Nicheloom

Market intelligence for builders — see what's gaining traction before it's crowded.

Rocky

Rust SQL engine with branches, replay, column lineage

Details

External ID
47935246
Source
HN
Company
—
Product
Rocky and Caveman Speak in Claurst CLI Save Big Token Amaze Amaze Amaze
Website domain
github.com
Launched
April 28, 2026
Cohort
—
Upvotes
122
Upvotes percentile
0.9138817480719794
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

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.

Enrichment

Theme
database infrastructure and developer tools
Vertical
Horizontal
Function
Data infrastructure
Audience
Developer
AI stance
Not AI
Project type
Commercial product
Normalized one-liner
sql engine with branches and replay
Manually corrected
False

Could you build this?

No Creating a transactional SQL database engine written in Rust featuring custom branchable storage, deterministic query replay, and column-level lineage requires deep database internals, compiler theory, and systems programming expertise.

What it would actually take: The architecture requires a custom SQL parser/analyzer, a cost-based query optimizer, an execution engine (or DataFusion extension), and an append-only, copy-on-write storage engine capable of snapshot branching and deterministic transaction replaying. Building accurate column-level lineage also demands static code analysis across arbitrary nested SQL subqueries and window functions.

Discussion

20 comments analyzed.

Competitors mentioned: dbt (data build tool), Dagster, Databricks, Iceberg

Concerns raised: Name 'Rocky' conflicts with existing popular Linux distribution, Missing proper attribution to prior work on branches and lineage, README doesn't clearly connect dots on benefits for data engineers, Limited to SQL-first approach, no Python support mentioned

Feature requests: Lineage diff command (compare lineage between branches), Nested branches support, Python language support beyond SQL, Cross-system merge semantics for branch operations

Competitors

Other products that read as similar to this one — 113 launches clear the similarity bar, closest 8 shown.

Attention rank: #15 of 114 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 181 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a data infrastructure tool for Media & entertainment yet.