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Sails  

Sails is a library for bringing your experience of writing applications utilizing Gear Protocol to the next level of simplicity and clarity. It deals with things like:

  • eliminating the necessity of writing some low-level boilerplate code and letting you to stay focused on your business problem
  • generated IDL file for your application
  • generated client allowing to interact with your application from code written in different languages and executed in different runtimes

NOTE

The Sails library is published under the name sails-rs on crates-io.

Getting started

Either use Sails CLI:

cargo install sails-cli
cargo sails new-program my-ping

Or add the following to your Cargo.toml

[dependencies]
sails-rs = { version = "*", features = ["debug"] }

And then in your lib.rs:

#![no_std]

use sails_rs::{gstd::debug, prelude::*};

struct MyPing;

#[service]
impl MyPing {
    pub const fn new() -> Self {
        Self
    }

    pub async fn ping(&mut self) -> bool {
        debug!("Ping called");
        true
    }
}

#[derive(Default)]
struct MyProgram;

#[program]
impl MyProgram {
    #[route("ping")]
    pub fn ping_svc(&self) -> MyPing {
        MyPing::new()
    }
}

Details

The entire idea of the Gear Protocol is based on the asynchronous version of the Request-Response Pattern. On-chain applications loaded onto Gear-based network receive and handle messages from the other on-chain or off-chain applications. Both can be treated as external consumers of services provided by your application, and the latter can represent ordinary people interacting with the network.

Application

Sails architecture for applications is based on a few key concepts.

The first one is service which is represented by an impl of some Rust struct marked with the #[service] attribute. The service main responsibility is implementing some aspect of application business logic.

A set of service's public methods defined by the impl is essentially a set of remote calls the service exposes to external consumers. Each such method working over a &mut self is treated as a command changing some state, whereas each method working over a &self is treated as a query keeping everything unchanged and returning some data. Both types of methods can accept some parameters passed by a client and can be synchronous or asynchronous. All the other service's methods and associated functions are treated as implementation details and ignored. The code generated behind the service by the #[service] attribute decodes an incoming request message and dispatches it to the appropriate method based on the method's name. On the method's completion, its result is encoded and returned as a response to a caller.

NOTE

In some cases, a command might need to return a certain amount of tokens (value) from the application's balance to the caller's one. This can be done via using a dedicated type, CommandReply<T>.

#[service]
impl MyService {
    // This is a command
    pub fn do_something(&mut self, p1: u32, p2: String) -> &'static [u8] {
        ...
    }

    // This is a command returning value along with the result
    pub fn withdraw(&mut self, amount: u64) -> CommandReply<()> {
        CommandReply::new(()).with_value(amount)
    }

    // This is a query
    pub fn something(&self, p1: Option<bool>) -> String {
        ...
    }
}

The second key concept is program which is similarly to the service represented by an impl of some Rust struct marked with the #[program] attribute. The program main responsibility is hosting one or more services and exposing them to the external consumers.

A set of its associated public functions returning Self are treated as application constructors. These functions can accept some parameters passed by a client and can be synchronous or asynchronous. One of them will be called once at the very beginning of the application lifetime, i.e. when the application is loaded onto the network. The returned program instance will live until the application stays on the network. If there are no such methods discovered, a default one with the following signature will be generated:

pub fn default() -> Self {
    Self
}

A set of program's public methods working over &self and having no other parameters are treated as exposed service constructors and are called each time when an incoming request message needs be dispatched to a selected service. All the other methods and associated functions are treated as implementation details and ignored. The code generated behind the program by the #[program] attribute receives an incoming request message from the network, decodes it and dispatches it to a matching service for actual processing. After that, the result is encoded and returned as a response to a caller. Only one program is allowed per application.

#[program]
impl MyProgram {
    // Application constructor
    pub fn new() -> Self {
        ...
    }

    // Yet another application constructor
    pub fn from_u32(p1: u32) -> Self {
        ...
    }

    // Service constructor
    pub fn ping_svc(&self) -> MyPing {
        ...
    }
}

And the final key concept is message routing. This concept doesn't have a mandatory representation in code, but can be altered by using the #[route] attribute applied to those public methods and associated functions described above. The concept itself is about rules for dispatching an incoming request message to a specific service's method using service and method names. By default, every service exposed via program is exposed using the name of the service constructor method converted into PascalCase. For example:

#[program]
impl MyProgram {
    // The `MyPing` service is exposed as `PingSvc`
    pub fn ping_svc(&self) -> MyPing {
        ...
    }
}

This behavior can be changed by applying the #[route] attribute:

#[program]
impl MyProgram {
    // The `MyPing` service is exposed as `Ping`
    #[route("ping")] // The specified name will be converted into PascalCase
    pub fn ping_svc(&self) -> MyPing {
        ...
    }
}

The same rules are applicable to service method names:

#[service]
impl MyPing {
    // The `do_ping` method is exposed as `Ping`
    #[route("ping")]
    pub fn do_ping(&mut self) {
        ...
    }

    // The `ping_count` method is exposed as `PingCount`
    pub fn ping_count(&self) -> u64 {
        ...
    }
}

Events

Sails offers a mechanism to emit events from your service while processing commands. These events serve as a means to notify off-chain subscribers about changes in the application state. In Sails, events are configured and emitted on a per-service basis through the events argument of the #[service] attribute. They are defined by a Rust enum, with each variant representing a separate event and its optional data. Once a service declares that it emits events, the #[service] attribute automatically generates the notify_on service method. This method can be called by the service to emit an event. For example:

fn counter_mut() -> &'static mut u32 {
    static mut COUNTER: u32 = 0;
    unsafe { &mut COUNTER }
}

struct MyCounter;

#[derive(Encode, TypeInfo)]
enum MyCounterEvent {
    Incremented(u32),
}

#[service(events = MyCounterEvent)]
impl MyCounter {
    pub fn new() -> Self {
        Self
    }

    pub fn increment(&mut self) {
        *counter_mut() += 1;
        self.notify_on(MyCounterEvent::Incremented(*counter_mut())).unwrap();
    }

    // This method is generated by the `#[service]` attribute
    fn notify_on(&mut self, event: MyCounterEvent) -> Result<()> {
        ...
    }
}

It's important to note that, internally, events use the same mechanism as any other message transmission in the Gear Protocol. This means an event is only published upon the successful completion of the command that emitted it.

Service Extending (Mixins)

A standout feature of Sails is its capability to extend (or mix in) existing services. This is facilitated through the use of the extends argument in the #[service] attribute. Consider you have Service A and Service B, possibly sourced from external crates, and you aim to integrate their functionalities into a new Service C. This integration would result in methods and events from Services A and B being seamlessly incorporated into Service C, as if they were originally part of it. In such a case, the methods available in Service C represent a combination of those from Services A and B. Should a method name conflict arise, where both Services A and B contain a method with the same name, the method from the service specified first in the extends argument takes precedence. This strategy not only facilitates the blending of functionalities but also permits the overriding of specific methods from the original services by defining a method with the same name in the new service. With event names, conflicts are not allowed. Unfortunately, the IDL generation process is the earliest when this can be reported as an error. For example:

struct MyServiceA;

#[service]
impl MyServiceA {
    pub fn do_a(&mut self) {
        ...
    }
}

struct MyServiceB;

#[service]
impl MyServiceB {
    pub fn do_b(&mut self) {
        ...
    }
}

struct MyServiceC;

#[service(extends = [MyServiceA, MyServiceB])]
impl MyServiceC {
    // New method
    pub fn do_c(&mut self) {
        ...
    }

    // Overridden method from MyServiceA
    pub fn do_a(&mut self) {
        ...
    }

    // do_b from MyServiceB will exposed due to the extends argument
}

Payload Encoding

An application written with Sails uses SCALE Codec to encode/decode data at its base.

Every incoming request message is expected to have the following format:

| SCALE encoded service name | SCALE encoded method name | SCALE encoded parameters |

Every outgoing response message has the following format:

| SCALE encoded service name | SCALE encoded method name | SCALE encoded result |

Every outgoing event message has the following format:

| SCALE encoded service name | SCALE encoded event name | SCALE encoded event data |

Client

Having robust interaction capabilities with applications is crucial. Sails offers several options for interaction.

Firstly, it supports manual interaction using the Gear Protocol. You can use:

  • The msg::send functions from the gstd crate to interact between applications.
  • The gclient crate to interact from off-chain code with an on-chain application.
  • The @gear-js/api library to interact with your program from JavaScript.

All you need to do is compose a byte payload according to the layout outlined in the Payload Encoding section and send it to the application.

Thanks to the generated IDL, Sails provides a way to interact with your application using generated clients with an interface similar to the one exposed by latter in a clearer way. Currently, Sails can generate client code for Rust and TypeScript.

When it comes to Rust, there are two options:

  • Use generated code that can encode and decode byte payloads for you, allowing you to continue using functions that send raw bytes.
  • Use fully generated code that can interact with your application in an RPC style.

For TypeScript see generated clients documentation.

Say you have an application that exposes a service MyService with a command do_something:

struct Output {
    m1: u32,
    m2: String,
}

#[service]
impl MyService {
    pub fn do_something(&mut self, p1: u32, p2: String) -> Output {
        ...
    }
}

#[program]
impl MyProgram {
    pub fn my_service(&self) -> MyService {
        MyService::new()
    }
}

Then, in a client application, provided the code generation happens in a Rust build script, you can use the generated code like this (option 1):

include!(concat!(env!("OUT_DIR"), "/my_service.rs"));

fn some_client_code() {
    let call_payload = my_service::io::DoSomething::encode_call(42, "Hello".to_string());
    let reply_bytes = gstd::msg::send_bytes_for_reply(target_app_id, call_payload, 0, 0).await.unwrap();
    let reply = my_service::io::DoSomething::decode_reply(&reply_bytes).unwrap();
    let m1 = reply.m1;
    let m2 = reply.m2;
}

Or like this (option 2):

include!(concat!(env!("OUT_DIR"), "/my_service.rs"));

fn some_client_code() {
    let mut my_service = MyService::new(remoting); // remoting is an abstraction provided by Sails
    let reply_ticket = client.do_something(42, "Hello".to_string())
        .with_reply_deposit(42)
        .publish(target_app_id)
        .await.unwrap();
    let reply = reply_ticket.reply().await.unwrap();
    let m1 = reply.m1;
    let m2 = reply.m2;
}

The second option provides you with an option to have your code testable, as the generated code depends on the trait which can be easily mocked.

As you may have noticed, the option 2 uses the concept of a remoting object, which needs to be passed to the client instantiation code. This object should implement the Remoting trait from the sails-rs crate. It abstracts the low-level communication details between client and the application. The sails-rs crate provides three implementations of this trait:

  • sails_rs::gstd::calls::GStdRemoting should be used when the client code is executed as a part of another on-chain application.
  • sails_rs::gclient::calls::GClientRemoting should be used when the client code is executed as a part of an off-chain application.
  • sails_rs::gstd::calls::GTestRemoting should be used when the client code is executed as a part of a tests utilizing the gtest crate.

When it comes to TypeScript, sails-js library can be used to interact with the program. Check out sails-js documentation for more details.

Writing Sagas (Advanced)

Occasionally, you may need to design a system where a business transaction spans multiple applications. In other words, a single business transaction may involve several local transactions within different applications. This challenge is typically addressed using a pattern called Saga. You can find detailed documentation about this pattern here and implementation guidelines here.

The process of addressing this issue in applications built with Sails is similar, but only the orchestration approach can be used, as Sails applications cannot catch events from each other. Additionally, it is important to handle infrastructure errors that can arise, particularly those related to the Gear Protocol and its concept of gas.

Let's refer to the guidelines and explore the nuances involved.

First, it is important to note that all the errors mentioned in the guidelines are business errors — only business errors can trigger compensation actions. In contrast, infrastructure errors are expected to be resolved through retries. This applies to both retriable and compensatable transactions. The key difference is that the former should never return a business error.

Normally, these retries are handled programmatically. However, when a RunOutOfGas error is received from another application, retrying is not an option. The only solution in this case is to propagate the error to the top-level caller, who will then need to attach more funds and attempt the entire business transaction again.

For retries to function correctly, local transactions must be idempotent (i.e., safe to retry without causing side effects, such as sending duplicate events). This can be ensured by assigning a unique identifier to each business transaction, which is passed to all actions within the Saga, whether it’s the initial attempt or a retry.

The Saga’s state must track this identifier to skip actions that were completed in previous attempts. Similarly, the actions themselves should recognize this identifier to prevent repeating changes that have already been made.

Another issue to consider is the possibility of receiving a Timeout error while waiting for a response from another application. In some systems, timeouts can be treated as business errors, triggering compensation. However, if the timeout is caused by infrastructure issues (e.g., network congestion), it can be handled similarly to the RunOutOfGas error.

There are optimization opportunities in such cases. Since the Gear Protocol guarantees that the caller will eventually receive a response (successful or not), one simple optimization is to increase the number of blocks allowed for waiting on a response. On the Vara network, this value is set to 100 blocks by default. Increasing it to 10,000 blocks would mitigate most network load issues without significantly raising transaction costs. This adjustment can be made using the with_wait_up_to method.

Another option is to use the with_reply_hook method, which involves additional logic to manage the Saga’s state. The reply hook can be triggered while the main code handles the Timeout error, allowing the caller to initiate another attempt. During this process, the timed-out action can be marked as completed in the Saga’s state, preventing it from being re-executed. However, it's worth considering whether this added complexity is necessary, as retrying an already completed idempotent action is harmless and only slightly increases the overall cost.

To summarize:

  • Implement an orchestrating Saga (orchestrator application) by maintaining its state.
  • Design calls to other applications as either compensatable or retriable transactions.
  • Record a list of actions needed to execute the transactions in the Saga’s state, along with the status of each action (e.g., not executed, succeeded, failed).
  • Ensure that each transaction's actions are implemented in an idempotent manner.
  • Prepare for 2 key infrastructure errors: RunOutOfGas and Timeout. The simplest approach is to propagate these errors to the top-level caller for retries.
    • For Timeout errors, optimize by increasing the number of blocks allowed for waiting on a response.
  • Keep in mind that every call to an application will eventually yield a response.

Examples

You can find all examples here along with some descriptions provided at the folder level. You can also find some explanatory comments in the code. Here is a brief overview of features mentioned above and showcased by the examples:

Exposing Services via Program

The examples are composed on a principle of a few programs exposing several services. See DemoProgram which demonstrates this, including the use of program's multiple constructors and the #[route] attribute for one of the exposed services. The example also includes Rust build script building the program as a WASM app ready for loading onto Gear network.

Basic Services

There are a couple of services which demonstrate basic service structure exposing some primitive methods operating based on input parameters and returning some results. They serve as an excellent starting point for developing your services. See Ping and ThisThat services. The latter, in addition to the basics, showcases the variety of types which can be used as parameters and return values in service methods.

Working with Data

In the real world, almost all apps work with some form of data, and apps developed using Sails are no exception. As discussed in the Application section, services are instantiated for every incoming request message, indicating that these services are stateless. However, there are a few ways to enable your services to maintain some state. In this case, the state will be treated as external to the service.

The most recommended way is demonstrated in the Counter service, where the data is stored as part of the program and passed to the service via RefCell. The service module merely defines the shape of the data but requires the data itself to be passed from the outside. This option provides you with full flexibility and allows you to unit test your services in a multi-threaded environment, ensuring the tests do not affect each other.

Another method is illustrated in the RmrkCatalog and RmrkResource services, where the data is stored in static variables within the service module. This strategy ensures that the state remains completely hidden from the outside, making the service entirely self-contained. However, this approach is not ideal for unit testing in a multi-threaded environment because each test can potentially influence others. Additionally, it's important not to overlook calling the service's seed method before its first use.

You can also explore other approaches, such as making a service require &'a mut for its data (which makes the service non-clonable), or using Cell (which requires data copying, incurring additional costs).

In all scenarios, except when using Cell, it's crucial to consider the static nature of data, especially during asynchronous calls within service methods. This implies that data accessed before initiating an asynchronous call might change by the time the call completes. See the RmrkResource service's add_part_to_resource method for more details.

Events

You can find an example of how to emit events from your service in the Counter and RmrkResource services.

Service Extending (Mixins)

An example of service extension is demonstrated with the Dog service, which extends the Mammal service from the same crate and the Walker service from a different crate. The service being extended must implement the Clone trait, while the extending service must implement the AsRef trait for the service being extended.

Using Generated Clients from Rust

The Demo Client crate showcases how to generate client code from an IDL file as a separate Rust crate. Alternatively, you can use the same approach directly in your application crate. See Rmrk Resource.

You can find various examples of how to interact with the application using the generated client code in Demo Tests. Check the comments in the code for more details.

Since the generated code is the same for all environments, whether it is an interaction from tests or from another application, the techniques for these interactions are the same. You can find an example of the interaction from an application in the Rmrk Resource service's add_part_to_resource method.

Bear in mind that working with the generated client requires the sails_rs crate to be in dependencies.

License

Licensed under either of Apache License, Version 2.0 or MIT license at your option. Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in `Sails` by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.