weaseldb/persistence.md

# Persistence Thread Design

## Overview

The persistence thread receives commit batches from the main processing pipeline and uploads them to S3. It uses a single-threaded design with connection pooling and batching for optimal performance.

## Architecture

**Input**: Commits arrive via `ThreadPipeline` interface from upstream processing
**Output**: Batched commits uploaded to S3 persistence backend
**Transport**: Single-threaded TCP client with connection pooling
**Protocol**: Higher layers handle HTTP, authentication, and S3-specific details

## Batching Strategy

The persistence thread collects commits into batches using two trigger conditions:

1. **Time Trigger**: `batch_timeout_ms` elapsed since batch collection started
2. **Size Trigger**: `batch_size_threshold` commits collected (can be exceeded by final commit)

**Flow Control**: When `max_in_flight_requests` reached, block until responses received.

## Main Processing Loop

### 1. Batch Collection

**No In-Flight Requests**:
- Use blocking acquire to get first commit batch
- Process immediately (no batching delay)

**With In-Flight Requests**:
- Check flow control: if at `max_in_flight_requests`, block for responses
- Collect commits using non-blocking acquire until trigger condition:
  - Check for available commits (non-blocking)
  - If `batch_size_threshold` reached → process batch immediately
  - If below threshold → use `epoll_wait(batch_timeout_ms)` for I/O and timeout
  - On timeout → process collected commits
- If no commits available and no in-flight requests → switch to blocking acquire

### 2. Connection Management

- Acquire healthy connection from pool
- Create new connections if pool below `target_pool_size`
- If no healthy connections available, block until one becomes available
- Maintain automatic pool replenishment

### 3. Data Transmission

- Write batch data to S3 connection using appropriate protocol
- Publish accepted transactions to subscriber system
- Track request as in-flight for flow control

### 4. I/O Event Processing

- Handle epoll events for all in-flight connections
- Monitor connection health via heartbeats
- Process incoming responses and detect connection failures

### 5. Response Handling

- **Ordered Acknowledgment**: Only acknowledge batch after all prior batches are durable
- Release batch via `StageGuard` destructor (publishes to next pipeline stage)
- Publish durability events to subscriber system
- Return healthy connection to pool

### 6. Failure Handling

- Remove failed connection from pool
- Retry batch with exponential backoff (up to `max_retry_attempts`)
- Backoff delays only affect the specific failing batch
- If retries exhausted, abort process or escalate error
- Initiate pool replenishment if below target

## Connection Pool

**Target Size**: `target_pool_size` connections (recommended: 2x `max_in_flight_requests`)
**Replenishment**: Automatic creation when below target
**Health Monitoring**: Heartbeat-based connection validation
**Sizing Rationale**: 2x multiplier ensures availability during peak load and connection replacement

## Key Design Properties

**Batch Ordering**: Batches may be retried out-of-order for performance, but acknowledgment to next pipeline stage maintains strict ordering.

**Backpressure**: Retry delays for failing batches create natural backpressure that eventually blocks the persistence thread when in-flight limits are reached.

**Graceful Shutdown**: On shutdown signal, drain all in-flight batches to completion before terminating.

## Configuration Parameters

| Parameter | Default | Description |
|-----------|---------|-------------|
| `batch_timeout_ms` | 1ms | Maximum time to wait collecting commits for batching |
| `batch_size_threshold` | 1MB | Threshold for triggering batch processing |
| `max_in_flight_requests` | 50 | Maximum concurrent requests to persistence backend |
| `target_pool_size` | 2x in-flight | Target number of connections to maintain |
| `max_retry_attempts` | 3 | Maximum retries for failed batches before aborting |
| `retry_base_delay_ms` | 100ms | Base delay for exponential backoff retries |

## Configuration Validation

**Required Constraints**:
- `batch_size_threshold` > 0 (must process at least one commit per batch)
- `max_in_flight_requests` > 0 (must allow at least one concurrent request)
- `max_in_flight_requests` < 1000 (required for single-call recovery guarantee)
- `target_pool_size` >= `max_in_flight_requests` (pool must accommodate all in-flight requests)
- `batch_timeout_ms` > 0 (timeout must be positive)
- `max_retry_attempts` >= 0 (zero disables retries)
- `retry_base_delay_ms` > 0 (delay must be positive if retries enabled)

**Performance Recommendations**:
- `target_pool_size` <= 2x `max_in_flight_requests` (optimal for performance)

## Recovery and Consistency

### Recovery Model

WeaselDB's batched persistence design enables efficient recovery while maintaining strict serializable consistency guarantees.

#### **Batch Ordering and Durability**

**Ordered Acknowledgment Property**: Batches may be retried out-of-order for performance, but acknowledgment to the next pipeline stage maintains strict ordering. This ensures that if batch N is acknowledged as durable, all batches 0 through N-1 are also guaranteed durable.

**Durability Watermark**: The system maintains a durable watermark indicating the highest consecutively durable batch ID. This watermark advances only when all preceding batches are confirmed persistent.

#### **Recovery Protocol**

WeaselDB uses a **sequential batch numbering** scheme with **S3 atomic operations** to provide efficient crash recovery and split-brain prevention without external coordination services.

**Batch Numbering Scheme**:
- Batch numbers start at `2^64 - 1` and count downward: `18446744073709551615, 18446744073709551614, 18446744073709551613, ...`
- Each batch is stored as S3 object `batches/{batch_number:020d}` with zero-padding
- S3 lexicographic ordering ensures recent batches (higher numbers) appear first in LIST operations

**Leadership and Split-Brain Prevention**:
- New persistence thread instances scan S3 to find the next available batch number
- Each batch write uses `If-None-Match="*"` to atomically claim the sequential batch number
- Only one instance can successfully claim each batch number, preventing split-brain scenarios
- Batch object content includes `leader_id` to identify which leader wrote each batch

**Recovery Scenarios**:

**Clean Shutdown**:
- All in-flight batches are drained to completion before termination
- Durability watermark accurately reflects all durable state
- No recovery required on restart

**Crash Recovery**:
1. **S3 Scan with Bounded Cost**: List S3 objects with prefix `batches/` and limit of 1000 objects
2. **Gap Detection**: Check for missing sequential batch numbers. WeaselDB never puts 1000 batches in flight concurrently, so a limit of 1000 is sufficient.
3. **Watermark Reconstruction**: Set durability watermark to highest consecutive batch number found
4. **Leadership Transition**: Begin writing batches starting from next available batch number. Skip past any batch numbers claimed in the durability watermark scan.

**Bounded Recovery Guarantee**: Since at most 999 batches can be in-flight during a crash, the durability watermark is guaranteed to be found within the first 1000 objects in S3. This ensures **O(1) recovery time** regardless of database size, with at most **one S3 LIST operation** required.

**Recovery Performance Limits**: To maintain single-call recovery guarantees, `max_in_flight_requests` is limited to **1000**, matching S3's maximum objects per LIST operation. This ensures recovery a single S3 API call is sufficient for recovery.