfier
BuyTicket ..> BuyTicketRequest
BuyTicket --> ForObtainingDateTime
BuyTicket --> ForStoringData
BuyTicket --> ForPaying
interface ForIssuingFines <<primary>>
struct CheckCarRequest
struct CheckCarResponse
ForIssuingFines ..> CheckCarRequest
ForIssuingFines ..> CheckCarResponse
class CheckCar <<function>>
CheckCar --> ForStoringData
CheckCar --> ForObtainingDateTime
CheckCar ..> CheckCarRequest
CheckCar ..> CheckCarResponse
@enduml
Notice that the IForParkingCars
and ForIssuingFines
primary interfaces are named differently from the Car Driver
and Parking Inspector
primary actors and BuyTicket
and CheckCar
use cases. This is not a mistake. Primary Port Interface names should reflect the Primary Actor role in a particular use case. There are no automatic rules for such a naming. Hopefully, the selected names are intuitive enough.
Notice also, that the Primary Interfaces are not directly implemented within the application
module and there is a disconnect between these interfaces and use case implementations.
This is also not a mistake. The concrete connection between the Primary Interface and the corresponding use case implementation depends on configuration, as reflected in the UML Class Diagram Below:
Fig 6: Configurator Class Diagram (”Buy Ticket” Use Case only)
DO NOT PUBLISH
@startuml
hide members
interface ForDrivingCars <<primary>>
interface ForStoringData
interface ForPaying
interface ForObtainingDateTime
class BuyTicket <<function>>
BuyTicket --> ForObtainingDateTime
BuyTicket --> ForStoringData
BuyTicket --> ForPaying
class ForDrivingCarsBackEnd implements ForDrivingCars
ForDrivingCarsBackEnd --> BuyTicket
class ForDrivingCarsClient implements ForDrivingCars
class ForDrivingCarsController
ForDrivingCarsController --> ForDrivingCars
class MockDataStore implements ForStoringData, ForPaying, ForObtainingDateTime
interface IBlueZoneApiFactory
class DirectCallApiFactory implements IBlueZoneApiFactory
DirectCallApiFactory ..> ForDrivingCarsBackEnd
class HttpRestApiFactory implements IBlueZoneApiFactory
class cloud.Api
cloud.Api --> ForDrivingCarsController
HttpRestApiFactory --> cloud.Api
HttpRestApiFactory ..> ForDrivingCarsController
HttpRestApiFactory ..> ForDrivingCarsClient
HttpRestApiFactory ..> ForDrivingCarsBackend
class Configurator
Configurator --> IBlueZoneApiFactory
Configurator --> MockDataStore
@enduml
Only elements related to the “Buy Ticket” Use Case implementation and essential connections are depicted to avoid clutter.
According to the class diagram above the Configurator
will decide which IBlueZoneApiFactory
implementation to use: DirectApiCallFactory
for local testing purposes or HttpRestApiFactory
for both local and remote testing via HTTP and production deployment.
Fig 7: Cloud Resources
The cloud resources diagram presented above reflects the outcome of the Wing compilation to the AWS target platform. It is quite different from the UML Class Diagram presented above and we have to conclude that various types of diagrams complement each other. The Cloud Resources diagram is important for understanding and controlling the system's operational aspects like cost, performance, reliability, resilience, and security.
The main challenge, as with previous diagrams, is the scale. With more cloud resources, the diagram will quickly be cluttered with too many details.
The current versions of all diagrams are more like useful illustrations than formal blueprints. Striking the right balance between accuracy and comprehension is a subject for future research. I addressed this issue in one of my early publications. Probably, it’s time to come back to this research topic.
The experience of porting the “Blue Zone” application, featured in the recently published book “Hexagonal Architecture Explained”, from Java to Wing led to the following interim conclusions
Throughout the preparation of this publication, I utilized several key tools to enhance the draft and ensure its quality.
The initial draft was crafted with the organizational capabilities of Notion's free subscription, facilitating the structuring and development of ideas.
For grammar and spelling review, the free version of Grammarly proved useful for identifying and correcting basic errors, ensuring the readability of the text.
The enhancement of stylistic expression and the narrative coherence checks were performed using the paid version of ChatGPT 4o. The ChatGPT 4o tool was also used to develop critical portions of the Trusted Wing Libraries: datetimex
and struct
in TypeScript.
UML Class Diagrams were produced with the free version of the PlantText UML online tool.
Java version of the “Blue Zone” application was developed by Juan Manuel Garrido de Paz, the book’s co-author. Juan Manuel Garrido de Paz sadly passed away in April 2024. May his memory be blessed and this report serves as a tribute to him.
While all advanced tools and resources significantly contributed to the preparation process, the concepts, solutions, and final decisions presented in this article are entirely my own, for which I bear full responsibility.
In this tutorial, we will build an AI-powered Q&A bot for your website documentation.
🌐 Create a user-friendly Next.js app to accept questions and URLs
🔧 Set up a Wing backend to handle all the requests
💡 Incorporate Langchain for AI-driven answers by scraping and analyzing documentation using RAG
🔄 Complete the connection between the frontend input and AI-processed responses.
Wing is an open-source framework for the cloud.
It allows you to create your application's infrastructure and code combined as a single unit and deploy them safely to your preferred cloud providers.
Wing gives you complete control over how your application's infrastructure is configured. In addition to its easy-to-learn programming language, Wing also supports Typescript.
In this tutorial, we'll use TypeScript. So, don't worry—your JavaScript and React knowledge is more than enough to understand this tutorial.
Here, you’ll create a simple form that accepts the documentation URL and the user’s question and then returns a response based on the data available on the website.
First, create a folder containing two sub-folders - frontend
and backend
. The frontend
folder contains the Next.js app, and the backend
folder is for Wing.
mkdir qa-bot && cd qa-bot
mkdir frontend backend
Within the frontend
folder, create a Next.js project by running the following code snippet:
cd frontend
npx create-next-app ./
Copy the code snippet below into the app/page.tsx
file to create the form that accepts the user’s question and the documentation URL:
"use client";
import { useState } from "react";
export default function Home() {
const [documentationURL, setDocumentationURL] = useState<string>("");
const [question, setQuestion] = useState<string>("");
const [disable, setDisable] = useState<boolean>(false);
const [response, setResponse] = useState<string | null>(null);
const handleUserQuery = async (e: React.FormEvent) => {
e.preventDefault();
setDisable(true);
console.log({ question, documentationURL });
};
return (
<main className='w-full md:px-8 px-3 py-8'>
<h2 className='font-bold text-2xl mb-8 text-center text-blue-600'>
Documentation Bot with Wing & LangChain
</h2>
<form onSubmit={handleUserQuery} className='mb-8'>
<label className='block mb-2 text-sm text-gray-500'>Webpage URL</label>
<input
type='url'
className='w-full mb-4 p-4 rounded-md border text-sm border-gray-300'
placeholder='https://www.winglang.io/docs/concepts/why-wing'
required
value={documentationURL}
onChange={(e) => setDocumentationURL(e.target.value)}
/>
<label className='block mb-2 text-sm text-gray-500'>
Ask any questions related to the page URL above
</label>
<textarea
rows={5}
className='w-full mb-4 p-4 text-sm rounded-md border border-gray-300'
placeholder='What is Winglang? OR Why should I use Winglang? OR How does Winglang work?'
required
value={question}
onChange={(e) => setQuestion(e.target.value)}
/>
<button
type='submit'
disabled={disable}
className='bg-blue-500 text-white px-8 py-3 rounded'
>
{disable ? "Loading..." : "Ask Question"}
</button>
</form>
{response && (
<div className='bg-gray-100 w-full p-8 rounded-sm shadow-md'>
<p className='text-gray-600'>{response}</p>
</div>
)}
</main>
);
}
The code snippet above displays a form that accepts the user’s question and the documentation URL, and logs them to the console for now.
Perfect! 🎉You’ve completed the application's user interface. Next, let’s set up the Wing backend.
Wing provides a CLI that enables you to perform various actions within your projects.
It also provides VSCode and IntelliJ extensions that enhance the developer experience with features like syntax highlighting, compiler diagnostics, code completion and snippets, and many others.
Before we proceed, stop your Next.js development server for now and install the Winglang CLI by running the code snippet below in your terminal.
npm install -g winglang@latest
Run the following code snippet to ensure that the Winglang CLI is installed and working as expected:
wing --version
Next, navigate to the backend
folder and create an empty Wing Typescript project. Ensure you select the empty
template and Typescript as the language.
wing new
Copy the code snippet below into the backend/main.ts
file.
import { cloud, inflight, lift, main } from "@wingcloud/framework";
main((root, test) => {
const fn = new cloud.Function(
root,
"Function",
inflight(async () => {
return "hello, world";
})
);
});
The main()
function serves as the entry point to Wing.
It creates a cloud function and executes at compile time. The inflight
function, on the other hand, runs at runtime and returns a Hello, world!
text.
Start the Wing development server by running the code snippet below. It automatically opens the Wing Console in your browser at http://localhost:3000
.
wing it
You've successfully installed Wing on your computer.
From the previous sections, you've created the Next.js frontend app within the frontend
folder and the Wing backend within the backend
folder.
In this section, you'll learn how to communicate and send data back and forth between the Next.js app and the Winglang backend.
First, install the Winglang React library within the backend folder by running the code below:
npm install @winglibs/react
Next, update the main.ts
file as shown below:
import { main, cloud, inflight, lift } from "@wingcloud/framework";
import React from "@winglibs/react";
main((root, test) => {
const api = new cloud.Api(root, "api", { cors: true })
;
//👇🏻 create an API route
api.get(
"/test",
inflight(async () => {
return {
status: 200,
body: "Hello world",
};
})
);
//👉🏻 placeholder for the POST request endpoint
//👇🏻 connects to the Next.js project
const react = new React.App(root, "react", { projectPath: "../frontend" });
//👇🏻 an environment variable
react.addEnvironment("api_url", api.url);
});
The code snippet above creates an API endpoint (/test
) that accepts GET requests and returns a Hello world
text. The main
function also connects to the Next.js project and adds the api_url
as an environment variable.
The API URL contained in the environment variable enables us to send requests to the Wing API route. Now, how do we retrieve the API URL within the Next.js app and make these requests?
Update the RootLayout
component within the Next.js app/layout.tsx
file as done below:
export default function RootLayout({
children,
}: Readonly<{
children: React.ReactNode;
}>) {
return (
<html lang='en'>
<head>
{/** ---👇🏻 Adds this script tag 👇🏻 ---*/}
<script src='./wing.js' defer />
</head>
<body className={inter.className}>{children}</body>
</html>
);
}
Re-build the Next.js project by running npm run build
.
Finally, start the Wing development server. It automatically starts the Next.js server, which can be accessed at http://localhost:3001
in your browser.
You've successfully connected the Next.js to Wing. You can also access data within the environment variables using window.wingEnv.<attribute_name>
.
In this section, you'll learn how to send requests to Wing, process these requests with LangChain and OpenAI, and display the results on the Next.js frontend.
First, let's update the Next.js app/page.tsx
file to retrieve the API URL and send user's data to a Wing API endpoint.
To do this, extend the JavaScript window
object by adding the following code snippet at the top of the page.tsx
file.
"use client";
import { useState } from "react";
interface WingEnv {
api_url: string;
}
declare global {
interface Window {
wingEnv: WingEnv;
}
}
Next, update the handleUserQuery
function to send a POST request containing the user's question and website's URL to a Wing API endpoint.
//👇🏻 sends data to the api url
const [response, setResponse] = useState<string | null>(null);
const handleUserQuery = async (e: React.FormEvent) => {
e.preventDefault();
setDisable(true);
try {
const request = await fetch(`${window.wingEnv.api_url}/api`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({ question, pageURL: documentationURL }),
});
const response = await request.text();
setResponse(response);
setDisable(false);
} catch (err) {
console.error(err);
setDisable(false);
}
};
Before you create the Wing endpoint that accepts the POST request, install the following packages within the backend
folder:
npm install @langchain/community @langchain/openai langchain cheerio
Cheerio enables us to scrape the software documentation webpage, while the LangChain packages allow us to access its various functionalities.
The LangChain OpenAI integration package uses the OpenAI language model; therefore, you'll need a valid API key. You can get yours from the OpenAI Developer's Platform.
Next, let’s create the /api
endpoint that handle incoming requests.
The endpoint will:
First, import the following into the main.ts
file:
import { main, cloud, inflight, lift } from "@wingcloud/framework";
import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { createRetrievalChain } from "langchain/chains/retrieval";
import React from "@winglibs/react";
Add the code snippet below within the main()
function to create the /api
endpoint:
api.post(
"/api",
inflight(async (ctx, request) => {
//👇🏻 accept user inputs from Next.js
const { question, pageURL } = JSON.parse(request.body!);
//👇🏻 initialize OpenAI Chat for LLM interactions
const chatModel = new ChatOpenAI({
apiKey: "<YOUR_OPENAI_API_KEY>",
model: "gpt-3.5-turbo-1106",
});
//👇🏻 initialize OpenAI Embeddings for Vector Store data transformation
const embeddings = new OpenAIEmbeddings({
apiKey: "<YOUR_OPENAI_API_KEY>",
});
//👇🏻 creates a text splitter function that splits the OpenAI result chunk size
const splitter = new RecursiveCharacterTextSplitter({
chunkSize: 200, //👉🏻 characters per chunk
chunkOverlap: 20,
});
//👇🏻 creates a document loader, loads, and scraps the page
const loader = new CheerioWebBaseLoader(pageURL);
const docs = await loader.load();
//👇🏻 splits the document into chunks
const splitDocs = await splitter.splitDocuments(docs);
//👇🏻 creates a Vector store containing the split documents
const vectorStore = await MemoryVectorStore.fromDocuments(
splitDocs,
embeddings //👉🏻 transforms the data to the Vector Store format
);
//👇🏻 creates a document retriever that retrieves results that answers the user's questions
const retriever = vectorStore.asRetriever({
k: 1, //👉🏻 number of documents to retrieve (default is 2)
});
//👇🏻 creates a prompt template for the request
const prompt = ChatPromptTemplate.fromTemplate(`
Answer this question.
Context: {context}
Question: {input}
`);
//👇🏻 creates a chain containing the OpenAI chatModel and prompt
const chain = await createStuffDocumentsChain({
llm: chatModel,
prompt: prompt,
});
//👇🏻 creates a retrieval chain that combines the documents and the retriever function
const retrievalChain = await createRetrievalChain({
combineDocsChain: chain,
retriever,
});
//👇🏻 invokes the retrieval Chain and returns the user's answer
const response = await retrievalChain.invoke({
input: `${question}`,
});
if (response) {
return {
status: 200,
body: response.answer,
};
}
return undefined;
})
);
The API endpoint accepts the user’s question and the page URL from the Next.js application, initialises ChatOpenAI
and OpenAIEmbeddings
, loads the documentation page, and retrieves the answers to the user’s query in the form of documents.
Then, splits the documents into chunks, saves the chunks in the MemoryVectorStore
, and enables us to fetch answers to the question using LangChain retrievers.
From the code snippet above, the OpenAI API key is entered directly into the code; this could lead to security breaches, making the API key accessible to attackers. To prevent this data leak, Winglang allows you to save private keys and credentials in variables called secrets
.
When you create a secret, Wing saves this data in a .env
file, ensuring it is secured and accessible.
Update the main()
function to fetch the OpenAI API key from the Wing Secret.
main((root, test) => {
const api = new cloud.Api(root, "api", { cors: true });
//👇🏻 creates the secret variable
const secret = new cloud.Secret(root, "OpenAPISecret", {
name: "open-ai-key",
});
api.post(
"/api",
lift({ secret })
.grant({ secret: ["value"] })
.inflight(async (ctx, request) => {
const apiKey = await ctx.secret.value();
const chatModel = new ChatOpenAI({
apiKey,
model: "gpt-3.5-turbo-1106",
});
const embeddings = new OpenAIEmbeddings({
apiKey,
});
//👉🏻 other code snippets & configurations
);
const react = new React.App(root, "react", { projectPath: "../frontend" });
react.addEnvironment("api_url", api.url);
});
secret
variable declares a name for the secret (OpenAI API key).lift().grant()
grants the API endpoint access to the secret value stored in the Wing Secret.inflight()
function accepts the context and request object as parameters, makes a request to LangChain, and returns the result.apiKey
using the ctx.secret.value()
function.Finally, save the OpenAI API key as a secret by running this command in your terminal.
Great, now our secrets are stored and we can interact with our application. Let's take a look at it in action!
Here is a brief demo:
Let's dig a little bit deeper into the Winglang docs to see what data our AI bot can extract.
So far, we have gone over the following:
Wing aims to bring back your creative flow and close the gap between imagination and creation. Another great advantage of Wing is that it is open-source. Therefore, if you are looking forward to building distributed systems that leverage cloud services or contribute to the future of cloud development, Wing is your best choice.
Feel free to contribute to the GitHub repository, and share your thoughts with the team and the large community of developrs.
The source code for this tutorial is available here.
Thank you for reading! 🎉
Building Slack apps can be a daunting task for beginners. Between understanding the Slack API, setting up a server to handle incoming requests, and deploying the app to a cloud provider, there are many steps involved. For instance setting up a slack app locally on your machine is simple enough, but then deploying it to a cloud provider can be challenging and might require re-architecting your app.
In this tutorial, I'm going to show you how to build a Slack app using Wing, making use of the Wing Console for local simulation and then deploying it to AWS with a single command!
Wing is an open source programming language for the cloud, that also provides a powerful and fun local development experience.
Wing combines infrastructure and runtime code in one language, enabling developers to stay in their creative flow, and to deliver better software, faster and more securely.
To take a closer look at Wing checkout our Github repository.
First, you need to install Wing on your machine (you'll need Node.js >= 20.x installed):
npm i -g winglang
You can check the CLI version like this (the minimum version required by this tutorial is 0.75.0):
wing --version
0.75.0
Ok, now that we have the Wing CLI installed we can use the Slack quick-start template to start building our Slack app.
$ mkdir my-slack-app
$ cd my-slack-app
$ wing new slack
This will create the following project structure:
my-slack-app/
├── main.w
├── package-lock.json
└── package.json
Let's take a look at the main.w
file, where we can see a code template for a simple Slack app that updates us on files uploaded to a bucket.
Note: your template will have more comments and explanations than the one below. I have removed them for brevity.
bring cloud;
bring slack;
let botToken = new cloud.Secret(name: "slack-bot-token");
let slackBot = new slack.App(token: botToken);
let inbox = new cloud.Bucket() as "file-process-inbox";
inbox.onCreate(inflight (key) => {
let channel = slackBot.channel("INBOX_PROCESSING_CHANNEL");
channel.post("New file: {key} was just uploaded to inbox!");
});
slackBot.onEvent("app_mention", inflight(ctx, event) => {
let eventText = event["event"]["text"].asStr();
log(eventText);
if eventText.contains("list inbox") {
let files = inbox.list();
let message = new slack.Message();
message.addSection({
fields: [
{
type: slack.FieldType.mrkdwn,
text: "*Current Inbox:*\n-{files.join("\n-")}"
}
]
});
ctx.channel.postMessage(message);
}
});
Note: Be sure to replace the
INBOX_PROCESSING_CHANNEL
with the name of the slack channel you want to post to.
We can see above the very first resource defined is a cloud.Secret
which is used to store the Slack bot token. This is a secure way to store sensitive information in Wing. So before we can get started we need to create a Slack app and get the bot token to use in our Wing app.
Create from Scratch
and give your app a name and select the workspace you want to deploy it to.
app_mentions:read
chat:write
chat:write.public
to add these head over to the OAuth & Permissions
section and add those permissions.
Once its installed you can copy the bot token and let's head back over to our Wing app.
Now that we have our bot token, we can add it to our application by running the wing secrets
command and pasting the token when prompted:
❯ wing secrets
1 secret(s) found
? Enter the secret value for slack-bot-token: [hidden]
Now that our bot token is stored our application is ready to run!
To run the app locally we can use the Wing Console, which simulates the cloud environment on your local machine. To start the console run:
wing it
This will open a browser window showing the Wing Console. You should see something similar to this:
Now in order to see the Slack bot in action, let's add some more code to our main.w
file. We will add a function that will make a new file each time it is called.
The following code can be appended to the main.w
file:
let counter = new cloud.Counter();
new cloud.Function(inflight () => {
let i = counter.inc();
inbox.put("file-{i}.txt", "Hello, Slack!");
}) as "Add File";
Once you save the file, the Wing Console will hot reload and you should now see a function resource we can play with that looks like this:
So now we can click on the Add File
function and interact with it in the right side panel. Go ahead and invoke the function a few times.
And BAM!! You should now be seeing messages in your Slack channel every time you invoke the function!
One thing we will notice is the Slack application is supposed to support the ability to list the files in the inbox. This is done by mentioning the Slack app and saying list inbox
.
If you try this now you will see absolutely nothing happens :) —— this is because we need to enable events in our slack app.
To do this head over to the Event Subscriptions
section in the Slack API dashboard and enable events. You will need to provide a URL for the Slack API to send events to. Luckily Wing makes providing this URL easy with builtin support for tunneling.
To get the tunnel URL go back to the Wing Console and Open a tunnel for this endpoint
After a moment the icon will change to a eye with a slash through it, now we can copy the url by right clicking the endpoint and selecting Copy URL
.
Next let's head over to the Event Subscriptions
section in the slack API dashboard and paste the URL in the Request URL
field. However, we need to append slack/events
to the end of the URL. So it should look something like this:
This should only take a few seconds to verify, and once its verified you can scroll down to the Subscribe to Bot Events
section and add the app_mention
event like so:
Lastly, don't forget to save your changes!
Now head back to your Slack channel where you received the messages earlier and mention your app and say list inbox
. Our Slack apps may have different names so it wont be exactly the same as the example below:
You should now see a message in the channel with the files in the inbox!
Awesome! You have now built a Slack app using Wing and tested it locally. Now let's deploy it to AWS!
Before we start you will need the following to follow along:
Getting our code ready for AWS is as simple as running 2 commands. The first thing we need to do is prepare the cloud.Secret
for the tf-aws
platform. This is done by running the wing secrets
command with the --platform
flag:
❯ wing secrets --platform tf-aws
1 secret(s) found
? Enter the secret value for slack-bot-token: [hidden]
Storing secrets in AWS Secrets Manager
Secret slack-bot-token does not exist, creating it.
1 secret(s) stored AWS Secrets Manager
This will result in the same prompt as before, but this time the secret will be stored in AWS Secrets Manager.
Next let's compile the application for tf-aws
:
❯ wing compile --platform tf-aws
This will compile the application and generate all the necessary Terraform files, and assets needed to deploy the application to AWS.
To deploy the code run the following commands:
terraform -chdir=./target/main.tfaws init
terraform -chdir=./target/main.tfaws apply -auto-approve
This will begin the deployment process and should only take about a minute to complete (barring internet connection issues). The result will show an output that contains a URL for the API Gateway endpoint and look something like this:
Apply complete! Resources: 30 added, 0 changed, 0 destroyed.
Outputs:
App_Api_Endpoint_Url_E233F0E8 = "https://p9y42fs0gg.execute-api.us-east-1.amazonaws.com/prod"
App_Slack_Request_Url_FF26641D = "https://p9y42fs0gg.execute-api.us-east-1.amazonaws.com/prod/slack/events"
The last step we need to do is to copy that App_Slack_Request_Url
and paste it into the Request URL
field in the Event Subscriptions
section in the Slack API dashboard. This will tell Slack to now send events to our deployed applications API Gateway endpoint.
You should see the URL verified in a few seconds.
DONT FORGET TO SAVE YOUR CHANGES!
Let's first test adding a file to the inbox, which in AWS is an s3 bucket. Navigate to the S3 console in AWS and find the bucket with the name that contains file-process-inbox
there will be some unique hashing to the end of it. For example, my bucket was named: file-process-inbox-c8419ccc-20240530151737187700000004
Upload any file on your machine to this bucket, and you should see a message in your Slack channel!
Lastly, let's test the list inbox
command.
And there you have it! You have successfully built a Slack app using Wing, tested it locally, and deployed it to AWS!
If you find yourself wanting to learn more about Wing, or had any issues with this tutorial, or just wanna chat, feel free to join our Discord server!
Winglang provides a solution for contributing to its Winglibs project. This is the way to go if you only need to wrap a particular cloud resource on one or more platforms. Just follow the guidelines. However, while developing the initial version of the Endor middleware framework, I had different needs.
First, the Endor library is in a very initial exploratory phase—far from a maturity level to be considered a contribution candidate for publishing in the public NPM Registry.
Second, it includes several supplementary and still immature tool libraries, such as Exceptions and Logging. These tools need to be published separately (see explanation below). Therefore, I needed a solution for managing multiple NPM Packages in one project.
Third, I wanted to explore how prospective Winglang customers will be able to manage their internal libraries.
For that goal, I decided to experiment with the AWS CodeArtifact service configured to play the role of my internal NPM Registry.
This publication is an experience report about the first phase, primarily focused on the developer’s experience with my Multi-Account, Multi-Platform, Multi-User (MAPU) environment, which I reported about here, here, and here. Specifically, I configured the AWS CodeArtifact Domain and Repository within my working account and postponed a more elaborate enterprise-grade system architecture to later stages. Let’s start with the overall solution overview.
Here is a brief description of the solution:
winglang
account, I created an AWS CodeArtifact Domain tentatively named <organizationID>-platform
.winglang-artifacts
.@winglibs
NPM Namespace. At the moment, this is a requirement determined by how the Winglang import system works.I found this arrangement suitable for a solo developer and researcher. A real organization, even of a middle size, will require some substantial adjustments — subject to further investigation.
Let’s now look at some technical implementation details.
Using Cloud Formation templates is always my preferred option. In this case, I created two simple Cloud Formation templates. One for creating an AWS CodeArtifact Domain resource:
{
"AWSTemplateFormatVersion": "2010-09-09",
"Description": "Template to create a CodeArtifact Domain; to be a part of platform template",
"Resources": {
"ArtifactDomain": {
"Type" : "AWS::CodeArtifact::Domain",
"Properties" : {
"DomainName" : "o-4e7dgfcrpx-platform"
}
}
}
}
And another - for creating an AWS CodeArtifact Repository resource:
{
"AWSTemplateFormatVersion": "2010-09-09",
"Description": "Template to create a CodeArtifact repository; to be a part of account template",
"Resources": {
"ArtifactRespository": {
"Type" : "AWS::CodeArtifact::Repository",
"Properties" : {
"Description" : "artifact repository for this <winglang> account",
"DomainName" : "o-4e7dgfcrpx-platform",
"RepositoryName": "winglang-artifacts",
"ExternalConnections" : [ "public:npmjs" ]
}
}
}
}
These templates are mere placeholders for future, more serious, development.
The same could be achieved with Winglang, as follows:
https://gist.github.com/eladb/5e1ddd1bd90c53d90b2195d080397381
Many thanks to Elad Ben-Israel for bringing this option to my attention. Currently, the whole MAPU system is implemented in Python and CloudFormation. Re-implementing it completely in Winglang would be a fascinating case study.
I followed the official guidelines and created the following Bash script:
export CODEARTIFACT_AUTH_TOKEN=$(\
aws codeartifact get-authorization-token \
--domain o-4e7dgfcrpx-platform \
--domain-owner 851725645964 \
--query authorizationToken \
--output text)
export REPOSITORY_ENDPOINT=$(\
aws codeartifact get-repository-endpoint \
--domain o-4e7dgfcrpx-platform \
--domain-owner 851725645964 \
--repository winglang-artifacts \
--format npm \
--query repositoryEndpoint \
--output text)
export REGISTRY=$(echo "$REPOSITORY_ENDPOINT" | sed 's|https:||')
npm config set registry=$REPOSITORY_ENDPOINT
npm config set $REGISTRY:_authToken=$CODEARTIFACT_AUTH_TOKEN
Here is a brief description of the script’s logic:
config
command to set the endpoint.config
command to set up session authentication.Placing this script in the [/etc/profile.d](https://www.linuxfromscratch.org/blfs/view/11.0/postlfs/profile.html)
ensures that it will be automatically executed at every user login thus making the whole communication with AWS CodeArtifact instead of the official [npmjs](https://docs.npmjs.com/cli/v8/using-npm/registry)
repository completely transparent for the end user.
Implementing this operation while addressing my specific needs required a more sophisticated logic reflected in the following script:
#!/bin/bash
set -euo pipefail
# Function to clean up tarball and extracted package
cleanup() {
rm *.tgz
rm -fR package
}
# Function to calculate the checksum of a package tarball
calculate_checksum() {
local tarball=$(ls *.tgz | head -n 1)
tar -xzf "$tarball"
cd package || exit 1
local checksum=$(\
tar \
--exclude='$lib' \
--sort=name \
--mtime='UTC 1970-01-01' \
--owner=0 \
--group=0 \
--numeric-owner -cf - . | sha256sum | awk '{print $1}')
cd ..
cleanup
echo "$checksum"
}
get_version() {
PACKAGE_VERSION=$(jq -r '.version' package.json)
}
publish() {
echo "Publishing new version: $PACKAGE_VERSION"
npm publish --access public --tag latest *.tgz
cleanup
exit 0
}
# Step 1: Read the package version from package.json
get_version
PACKAGE_NAME=$(jq -r '.name' package.json)
# Step 2: Check the latest version in the npm registry
LATEST_VERSION=$(npm show "$PACKAGE_NAME" version 2>/dev/null || echo "")
# Step 3: Prepare wing package
wing pack
# Step 4: If the versions are not equal, publish the new version
if [[ "$PACKAGE_VERSION" != "$LATEST_VERSION" ]]; then
publish
else
CURRENT_CHECKSUM=$(calculate_checksum)
# Download the latest package tarball
npm pack "$PACKAGE_NAME@$LATEST_VERSION" > /dev/null 2>&1
LATEST_CHECKSUM=$(calculate_checksum)
# Step 5: Compare the checksums
if [[ "$CURRENT_CHECKSUM" == "$LATEST_CHECKSUM" ]]; then
echo "No changes detected. Checksum matches the latest published version."
exit 0
else
echo $CURRENT_CHECKSUM
echo $LATEST_CHECKSUM
echo "Checksums do not match. Bumping patch version..."
npm version patch
wing pack
get_version
publish
fi
fi
Here is a brief explanation of what happens in this script:
[jq](https://jqlang.github.io/jq/)
command, extract the package name and version from the package.json
file.[npm show](https://docs.npmjs.com/cli/v10/commands/npm-view)
command, extract the package version number from the registry.[wing pack](https://www.winglang.io/docs/libraries)
command, prepare the package .tgz file.[npm publish](https://docs.npmjs.com/cli/v10/commands/npm-publish)
command.[npm version patch](https://docs.npmjs.com/cli/v10/commands/npm-version)
command, automatically bump up the [patch](https://symver.org/)
version number, rebuild the .tgz file, and publish the new version.Reliable checksum validation was the most challenging part of developing this script. The wing pack
command creates a special @lib
folder within the resulting .tgz
archive. This folder introduces some randomness and can be affected by several factors, including Winglang compiler upgrades. Additionally, the .tgz
file checksum calculation is sensitive to the order and timestamps of individual files. As a result, comparing the results of direct checksum calculation for the current and published packages was not an option.
To overcome these limitations, new archives are created with the @lib
folder excluded and file order and timestamps normalized. The assistance of the ChatGPT 4o tool proved instrumental, especially in addressing this challenge.
In the current implementation, I keep this script in my home directory and invoke it from a common [Build.mk](http://Build.mk)
Makefile used for all libraries (this might change in the future):
.PHONY: all compile-deps build-ts prepare test publish
all: publish
update-deps:
npm install && npm update
compile-ts: update-deps
ifneq ($(wildcard tsconfig.json),)
@echo "tsconfig.json found, running tsc..."
tsc
else
@echo "tsconfig.json not found, skipping TypeScript compilation."
endif
test: compile-ts
wing test -t sim ./test/*.test.w
publish: test
~/publish-npm.sh
To explain why I chose this particular way of publishing logic, I need to explain my overall project structure, illustrated in the diagram below:
The top of the diagram above reflects the NMP packages involved and their dependencies are depicted at the top, while the bottom part reflects my project folder structure.
The endor
package is the ultimate goal of this development activity: an exploratory middleware framework for the Winglang programming language. Its efficacy is validated by a separate todo.endor.w
application. Initially, both modules were kept together. However, keeping pure application parts separate from the infrastructure became progressively challenging.
The endor
package uses three auxiliary packages logging
, exception
, and datetimex
. These three packages are potential candidates to be contributed to the Winglibs project. However, they are still under active experimentation and development and are kept within the same Github repository.
Additionally, the endor
package depends on other packages published on the public [npmjs](https://docs.npmjs.com/cli/v8/using-npm/registry)
registry. Some of these packages, such as dynamodb
and jwt
belong to the same @winglibs
namespace, while others do not.
I face a mixed-case challenge: the system already has a modular structure, but all components are under intensive development, requiring instant propagation of changes. As a solo developer and researcher, I still do not need more sophisticated CI/CD solutions, but rather employ a master Makefile to pull everything together:
.PHONY: all \
update_npm \
update_wing \
update_tsc \
make_datetimex \
make_exception \
make_logging \
make_endor
all: update_wing make_endor
update_npm:
sudo npm update -g npm
update_tsc: update_npm
sudo npm update -g tsc
update_wing: update_tsc
sudo npm update -g winglang
make_datetimex:
$(MAKE) -C ./datetimex -f ../Build.mk
make_logging: make_datetimex
$(MAKE) -C ./logging -f ../Build.mk
make_exception:
$(MAKE) -C ./exception -f ../Build.mk
make_endor: make_exception make_logging
$(MAKE) -C ./endor -f ../Build.mk
The todo.endor.w
Makefile looks like this:
.PHONY: all update_wing install_endor test_local
cloud ?= aws
target := target/main.tf$(cloud)
update_npm:
sudo npm update -g npm
update_wing: update_npm
sudo npm update -g winglang
install_endor:
npm install && npm update
build_ts:
tsc
test_local: update_wing install_endor build_ts test_app
test_app:
wing test -t sim ./test/*.w
test_remote:
wing test -t tf-$(cloud) ./test/service.test.w
run_local:
wing run -t sim ./dev.main.w
compile:
wing compile ./main.w -t tf-$(cloud)
tf-init: compile
( \
cd $(target) ;\
terraform init \
)
deploy: tf-init
( \
cd $(target) ;\
terraform apply -auto-approve \
)
destroy:
( \
cd $(target) ;\
terraform destroy -auto-approve \
)
This arrangement allows me to keep modules isolated, make changes in several places where appropriate, and perform fully automated build and verification without needing manual version updates within multiple package.json
files.
Specifying cross-package dependencies is another point to pay attention to. Here is the endor
package specification:
{
"name": "@winglibs/endor",
"description": "Wing middleware framework library",
"repository": {
"type": "git",
"url": "https://github.com/asterkin/endor.w.git",
"directory": "endor"
},
"version": "0.0.19",
"author": {
"email": "asher.sterkin@gmail.com",
"name": "Asher Sterkin"
},
"license": "MIT",
"peerDependencies": {
"@authenio/samlify-node-xmllint": "2.x.x",
"@winglibs/dynamodb": "0.x.x",
"@winglibs/jwt": "0.x.x",
"qs": "6.x.x",
"samlify": "2.x.x",
"ws": "8.x.x",
"inflection": "3.x.x",
"@winglibs/exception": "0.x.x",
"@winglibs/logging": "0.x.x"
}
}
Notice that, unlike traditional formats, all dependencies are specified using the x
placeholder without the leading ^
symbol. This is because, with the ^
prefix included, the most up-to-date versions are brought in only for the final todo.endor.w
application, whereas I needed them to be used in the dependent modules' unit tests. Using the x
placeholder instead does the job.
In summary, while not final, the described solution provides good enough treatment for all essential requirements at the current stage of the system evolution. As the system grows, adequate adjustments will be implemented and reported. Stay tuned.
Throughout the preparation of this publication, I utilized several key tools to enhance the draft and ensure its quality.
The initial draft was crafted with the organizational capabilities of Notion's free subscription, facilitating the structuring and development of ideas.
For grammar and spelling review, the free version of Grammarly proved useful for identifying and correcting basic errors, ensuring the readability of the text.
The enhancement of stylistic expression and the narrative coherence checks were performed using the paid version of ChatGPT 4o. The ChatGPT 4o tool was also used for developing the package publishing script and creation of NMP elements icons.
While these advanced tools and resources significantly contributed to the preparation process, the concepts, solutions, and final decisions presented in this article are entirely my own, for which I bear full responsibility.
By the end of this article, you will build and deploy a ChatGPT Client using Wing and Next.js.
This application can run locally (in a local cloud simulator) or deploy it to your own cloud provider.
Building a ChatGPT client and deploying it to your own cloud infrastructure is a good way to ensure control over your data.
Deploying LLMs to your own cloud infrastructure provides you with both privacy and security for your project.
Sometimes, you may have concerns about your data being stored or processed on remote servers when using proprietary LLM platforms like OpenAI’s ChatGPT, either due to the sensitivity of the data being fed into the platform or for other privacy reasons.
In this case, self-hosting an LLM to your cloud infrastructure or running it locally on your machine gives you greater control over the privacy and security of your data.
Wing is a cloud-oriented programming language that lets you build and deploy cloud-based applications without worrying about the underlying infrastructure. It simplifies the way you build on the cloud by allowing you to define and manage your cloud infrastructure and your application code within the same language. Wing is cloud agnostic - applications built with it can be compiled and deployed to various cloud platforms.
To follow along, you need to:
To get started, you need to install Wing on your machine. Run the following command:
npm install -g winglang
Confirm the installation by checking the version:
wing --version
mkdir assistant
cd assistant
npx create-next-app@latest frontend
mkdir backend && cd backend
wing new empty
We have successfully created our Wing and Next.js projects inside the assistant directory. The name of our ChatGPT Client is Assistant. Sounds cool, right?
The frontend and backend directories contain our Next and Wing apps, respectively. wing new empty
creates three files: package.json
, package-lock.json
, and main.w
. The latter is the app’s entry point.
The Wing simulator allows you to run your code, write unit tests, and debug your code inside your local machine without needing to deploy to an actual cloud provider, helping you iterate faster.
Use the following command to run your Wing app locally:
wing it
Your Wing app will run on localhost:3000
.
npm i @winglibs/openai @winglibs/react
main.w
file. Let's also import all the other libraries we’ll need.bring openai
bring react
bring cloud
bring ex
bring http
bring
is the import statement in Wing. Think of it this way, Wing uses bring
to achieve the same functionality as import
in JavaScript.
cloud
is Wing’s Cloud library. It exposes a standard interface for Cloud API, Bucket, Counter, Domain, Endpoint, Function and many more cloud resources. ex
is a standard library for interfacing with Tables and cloud Redis database, and http
is for calling different HTTP methods - sending and retrieving information from remote resources.
We will use gpt-4-turbo
for our app but you can use any OpenAI model.
Create a Class
to initialize your OpenAI API. We want this to be reusable.
We will add a personality
to our Assistant
class so that we can dictate the personality of our AI assistant when passing a prompt to it.
let apiKeySecret = new cloud.Secret(name: "OAIAPIKey") as "OpenAI Secret";
class Assistant {
personality: str;
openai: openai.OpenAI;
new(personality: str) {
this.openai = new openai.OpenAI(apiKeySecret: apiKeySecret);
this.personality = personality;
}
pub inflight ask(question: str): str {
let prompt = `you are an assistant with the following personality: ${this.personality}. ${question}`;
let response = this.openai.createCompletion(prompt, model: "gpt-4-turbo");
return response.trim();
}
}
Wing unifies infrastructure definition and application logic using the preflight
and inflight
concepts respectively.
Preflight code (typically infrastructure definitions) runs once at compile time, while inflight code will run at runtime to implement your app’s behavior.
Cloud storage buckets, queues, and API endpoints are some examples of preflight. You don’t need to add the preflight keyword when defining a preflight, Wing knows this by default. But for an inflight block, you need to add the word “inflight” to it.
We have an inflight block in the code above. Inflight blocks are where you write asynchronous runtime code that can directly interact with resources through their inflight APIs.
Let's walk through how we will secure our API keys because we definitely want to take security into account.
Let's create a .env
file in our backend’s root and pass in our API Key:
OAIAPIKey = Your_OpenAI_API_key
We can test our OpenAI API keys locally referencing our .env file, and then since we are planning to deploy to AWS, we will walk through setting up the AWS Secrets Manager.
First, let's head over to AWS and sign into the Console. If you don't have an account, you can create one for free.
Navigate to the Secrets Manager and let's store our API key values.
We have stored our API key in a cloud secret named OAIAPIKey
. Copy your key and we will jump over to the terminal and connect to our secret that is now stored in the AWS Platform.
wing secrets
Now paste in your API Key as the value in the terminal. Your keys are now properly stored and we can start interacting with our app.
Storing your AI's responses in the cloud gives you control over your data. It resides on your own infrastructure, unlike proprietary platforms like ChatGPT, where your data lives on third-party servers that you don’t have control over. You can also retrieve these responses whenever you need them.
Let’s create another class that uses the Assistant class to pass in our AI’s personality and prompt. We would also store each model’s responses as txt
files in a cloud bucket.
let counter = new cloud.Counter();
class RespondToQuestions {
id: cloud.Counter;
gpt: Assistant;
store: cloud.Bucket;
new(store: cloud.Bucket) {
this.gpt = new Assistant("Respondent");
this.id = new cloud.Counter() as "NextID";
this.store = store;
}
pub inflight sendPrompt(question: str): str {
let reply = this.gpt.ask("{question}");
let n = this.id.inc();
this.store.put("message-{n}.original.txt", reply);
return reply;
}
}
We gave our Assistant the personality “Respondent.” We want it to respond to questions. You could also let the user on the frontend dictate this personality when sending in their prompts.
Every time it generates a response, the counter increments, and the value of the counter is passed into the n
variable used to store the model’s responses in the cloud. However, what we really want is to create a database to store both the user prompts coming from the frontend and our model’s responses.
Let's define our database.
Wing has ex.Table
built-in - a NoSQL database to store and query data.
let db = new ex.Table({
name: "assistant",
primaryKey: "id",
columns: {
question: ex.ColumnType.STRING,
answer: ex.ColumnType.STRING
}
});
We added two columns in our database definition - the first to store user prompts and the second to store the model’s responses.
We want to be able to send and receive data in our backend. Let’s create POST and GET routes.
let api = new cloud.Api({ cors: true });
api.post("/assistant", inflight((request) => {
// POST request logic goes here
}));
api.get("/assistant", inflight(() => {
// GET request logic goes here
}));
let myAssistant = new RespondToQuestions(store) as "Helpful Assistant";
api.post("/assistant", inflight((request) => {
let prompt = request.body;
let response = myAssistant.sendPrompt(JSON.stringify(prompt));
let id = counter.inc();
// Insert prompt and response in the database
db.insert(id, { question: prompt, answer: response });
return cloud.ApiResponse({
status: 200
});
}));
In the POST route, we want to pass the user prompt received from the frontend into the model and get a response. Both prompt and response will be stored in the database. cloud.ApiResponse
allows you to send a response for a user’s request.
Add the logic to retrieve the database items when the frontend makes a GET request.
Add the logic to retrieve the database items when the frontend makes a GET request.
api.get("/assistant", inflight(() => {
let questionsAndAnswers = db.list();
return cloud.ApiResponse({
body: JSON.stringify(questionsAndAnswers),
status: 200
});
}));
Our backend is ready. Let's test it out in the local cloud simulator.
Run wing it
.
Lets go over to localhost:3000
and ask our Assistant a question.
Both our question and the Assistant’s response has been saved to the database. Take a look.
We need to expose the API URL of our backend to our Next frontend. This is where the react library installed earlier comes in handy.
let website = new react.App({
projectPath: "../frontend",
localPort: 4000
});
website.addEnvironment("API_URL", api.url);
Add the following to the layout.js
of your Next app.
import { Inter } from "next/font/google";
import "./globals.css";
const inter = Inter({ subsets: ["latin"] });
export const metadata = {
title: "Create Next App",
description: "Generated by create next app",
};
export default function RootLayout({ children }) {
return (
<html lang="en">
<head>
<script src="./wing.js" defer></script>
</head>
<body className={inter.className}>{children}</body>
</html>
);
}
We now have access to API_URL
in our Next application.
Let’s implement the frontend logic to call our backend.
import { useEffect, useState, useCallback } from 'react';
import axios from 'axios';
function App() {
const [isThinking, setIsThinking] = useState(false);
const [input, setInput] = useState("");
const [allInteractions, setAllInteractions] = useState([]);
const retrieveAllInteractions = useCallback(async (api_url) => {
await axios ({
method: "GET",
url: `${api_url}/assistant`,
}).then(res => {
setAllInteractions(res.data)
})
}, [])
const handleSubmit = useCallback(async (e)=> {
e.preventDefault()
setIsThinking(!isThinking)
if(input.trim() === ""){
alert("Chat cannot be empty")
setIsThinking(true)
}
await axios({
method: "POST",
url: `${window.wingEnv.API_URL}/assistant`,
headers: {
"Content-Type": "application/json"
},
data: input
})
setInput("");
setIsThinking(false);
await retrieveAllInteractions(window.wingEnv.API_URL);
})
useEffect(() => {
if (typeof window !== "undefined") {
retrieveAllInteractions(window.wingEnv.API_URL);
}
}, []);
// Here you would return your component's JSX
return (
// JSX content goes here
);
}
export default App;
The retrieveAllInteractions
function fetches all the questions and answers in the backend’s database. The handSubmit
function sends the user’s prompt to the backend.
Let’s add the JSX implementation.
import { useEffect, useState } from 'react';
import axios from 'axios';
import './App.css';
function App() {
// ...
return (
<div className="container">
<div className="header">
<h1>My Assistant</h1>
<p>Ask anything...</p>
</div>
<div className="chat-area">
<div className="chat-area-content">
{allInteractions.map((chat) => (
<div key={chat.id} className="user-bot-chat">
<p className='user-question'>{chat.question}</p>
<p className='response'>{chat.answer}</p>
</div>
))}
<p className={isThinking ? "thinking" : "notThinking"}>Generating response...</p>
</div>
<div className="type-area">
<input
type="text"
placeholder="Ask me any question"
value={input}
onChange={(e) => setInput(e.target.value)}
/>
<button onClick={handleSubmit}>Send</button>
</div>
</div>
</div>
);
}
export default App;
Navigate to your backend directory and run your Wing app locally using the following command
cd ~assistant/backend
wing it
Also run your Next.js frontend:
cd ~assistant/frontend
npm run dev
Let’s take a look at our application.
Let’s ask our AI Assistant a couple developer questions from our Next App.
We’ve seen how our app can work locally. Wing also allows you to deploy to any cloud provider including AWS. To deploy to AWS, you need Terraform and AWS CLI configured with your credentials.
tf-aws
. The command instructs the compiler to use Terraform as the provisioning engine to bind all our resources to the default set of AWS resources.cd ~/assistant/backend
wing compile --platform tf-aws main.w
cd ./target/main.tfaws
terraform init
terraform apply
Note: terraform apply
takes some time to complete.
You can find the complete code for this tutorial here.
As I mentioned earlier, we should all be concerned with our apps security, building your own ChatGPT client and deploying it to your cloud infrastructure gives your app some very good safeguards.
We have demonstrated in this tutorial how Wing provides a straightforward approach to building scalable cloud applications without worrying about the underlying infrastructure.
If you are interested in building more cool stuff, Wing has an active community of developers, partnering in building a vision for the cloud. We'd love to see you there.
Just head over to our Discord and say hi!
Winglang's unique capability to uniformly handle both preflight (cloud resource configuration) and inflight (cloud events processing) logic opens up meta-programming possibilities akin to Lisp macros. This allows for the dynamic adjustment of service configurations to various deployment targets—such as DEV, TEST, STAGE, and PROD—at the build stage. By doing so, it optimizes cost, security, and performance without compromising the integrity of the core service logic, which remains largely insulated from middleware framework details. This level of flexibility is unmatched by more traditional cloud middleware libraries, such as PowerTools for AWS Lambda, which I explored in the first part of this series.
In this part, I will explore how a middleware framework can leverage the Template Method Design Pattern. This design pattern has proven instrumental in defining the common elements of the REST API Create/Retrieve/Update/Delete (CRUD) request handling flow, while still allowing enough flexibility to accommodate the specifics of each request.
Specifically, the application of the Template Method Design Pattern to define a common request-handling workflow has demonstrated the following benefits:
Nevertheless, this approach has limitations. A high number of deployment targets, services, resources, and function permutations may necessitate maintaining a large number of templates—a common challenge in any Engineering Platform based on blueprints.
Exploring whether these limitations can be surmounted using Winglang's unique capabilities will be the focus of future research.
As clarified in the previous publication,
Common middleware services augment distribution middleware by defining higher-level domain-independent reusable services that allow application developers to concentrate on programming business logic, without the need to write the “plumbing” code required to develop distributed applications via lower-level middleware directly.
In our pursuit, we specifically aim to define common service configurations that are adaptable to various development targets. Such configurations are not only reusable across multiple services and resources within the same team or organization but are also distinct enough not to be overgeneralized in the form of a framework but rather to merit integration into a tailored Engineering Platform. This balance ensures that while the configurations maintain a high level of generality, they remain sufficiently detailed to support the unique needs of different projects within the organization.
Here is an example of how common service configurations can be implemented using Winglang. Typically, this or a similar code snippet will be a part of a larger solution integrated into an organization's or team's engineering platform:
bring endor;
bring cloud;
bring logging;
//Could be a part of an organization or team engineering platform
pub class ServiceFactory impl endor.IRestApiHandlerTemplate {
_tools: endor.ApiTools;
_mode: endor.Mode;
pub logger: logging.Logger;
pub api: cloud.Api;
_getLoggingLevel(mode: endor.Mode): logging.Level {
if mode == endor.Mode.PROD {
return logging.Level.INFO;
} elif mode == endor.Mode.DEV {
return logging.Level.TRACE;
} else {
return logging.Level.DEBUG;
}
}
_getToolsOptions(
mode: endor.Mode,
logger: logging.Logger
): endor.ApiToolsOptions {
if mode == endor.Mode.DEV {
return endor.ApiToolsOptions{
logger: logger,
statusMessage: Map<str>{} // leave original error messages intact
};
}
return endor.ApiToolsOptions{
logger: logger
}; // use defaults
}
new(serviceName: str, mode: endor.Mode) {
this._mode = mode;
this.logger = new logging.Logger(
this._getLoggingLevel(mode),
serviceName);
this._tools = new endor.ApiTools(
this._getToolsOptions(mode,
this.logger));
this.api = new cloud.Api();
}
_getProdApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
getHomePage: (inflight (Json, str): str)?
): endor.RestApiBuilder {
let tokenFactory = new endor.FixedSecretTokenFiltersFactory();
let cookieFactory = new endor.CookieAuthFiltersFactory(tokenFactory);
let apiBuilder = new endor.RestApiBuilder(
this.api,
resource,
this,
cookieFactory,
responseFormats);
if getHomePage != nil {
apiBuilder.samlLogin(cookieFactory, getHomePage!);
}
return apiBuilder;
}
_getDevApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
getHomePage: (inflight (Json, str): str)?
): endor.RestApiBuilder {
let session = Json {
userID: "test-user",
fullName: "Test User"
};
let stubAuth = new endor.ApiStubAuthFactory(session);
let apiBuilder = new endor.RestApiBuilder(
this.api,
resource,
this,
stubAuth,
responseFormats);
if getHomePage != nil {
apiBuilder.stubLogin(session, getHomePage!);
}
return apiBuilder;
}
_getDefaultApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
password: str
): endor.RestApiBuilder {
let basicAuth = new endor.ApiBasicAuthFactory(password);
let apiBuilder = new endor.RestApiBuilder(
this.api,
resource,
this,
basicAuth,
responseFormats);
return apiBuilder;
}
pub getApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
getHomePage: (inflight (Json, str): str)?,
password: str?
): endor.RestApiBuilder {
if this._mode == endor.Mode.PROD {
return this._getProdApiBuilder(
resource,
responseFormats,
getHomePage);
} elif this._mode == endor.Mode.DEV {
return this._getDevApiBuilder(
resource,
responseFormats,
getHomePage);
} else {
return this._getDefaultApiBuilder(
resource,
responseFormats,
password!);
}
}
pub makeRequestHandler(
functionName: str,
proc: inflight (cloud.ApiRequest): cloud.ApiResponse
): inflight(cloud.ApiRequest): cloud.ApiResponse {
let handler = inflight (request: cloud.ApiRequest): cloud.ApiResponse => {
let var response = cloud.ApiResponse{};
try {
this._tools.logRequest(functionName, request);
response = proc(request);
} catch err {
response = this._tools.errorResponse(err);
}
this._tools.logResponse(functionName, response);
response = this._tools.responseMessage(response);
return response;
};
return handler;
}
}
This example is quite detailed, so we will break it down section by section to fully understand its structure and functionality.
This module, by convention named middleware.w
, features a Winglang preflight class called ServiceFactory
. This class encapsulates the following public resources and methods, which are crucial for the middleware's operation:
api: [cloud.Api](https://www.winglang.io/docs/standard-library/cloud/api)
: resource from the Winglang Standard Librarylogger: logging.Logger
: resource, extending the Winglang libraries, which was discussed previously in terms of its utility and possible implementation implementation heremakeRequestHandler()
: Factory Method that applies the Template Method Design Pattern tailored to each request handlergetApiBuilder()
: another Factory Method this time applying the Builder Design Pattern to configure specific resource API calls pub makeRequestHandler(
functionName: str,
proc: inflight (cloud.ApiRequest): cloud.ApiResponse
): inflight(cloud.ApiRequest): cloud.ApiResponse {
let handler = inflight (request: cloud.ApiRequest): cloud.ApiResponse => {
let var response = cloud.ApiResponse{};
try {
this._tools.logRequest(functionName, request);
response = proc(request);
} catch err {
response = this._tools.errorResponse(err);
}
this._tools.logResponse(functionName, response);
response = this._tools.responseMessage(response);
return response;
};
return handler;
}
This Factory Method plays a central role in implementing the Common Service Middleware. It is a Winglang preflight method that obtains two parameters:
functionName
: The name of the function handling the API request, used for logging purposes.proc
: a Winglang inflight function that transforms cloud.ApiRequest
into a cloud.ApiResponse
Internally, it defines a Winglang inflight function, called handler
that encapsulates a typical API request processing workflow:
cloud.ApiRequest
.proc
function to obtain a cloud.ApiResponse
.cloud.ApiResponse
.cloud.ApiResponse
.Operations for logging and error handling are managed using the auxiliary endor.ApiTools
class, which we will explore in further detail later.
The getApiBuilder()
method, another key component implemented as a Factory Method, dynamically creates a properly configured API Builder for a specific resource, depending on the deployment mode:
pub getApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
getHomePage: (inflight (Json, str): str)?,
password: str?
): endor.RestApiBuilder {
if this._mode == endor.Mode.PROD {
return this._getProdApiBuilder(
resource,
responseFormats,
getHomePage);
} elif this._mode == endor.Mode.DEV {
return this._getDevApiBuilder(
resource,
responseFormats,
getHomePage);
} else {
return this._getDefaultApiBuilder(
resource,
responseFormats,
password!);
}
}
This Winglang preflight method accepts four parameters:
resource
: A Winglang Struct defining the API resource, including its names and HTTP paths for singular and plural operations.responseFormats
: A Winglang Array specifying supported response formats, such as application/json
, text/html
, or text/plain
.getHomePage
: An optional Winglang inflight function to fetch the home page content using session data and the required format.password
: An optional string for temporary use in HTTP Basic Authentication.Based on the _mode
field, the method directs the construction of the appropriate builder:
_getProdApiBuilder
for the PROD
mode._getDevApiBuilder
for the DEV
mode._getDefaultApiBuilder
in other cases.The main variability point that distinguishes these three options is the authentication strategy applied to each API request. Let's examine the specifics of each builder's implementation.
_getProdApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
getHomePage: (inflight (Json, str): str)?
): endor.RestApiBuilder {
let tokenFactory = new endor.FixedSecretTokenFiltersFactory();
let cookieFactory = new endor.CookieAuthFiltersFactory(tokenFactory);
let apiBuilder = new endor.RestApiBuilder(
this.api,
resource,
this,
cookieFactory,
responseFormats);
if getHomePage != nil {
apiBuilder.samlLogin(cookieFactory, getHomePage!);
}
return apiBuilder;
}
For production environments, the security of API requests is paramount. This method implements stringent authentication protocols using JSON Web Tokens (JWT) embedded within Cookie HTTP Headers. The JWTs are signed with a fixed random key, a cost-effective measure that maintains robust security for services at this level.
This Winglang preflight method sets up the described security configuration. Additionally, if the getHomePage
parameter is not nil
, the method configures an extra HTTP request handler for SAML-based authentication, offering another layer of security and user verification (further details on this process can be found here).
_getDevApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
getHomePage: (inflight (Json, str): str)?
): endor.RestApiBuilder {
let session = Json {
userID: "test-user",
fullName: "Test User"
};
let stubAuth = new endor.ApiStubAuthFactory(session);
let apiBuilder = new endor.RestApiBuilder(
this.api,
resource,
this,
stubAuth,
responseFormats);
if getHomePage != nil {
apiBuilder.stubLogin(session, getHomePage!);
}
return apiBuilder;
}
For development purposes, especially when using the Winglang Simulator in interactive mode, security requirements can be relaxed. In this mode, authentic security is not required, allowing developers to focus on functionality and flow without the overhead of complex security protocols.
This Winglang preflight method implements such a setup. It uses a stub authentication system based on predefined user session data. Furthermore, if the getHomePage
parameter is supplied and not nil
, the method adds an HTTP request handler that simulates the login process, maintaining the integrity of the user experience even in a simulated environment.
_getDefaultApiBuilder(
resource: endor.ApiResource,
responseFormats: Array<str>,
password: str
): endor.RestApiBuilder {
let basicAuth = new endor.ApiBasicAuthFactory(password);
let apiBuilder = new endor.RestApiBuilder(
this.api,
resource,
this,
basicAuth,
responseFormats);
return apiBuilder;
}
For test and stage modes, where end-to-end testing is routinely performed, a balance between security, cost-efficiency, and performance is essential. Unlike local simulations that utilize stub authentication similar to the development environment, end-to-end tests in real cloud platforms like AWS require more robust security.
This Winglang preflight method achieves this by implementing HTTP Basic Authentication. It utilizes a dynamically generated password to ensure security while maintaining cost efficiency. The use of HTTP Basic Authentication, where credentials are passed directly in the header, eliminates the need for separate login request handling, streamlining the testing process.
Proper initialization of the ServiceFactory
object is crucial for the functionality of its public methods and fields. Below is a detailed look into its initialization process:
bring endor;
bring cloud;
bring logging;
//Could be a part of organization or team engineering platform
pub class ServiceFactory impl endor.IRestApiHandlerTemplate {
_tools: endor.ApiTools;
_mode: endor.Mode;
pub logger: logging.Logger;
pub api: cloud.Api;
_getLoggingLevel(mode: endor.Mode): logging.Level {
if mode == endor.Mode.PROD {
return logging.Level.INFO;
} elif mode == endor.Mode.DEV {
return logging.Level.TRACE;
} else {
return logging.Level.DEBUG;
}
}
_getToolsOptions(
mode: endor.Mode,
logger: logging.Logger
): endor.ApiToolsOptions {
if mode == endor.Mode.DEV {
return endor.ApiToolsOptions{
logger: logger,
statusMessage: Map<str>{} // leave original error messages intact
};
}
return endor.ApiToolsOptions{
logger: logger
}; // use defaults
}
new(serviceName: str, mode: endor.Mode) {
this._mode = mode;
this.logger = new logging.Logger(
this._getLoggingLevel(mode),
serviceName);
this._tools = new endor.ApiTools(
this._getToolsOptions(mode,
this.logger));
this.api = new cloud.Api();
}
The new()
constructor method is designed to take two parameters:
serviceName
: Utilized in all log messages to identify service-specific operations.mode
: Determines the appropriate configuration settings for different operational environments.The initialization sequence performs the following steps:
mode
to dictate the behavior of the getApiBuilder()
method.Logger
object with a logging level based on mode
:
endor.ApiTools
object with settings influenced by mode
:
This ServiceFactory
class, by implementing the endor.IRestApiHandlerTemplate
interface, seamlessly integrates with the endor.RestApiBuilder
. This allows the latter to utilize the makeRequestHandler()
method of ServiceFactory
, thus ensuring consistent handling of all API requests.
The common middleware service configuration described previously hides a substantial portion of the system infrastructure complexity, allowing specific service configurations to be defined with ease and precision. A practical implementation of this approach can be seen in the TodoService
, first introduced in the previous publication:
bring endor;
bring cloud;
bring logging;
bring "./core" as core;
bring "./adapters" as adapters;
bring "./middleware.w" as middleware;
pub class TodoService {
_api: cloud.Api;
//TODO: true content negotiation; unit test?; move to adapters??
_getResponseFormatters(
mode: endor.Mode,
resource: endor.ApiResource
): Map<core.ITodoFormatter> {
if mode == endor.Mode.TEST {
return Map<core.ITodoFormatter> {
"application/json" => new adapters.TodoJsonFormatter()
};
}
return Map<core.ITodoFormatter> {
"text/html" => new adapters.TodoHtmlFormatter(resource.htmlPath),
"text/plain" => new adapters.TodoTextFormatter(),
};
}
new(mode: endor.Mode, password: str?) {
let serviceName = "Todo Service";
let factory = new middleware.ServiceFactory(serviceName, mode);
let resource = new endor.ApiResource("Task");
let responseFormatters = this._getResponseFormatters(mode, resource);
let repository = new adapters.TaskTableRepository(factory.logger);
let handler = new core.TodoHandler(
repository,
responseFormatters,
factory.logger
);
let apiBuilder = factory.getApiBuilder(
mode,
resource,
responseFormatters.keys(),
handler.getHomePage(),
password
);
apiBuilder.retrieveResources(handler.getAllTasks());
apiBuilder.createResource(handler.createTask());
apiBuilder.replaceResource(handler.replaceTask());
apiBuilder.deleteResource(handler.deleteTask());
this._api = factory.api;
}
pub getUrl(): str {
return this._api.url;
}
}
The TodoService
class serves as a Winglang preflight entity that orchestrates the integration of the service core, its adapters, and middleware. It includes a public getUrl()
method for testing purposes.
The new()
constructor method takes two parameters:
mode
: Determines the output format selection and middleware configuration.password
: An optional string for HTTP Basic Authentication in scenarios requiring secure access.The service is initialized through the following steps:
ServiceFactory
with serviceName
and mode
.Task
resource descriptor that translates to the /tasks
HTTP path.mode
, it configures response formatters:
application/json
for TEST mode.text/html
and text/plain
for PROD and DEV modes.core.TodoHandler
with necessary dependencies, including a logger from ServiceFactory
.ServiceFactory.getApiBuilder()
to link TodoHandler
methods to respective REST API endpoints.TodoHandler
methods to the corresponding REST API calls.cloud.Api
object to handle URL retrieval via getUrl()
.This TodoService
can be instantiated with different target modes and optionally a random password, resulting in three primary configurations for various environments:
let _service = new service.TodoService(endor.Mode.PROD);
let _service = new service.TodoService(endor.Mode.DEV);
let _service = new service.TodoService(endor.Mode.TEST, _password);
While detailed code snippets and textual descriptions provide precise insight into design decisions, they often fall short of conveying a holistic view. For a broader perspective, visual representations are invaluable, especially when dealing with concepts as abstract as higher-order programming utilized by Winglang's preflight/inflight mechanisms. This section aims to bridge this understanding gap through graphical illustrations.
To visualize core system components and their relationships a UML Class diagram is a suitable tool:
In this diagram, solid arrows indicate permanent references, dashed arrows depict temporal interactions, and diamond-ended lines show component aggregations. This static representation helps delineate how components are interconnected during the preflight phase.
However, to visualize what happens dynamically at the inflight stage, a more specialized notation is necessary. Traditional UML communication and sequence diagrams were found inadequate, prompting the creation of a custom notation specifically designed for this purpose.
Illustrated below is the createTask
with HTTP Basic Authentication scenario:
In this diagram:
cloud.Api
to convert it into a cloud.ApiRequest
and invokes the appropriate inflight function, typically specified within the RestApiHandlerTemplate
(here, implemented by ServiceFactory
).try
block, where the logRequest
function from ApiTools
logs the request, and the plugged-in function (createResource
from RestApiBuilder
) processes the request. Upon error, errorResponse
from ApiTools
converts exceptions into cloud.ApiResponse
. Regardless of the outcome, logResponse
and responseMessage
from ApiTools
are invoked to finalize the processing.createResource
function within RestApiBuilder
initiates authentication via a plugged-in auth
function (provided by BasicAuthFactory
), which extracts user data and passes control to createResource
of RestApiFactory
.createResource
in RestApiFactory
parses HTTP headers and body data and calls the createTask
function provided by TodoHandler
, which handles the core business logic.This dynamic interaction is indeed complex, highlighting the intricate and interconnected nature of the system. The proposed middleware design aims to shield users from this complexity in daily operations. However, when issues arise, creating a precise graphical representation of the underlying