RisingWave is a production-grade distributed streaming SQL database. The codebase implements a multi-node cluster composed of SQL frontend nodes (parser, binder, optimizer, planner), compute nodes running both streaming executors and batch execution, a meta service for catalog/scheduling/checkpoint coordination, and a custom LSM-tree storage engine (Hummock) over object storage. It speaks the PostgreSQL wire protocol, supports incremental CREATE MATERIALIZED VIEW over streaming inputs, and serves real-time queries and sinks. The repository is primarily an application, despite containing extensive benchmarks and deterministic simulation testing: those artifacts validate distributed-system correctness rather than performing data research.
Feldera is a real-time streaming SQL analytics platform built on the DBSP (Differential Dataflow) incremental computation engine. It compiles SQL into efficient incremental circuits that process streaming data with sub-millisecond latency, supporting complex queries including joins, aggregations, window functions, and recursive queries. The system is deployed as a pipeline manager (Rust) with a REST API, a web console (Svelte), a Python SDK, a dbt adapter, and a CLI tool. It integrates with numerous data sources and sinks (Kafka, Postgres, Delta Lake, Iceberg, HTTP, NATS, Redis, etc.) and provides fault tolerance via checkpointing and storage backends. The architecture is polyglot: a Java-based SQL compiler (using Apache Calcite) translates SQL to Rust code that runs on the DBSP runtime; the pipeline manager orchestrates compilation, deployment, and lifecycle; the web console offers a full UI for pipeline management, monitoring, and profiling. The codebase also includes a Spark SQL translator (felderize) and extensive benchmark suites (Nexmark, LDBC). While primarily an application, it has strong research roots in incremental view maintenance and formal monotonicity analysis, reflected in the compiler's optimization passes and the DBSP runtime's operator design. Key reusable components include the adapter library (connector traits), the Feldera types crate (shared configs and errors), the common UI component library, and the Python client SDK. The project is actively maintained with frequent commits, comprehensive CI/CD, and a growing test suite covering SQL semantics across many data types and edge cases.