Crates.io | rush_core |
lib.rs | rush_core |
version | 0.1.1 |
source | src |
created_at | 2023-10-09 11:27:18.536586 |
updated_at | 2023-10-09 11:33:22.083896 |
description | The rules engine is based on the rete algorithm |
homepage | |
repository | https://github.com/woshihaoren4/rush |
max_upload_size | |
id | 997900 |
size | 28,564 |
Rush is a universal rules engine.
Rush provides a general computational abstraction. There can be multiple implementations and any combination.
Conditional parallel computing
const SIMPLE_RULE: &'static str = "
rule COMPLEX_RULE
when
age > 18
then
stage = 'adult'
";
fn main(){
let rh = Rush::from(Into::<ExprEngine>::into([SIMPLE_RULE]));
let res:HashMap<String,String> = rh.flow(r#"{"age":19}"#.parse::<Value>().unwrap()).unwrap();
assert_eq!(res.get("stage").unwrap().as_str(),"adult");
}
Here, regular expressions are generated directly. You can actually implement some of them yourself to integrate with your own services.
The direct parsing rules are as follows:
The keyword cannot be repeated: when,then
rule [name] [description] [engine/default:expr] [...]
when
[condition 1];
[condition 2];
...
[condition n];
then
[key1 = execute 1];
[key2 = execute 2];
...
[keyn = execute n];
contain([1,2.3,'hello'],1)
You can add functions just like normal rust functions
let rh = rh
.register_function("abs", |i: i64| Ok(i.abs()));
const LUA_RULE_SCRIPT: &'static str = r#"
rule LUA_RULE_SCRIPT _ lua
lua_script:
function handle(req)
local resp = {}
if req.source == ONLINE_CHANNEL then
resp.message = "线上渠道"
elseif req.source == OFFLINE_CHANNEL then
resp.message = "线下渠道"
else
resp.message = "未知渠道:"..req.source
end
return resp
end
return {handle_function="handle"}
"#;
Compile a wasm file with the following code, use command cargo build --target wasm32-unknown-unknown --release
extern "C" {
fn success(ptr: *const u8, len: usize) -> u32;
}
#[no_mangle]
pub extern "C" fn handle(ptr: *mut c_char, len: u32) -> u32 {
unsafe {
let slice = std::slice::from_raw_parts(ptr, len as usize);
let cs = CStr::from_ptr(slice.as_ptr());
let cs = CString::from(cs);
let s = cs.to_str().unwrap();
let s = format!(r#"{{"input":{}}}"#, s);
success(s.as_ptr(), s.len());
}
1u32
}
Refer to this file in the rule and call:
const WASM_RULE: &'static str = "
rule WASM_RULE _ wasm
wasm_file: wasm_example/wasm_example_one.wasm
";
#[tokio::test]
async fn test_wasm_build() {
let rt = WasmRuntimeFactory::new().async_build(WASM_RULE).await.unwrap();
let result: HashMap<String, String> =
rt.async_flow(Value::String("hello".into())).await.unwrap();
assert_eq!(result.get("input").unwrap().as_str(), "hello");
}
If you're concerned about the overhead of this library, a good range of benchmarks are built into this repo. You can run them with cargo bench -- --verbose
The library is built with an eye towards being quick, but has not been aggressively profiled and optimized. For most applications, though, it is completely fine.
Here are my test results,at MacBook Pro,CPU 2.6 GHz Intel Core i7, [lowest, average, highest]
assign_simple_parse time: [620.70 ns 625.08 ns 630.18 ns]
rule_full_parse time: [7.5513 µs 7.5794 µs 7.6094 µs]
multi_flow time: [15.363 µs 15.721 µs 16.184 µs]
sync_flow time: [2.9953 µs 3.0295 µs 3.0700 µs]
single_parse time: [165.08 ns 174.83 ns 186.49 ns]
simple_parse time: [2.6358 µs 2.6470 µs 2.6591 µs]
full_parse time: [19.868 µs 20.089 µs 20.356 µs]
have_function_rush time: [6.9074 µs 6.9507 µs 7.0011 µs]
lua_async_flow time: [10.731 µs 10.847 µs 10.970 µs]
wasm_async_flow time: [8.7260 µs 8.8046 µs 8.8806 µs]
Expressions are currently supported as cells of count. Future support for lua and wasm is also planned。
This project is licensed under the MIT general use license. You're free to integrate, fork, and play with this code as you feel fit without consulting the author, as long as you provide proper credit to the author in your works.