$ show pathwaycom/pathway
Pathway is a high-performance live data processing framework built on top of timely dataflow and differential dataflow, providing a Python API for defining streaming and batch data pipelines. It is designed for real-time ETL, event processing, AI/ML pipelines (including RAG), and incremental computation with strong consistency guarantees. The architecture consists of a Rust core engine that handles dataflow execution, scheduling, persistence, and connectors, while the Python layer provides a declarative API for table operations, UDFs, and integrations with external systems like Kafka, databases, vector stores, and cloud storage. Key entry points include the `pathway` CLI, the Python `pw` module, and the Rust engine library. Data flows through connectors (sources/sinks), transformation operators (joins, groupby, windowing, etc.), and optional persistence for fault tolerance. External dependencies include Kafka, S3, PostgreSQL, various vector databases, and LLM services.
The LLM identified 4 files worth highlighting for reuse:
Implements columnar storage optimization for timely dataflow containers, enabling efficient memory layout and zero-copy serialization for dataflow messages. This component is self-contained within the `timely_container` crate and can be extracted for use in other data-intensive systems requiring columnar data representation.
Provides reference-counted wrappers (`TraceRc`, `TraceBox`) for sharing differential dataflow traces across multiple scopes or operators without cloning the underlying data. This pattern enables efficient memory reuse in complex dataflow graphs and can be adapted for other incremental computation frameworks.
Implements REST API servers for LLM-based question answering and document retrieval, supporting multiple backends (OpenAI, Bedrock, LiteLLM) and configurable prompt templates. This module is relatively self-contained and can be reused as a lightweight RAG serving layer in other Python applications.
Manages persistence state with snapshot versioning, metadata tracking, and cleanup logic for fault-tolerant stream processing. The implementation handles versioned snapshots, frontier tracking, and garbage collection, forming a reusable persistence layer for any dataflow engine requiring exactly-once semantics.