Rocky – Rust SQL engine with branches, replay, column lineage
RepositoryFreeHi HN, I'm Hugo. I've been building Rocky over the past month, shipping fast in the open. The binary is on GitHub Releases, `dagster-rocky` on PyPI, and the VS Code extension on the Marketplace. I held off on a broader announcement until the trust-system surface was coherent enough to talk
- Best for
- branch-based sql query execution, replayable query execution, column lineage tracking
- Type
- Repository · Free
- Score
- 42/100
- Best alternative
- Tavily MCP Server
Capabilities3 decomposed
branch-based sql query execution
Medium confidenceRocky implements a unique branching mechanism that allows users to create multiple versions of SQL queries and their results. This is achieved through a version control-like system where each branch can be independently modified and replayed, enabling experimentation without affecting the main dataset. The architecture leverages Rust's memory safety features to ensure efficient execution and rollback capabilities.
The branching mechanism is integrated directly into the SQL execution engine, allowing for seamless version control of queries.
More efficient than traditional SQL engines that lack branching capabilities, enabling true experimentation without risk.
replayable query execution
Medium confidenceRocky allows users to replay previous SQL queries and their results, leveraging a logging system that captures the state of the database at the time of execution. This capability is built using Rust's efficient data structures to minimize overhead and ensure quick retrieval of past states. Users can specify which execution to replay, making it easy to reproduce results or debug issues.
Utilizes a lightweight logging mechanism that captures execution context, allowing for precise replay without significant performance penalties.
More responsive than traditional logging systems, enabling instant replay of SQL queries without extensive overhead.
column lineage tracking
Medium confidenceRocky provides detailed tracking of column lineage, allowing users to see how data flows through their SQL queries. This is implemented using metadata annotations that are captured during query parsing and execution. Users can query the lineage information to understand dependencies and transformations applied to specific columns, enhancing data governance and compliance.
The lineage tracking is integrated at the query parsing level, providing real-time insights into data transformations without additional tooling.
More comprehensive than traditional lineage tools, which often require separate integrations or manual tracking.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓data engineers working on complex SQL queries
- ✓data analysts needing to verify past results
- ✓data scientists and compliance officers
Known Limitations
- ⚠Branching may introduce overhead in query execution time due to state management
- ⚠Limited to SQL syntax supported by Rocky
- ⚠Replay functionality is limited to the scope of logged queries
- ⚠Storage requirements may increase with extensive logging
- ⚠Lineage tracking may slow down query parsing for complex queries
- ⚠Requires additional storage for lineage metadata
Requirements
Input / Output
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