ZEN Engine
ZEN Engine is business friendly Open-Source Business Rules Engine
(BRE) to execute decision models according to the GoRules JSON
Decision Model (JDM) standard. It is written in Rust and provides
native bindings for NodeJS and Python. ZEN Engine allows to load
and execute JSON Decision Model (JDM) from JSON files.
Resources
Documentation
Online Rules Engine Editor
Installation
Add the following to your Cargo.toml file:
[dependencies]
zen-engine = "0"
Usage
To execute a simple decision using a Noop (default) loader you can use the code below.
use serde_json::json;
use zen_engine::DecisionEngine;
use zen_engine::model::DecisionContent;
async fn evaluate() {
let decision_content: DecisionContent = serde_json::from_str(include_str!("jdm_graph.json")).unwrap();
let engine = DecisionEngine::default();
let decision = engine.create_decision(decision_content.into());
let result = decision.evaluate(&json!({ "input": 12 })).await;
}
Alternatively, you may create decision indirectly without constructing the engine utilising
Decision::from function.
Loaders
For more advanced use cases where you want to load multiple decisions and utilise graphs you
may use one of the following pre-made loaders:
- FilesystemLoader - with a given path as a root it tries to load a decision based on relative path
- MemoryLoader - works as a HashMap (key-value store)
- ClosureLoader - allows for definition of simple async callback function which takes key as a parameter
and returns an
Arc<DecisionContent> instance
- NoopLoader - (default) fails to load decision, allows for usage of create_decision
(mostly existing for streamlining API across languages)
Filesystem loader
Assuming that you have a folder with decision models (.json files) which is located under /app/decisions,
you may use FilesystemLoader in the following way:
use serde_json::json;
use zen_engine::DecisionEngine;
use zen_engine::loader::{FilesystemLoader, FilesystemLoaderOptions};
async fn evaluate() {
let engine = DecisionEngine::new(FilesystemLoader::new(FilesystemLoaderOptions {
keep_in_memory: true, root: "/app/decisions"
}));
let context = json!({ "customer": { "joinedAt": "2022-01-01" } });
{
let promotion_decision = engine.get_decision("commercial/promotion.json").await.unwrap();
let result = promotion_decision.evaluate(&context).await.unwrap();
}
{
let result = engine.evaluate("commercial/promotion.json", &context).await.unwrap();
}
}
Custom loader
You may create a custom loader for zen engine by implementing DecisionLoader trait.
Here's an example of how MemoryLoader has been implemented.
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use zen_engine::loader::{DecisionLoader, LoaderError, LoaderResponse};
use zen_engine::model::DecisionContent;
#[derive(Debug, Default)]
pub struct MemoryLoader {
memory_refs: RwLock<HashMap<String, Arc<DecisionContent>>>,
}
impl MemoryLoader {
pub fn add<K, D>(&self, key: K, content: D)
where
K: Into<String>,
D: Into<DecisionContent>,
{
let mut mref = self.memory_refs.write().unwrap();
mref.insert(key.into(), Arc::new(content.into()));
}
pub fn get<K>(&self, key: K) -> Option<Arc<DecisionContent>>
where
K: AsRef<str>,
{
let mref = self.memory_refs.read().unwrap();
mref.get(key.as_ref()).map(|r| r.clone())
}
pub fn remove<K>(&self, key: K) -> bool
where
K: AsRef<str>,
{
let mut mref = self.memory_refs.write().unwrap();
mref.remove(key.as_ref()).is_some()
}
}
impl DecisionLoader for MemoryLoader {
fn load<'a>(&'a self, key: &'a str) -> impl Future<Output=LoaderResponse> + 'a {
async move {
self.get(&key)
.ok_or_else(|| LoaderError::NotFound(key.to_string()).into())
}
}
}