# StreamWeave Product Requirements Document
## Project Overview
StreamWeave is a composable, async, stream-first computation framework in pure Rust. It provides a fluent API for building fully composable, async data pipelines. The framework is built around the concept of streaming data with a focus on simplicity, composability, and performance.
## Current State
The following features are already implemented:
- Pure Rust API with zero-cost abstractions
- Full async/await compatibility via `futures::Stream`
- Fluent pipeline-style API with type-safe builder pattern
- Comprehensive error handling system with multiple strategies (Stop, Skip, Retry, Custom)
- Code-as-configuration — no external DSLs
- Comprehensive test infrastructure
- File-based producers and consumers
- Common transformers (Map, Batch, RateLimit, CircuitBreaker, Retry, Filter, Window, Delay)
- HTTP middleware support with Axum integration
- WebSocket support
- Server-Sent Events support
## Architecture
The framework uses three primary building blocks:
1. **Producer**: Starts a stream of data (implements the `Producer` trait)
2. **Transformer**: Transforms stream items (implements the `Transformer` trait)
3. **Consumer**: Consumes the stream (implements the `Consumer` trait)
All components are chained together using the `PipelineBuilder` with a type-state pattern for compile-time validation.
## Planned Features (Requirements)
### 1. Stateful Processing
Add support for stateful stream processing where transformers can maintain and update state across items.
- Implement a `StatefulTransformer` trait that allows maintaining state
- Provide state persistence options (in-memory, external store)
- Support for state snapshots and recovery
- Add state aggregation patterns (running totals, moving averages)
### 2. Exactly-Once Processing Semantics
Implement exactly-once processing guarantees for reliable stream processing.
- Add transaction support for pipelines
- Implement idempotent processing patterns
- Add deduplication mechanisms
- Support checkpointing and recovery
- Implement offset tracking for resumable processing
### 3. Distributed Processing
Support distributed stream processing across multiple nodes.
- Design distributed architecture (coordinator/worker model)
- Implement data partitioning strategies
- Add inter-node communication (gRPC or custom protocol)
- Support work stealing and load balancing
- Implement fault tolerance and node recovery
### 4. Windowing Operations
Implement comprehensive windowing operations for time-based and count-based processing.
- Tumbling windows (fixed, non-overlapping)
- Sliding windows (overlapping)
- Session windows (gap-based)
- Global windows
- Custom window implementations
- Late data handling
- Watermarks for event-time processing
### 5. Additional Data Format Support
Add support for more data formats and serialization.
- JSON streaming (with serde integration)
- CSV parsing and generation
- Parquet file support
- Avro format support
- MessagePack support
- Protocol Buffers integration
- Arrow format for columnar data
### 6. Specialized Transformers
Add more specialized transformers for common use cases.
- `FlatMap` transformer (one-to-many mapping)
- `Reduce` transformer (aggregation)
- `GroupBy` transformer (keyed grouping)
- `Join` transformer (stream joining)
- `Sort` transformer (ordered output)
- `Distinct` transformer (deduplication)
- `Sample` transformer (statistical sampling)
- `Throttle` transformer (backpressure control)
### 7. Fan-In/Fan-Out Support
Implement support for splitting and merging streams.
- Stream splitting (one input to multiple outputs)
- Stream merging (multiple inputs to one output)
- Round-robin distribution
- Broadcast to all consumers
- Conditional routing based on content
- Load-balanced fan-out
### 8. WASM Optimizations
Optimize for WebAssembly deployment.
- WASM-specific memory management
- Browser-compatible async runtime integration
- Size optimization for WASM bundles
- WASM-specific documentation and examples
- Feature flags for WASM vs native builds
- Testing infrastructure for WASM targets
### 9. Reusable Pipeline Components
Enable pipeline composition and reuse.
- Pipeline templates/blueprints
- Parameterized pipelines
- Pipeline serialization/deserialization
- Hot-reload of pipeline configurations
- Pipeline versioning
### 10. Specialized Producers and Consumers
Add more specialized I/O components.
**Producers:**
- Kafka producer
- Redis Streams producer
- MQTT producer
- Database query producer (SQL)
- gRPC streaming producer
- Timer/interval producer
- HTTP polling producer
**Consumers:**
- Kafka consumer
- Redis Streams consumer
- MQTT consumer
- Database sink consumer
- gRPC streaming consumer
- Elasticsearch consumer
- S3/Cloud storage consumer
### 11. Machine Learning Integration
Add ML pipeline support.
- Model inference transformer
- Feature extraction utilities
- Batch inference optimization
- Model hot-swapping
- Integration with common ML frameworks (ONNX, TensorFlow Lite)
- Streaming prediction pipelines
### 12. Monitoring and Metrics
Implement comprehensive observability.
- Metrics collection (throughput, latency, error rates)
- OpenTelemetry integration
- Prometheus metrics export
- Distributed tracing support
- Health check endpoints
- Custom metric definitions
- Real-time dashboards integration
### 13. SQL-Like Querying
Add SQL-like query interface for streams.
- SELECT-style projections
- WHERE-style filtering
- GROUP BY aggregations
- JOIN operations across streams
- ORDER BY with streaming sort
- LIMIT and OFFSET support
- Query optimization
### 14. Visualization Tools
Create tools for pipeline visualization and debugging.
- Pipeline DAG visualization
- Real-time data flow visualization
- Performance profiling visualization
- Interactive pipeline builder UI
- Debug mode with step-through execution
- Pipeline comparison tools
## Technical Requirements
- All features must maintain backward compatibility
- Performance must not regress for existing functionality
- All public APIs must have comprehensive documentation
- Test coverage must remain above 90%
- WASM compatibility must be preserved for applicable features
- Follow existing code patterns and architecture
## Success Criteria
- All planned features implemented and tested
- Documentation updated for all new features
- Examples provided for each major feature
- Performance benchmarks for critical paths
- Community feedback incorporated