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//! # Cano: Type-Safe Async Workflow Engine
//!
//! Cano is an async workflow orchestration engine for Rust built on Finite State Machines (FSM).
//! States are user-defined enums; the engine guarantees type-safe transitions between them.
//!
//! Well-suited for:
//! - Data pipelines: ETL jobs with parallel processing (Split/Join) and aggregation
//! - AI agents: Multi-step inference chains with shared context
//! - Background systems: Scheduled maintenance, periodic reporting, cron jobs
//!
//! ## Quick Start
//!
//! ```rust
//! use cano::prelude::*;
//!
//! #[derive(Debug, Clone, PartialEq, Eq, Hash)]
//! enum Step { Fetch, Process, Done }
//!
//! // A typed configuration struct shared with tasks via Resources.
//! struct FetchConfig { batch_size: usize }
//! #[resource]
//! impl Resource for FetchConfig {}
//!
//! struct FetchTask;
//! struct ProcessTask;
//!
//! #[task]
//! impl Task<Step> for FetchTask {
//! async fn run(&self, res: &Resources) -> Result<TaskResult<Step>, CanoError> {
//! let config = res.get::<FetchConfig, _>("config")?;
//! let store = res.get::<MemoryStore, _>("store")?;
//! // Produce `batch_size` items and store them for the next task.
//! let data: Vec<u32> = (1..=(config.batch_size as u32)).collect();
//! store.put("data", data)?;
//! Ok(TaskResult::Single(Step::Process))
//! }
//! }
//!
//! #[task]
//! impl Task<Step> for ProcessTask {
//! async fn run(&self, res: &Resources) -> Result<TaskResult<Step>, CanoError> {
//! let store = res.get::<MemoryStore, _>("store")?;
//! let data: Vec<u32> = store.get("data")?;
//! store.put("sum", data.iter().sum::<u32>())?;
//! Ok(TaskResult::Single(Step::Done))
//! }
//! }
//!
//! # #[tokio::main]
//! # async fn main() -> Result<(), CanoError> {
//! let store = MemoryStore::new();
//! let resources = Resources::new()
//! .insert("config", FetchConfig { batch_size: 3 })
//! .insert("store", store.clone());
//! let workflow = Workflow::new(resources)
//! .register(Step::Fetch, FetchTask)
//! .register(Step::Process, ProcessTask)
//! .add_exit_state(Step::Done);
//!
//! let final_state = workflow.orchestrate(Step::Fetch).await?;
//! assert_eq!(final_state, Step::Done);
//!
//! // The sum of 1..=3 is 6.
//! assert_eq!(store.get::<u32>("sum")?, 6);
//! # Ok(())
//! # }
//! ```
//!
//! ## Resource-Free Workflows
//!
//! When tasks do not need shared resources, use [`Workflow::bare`] and implement
//! [`run_bare`](task::Task::run_bare) instead of `run`:
//!
//! ```rust
//! use cano::prelude::*;
//!
//! #[derive(Debug, Clone, PartialEq, Eq, Hash)]
//! enum Step { Compute, Done }
//!
//! struct ComputeTask;
//!
//! #[task]
//! impl Task<Step> for ComputeTask {
//! async fn run_bare(&self) -> Result<TaskResult<Step>, CanoError> {
//! // Pure computation — no resource lookup needed.
//! let result: u32 = (1..=100).sum();
//! assert_eq!(result, 5050);
//! Ok(TaskResult::Single(Step::Done))
//! }
//! }
//!
//! # #[tokio::main]
//! # async fn main() -> Result<(), CanoError> {
//! let workflow = Workflow::bare()
//! .register(Step::Compute, ComputeTask)
//! .add_exit_state(Step::Done);
//!
//! let final_state = workflow.orchestrate(Step::Compute).await?;
//! assert_eq!(final_state, Step::Done);
//! # Ok(())
//! # }
//! ```
//!
//! ## Core Concepts
//!
//! ### Finite State Machines (FSM)
//!
//! Workflows in Cano are state machines. You define your states as an `enum`, register
//! a [`Task`] handler for each state, and the engine manages transitions.
//!
//! ### Tasks
//!
//! [`Task`] is the single interface for processing logic — implement one of `run` /
//! `run_bare`. Several specialized task models build on it:
//! - [`RouterTask`] trait: side-effect-free branching; reads resources, returns the next
//! state without writing anything. Registered with [`Workflow::register_router`] — the
//! engine dispatches it like a normal state but writes **no** `CheckpointRow`.
//! - [`PollTask`] trait: "wait-until" loop; `poll()` returns [`PollOutcome::Ready`] or
//! [`PollOutcome::Pending { delay_ms }`](PollOutcome::Pending) on each call. Defaults to
//! [`TaskConfig::minimal()`](task::TaskConfig::minimal) (no retries). Registered with
//! [`Workflow::register`] — the engine applies `attempt_timeout` if set via `config()`.
//! - [`BatchTask`] trait: fan-out over data items via `load`/`process_item`/`finish`.
//! - [`SteppedTask`] trait: resumable iterative work via `step()` with a serializable cursor.
//!
//! Every [`RouterTask`], [`PollTask`], [`BatchTask`], and [`SteppedTask`] automatically
//! implements [`Task`] via companion impls emitted by the `#[task::router]`,
//! `#[task::poll]`, `#[task::batch]`, and `#[task::stepped]` macros respectively.
//!
//! ### Parallel Execution (Split/Join)
//!
//! Run tasks concurrently with [`Workflow::register_split`] and join results using
//! strategies: `All`, `Any`, `Quorum(n)`, `Percentage(f64)`, `PartialResults(min)`,
//! or `PartialTimeout`.
//!
//! ### Store
//!
//! [`MemoryStore`] provides a thread-safe `Arc<RwLock<HashMap>>` for sharing typed data
//! between states. For custom backends, register a concrete storage resource in [`Resources`].
//!
//! ### Observability
//!
//! Beyond the optional `tracing` integration, implement [`WorkflowObserver`] and attach it
//! with [`Workflow::with_observer`] to receive synchronous lifecycle and failure callbacks
//! (state entry, task start/success/failure, retry, circuit-open, checkpoint, resume). With
//! the `tracing` feature enabled, [`TracingObserver`] is a ready-made observer that re-emits
//! those callbacks as `tracing` events. Resources can also report their own [`HealthStatus`]
//! via [`Resource::health`], aggregated by [`Resources::check_all_health`].
//!
//! ### Crash Recovery
//!
//! Attach a [`CheckpointStore`] with [`Workflow::with_checkpoint_store`] (plus
//! [`Workflow::with_workflow_id`]) and the FSM records one [`CheckpointRow`] per state
//! entered — *before* that state's task runs. After a crash, [`Workflow::resume_from`]
//! reloads the run, re-enters the FSM at the last checkpointed state, and continues
//! forward; the resumed state's task re-runs, so tasks at and after the resume point must
//! be idempotent. The trait is backend-agnostic; with the `recovery` feature,
//! `RedbCheckpointStore` is a ready-made embedded, ACID implementation. Workflows
//! without a checkpoint store pay nothing. See the `workflow_recovery` example.
//!
//! ### Sagas & Compensation
//!
//! For steps that mutate external systems, implement [`CompensatableTask`] (its `run`
//! returns the next state *and* an `Output`; its `compensate` undoes the step given that
//! `Output`) and register it with [`Workflow::register_with_compensation`]. The engine
//! keeps a per-run compensation stack; if a later state fails, it drains the stack in
//! reverse and runs each `compensate`. A clean rollback returns the original error (and
//! clears the checkpoint log if one is attached); a failed `compensate` produces
//! [`CanoError::CompensationFailed`] with the original error plus every compensation
//! error. With a checkpoint store, outputs are persisted, so a resumed run can still
//! compensate work done before the crash. See the `saga_payment` example.
//!
//! ## Processing Lifecycle
//!
//! **Task**: Single `run()` / `run_bare()` method — full control over execution flow.
//!
//! **RouterTask**: Single `route()` method — side-effect-free; reads resources, returns
//! the next state. Dispatched like a normal state but leaves no checkpoint row.
//!
//! **PollTask**: Single `poll()` method — returns [`PollOutcome::Ready(TaskResult)`](PollOutcome::Ready)
//! or [`PollOutcome::Pending { delay_ms }`](PollOutcome::Pending); the engine loops until `Ready`. Defaults
//! to `TaskConfig::minimal()` (no retries). Use `config().with_attempt_timeout(dur)` to cap
//! total wall-clock time; registered with [`Workflow::register`] like any other task.
//!
//! ## Module Overview
//!
//! - [`mod@task`]: The [`Task`] trait — single `run()` / `run_bare()` method
//! - [`task::router`]: The [`RouterTask`] trait — side-effect-free branching via `route()`; registered with [`Workflow::register_router`]
//! - [`task::poll`]: The [`PollTask`] trait — wait-until loop via `poll()`; registered with [`Workflow::register`]
//! - [`task::batch`]: The [`BatchTask`] trait — fan-out over data items via `load`/`process_item`/`finish`; registered with [`Workflow::register`]
//! - [`task::stepped`]: The [`SteppedTask`] trait — resumable iterative work via `step()` with a serializable cursor; registered with [`Workflow::register_stepped`] (persists the cursor when a checkpoint store is attached)
//! - [`workflow`]: [`Workflow`] — FSM orchestration with Split/Join support
//! - `scheduler` (requires `scheduler` feature): `Scheduler` (builder) and `RunningScheduler` (live handle) — cron and interval scheduling
//! - [`mod@resource`]: [`Resource`] trait, [`Resources`] dictionary, and [`HealthStatus`] — lifecycle-aware resource management and health probes
//! - [`observer`]: [`WorkflowObserver`] — synchronous lifecycle/failure event hooks (and [`TracingObserver`], behind the `tracing` feature)
//! - [`recovery`]: [`CheckpointStore`] / [`CheckpointRow`] — append-only checkpoint log for crash recovery (and `RedbCheckpointStore`, behind the `recovery` feature)
//! - [`saga`]: [`CompensatableTask`] — pair a forward step with a compensating action; failures roll back via [`Workflow::register_with_compensation`]
//! - [`store`]: [`MemoryStore`] — a typed in-memory store that implements [`Resource`]
//! - [`error`]: [`CanoError`] variants and the [`CanoResult`] alias
//!
//! ## Getting Started
//!
//! 1. Run an example: `cargo run --example workflow_simple`
//! 2. Read the module docs — each module has detailed documentation and examples
//! 3. Run benchmarks: `cargo bench --bench workflow_performance`
// Core public API - simplified imports
pub use ;
pub use ;
pub use WorkflowObserver;
pub use ;
pub use ;
pub use ;
pub use ;
pub use ;
pub use ;
pub use ;
pub use RedbCheckpointStore;
pub use MemoryStore;
pub use ;
pub use ;
pub use TracingObserver;
pub use ;
/// Attribute macro applied to `Task` trait definitions and `impl Task` blocks
/// to rewrite `async fn` methods into ones returning
/// `Pin<Box<dyn Future<Output = ...> + Send + 'async_trait>>`.
///
/// The separate name makes `#[cano::task]` self-documenting at impl sites.
/// The implementation lives in the [`cano-macros`] sibling crate.
///
/// Processing-model macros are namespaced under `cano::task::` and `cano::saga::`:
/// - `#[cano::task::router]` — for `impl RouterTask` blocks
/// - `#[cano::task::poll]` — for `impl PollTask` blocks
/// - `#[cano::task::batch]` — for `impl BatchTask` blocks
/// - `#[cano::task::stepped]` — for `impl SteppedTask` blocks
/// - `#[cano::saga::task]` — for `impl CompensatableTask` blocks
///
/// [`cano-macros`]: https://docs.rs/cano-macros
pub use task;
/// Attribute macro applied to the `Resource` trait definition and `impl Resource` blocks.
///
/// See [`macro@task`] for the rewrite shape; this macro is functionally identical and
/// differs only in name.
pub use resource;
/// Attribute macro applied to the `CheckpointStore` trait definition and
/// `impl CheckpointStore` blocks.
///
/// See [`macro@task`] for the rewrite shape; this macro is functionally identical and
/// differs only in name.
pub use checkpoint_store;
/// Derive macro that generates a `from_resources` associated function for a struct.
///
/// See [`FromResources`] for the full specification. Each
/// field must be `Arc<T>`; annotate it with `#[res("key")]` or
/// `#[res(EnumKey::Variant)]`.
pub use FromResources;
/// Derive macro that generates an empty `Resource` impl for a struct.
///
/// Equivalent to writing `#[resource] impl Resource for MyStruct {}` by hand,
/// but derived directly from the struct definition. The trait's no-op `setup`
/// and `teardown` defaults take effect automatically.
///
/// In the prelude, this is exported as [`Resource`] alongside the trait of the
/// same name — Rust's separate macro and type namespaces let them coexist.
pub use Resource;
// Convenience re-exports for common patterns