This project consists of four main steps that, when put together, can run Cub source code from a raw string to an executed result.
🛬 source code |
➡️ | Lexer | ➡️ | Parser | ➡️ | Compiler | ➡️ | Interpreter | ➡️ | result 🛫 |
---|
The following table describes the I/O
of each step in the pipeline:
Input | Output | |
---|---|---|
Lexer | Source code | Tokens |
Parser | Tokens | AST |
Compiler | AST | Scorpion Bytecode |
Interpreter | Scorpion Bytecode | Execution result |
Note: Each step in the pipeline is independent from all others. This makes unit testing very straight forward.
In practice it is common to want to compile source code once and execute it multiple times. The following pipelines provide this in an efficient way:
Pipeline 1:
🛬 source code |
➡️ | Lexer | ➡️ | Parser | ➡️ | Compiler | ➡️ | Bytecode |
➡️ | encode 🛫 |
---|
This pipeline can be written in Swift as:
public func compileToBytecode(_ source: String) throws -> BytecodeBody {
return try (lexer |> parse |> compile)(source)
}
This makes use of the custom |>
(pipe) operator.
Pipeline 2:
🛬 decode |
➡️ | Bytecode |
➡️ | Interpreter | ➡️ | result 🛫 |
---|
The encoding/decoding will typically be followed by writing/reading the bytecode to disk, to enable efficient distribution.
Generally the performance of the interpreter step (pipeline 2) is deemed more important than compilation time (pipeline 1). A concrete example of this is compile time code optimization: this will, by definition, slow down compilation time. But the performance gains at runtime are worth it.