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docs: improve documentation and READMEs to explain map/array steps #228

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220 changes: 148 additions & 72 deletions pkgs/core/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,106 @@ The SQL Core handles the workflow lifecycle through these key operations:

<a href="./assets/flow-lifecycle.svg"><img src="./assets/flow-lifecycle.svg" alt="Flow Lifecycle" width="25%" height="25%"></a>

## Step Types

pgflow supports two fundamental step types that control how tasks are created and executed:

### Single Steps (Default)

Single steps are the standard step type where each step creates exactly one task when started. These steps process their input as a whole and return a single output value.

```sql
-- Regular single step definition
SELECT pgflow.add_step('my_flow', 'process_data');
```

### Map Steps

Map steps enable parallel processing of arrays by automatically creating multiple tasks - one for each array element. The system handles task distribution, parallel execution, and output aggregation transparently.

```sql
-- Map step definition (step_type => 'map')
SELECT pgflow.add_step(
flow_slug => 'my_flow',
step_slug => 'process_items',
deps_slugs => ARRAY['fetch_items'],
step_type => 'map'
);
```

#### Key Characteristics

- **Multiple Task Creation**: The SQL core creates N tasks for a map step (one per array element), unlike single steps which create one task
- **Element Distribution**: The SQL core distributes individual array elements to tasks based on `task_index`
- **Output Aggregation**: The SQL core aggregates task outputs back into an array for dependent steps
- **Constraint**: Map steps can have at most one dependency (which must return an array), or zero dependencies (then flow input must be an array)

#### Map Step Execution Flow

1. **Array Input Validation**: The SQL core validates that the input is an array
2. **Task Creation**: The SQL core creates N tasks with indices 0 to N-1
3. **Element Distribution**: The SQL core assigns `array[task_index]` as input to each task
4. **Parallel Execution**: Edge workers execute tasks independently in parallel
5. **Output Collection**: The SQL core aggregates outputs preserving array order
6. **Dependent Activation**: The SQL core passes the aggregated array to dependent steps

#### Root Map vs Dependent Map

**Root Map Steps** process the flow's input array directly:
```sql
-- Root map: no dependencies, processes flow input
SELECT pgflow.add_step(
flow_slug => 'batch_processor',
step_slug => 'process_each',
step_type => 'map'
);

-- Starting the flow with array input
SELECT pgflow.start_flow(
flow_slug => 'batch_processor',
input => '[1, 2, 3, 4, 5]'::jsonb
);
```

**Dependent Map Steps** process another step's array output:
```sql
-- Dependent map: processes the array from 'fetch_items'
SELECT pgflow.add_step(
flow_slug => 'data_pipeline',
step_slug => 'transform_each',
deps_slugs => ARRAY['fetch_items'],
step_type => 'map'
);
```

#### Edge Cases and Special Behaviors

1. **Empty Array Cascade**: When a map step receives an empty array (`[]`):
- The SQL core completes it immediately without creating tasks
- The completed map step outputs an empty array
- Any dependent map steps also receive empty arrays and complete immediately
- This cascades through the entire chain of map steps in a single transaction
- Example: `[] → map1 → [] → map2 → [] → map3 → []` all complete together

2. **NULL Values**: NULL array elements are preserved and distributed to their respective tasks

3. **Non-Array Input**: The SQL core fails the step when input is not an array

4. **Type Violations**: When a single step outputs non-array data to a map step, the SQL core fails the entire run (stores the invalid output for debugging, archives all queued messages, prevents orphaned tasks)

#### Implementation Details

Map steps utilize several database fields for state management:
- `initial_tasks`: Number of tasks to create (NULL until array size is known)
- `remaining_tasks`: Tracks incomplete tasks for the step
- `task_index`: Identifies which array element each task processes
- `step_type`: Column value 'map' triggers map behavior

The aggregation process ensures:
- **Order Preservation**: Task outputs maintain array element ordering
- **NULL Handling**: NULL outputs are included in the aggregated array
- **Atomicity**: Aggregation occurs within the same transaction as task completion

## Example flow and its life

Let's walk through creating and running a workflow that fetches a website,
Expand Down Expand Up @@ -274,81 +374,17 @@ delay = base_delay * (2 ^ attempts_count)

Timeouts are enforced by setting the message visibility timeout to the step's timeout value plus a small buffer. If a worker doesn't acknowledge completion or failure within this period, the task becomes visible again and can be retried.

## TypeScript Flow DSL
## Workflow Definition with TypeScript DSL

> [!NOTE]
> TypeScript Flow DSL is a Work In Progress and is not ready yet!

### Overview

While the SQL Core engine handles workflow definitions and state management, the primary way to define and work with your workflow logic is via the Flow DSL in TypeScript. This DSL offers a fluent API that makes it straightforward to outline the steps in your flow with full type safety.

### Type Inference System

The most powerful feature of the Flow DSL is its **automatic type inference system**:

1. You only need to annotate the initial Flow input type
2. The return type of each step is automatically inferred from your handler function
3. These return types become available in the payload of dependent steps
4. The TypeScript compiler builds a complete type graph matching your workflow DAG

This means you get full IDE autocompletion and type checking throughout your workflow without manual type annotations.

### Basic Example

Here's an example that matches our website analysis workflow:

```ts
// Provide a type for the input of the Flow
type Input = {
url: string;
};

const AnalyzeWebsite = new Flow<Input>({
slug: 'analyze_website',
maxAttempts: 3,
baseDelay: 5,
timeout: 10,
})
.step(
{ slug: 'website' },
async (input) => await scrapeWebsite(input.run.url)
)
.step(
{ slug: 'sentiment', dependsOn: ['website'], timeout: 30, maxAttempts: 5 },
async (input) => await analyzeSentiment(input.website.content)
)
.step(
{ slug: 'summary', dependsOn: ['website'] },
async (input) => await summarizeWithAI(input.website.content)
)
.step(
{ slug: 'saveToDb', dependsOn: ['sentiment', 'summary'] },
async (input) =>
await saveToDb({
websiteUrl: input.run.url,
sentiment: input.sentiment.score,
summary: input.summary,
}).status
);
```

### How Payload Types Are Built

The payload object for each step is constructed dynamically based on:
The SQL Core is the DAG orchestration engine that handles dependency resolution, step state management, and task spawning. However, workflows are defined using the TypeScript Flow DSL, which compiles user intent into the SQL primitives that populate the definition tables (`flows`, `steps`, `deps`).

1. **The `run` property**: Always contains the original workflow input
2. **Dependency outputs**: Each dependency's output is available under a key matching the dependency's ID
3. **DAG structure**: Only outputs from direct dependencies are included in the payload
See the [@pgflow/dsl package](../dsl/README.md) for complete documentation on:
- Expressing workflows with type-safe method chaining
- Step types (`.step()`, `.array()`, `.map()`)
- Compilation to SQL migrations
- Type inference and handler context

This means your step handlers receive exactly the data they need, properly typed, without any manual type declarations beyond the initial Flow input type.

### Benefits of Automatic Type Inference

- **Refactoring safety**: Change a step's output, and TypeScript will flag all dependent steps that need updates
- **Discoverability**: IDE autocompletion shows exactly what data is available in each step
- **Error prevention**: Catch typos and type mismatches at compile time, not runtime
- **Documentation**: The types themselves serve as living documentation of your workflow's data flow
The SQL Core executes these compiled definitions, managing when steps are ready, how many tasks to create (1 for single steps, N for map steps), and how to aggregate results.

## Data Flow

Expand Down Expand Up @@ -385,6 +421,46 @@ The `saveToDb` step depends on both `sentiment` and `summary`:
}
```

### Map Step Handler Inputs

Map step tasks receive a fundamentally different input structure than single step tasks. Instead of receiving an object with `run` and dependency keys, **map tasks receive only their assigned array element**:

#### Example: Processing user IDs

```json
// Flow input (for root map) or dependency output:
["user123", "user456", "user789"]

// What each map task receives:
// Task 0: "user123"
// Task 1: "user456"
// Task 2: "user789"

// NOT this:
// { "run": {...}, "dependency": [...] }
```

This means:
- Map handlers process individual elements in isolation
- Map handlers cannot access the original flow input (`run`)
- Map handlers cannot access other dependencies
- Map handlers focus solely on transforming their assigned element

#### Map Step Outputs Become Arrays

When a step depends on a map step, it receives the aggregated array output:

```json
// If 'process_users' is a map step that processed ["user1", "user2"]
// and output [{"name": "Alice"}, {"name": "Bob"}]

// A step depending on 'process_users' receives:
{
"run": { /* original flow input */ },
"process_users": [{"name": "Alice"}, {"name": "Bob"}] // Full array
}
```

### Run Completion

When all steps in a run are completed, the run status is automatically updated to 'completed' and its output is set. The output is an aggregation of all the outputs from final steps (steps that have no dependents):
Expand Down
80 changes: 73 additions & 7 deletions pkgs/dsl/README.md
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Expand Up @@ -93,35 +93,71 @@ The standard method for adding steps to a flow. Each step processes input and re

#### `.array()` - Array-Returning Steps

A semantic wrapper around `.step()` that provides type enforcement for steps that return arrays. Useful for data fetching or collection steps.
A semantic wrapper around `.step()` that provides type enforcement for steps that return arrays. Useful for data fetching or collection steps that will be processed by map steps.

```typescript
// Fetch an array of items to be processed
.array(
{ slug: 'fetch_items' },
async () => [1, 2, 3, 4, 5]
)

// With dependencies - combining data from multiple sources
.array(
{ slug: 'combine_results', dependsOn: ['source1', 'source2'] },
async (input) => [...input.source1, ...input.source2]
)
```

**Key Points:**
- Return type is enforced to be an array at compile time
- Commonly used as input for subsequent map steps
- Can depend on other steps just like regular steps

#### `.map()` - Array Processing Steps

Processes arrays element-by-element, similar to JavaScript's `Array.map()`. The handler receives individual items instead of the full input object.

**Two Modes of Operation:**

1. **Root Map** (no `array:` property): Processes the flow's input array directly
- The flow input MUST be an array when using root maps
- Omitting the `array:` property tells pgflow to use the flow input

2. **Dependent Map** (with `array:` property): Processes another step's array output
- The `array:` property specifies which step's output to process
- That step must return an array

```typescript
// Root map - processes flow input array
// ROOT MAP - No array: property means use flow input
// Flow input MUST be an array (e.g., ["hello", "world"])
new Flow<string[]>({ slug: 'process_strings' })
.map({ slug: 'uppercase' }, (item) => item.toUpperCase());
.map(
{ slug: 'uppercase' }, // No array: property!
(item) => item.toUpperCase()
);
// Each string in the input array gets uppercased in parallel

// Dependent map - processes another step's output
// DEPENDENT MAP - array: property specifies the source step
new Flow<{}>({ slug: 'data_pipeline' })
.array({ slug: 'numbers' }, () => [1, 2, 3])
.map({ slug: 'double', array: 'numbers' }, (n) => n * 2)
.map({ slug: 'square', array: 'double' }, (n) => n * n);
.map(
{ slug: 'double', array: 'numbers' }, // Processes 'numbers' output
(n) => n * 2
)
.map(
{ slug: 'square', array: 'double' }, // Chains from 'double'
(n) => n * n
);
// Results: numbers: [1,2,3] → double: [2,4,6] → square: [4,16,36]
```

**Key differences from regular steps:**
- Uses `array:` instead of `dependsOn:` for specifying the single array dependency
- Uses `array:` to specify dependency (not `dependsOn:`)
- When `array:` is omitted, uses flow input array (root map)
- Handler signature is `(item, context) => result` instead of `(input, context) => result`
- Return type is always an array
- Map steps can have at most one dependency (the array source)
- Generates SQL with `step_type => 'map'` parameter for pgflow's map processing

**Type Safety:**
Expand All @@ -141,9 +177,39 @@ new Flow<{}>({ slug: 'user_flow' })
});
```

**Common Patterns:**

```typescript
// Batch processing - process multiple items in parallel
new Flow<number[]>({ slug: 'batch_processor' })
.map({ slug: 'validate' }, (item) => {
if (item < 0) throw new Error('Invalid item');
return item;
})
.map({ slug: 'process', array: 'validate' }, async (item) => {
// Each item processed in its own task
return await expensiveOperation(item);
});

// Data transformation pipeline
new Flow<{}>({ slug: 'etl_pipeline' })
.step({ slug: 'fetch_urls' }, () => ['url1', 'url2', 'url3'])
.map({ slug: 'scrape', array: 'fetch_urls' }, async (url) => {
return await fetchContent(url);
})
.map({ slug: 'extract', array: 'scrape' }, (html) => {
return extractData(html);
})
.step({ slug: 'aggregate', dependsOn: ['extract'] }, (input) => {
// input.extract is the aggregated array from all map tasks
return consolidateResults(input.extract);
});
```

**Limitations:**
- Can only depend on a single array-returning step
- TypeScript may not track type transformations between chained maps (use type assertions if needed)
- Root maps require the entire flow input to be an array

### Context Object

Expand Down
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