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docs/COMPARISONS.md
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@@ -66,36 +66,18 @@ graph.update_dependency(y, sum, 1)?;
## Quick Comparison
| Project | License | Runtime | Type Safety | API Complexity | Performance vs dagx | Key Features |
| ------------------------------------------------------------- | -------------- | ------------------- | --------------------- | -------------- | ---------------------------- | ------------------------------------------------------------------------------------ |
| **dagx** | MIT | Any async runtime | Compile-time | Simple | Baseline (see benchmarks) | Primitives-as-scheduler, inline fast-path, automatic Arc wrapping, up to 8 deps/task |
| [**dagrs**](https://github.com/dagrs-dev/dagrs) (v0.5) | MIT/Apache-2.0 | Creates own runtime | Runtime (async_trait) | Complex | 1.04-129x slower (see below) | Flow-based Programming, cyclic graphs, loops, conditional nodes, YAML config |
| [**async_dag**](https://github.com/chubei-oppen/async_dag) | MIT | Any async runtime | Compile-time | Medium | No benchmarks | Slot-based dependencies, Graph/TryGraph modes, maximum parallelism |
| [**dag-flow**](https://github.com/makisevon/dag-flow) | MIT/Apache-2.0 | Any async runtime | Runtime (HashMap) | Complex | No benchmarks | Experimental, all tasks run simultaneously, weak dependencies |
| [**RenovZ/dag-runner**](https://github.com/RenovZ/dag-runner) | MIT | Tokio only | Unclear | Simple | No benchmarks | Edge-based API, cycle detection, stops on first error |
| [**tasksitter**](https://github.com/lionkor/tasksitter) | Unspecified | Unclear | Unclear | Medium | No benchmarks | Cyclic graphs, dynamic runtime modification, pause/resume |
| Project | License | Runtime | Type Safety | API Complexity | Performance vs dagx | Key Features |
| ------------------------------------------------------------- | -------------- | ------------------- | --------------------- | -------------- | ------------------------- | ------------------------------------------------------------------------------------ |
| **dagx** | MIT | Any async runtime | Compile-time | Simple | Baseline (see benchmarks) | Primitives-as-scheduler, inline fast-path, automatic Arc wrapping, up to 8 deps/task |
| [**dagrs**](https://github.com/dagrs-dev/dagrs) (v0.5) | MIT/Apache-2.0 | Creates own runtime | Runtime (async_trait) | Complex | 1-100x slower (see below) | Flow-based Programming, cyclic graphs, loops, conditional nodes, YAML config |
| [**async_dag**](https://github.com/chubei-oppen/async_dag) | MIT | Any async runtime | Compile-time | Medium | No benchmarks | Slot-based dependencies, Graph/TryGraph modes, maximum parallelism |
| [**dag-flow**](https://github.com/makisevon/dag-flow) | MIT/Apache-2.0 | Any async runtime | Runtime (HashMap) | Complex | No benchmarks | Experimental, all tasks run simultaneously, weak dependencies |
| [**RenovZ/dag-runner**](https://github.com/RenovZ/dag-runner) | MIT | Tokio only | Unclear | Simple | No benchmarks | Edge-based API, cycle detection, stops on first error |
| [**tasksitter**](https://github.com/lionkor/tasksitter) | Unspecified | Unclear | Unclear | Medium | No benchmarks | Cyclic graphs, dynamic runtime modification, pause/resume |
_GitHub stars (as of 2025): dagrs (449), async_dag (25), dag-flow (2), others (0-2)_
**Performance**: As noted in the README, dagx is **1-100x faster** than dagrs across all benchmark patterns. The inline fast-path optimization eliminates spawning overhead for sequential workloads while maintaining excellent parallel performance. Most real-world DAGs mix sequential and parallel patterns. dagx automatically optimizes for both, performing up to 2 orders of magnitude faster than dagrs regardless of workload shape.
## Performance Benchmarks vs dagrs
Direct comparison benchmarks (lower is better):
| Workload | Tasks | dagx | dagrs | Speedup |
| ----------------- | ------ | ------- | ------- | ------------------ |
| Sequential chain | 5 | 3.0 µs | 385 µs | **129x faster** 🚀 |
| Sequential chain | 100 | 79 µs | 703 µs | **8.9x faster** |
| Diamond pattern | 4 | 11 µs | 387 µs | **34x faster** |
| Fan-out (1→100) | 101 | 155 µs | 595 µs | **3.85x faster** |
| Independent tasks | 10,000 | 12.7 ms | 13.3 ms | **1.04x faster** |
**Summary**: dagx is **1.04-129x faster** than dagrs across all benchmark patterns. The inline fast-path optimization eliminates spawning overhead for sequential workloads while maintaining excellent parallel performance.
**How does dagx achieve this?** The inline fast-path detects single-task layers (common in sequential chains) and executes them directly without spawning overhead. For parallel workloads, tasks still spawn to maximize concurrency. This adaptive execution strategy combines the best of both worlds: sub-microsecond overhead for sequential work, true parallelism for concurrent work.
**Key insight**: Most real-world DAGs mix sequential and parallel patterns. dagx automatically optimizes for both, delivering 1.04-129x better performance than dagrs regardless of workload shape.
_Benchmarks run on AMD Ryzen 7 7840U (Zen 4) @ 3.3GHz. Run `cargo bench` to test on your hardware._
_Benchmarks run on Intel i9-13950HX @ 5.5GHz. Run `cargo bench` to test on your hardware._
## Detailed Comparison
@@ -107,7 +89,6 @@ _Benchmarks run on AMD Ryzen 7 7840U (Zen 4) @ 3.3GHz. Run `cargo bench` to test
- Most mature (449 GitHub stars, active community)
- Rich feature set: Flow-based Programming, cyclic graphs, loops, conditional nodes
- YAML configuration support for declarative workflows
- Designed for complex orchestration patterns
**Trade-offs**:
@@ -115,29 +96,7 @@ _Benchmarks run on AMD Ryzen 7 7840U (Zen 4) @ 3.3GHz. Run `cargo bench` to test
- Creates own Tokio runtime internally (not runtime-agnostic, cannot be nested)
- More complex API: `Action` trait, `InChannels`/`OutChannels`, `NodeTable`, `Content` wrappers, manual node ID tracking
- Uses `async_trait` for type erasure (runtime overhead)
- **1.04-129x slower than dagx** across all benchmark patterns (see comparison benchmarks)
**API Style** (v0.5):
```rust
// Define action with channels for communication
#[async_trait]
impl Action for MyTask {
async fn run(&self, input: &mut InChannels,
output: &mut OutChannels, env: Arc<EnvVar>) -> Output {
let inputs: Vec<T> = input.map(|c| ...).await; // Manually extract
output.broadcast(Content::new(result)).await; // Manually wrap
Output::Out(Some(Content::new(result)))
}
}
let mut table = NodeTable::new();
let node = DefaultNode::with_action("name".into(), MyTask, &mut table);
let node_id = node.id(); // Must capture ID before moving
graph.add_node(node);
graph.add_edge(source_id, vec![target_id]);
graph.set_env(EnvVar::new(table)); graph.start().unwrap();
```
- **Slower than dagx** across all benchmark patterns (see comparison benchmarks)
### async_dag (Clean Type Safety)
@@ -154,8 +113,7 @@ graph.set_env(EnvVar::new(table)); graph.start().unwrap();
- Medium API complexity: slot-based dependency management
- Must manually specify slot indices (0, 1, etc.) when connecting tasks
- Less mature (25 stars)
- No performance benchmarks
- Less mature
**API Style**:
@@ -188,7 +146,7 @@ graph.run().await;
- Runtime type safety via `HashMap<String, Input>`
- Complex API: implement `Task` trait with `id()`, `dependencies()`, `run()`
- Named dependencies (string-based lookup)
- Very early stage (2 stars)
- Very early stage
**API Style**:
@@ -219,7 +177,7 @@ impl Task<String, Bytes> for MyTask {
- Requires Tokio runtime
- Manual channel setup for task communication
- Type safety model unclear
- Very early stage (0 stars)
- Very early stage
**API Style**:
@@ -245,29 +203,26 @@ dag.run().await?;
- Limited documentation
- Runtime and type safety model unclear
- Very early stage (0 stars)
- Very early stage
## When to Choose dagx
Choose dagx when you value:
- **Performance**: 1.04-129x faster than dagrs across all workload patterns (see benchmarks)
- **Runtime flexibility**: Works with Tokio, async-std, smol, or any async runtime
- **Compile-time safety**: Full type safety with no runtime type errors in the public API
- **Minimal overhead**: ~790 ns per task (sequential), ~1.3 µs per task (parallel)
- **Performance**: Faster than dagrs across all workload patterns (see benchmarks)
- **Runtime flexibility**: Works with Tokio, smol, or any async runtime
- **Compile-time safety**: Full type safety with no runtime type errors possible
- **Simple, ergonomic API**: `#[task]` macro, `add_task()`, `depends_on()` - that's it
- **Automatic optimizations**: Arc wrapping, inline execution, adaptive spawning - all transparent
- **Predictable performance**: Linear scaling, no hidden complexity, consistent sub-µs overhead
dagx is **not** the right choice if you need:
- Cyclic graphs or dynamic flow control (loops, conditions) → Consider **dagrs** or **tasksitter**
- More than 8 dependencies per task → Consider **dagrs** or **async_dag**
- YAML-based configuration → Consider **dagrs**
## When to Consider Alternatives
- **Choose dagrs** if you need advanced flow control (loops, conditionals, cyclic graphs), YAML configuration, or are already committed to Tokio and want a mature, feature-rich solution
- **Choose dagrs** if you need advanced flow control (loops, conditionals, cyclic graphs), or are already committed to Tokio and want a mature, feature-rich solution
- **Choose async_dag** if you want compile-time type safety with runtime flexibility and the slot-based API appeals to you
- **Choose dag-flow** if you're building experimental projects and the all-tasks-run-simultaneously model fits your use case
- **Choose RenovZ/dag-runner** if you need the simplest possible edge-based API and are already using Tokio