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# Dataflow-rs
**A high-performance rules engine for IFTTT-style automation in Rust with zero-overhead JSONLogic evaluation.**
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.rust-lang.org)
[](https://crates.io/crates/dataflow-rs)
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---
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.
## How It Works: IF → THEN → THAT
```
┌─────────────────────────────────────────────────────────────────┐
│ 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`
## Core Concepts
| **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.
## Getting Started
### 1. Add to `Cargo.toml`
```toml
[dependencies]
dataflow-rs = "2.1"
tokio = { version = "1", features = ["rt-multi-thread", "macros"] }
serde_json = "1.0"
```
### 2. Define Rules in JSON
```json
{
"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]}]}
}
]
}
}
}
]
}
```
### 3. Run the Engine
```rust
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(())
}
```
### Using Rules Engine Aliases
```rust
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);
```
## Key Features
- **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 `AsyncFunctionHandler` trait.
- **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.
## Architecture
### Compilation Phase (Startup)
1. All JSONLogic expressions compiled once when the Engine is created
2. Compiled logic cached with Arc for zero-copy sharing
3. Validates all expressions early, failing fast on errors
### Execution Phase (Runtime)
1. **Engine** evaluates each rule's condition against the message context
2. Matching rules execute their actions with pre-compiled logic (zero compilation overhead)
3. `process_message()` for normal execution, `process_message_with_trace()` for debugging
4. Each action can be async, enabling I/O operations without blocking
## Performance
- **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
```bash
cargo run --example benchmark # Performance benchmark
cargo run --example rules_engine # IFTTT-style rules engine demo
cargo run --example complete_workflow # Parse → Transform → Validate pipeline
```
## Custom Functions
Extend the engine with your own async actions:
```rust
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));
```
## Built-in Functions
| `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 |
### Filter (Pipeline Control Flow)
The `filter` function evaluates a JSONLogic condition and controls pipeline execution:
```json
{
"function": {
"name": "filter",
"input": {
"condition": {"==": [{"var": "data.status"}, "active"]},
"on_reject": "halt"
}
}
}
```
- `on_reject: "halt"` — stops the entire workflow when the condition is false
- `on_reject: "skip"` — skips just the current task and continues
### Log (Structured Logging)
The `log` function outputs structured log messages using the `log` crate:
```json
{
"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.
## Channel Routing
Route messages to specific workflow channels for efficient O(1) dispatch:
```rust
// 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.
## Workflow Lifecycle
Workflows support lifecycle management fields:
```json
{
"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": [...]
}
```
| `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.
## Engine Hot Reload
Swap workflows at runtime without losing custom function registrations:
```rust
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
```
## Related Packages
| [@goplasmatic/dataflow-wasm](https://www.npmjs.com/package/@goplasmatic/dataflow-wasm) | WebAssembly bindings for browser execution |
| [@goplasmatic/dataflow-ui](https://www.npmjs.com/package/@goplasmatic/dataflow-ui) | React components for rule visualization and debugging |
## Contributing
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.
## About Plasmatic
Dataflow-rs is developed by the team at [Plasmatic](https://github.com/GoPlasmatic). We're passionate about building open-source tools for data processing and automation.
## License
This project is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.