Dataflow-rs

A high-performance rules engine for IFTTT-style automation in Rust with zero-overhead JSONLogic evaluation.

Dataflow-rs is a lightweight, embeddable 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.

⚡ Blazing Fast Performance

Dataflow-rs is built for high-throughput hot paths. By compiling all JSONLogic expressions once at engine startup, runtime evaluation runs with zero allocations, zero parsing overhead, and predictable latency.

A multi-threaded benchmark (1,000,000 concurrent events) on a 10-core machine yields:

• Throughput: ~640,000 messages/sec
• Median (P50) Latency: 6 μs
• Tail (P99) Latency: 51 μs
• Tail (P99.9) Latency: 93 μs

🧩 Full-Stack Ecosystem

Go beyond backend microservices. Use the same rule definitions across your entire stack:

1. Rust Backend: Run natively with maximum speed and concurrency using dataflow-rs.
2. Browser & Edge: Run client-side validations or edge routing using WebAssembly bindings via @goplasmatic/dataflow-wasm.
3. React UI Admin Portal: Let users and developers visualize, edit, and step-by-step debug rules using @goplasmatic/dataflow-ui.

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

Both naming conventions are fully supported — use whichever fits your mental model.

Why dataflow-rs?

If you need dynamic business rules or user-customizable workflows, writing manual if/else checks makes your code rigid, while running full orchestrators (like Temporal or Zeebe) adds heavy infrastructure overhead and milliseconds of network latency. Dataflow-rs gives you the best of both worlds:

Getting Started

1. Add to Cargo.toml

[dependencies]
dataflow-rs = "3.0"
tokio = { version = "1", features = ["rt-multi-thread", "macros"] }
serde_json = "1.0"

2. Define Rules in 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

use dataflow_rs::{Engine, Workflow};
use dataflow_rs::engine::message::Message;
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Paste the JSON rule defined in Step 2:
    let rule_json = r#"{
        "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]}]}
                            }
                        ]
                    }
                }
            }
        ]
    }"#;

    let workflow = Workflow::from_json(rule_json)?;

    // Create engine — all JSONLogic compiled once here
    let engine = Engine::builder().with_workflow(workflow).build()?;

    // Process a message
    let payload = json!({"order": {"total": 1500}});
    let mut message = Message::from_value(&payload);
    engine.process_message(&mut message).await?;

    println!("Discount: {}", message.data()["order"]["discount"]); // 150
    println!("Final Total: {}", message.data()["order"]["final_total"]); // 1350
    Ok(())
}

Handling Errors — Two Channels

process_message reports errors through two complementary channels:

• Result::Err signals that the engine stopped early (a task failed without continue_on_error, or an engine-level error occurred).
• message.errors() always contains every error encountered, including errors from tasks that ran with continue_on_error = true and so didn't short-circuit the workflow.

A short-circuit ? will surface only the first kind. For full coverage:

use dataflow_rs::{Engine, Workflow};
use dataflow_rs::engine::message::Message;
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let engine = Engine::builder()
        .with_workflow(Workflow::from_json(r#"{ ... }"#)?)
        .build()?;

    let mut message = Message::from_value(&json!({"order": {"total": 1500}}));

    // `continue_on_error` tasks may record errors here without returning Err.
    if let Err(e) = engine.process_message(&mut message).await {
        eprintln!("engine halted: {e}");
    }

    // Always iterate `message.errors()` to see everything that went wrong.
    for err in message.errors() {
        eprintln!(
            "[{workflow_id}/{task_id}] {msg}",
            workflow_id = err.workflow_id.as_deref().unwrap_or("-"),
            task_id = err.task_id.as_deref().unwrap_or("-"),
            msg = err.message,
        );
    }

    Ok(())
}

Using Rules Engine Aliases

use dataflow_rs::{RulesEngine, Rule, Action};

// These are type aliases — same types, rules-engine terminology
let rule = Rule::from_json(r#"{ ... }"#)?;
let engine = RulesEngine::builder().with_workflow(rule).build()?;

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

On a 10-core machine processing 1,000,000 messages concurrently (Tokio multi-threaded runtime, --release; per message: 1 parse + 6 mappings + 3 validations):

Why it's fast:

• Pre-Compilation: All JSONLogic compiled at startup, zero runtime parsing
• Arc-Wrapped Logic: Zero-copy sharing of compiled expressions across threads
• Arena Evaluation: Consecutive sync tasks evaluate against one bump-arena view of the context; map writes are spliced into it in place instead of re-cloning the written subtree
• Precomputed Paths: Mapping, parse, and publish target paths are split and interned at compile time — the hot path never re-parses a path string
• Async I/O: Non-blocking operations for external services via Tokio

Tuning tip: if you never read audit trails, build messages with Message::builder().capture_changes(false) — skipping the per-mapping old/new value snapshots is the largest single lever in mapping-heavy workloads. See the performance guide for more.

Run the benchmarks and examples yourself:

cargo run --example benchmark --release             # Full throughput + latency percentiles
cargo run --example realistic_benchmark --release   # ISO 20022 -> SwiftMT-style workload
cargo run --example micro_aggregate_bench --release # Aggregate-heavy (reduce/map) workload
cargo run --example hello_world           # Minimal getting-started example
cargo run --example rules_engine          # IFTTT-style rules engine demo
cargo run --example complete_workflow     # Parse → Transform → Validate pipeline
cargo run --example custom_function       # Extending the engine with custom handlers
cargo run --example error_handling        # Error handling patterns

Custom Functions

Extend the engine with your own async actions. Each handler declares a typed Input (deserialized once at engine init), receives a TaskContext that records audit-trail changes automatically, and returns a TaskOutcome:

use async_trait::async_trait;
use dataflow_rs::{AsyncFunctionHandler, Engine, Result, TaskContext, TaskOutcome};
use datavalue::OwnedDataValue;
use serde::Deserialize;
use serde_json::json;

/// Typed config for the handler — fails at `Engine::new()` if malformed,
/// not on first message.
#[derive(Deserialize)]
pub struct NotifyInput {
    pub channel: String,
}

pub struct NotifyManager;

#[async_trait]
impl AsyncFunctionHandler for NotifyManager {
    type Input = NotifyInput;

    async fn execute(
        &self,
        ctx: &mut TaskContext<'_>,
        input: &NotifyInput,
    ) -> Result<TaskOutcome> {
        // Your custom async logic here (HTTP calls, DB writes, etc.)
        ctx.set(
            "data.notified_channel",
            OwnedDataValue::from(&json!(input.channel)),
        );
        Ok(TaskOutcome::Success)
    }
}

// Register handlers via the builder. `.register("name", h)` accepts any
// `AsyncFunctionHandler` and boxes it internally.
let engine = Engine::builder()
    .with_workflows(workflows)
    .register("notify_manager", NotifyManager)
    .build()?;

Built-in Functions

Filter (Pipeline Control Flow)

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 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:

{
    "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:

// 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:

{
    "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": [...]
}

All fields are optional and backward-compatible with existing configurations.

Engine Hot Reload

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

Visualize & Debug Rules

Because every rule is plain JSON, the React UI can render it: JSONLogic expressions become readable flow diagrams, and the debugger steps through execution with a message diff after every task.

Ecosystem

📖 Documentation: User Guide & API Reference · Interactive Playground · Visual Debugger

Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository and clone your fork
2. Run tests: cargo test to ensure everything passes
3. Make changes and add tests for any new features
4. Run the benchmark before and after: cargo run --example benchmark --release
5. Submit a pull request with a clear description of your changes

See the CHANGELOG for recent changes and release history.

About Plasmatic

Dataflow-rs is developed by the team at Plasmatic. 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 file for more details.