AIR

Overview

This crate defines the core of the AIR interpreter intended to execute scripts to control execution flow in the Fluence network. From a high level point of view, the interpreter can be considered as a state transition function that takes two states, merges them, and then produces a new state.

Interpreter interface

This interpreter has only one export function called invoke and no import functions. The export function has the following signature:

pub fn executed_air(
    /// AIR script to execute.
    air: String,
    
    /// Previous data that should be equal to the last returned by the interpreter. 
    prev_data: Vec<u8>,
    
    /// So-called current data that was sent with a particle from the interpreter on some other peer.
    data: Vec<u8>,
    
    /// Running parameters that includes different settings.
    params: RunParameters,
    
    /// Results of calling services.
    call_results: Vec<u8>,
) -> InterpreterOutcome {...}

pub struct InterpreterOutcome {
    /// A return code, where 0 means success.
    pub ret_code: i32,

    /// Contains error message if ret_code != 0.
    pub error_message: String,

    /// Contains so-called new data that should be preserved in an executor of this interpreter
    /// regardless of ret_code value.
    pub data: Vec<u8>,

    /// Public keys of peers that should receive data.
    pub next_peer_pks: Vec<String>,

    /// Collected parameters of all met call instructions that could be executed on a current peer.
    pub call_requests: Vec<u8>,
}

As it was already mentioned in the previous section, invoke takes two states (prev_data and current_data) and returns a new state (new_data). Additionally, it takes AIR script that should be executed, some run parameters (such as init_peer_id and current_peer_id), and call_results, results of services calling. As a result it provides the IntepreterOutcome structure described in the code snippet above.

Main properties

Let's consider the interpreter with respect to data first, because previous, current and resulted data are the most interesting parts of arguments and the outcome. Assuming X is a set of all possible values that data could have, we'll denote executed_air export function as f: X * X -> X. It could be seen that with respect to data f forms a magma.

Even more, f is an idempotent non-commutative monoid, because:

1. f is associative: forall a, b, c from X: f(f(a,b), c) = f(a, f(b,c))
2. f has a neutral element: exists e, forall a from X: f(e, a) = f(a, e) = a, where e is a data with an empty trace
3. f is a non-commutative function: exists a, b from X: f(a, b) != f(b, a)
4. X could be constructed from a four based elements that form the ExecutedState enum (that why this monoid is free)
5. f satisfies these idempotence properties:
   1. forall x from X: f(x, x) = x
   2. forall a, b from X: f(a, b) = c, f(c, b) = c, f(c, a) = c

Interaction with the interpreter

The interpreter allows a peer (either a node or a browser) to call service asynchronously by collecting all arguments and other necessary stuff from each call instruction that could be called during the execution and return them in InterpreterOutcome. A host should then execute them at any time and call back the interpreter providing executed service results as the call_results argument.

A scheme of interacting with the interpreter should look as follows:

1. For each new current_data received from a network, a host should call the interpreter with corresponding prev_data and current_data and empty call_results. prev_data here is last new_data returned from the interpreter.

2. Having obtained a result of the interpreter, there could be non-empty next_peer_ids and non-empty call_requests in InterpreterOutcome:

   1. re next_peer_pks: it's a peer duty to decide whether it should send particle after each interpreter call or after the whole particle execution, i.e. after completing all call_requests.
   2. re call_requests: call_requests is a HashMap<u32, CallRequestParams> and it's important for host to keep that correspondence between u32 and call CallRequestParams, because they should be used when results are passed back on step 3.

3. If call_requests was non-empty on step 2, a peer must call the interpreter again with supplied call results (HashMap<u32, CallServiceResult>) following next rules:

• current_data here shouldn't be supplied (actually, it could be supplied because of f is idempotent, but it's unnecessary and slow down a bit an interpreter execution)
• it's not necessary to supply call_results before handling the next particle, actually a peer could supply it in any moment
• a peer must preserve new_data after each execution step. It's important, because f is non-commutative and the interpreter save an additional info in data expecting to see the resulted data back as prev_data on the next launch. Then this flow should be repeated starting from point 2.

4. If call_requests was empty, the whole execution is completed, new_data must be preserved and particle send for all new_peer_pks as usual.

An example of interaction can be found in tests.