update docs describing architectural pattern (orchestrated state machines)

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Stuart Axelbrooke 2025-11-21 17:08:51 +08:00
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# DSL generated code
**/generated/
!/databuild/databuild.rs

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Please reference these for any related work, as they indicate key technical bias/direction of the project.
## Architecture Pattern
DataBuild implements **Orchestrated State Machines** - a pattern where the application core is composed of:
- **Type-safe state machines** for domain entities (Want, JobRun, Partition)
- **Dependency graphs** expressing relationships between entities
- **Orchestration logic** that coordinates state transitions based on dependencies
This architecture provides compile-time correctness, observability through event sourcing, and clean separation between entity behavior and coordination logic. See [`docs/orchestrated-state-machines.md`](docs/orchestrated-state-machines.md) for the full theory and implementation patterns.
**Key implications for development:**
- Model entities as explicit state machines with type-parameterized states
- Use consuming methods for state transitions (enforces immutability)
- Emit events to BEL for all state changes (observability)
- Centralize coordination logic in the Orchestrator (separation of concerns)
## Tenets
- Declarative over imperative wherever possible/reasonable.

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# Orchestrated State Machines: A Theory of Application Architecture
## Overview
DataBuild's core architecture exemplifies a pattern we call **Dependency-Aware State Machine Orchestration** or **Stateful Dataflow Architecture**. This document crystallizes the theory behind this approach and its applications.
## The Pattern
At its essence, the pattern is:
```
Application = State Machines + Dependency Graph + Orchestration Logic
```
Where:
- **State Machines**: Individual entities (Want, JobRun, Partition) with well-defined, type-safe states
- **Dependency Graph**: Relationships between entities (wants depend on partitions, partitions depend on job runs)
- **Orchestration Logic**: Coordination rules that trigger state transitions when dependencies are satisfied
## Key Components
### 1. State Machines
Each domain entity is modeled as an explicit state machine with:
- **Well-defined states** (NotStarted, Running, Completed, Failed, etc.)
- **Type-safe transitions** enforced at compile time
- **Immutable state progression** via consuming methods
Example from DataBuild:
```rust
pub enum JobRun<B: JobRunBackend> {
NotStarted(JobRunWithState<B, B::NotStartedState>),
Running(JobRunWithState<B, B::RunningState>),
Completed(JobRunWithState<B, B::CompletedState>),
Failed(JobRunWithState<B, B::FailedState>),
// ...
}
// Can ONLY call run() on NotStarted jobs - compiler enforces this!
impl<B: JobRunBackend> JobRunWithState<B, B::NotStartedState> {
pub fn run(self, env) -> Result<JobRunWithState<B, B::RunningState>, Error>
}
```
### 2. Dependency Graph
Entities are connected through explicit dependencies:
- Wants → Partitions (wants request specific partitions)
- Partitions → JobRuns (jobs build partitions)
- JobRuns → Partitions (jobs declare what they built)
### 3. Orchestration Logic
A central orchestrator:
- Observes all entity states
- Evaluates dependency conditions
- Triggers state transitions when conditions are met
- Maintains global consistency invariants
## Core Principles
1. **Model domain entities as explicit state machines** - Don't hide state in boolean flags
2. **Express dependencies as a graph** - Make relationships first-class
3. **Centralize coordination logic** - Separate entity behavior from system coordination
4. **Make state transitions event-sourced** - Append-only log enables time-travel and auditability
5. **Use types to enforce valid transitions** - Catch errors at compile time, not runtime
## Advantages
### Type Safety
Compile-time guarantees prevent invalid state transitions:
```rust
// This will not compile:
let job = JobRun::Running(running_job);
job.run(); // ERROR: no method `run` found for `JobRun<Running>`
```
### Observability
Event-sourced state transitions provide complete audit trail:
- What's running? Query running jobs
- What failed? Filter by failed state
- When did it transition? Check BEL timestamps
### Testability
- State machines can be tested in isolation
- Orchestration logic can be tested with mock state machines
- Dependency resolution can be tested independently
### Incremental Progress
System can be stopped and restarted:
- State is persisted in BEL
- Resume from last known state
- No need to restart from beginning
### Correctness
- Type system prevents impossible states
- Event log provides ground truth
- Dependency graph ensures proper ordering
## Real-World Applications
This pattern is the **fundamental architecture** of:
**Build Systems**
- Bazel, Buck, Pants - artifacts depend on other artifacts
- Your "builds" are literally builds
**Workflow Engines**
- Temporal, Prefect, Airflow - DAG of tasks with state
- Each task is a state machine, orchestrator schedules based on dependencies
**Data Orchestration**
- Dagster, Kedro - data assets with lineage
- Partitions are data assets, jobs are transformations
**Game Engines**
- Entity Component Systems - entities have state
- Game loop orchestrates entity state transitions
**Business Process Management**
- BPMN engines - business processes as state machines
- Workflow engine coordinates process instances
## When to Use This Pattern
This architecture is particularly powerful for systems where:
- **Eventual consistency** is acceptable (not strict ACID transactions)
- **Incremental progress** is important (can checkpoint and resume)
- **Observability** is critical (need to know what's happening)
- **Correctness** matters (type-safe transitions prevent bugs)
- **Concurrency** is inherent (multiple things happening simultaneously)
- **Dependencies** are complex (can't just process sequentially)
## Implementation Lessons
### Use Drain for State Transitions
Clean pattern for moving entities through states:
```rust
fn schedule_queued_jobs(&mut self) -> Result<()> {
let mut new_jobs = Vec::new();
for job in self.job_runs.drain(..) {
let transitioned = match job {
JobRun::NotStarted(ns) => JobRun::Running(ns.run(None)?),
other => other, // Pass through unchanged
};
new_jobs.push(transitioned);
}
self.job_runs = new_jobs;
Ok(())
}
```
### Parameterize State for Type Safety
```rust
pub struct JobRunWithState<Backend, State> {
job_run_id: Uuid,
state: State, // Type parameter enforces valid operations
}
```
### Event Sourcing for Auditability
All state changes emit events to append-only log:
```rust
self.bel.append_event(&JobRunSuccessEvent {
job_run_id,
timestamp
})?;
```
### Separate Entity Logic from Coordination
- Entity state machines: "What transitions are valid for me?"
- Orchestrator: "Given all entity states, what should happen next?"