update docs describing architectural pattern (orchestrated state machines)
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@ -19,3 +19,4 @@ logs/databuild/
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# DSL generated code
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**/generated/
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!/databuild/databuild.rs
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15
AGENTS.md
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AGENTS.md
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@ -16,6 +16,21 @@ DataBuild is a bazel-based data build system. Key files:
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Please reference these for any related work, as they indicate key technical bias/direction of the project.
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## Architecture Pattern
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DataBuild implements **Orchestrated State Machines** - a pattern where the application core is composed of:
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- **Type-safe state machines** for domain entities (Want, JobRun, Partition)
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- **Dependency graphs** expressing relationships between entities
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- **Orchestration logic** that coordinates state transitions based on dependencies
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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.
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**Key implications for development:**
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- Model entities as explicit state machines with type-parameterized states
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- Use consuming methods for state transitions (enforces immutability)
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- Emit events to BEL for all state changes (observability)
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- Centralize coordination logic in the Orchestrator (separation of concerns)
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## Tenets
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- Declarative over imperative wherever possible/reasonable.
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173
docs/design/orchestrated-state-machines.md
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173
docs/design/orchestrated-state-machines.md
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@ -0,0 +1,173 @@
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# Orchestrated State Machines: A Theory of Application Architecture
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## Overview
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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.
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## The Pattern
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At its essence, the pattern is:
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```
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Application = State Machines + Dependency Graph + Orchestration Logic
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```
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Where:
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- **State Machines**: Individual entities (Want, JobRun, Partition) with well-defined, type-safe states
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- **Dependency Graph**: Relationships between entities (wants depend on partitions, partitions depend on job runs)
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- **Orchestration Logic**: Coordination rules that trigger state transitions when dependencies are satisfied
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## Key Components
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### 1. State Machines
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Each domain entity is modeled as an explicit state machine with:
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- **Well-defined states** (NotStarted, Running, Completed, Failed, etc.)
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- **Type-safe transitions** enforced at compile time
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- **Immutable state progression** via consuming methods
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Example from DataBuild:
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```rust
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pub enum JobRun<B: JobRunBackend> {
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NotStarted(JobRunWithState<B, B::NotStartedState>),
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Running(JobRunWithState<B, B::RunningState>),
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Completed(JobRunWithState<B, B::CompletedState>),
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Failed(JobRunWithState<B, B::FailedState>),
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// ...
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}
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// Can ONLY call run() on NotStarted jobs - compiler enforces this!
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impl<B: JobRunBackend> JobRunWithState<B, B::NotStartedState> {
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pub fn run(self, env) -> Result<JobRunWithState<B, B::RunningState>, Error>
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}
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```
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### 2. Dependency Graph
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Entities are connected through explicit dependencies:
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- Wants → Partitions (wants request specific partitions)
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- Partitions → JobRuns (jobs build partitions)
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- JobRuns → Partitions (jobs declare what they built)
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### 3. Orchestration Logic
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A central orchestrator:
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- Observes all entity states
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- Evaluates dependency conditions
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- Triggers state transitions when conditions are met
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- Maintains global consistency invariants
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## Core Principles
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1. **Model domain entities as explicit state machines** - Don't hide state in boolean flags
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2. **Express dependencies as a graph** - Make relationships first-class
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3. **Centralize coordination logic** - Separate entity behavior from system coordination
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4. **Make state transitions event-sourced** - Append-only log enables time-travel and auditability
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5. **Use types to enforce valid transitions** - Catch errors at compile time, not runtime
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## Advantages
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### Type Safety
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Compile-time guarantees prevent invalid state transitions:
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```rust
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// This will not compile:
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let job = JobRun::Running(running_job);
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job.run(); // ERROR: no method `run` found for `JobRun<Running>`
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```
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### Observability
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Event-sourced state transitions provide complete audit trail:
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- What's running? Query running jobs
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- What failed? Filter by failed state
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- When did it transition? Check BEL timestamps
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### Testability
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- State machines can be tested in isolation
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- Orchestration logic can be tested with mock state machines
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- Dependency resolution can be tested independently
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### Incremental Progress
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System can be stopped and restarted:
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- State is persisted in BEL
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- Resume from last known state
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- No need to restart from beginning
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### Correctness
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- Type system prevents impossible states
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- Event log provides ground truth
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- Dependency graph ensures proper ordering
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## Real-World Applications
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This pattern is the **fundamental architecture** of:
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**Build Systems**
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- Bazel, Buck, Pants - artifacts depend on other artifacts
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- Your "builds" are literally builds
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**Workflow Engines**
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- Temporal, Prefect, Airflow - DAG of tasks with state
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- Each task is a state machine, orchestrator schedules based on dependencies
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**Data Orchestration**
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- Dagster, Kedro - data assets with lineage
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- Partitions are data assets, jobs are transformations
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**Game Engines**
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- Entity Component Systems - entities have state
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- Game loop orchestrates entity state transitions
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**Business Process Management**
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- BPMN engines - business processes as state machines
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- Workflow engine coordinates process instances
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## When to Use This Pattern
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This architecture is particularly powerful for systems where:
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- **Eventual consistency** is acceptable (not strict ACID transactions)
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- **Incremental progress** is important (can checkpoint and resume)
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- **Observability** is critical (need to know what's happening)
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- **Correctness** matters (type-safe transitions prevent bugs)
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- **Concurrency** is inherent (multiple things happening simultaneously)
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- **Dependencies** are complex (can't just process sequentially)
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## Implementation Lessons
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### Use Drain for State Transitions
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Clean pattern for moving entities through states:
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```rust
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fn schedule_queued_jobs(&mut self) -> Result<()> {
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let mut new_jobs = Vec::new();
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for job in self.job_runs.drain(..) {
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let transitioned = match job {
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JobRun::NotStarted(ns) => JobRun::Running(ns.run(None)?),
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other => other, // Pass through unchanged
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};
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new_jobs.push(transitioned);
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}
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self.job_runs = new_jobs;
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Ok(())
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}
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```
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### Parameterize State for Type Safety
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```rust
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pub struct JobRunWithState<Backend, State> {
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job_run_id: Uuid,
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state: State, // Type parameter enforces valid operations
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}
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```
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### Event Sourcing for Auditability
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All state changes emit events to append-only log:
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```rust
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self.bel.append_event(&JobRunSuccessEvent {
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job_run_id,
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timestamp
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})?;
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```
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### Separate Entity Logic from Coordination
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- Entity state machines: "What transitions are valid for me?"
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- Orchestrator: "Given all entity states, what should happen next?"
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