A high-performance rules engine for IFTTT-style automation in Rust with zero-overhead JSONLogic evaluation.
Dataflow-rs is a lightweight rules engine that lets you define IF → THEN → THAT automation in JSON. Rules are evaluated using pre-compiled JSONLogic for zero runtime overhead, and actions execute asynchronously for high throughput. Whether you're routing events, validating data, or building complex automation pipelines, Dataflow-rs gives you enterprise-grade performance with minimal complexity.
┌─────────────────────────────────────────────────────────────────┐
│ Rule (Workflow) │
│ │
│ IF condition matches → JSONLogic against any field │
│ THEN execute actions (tasks) → map, validate, custom logic │
│ THAT chain more rules → priority-ordered execution │
└─────────────────────────────────────────────────────────────────┘
Example: IF order.total > 1000 THEN apply_discount AND notify_manager
| Rules Engine | Workflow Engine | Description |
|---|---|---|
| Rule | Workflow | A condition + actions bundle — IF condition THEN execute actions |
| Action | Task | An individual processing step (map, validate, or custom function) |
| RulesEngine | Engine | Evaluates rules against messages and executes matching actions |
Both naming conventions are fully supported — use whichever fits your mental model.
[dependencies]
dataflow-rs = "2.1"
tokio = { version = "1", features = ["rt-multi-thread", "macros"] }
serde_json = "1.0"{
"id": "premium_order",
"name": "Premium Order Processing",
"condition": {">=": [{"var": "data.order.total"}, 1000]},
"tasks": [
{
"id": "apply_discount",
"name": "Apply Premium Discount",
"function": {
"name": "map",
"input": {
"mappings": [
{
"path": "data.order.discount",
"logic": {"*": [{"var": "data.order.total"}, 0.1]}
},
{
"path": "data.order.final_total",
"logic": {"-": [{"var": "data.order.total"}, {"*": [{"var": "data.order.total"}, 0.1]}]}
}
]
}
}
}
]
}use dataflow_rs::{Engine, Workflow};
use dataflow_rs::engine::message::Message;
use serde_json::json;
use std::sync::Arc;
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let workflow = Workflow::from_json(r#"{ ... }"#)?; // Your rule JSON
// Create engine — all JSONLogic compiled once here
let engine = Engine::new(vec![workflow], None);
// Process a message
let payload = Arc::new(json!({"order": {"total": 1500}}));
let mut message = Message::new(payload);
engine.process_message(&mut message).await?;
println!("Discount: {}", message.data()["order"]["discount"]); // 150
Ok(())
}use dataflow_rs::{RulesEngine, Rule, Action};
// These are type aliases — same types, rules-engine terminology
let rule = Rule::from_json(r#"{ ... }"#)?;
let engine = RulesEngine::new(vec![rule], None);- IF → THEN → THAT Model: Define rules with JSONLogic conditions, execute actions, chain with priority ordering.
- Zero Runtime Compilation: All JSONLogic expressions pre-compiled at startup for optimal performance.
- Full Context Access: Conditions can access any field —
data,metadata,temp_data. - Async-First Architecture: Native async/await support with Tokio for high-throughput processing.
- Execution Tracing: Step-by-step debugging with message snapshots after each action.
- Built-in Functions: Parse, Map, Validate, Filter, Log, and Publish for complete data pipelines.
- Pipeline Control Flow: Filter/gate function to halt workflows or skip tasks based on conditions.
- Channel Routing: Route messages to specific workflow channels with O(1) lookup.
- Workflow Lifecycle: Manage workflow status (active/paused/archived), versioning, and tagging.
- Hot Reload: Swap workflows at runtime without re-registering custom functions.
- Extensible: Add custom async actions by implementing the
AsyncFunctionHandlertrait. - Typed Integration Configs: Pre-validated configs for HTTP, Enrich, and Kafka integrations.
- WebAssembly Support: Run rules in the browser with
@goplasmatic/dataflow-wasm. - React UI Components: Visualize and debug rules with
@goplasmatic/dataflow-ui. - Auditing: Full audit trail of all changes as data flows through the pipeline.
- All JSONLogic expressions compiled once when the Engine is created
- Compiled logic cached with Arc for zero-copy sharing
- Validates all expressions early, failing fast on errors
- Engine evaluates each rule's condition against the message context
- Matching rules execute their actions with pre-compiled logic (zero compilation overhead)
process_message()for normal execution,process_message_with_trace()for debugging- Each action can be async, enabling I/O operations without blocking
- Pre-Compilation: All JSONLogic compiled at startup, zero runtime overhead
- Arc-Wrapped Logic: Zero-copy sharing of compiled expressions
- Context Arc Caching: 50% improvement via cached Arc context
- Async I/O: Non-blocking operations for external services
- Predictable Latency: No runtime allocations for logic evaluation
cargo run --example benchmark # Performance benchmark
cargo run --example rules_engine # IFTTT-style rules engine demo
cargo run --example complete_workflow # Parse → Transform → Validate pipelineExtend the engine with your own async actions:
use async_trait::async_trait;
use dataflow_rs::engine::{
AsyncFunctionHandler, FunctionConfig,
error::Result, message::{Change, Message}
};
use datalogic_rs::DataLogic;
use serde_json::json;
use std::collections::HashMap;
use std::sync::Arc;
pub struct NotifyManager;
#[async_trait]
impl AsyncFunctionHandler for NotifyManager {
async fn execute(
&self,
message: &mut Message,
config: &FunctionConfig,
datalogic: Arc<DataLogic>,
) -> Result<(usize, Vec<Change>)> {
// Your custom async logic here (HTTP calls, DB writes, etc.)
Ok((200, vec![]))
}
}
// Register when creating the engine:
let mut custom_functions: HashMap<String, Box<dyn AsyncFunctionHandler + Send + Sync>> = HashMap::new();
custom_functions.insert("notify_manager".to_string(), Box::new(NotifyManager));
let engine = Engine::new(workflows, Some(custom_functions));| Function | Purpose | Modifies Data |
|---|---|---|
parse_json |
Parse JSON from payload into data context | Yes |
parse_xml |
Parse XML string into JSON data structure | Yes |
map |
Data transformation using JSONLogic | Yes |
validation |
Rule-based data validation | No (read-only) |
filter |
Pipeline control flow — halt workflow or skip task | No |
log |
Structured logging with JSONLogic expressions | No |
publish_json |
Serialize data to JSON string | Yes |
publish_xml |
Serialize data to XML string | Yes |
The filter function evaluates a JSONLogic condition and controls pipeline execution:
{
"function": {
"name": "filter",
"input": {
"condition": {"==": [{"var": "data.status"}, "active"]},
"on_reject": "halt"
}
}
}on_reject: "halt"— stops the entire workflow when the condition is falseon_reject: "skip"— skips just the current task and continues
The log function outputs structured log messages using the log crate:
{
"function": {
"name": "log",
"input": {
"level": "info",
"message": {"cat": ["Processing order ", {"var": "data.order.id"}]},
"fields": {
"total": {"var": "data.order.total"},
"user": {"var": "data.user.name"}
}
}
}
}Log levels: trace, debug, info, warn, error. Messages and fields support JSONLogic expressions.
Route messages to specific workflow channels for efficient O(1) dispatch:
// Workflows define their channel
// { "id": "order_rule", "channel": "orders", "status": "active", ... }
// Process only workflows on a specific channel
engine.process_message_for_channel("orders", &mut message).await?;Only active workflows are included in channel routing. Workflows default to the "default" channel.
Workflows support lifecycle management fields:
{
"id": "my_rule",
"channel": "orders",
"version": 2,
"status": "active",
"tags": ["premium", "high-priority"],
"created_at": "2025-01-15T10:00:00Z",
"updated_at": "2025-06-01T14:30:00Z",
"tasks": [...]
}| Field | Type | Default | Description |
|---|---|---|---|
channel |
string | "default" |
Channel for message routing |
version |
number | 1 |
Workflow version |
status |
string | "active" |
active, paused, or archived |
tags |
array | [] |
Arbitrary tags for organization |
created_at |
datetime | null |
Creation timestamp (ISO 8601) |
updated_at |
datetime | null |
Last update timestamp (ISO 8601) |
All fields are optional and backward-compatible with existing configurations.
Swap workflows at runtime without losing custom function registrations:
let new_workflows = vec![Workflow::from_json(r#"{ ... }"#)?];
let new_engine = engine.with_new_workflows(new_workflows);
// Old engine remains valid for in-flight messages| Package | Description |
|---|---|
| @goplasmatic/dataflow-wasm | WebAssembly bindings for browser execution |
| @goplasmatic/dataflow-ui | React components for rule visualization and debugging |
We welcome contributions! Feel free to fork the repository, make your changes, and submit a pull request. Please make sure to add tests for any new features.
Dataflow-rs is developed by the team at Plasmatic. We're passionate about building open-source tools for data processing and automation.
This project is licensed under the Apache License, Version 2.0. See the LICENSE file for more details.