Rocky – Rust SQL engine with branches, replay, column lineage

Hacker News

Hi 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 about as one thing. The governance waveplan — column classification, per-env masking, 8-field audit trail on every run, `rocky compliance` rollup, role-graph reconciliation, retention policies — landed end-to-end last week in engine-v1.16.0 and rounded out in v1.17.4 (tagged 2026-04-26). That's the milestone I'd been waiting for. The pitch: keep Databricks or Snowflake. Bring Rocky for the DAG. Rocky is a Rust-based control plane for warehouse pipelines. Storage and compute stay with your warehouse. Rocky owns the graph — dependencies, compile-time types, drift, incremental logic, cost, lineage, governance. The things your current stack can't give you because it doesn't own the DAG. A few things I think are interesting: - Branches + replay. `rocky branch create stg` gives you a logical copy of a pipeline's tables (schema-prefix today; native Delta SHALLOW CLONE and Snowflake zero-copy are next). `rocky replay <run_id>` reconstructs which SQL ran against which inputs. Git-grade workflow on a warehouse. - Column-level lineage from the compiler, not a post-hoc graph crawl. The type checker traces columns through joins, CTEs, and windows. VS Code surfaces it inline via LSP. - Governance as a first-class surface. Column classification tags plus per-env masking policies, applied to the warehouse via Unity Catalog (Databricks) or masking policies (Snowflake). 8-field audit trail on every run. `rocky compliance` rollup that CI can gate on. Role-graph reconciliation via SCIM + per-catalog GRANT. Retention policies with a warehouse-side drift probe. - Cost attribution. Every run produces per-model cost (bytes, duration). `[budget]` blocks in `rocky.toml`; breaches fire a `budget_breach` hook event. - Compile-time portability + blast radius. Dialect-divergence lint across Databricks / Snowflake / BigQuery / DuckDB (12 constructs). `SELECT *` downstream-impact lint. - Schema-grounded AI. Generated SQL goes through the compiler — AI suggestions type-check before they can land. What Rocky isn't: - Not a warehouse — it's the control plane on top. - Not a Fivetran replacement. `rocky load` handles files (CSV/Parquet/JSONL); for SaaS sources use Fivetran, Airbyte, or warehouse-native CDC. - Not dbt Cloud — no hosted UI, no managed scheduler. First-class Dagster integration if you need orchestration. Adapters: Databricks (GA), Snowflake (Beta), BigQuery (Beta), DuckDB (local dev / playground). Apache 2.0. I'd love feedback on the trust-system framing, the governance surface (particularly classification-to-masking resolution in `rocky compile` and the `rocky compliance` CI gate), the branches/replay design, the cost-attribution primitives, or anything else that catches your eye. Happy to go deep in the thread.

Visit product View on Hacker NewsApr 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, code, open · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

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AppSumoMay struggle as an AppSumo deal · Strong signals: plus, host · Missing: platform, intuitive, reviews

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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