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Top 23 Rust SQL Projects
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Project mention: Show HN: BlitzGraph – Supabase for graphs, built for LLM agents | news.ycombinator.com | 2026-06-23
Hello! I would say about 50% of them were bugs, 25% were features/mechanisms I loved from the the other ones, and 25% trying to push a better way to model things, which is what I never got in none of them. Typedb would be close if entities could evolve and belong to multiple types, as well as if they focused on making it adapted for app creation, but it wasn't their priority. In surrealdb I opened several issues around enhancing the graph db part, add some topological ordering to mutations the way typedb does it and several other topics. The most recent ones are in surrealdb's repo: https://github.com/surrealdb/surrealdb/issues?q=is%3Aissue%2.... Half of them have been achieved but the other half remains. With blitzgraph I tried to take the best ideas and pracrices from each, keep the tradeoffs loww, and lean as hard as possible into being AI-agent-first
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Kargo
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turso
A SQL database in Rust: SQLite-compatible, now also speaking Postgres (experimental). The LLVM of databases.
TPC-H is the recommended approach in Turso's CONTRIBUTING.md - https://github.com/tursodatabase/turso/blob/main/CONTRIBUTIN...
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The incumbents in the semantic layer space are MetricFlow (dbt’s semantic layer engine) and Cube.js; worth mentioning is Lightdash, which used to be more of a dashboard solution but is now increasingly beefing up its semantic layer side. A project to watch is Apache Ossie (formerly Open Semantic Interchange), which aims to become an open source semantic layer standard. At the moment it only has a limited spec for specifying datasets and no reference semantics for querying, so definitely worth following but not a full solution at the moment. Finally, we will cover SLayer, a recent entrant aiming to be used by agents as well as humans.
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sqlx
🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite. (by transact-rs)
Project mention: What ORMs have taught me: just learn SQL (2014) | news.ycombinator.com | 2026-07-04If you use Rust and like to write SQL, check out SQLx: https://github.com/transact-rs/sqlx
Or diesel-rs for ORM
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prql
PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
Project mention: Things I want in a modern relational query language | news.ycombinator.com | 2026-08-23I don't take it as too-much-syntax in the brain, but all of the problems bad syntax causes. We could still be writing code in assembly or C, but we have found that different languages make things easier or safer to construct.
I can trivially handle having to repeatedly bounce to the top-then-to-the-bottom of a query I am writing because I want to change the group-by or sorting order, but that is annoying friction. Since the language does not compose well, you need to keep most of the query in your head and cannot build it up piecemeal as easily as something like PRQL (https://prql-lang.org/)
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You can find all the techniques described in this blog post in a single-file example application.
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AppSignal
Monitoring that respects your time & budget. APM, error tracking, and dashboards for modern web apps. Ten-minute setup, transparent flat pricing, and support from engineers who actually use the product.
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databend
Data Agent Ready Warehouse : One for Analytics, Search, AI, Python Sandbox. — rebuilt from scratch. Unified architecture on your S3.
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Here are a few examples you might find credible:
- pola-rs/polars: https://github.com/pola-rs/polars/pull/26823 - ~2.7x median speedup of primitive-to-boolean casting credited to Claude Opus 4.6
- pydantic/monty: https://github.com/pydantic/monty/pull/643 - ~53x speedup of bytes substring search generated with Claude Code
- pola-rs/polars: https://github.com/pola-rs/polars/pull/28363 - up to 43.9x speedup of concat_list using a dedicated Arrow kernel, code generated with Cursor + Claude Opus 4.8/Sol
- duckdb/duckdb: https://github.com/duckdb/duckdb/pull/22076 - up to 490x speedup of GeoParquet spatial queries using row-group pruning, generated with Claude Code / co-authored by Claude Sonnet 4.6
- apache/datafusion: https://github.com/apache/datafusion/pull/24086 - 6.35x speedup of TPC-H SF10 Parquet scans under simulated object-store latency, generated with Claude Code
- duckdb/duckdb: https://github.com/duckdb/duckdb/pull/22013 - up to 48% reduction in query time by fusing DISTINCT ON into the hash-join build side, generated with Claude Code
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paradedb
One Postgres for your application data, full-text search, vector retrieval, and aggregations. Home of the pg_search extension.
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Project mention: Ask HN: Has AI stolen the satisfaction from programming? | news.ycombinator.com | 2025-10-13
10) Many other smaller features
This would be very hard to achieve without AI for most one-person teams. Although tbf not impossible.
> The new way: The entire premise of AI coding tools is to automate the thinking, not just the typing. You're supposed to describe a problem and get a solution without understanding the details. That's the labor-saving promise.
I think here the OP introduces a strawman since as many people have pointed out, the labour saving happens in automating menial tasks.
> >I was thinking of all the classic exploratory learning blog posts. Things that sounded fun. Writing a toy database to understand how they work, implementing a small Redis clone. Now that feels stupid. Like I'd be wasting time on details the AI is supposed to handle.
On the contrary. Reading ToyDB[1] source code helped me understand MVCC and Isolation levels. That's knowledge that's valuable for an systems architect since at the end LLMs are just fancy word generators.
[1] https://github.com/erikgrinaker/toydb
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One addition: Postgres for all your GPU-based machine learning training and inference needs: https://github.com/postgresml/postgresml
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greptimedb
The open-source observability database. One columnar engine for metrics, logs, and traces, on object storage.
We also started with the typical kube-prometheus-stack, but we don’t like Prometheus/PromQL. Moreover, it only solves the „metrics“ part - to handle logs and traces, more quite heavy and complex components have to be added to the observability stack.
This didn‘t feel right, so we looked around and found greptimedb https://github.com/GreptimeTeam/greptimedb, which simplifies the whole stack. It‘s designed to handle metrics, logs, and traces. We collect metrics and logs via OpenTelemetry, and visualize them with Grafana. It provides endpoints for Postgres, MySQL, PromQL; we‘re happy to be able to build dashboards using SQL as that’s where we have the most knowledge.
The benchmarks look promising, but our k8s clusters aren’t huge anyway. As a platform engineer, we appreciate the simplicity of our observability stack.
Any other happy greptimedb users around here? Together with OTel, we think we can handle all future obs needs.
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materialize
The live data layer for apps and AI agents. Create up-to-the-second views into your business, just using SQL (by MaterializeInc)
Project mention: ANN v3: 200ms p99 query latency over 100B vectors | news.ycombinator.com | 2026-01-25I agree our sample may not be representative but we try to stay focused on the current and next crop of tpuf customers. So far "CI prohibits network access during tests" just hasn't come up as a pain point for any of them, but as I mentioned in another comment [0], we're definitely keeping an open mind about introducing an offline dev experience.
At my last company an engineer spent a year implementing Bazel [0][1] only to have it ripped out after they left [2] due to the maintenance burden. You might say it was a little bit of a hassle. :)
[0]: https://news.ycombinator.com/item?id=46758156
[1]: https://github.com/MaterializeInc/materialize/pull/24243
[2]: https://github.com/MaterializeInc/materialize/pull/31006
[3]: https://github.com/MaterializeInc/materialize/pull/33895
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I recently added autocomplete to rainfrog, the terminal UI database tool I've been working on: https://github.com/achristmascarl/rainfrog
I initially made it as a lightweight, keyboard-centric alternative to dbeaver/pgadmin, and it now covers 95%+ of the database-related tasks I need to do at my job.
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readyset
Readyset is a MySQL and Postgres wire-compatible caching layer that sits in front of existing databases to speed up queries and horizontally scale read throughput. Under the hood, ReadySet caches the results of cached select statements and incrementally updates these results over time as the underlying data changes.
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For tools with local storage (todo, habit, contacts, stash), SQLite handles persistence via rusqlite. No database server, no config files to manage. The database lives at ~/.local/share/dee-toolname/data.db and everything just works.
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tokio-postgres handles this by just dispatching the "ROLLBACK" command in impl Drop and ignoring the response. https://github.com/rust-postgres/rust-postgres/blob/a7a49a90...
Is this not enough? What could go wrong? If the network connection dies or the task is cancelled, I'm assuming the database server cleans up the connection state and does a rollback automatically.
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GQL
GitQL is a extensible SQL-like query language and SDK to perform queries on various data sources such .git files with supports of most of SQL features such as grouping, ordering and aggregation and window functions and allow customization like user-defined types and functions
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Rust SQL discussion
Rust SQL related posts
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Io_uring Without Readahead
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A better SQL in 11 lines of code
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AWS Acquires DuckDB
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Things I want in a modern relational query language
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PostgreSQL for Everything
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Show HN: Parquity – find and reproduce Parquet interoperability failures
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Is it all just vapourware?
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A note from our sponsor - SaaSHub
www.saashub.com | 15 Sep 2026
Index
What are some of the best open-source SQL projects in Rust? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | surrealdb | 33,013 |
| 2 | turso | 24,244 |
| 3 | cube.js | 20,813 |
| 4 | sqlx | 17,461 |
| 5 | diesel | 14,177 |
| 6 | prql | 10,914 |
| 7 | sea-orm | 9,890 |
| 8 | databend | 9,440 |
| 9 | datafusion | 9,308 |
| 10 | paradedb | 9,253 |
| 11 | toydb | 7,281 |
| 12 | postgresml | 6,807 |
| 13 | greptimedb | 6,661 |
| 14 | materialize | 6,368 |
| 15 | rainfrog | 5,323 |
| 16 | readyset | 5,279 |
| 17 | arroyo | 5,031 |
| 18 | fselect | 4,458 |
| 19 | rusqlite | 4,385 |
| 20 | rust-postgres | 4,000 |
| 21 | qsv | 3,783 |
| 22 | GQL | 3,512 |
| 23 | datafusion-sqlparser-rs | 3,455 |
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