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Top 23 Rust Distributed 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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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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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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Daft
High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale
Project mention: 650GB of Data (Delta Lake on S3). Polars vs. DuckDB vs. Daft vs. Spark | news.ycombinator.com | 2025-11-13Hey everyone, I'm a software engineer at Eventual, the team behind Daft! Huge thanks to the op for the benchmark, we're a huge fan of your blog posts and this gave us some really useful insights. For context, Daft is a high-performance data processing engine for AI workloads that works both on single-node and distributed setups.
We're actively looking into the results of the benchmark and hope to share some of our findings soon. From initial results, we found a lot of potential optimizations we could make to our deltalake reader to improve parallelism and our groupby operator to improve pipelining for count aggregations. We're hoping to roll our these improvements over the next couple of releases.
If you're interested to learn more about our findings, check out our GitHub (https://github.com/Eventual-Inc/Daft) or follow us on Twitter (https://x.com/daftengine) and LinkedIn (https://www.linkedin.com/showcase/daftengine) for updates. Also if Daft sounds interesting to you, give us a try via pip install daft!
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wasmCloud
wasmCloud is an open source Cloud Native Computing Foundation (CNCF) project that enables teams to build, manage, and scale polyglot apps across any cloud, K8s, or edge.
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Project mention: Show HN: Luna, an in-memory SQL Server for object storage data | news.ycombinator.com | 2025-09-16
Hi HN,
Chew here, CTO at [Alphaus](https://alphaus.cloud/en/). We've been running a similar version of Luna at Alphaus, to cache really huge CSVs and PARQUETs in memory (hundreds of GBs per account across multiple files). It's written in Go, and uses DuckDB and Apache Arrow behind the scenes.
We're now at a point where a single VM's memory is really not enough to hold these huge files (the really hi-mem VMs are not yet available in our region) so we're thinking of distributing the cache across multiple hi-mem VMs (with disk spill-over). I've been "itching" to rewrite this software using a non-GC language, and so I thought it's a good timing to do an OSS version of it. I did consider C++ (and Zig) but I think Rust is a good fit here.
I've looked at [Ballista](https://github.com/apache/datafusion-ballista) actually as I'm quite familiar with DataFusion, and of course, MotherDuck, but the amount of custom duct-tapes we do behind the scenes made me think it's going to be worth the effort.
And so, sharing to you, "Luna". Still alpha-level, and still a lot of things missing, but the main goal is an easy-to-use, easy-to-deploy, columnar in-memory SQL server process, leveraging DuckDB and Apache Arrow, with more focus on cloud and object storage data. And a distributed/cluster version of Luna as well; sort of alternative to Ballista.
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nanocl
Work in progress distributed system that simplifies the orchestration of containers and virtual machines.
Nanocl is a container orchestration platform that allows you to manage and deploy containerized applications easily. It provides a simple and efficient way to run containers on your infrastructure, making it easier to manage and scale your applications.
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nodedb
The memory & storage engine for AI agents. Multi-model, edge-to-cloud, PostgreSQL-compatible.
Project mention: Show HN: NodeDB – High Perfomance Multi-Model Database | news.ycombinator.com | 2026-05-11 -
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sayiir
Sayiir: simple, embeddable durable workflow engine in Rust with Node.js & Python bindings. Checkpoint-based recovery, no deterministic replay. A simplified alternative to Temporal, Restate, and Airflow.
Project mention: What I learned building a workflow engine from scratch in Rust | dev.to | 2026-02-27If any of this was interesting — whether you're building your own engine, evaluating workflow tools, or just like reading about systems design — the [source is on GitHub (https://github.com/yacinehmito/sayiir) and the docs have a 5-minute getting started guide.
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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 Distributed discussion
Rust Distributed related posts
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Show HN: BlitzGraph – Supabase for graphs, built for LLM agents
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SurrealMX: In-memory storage with time travel and persistent storage
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How to use Surrealism to build your own custom SurrealDB extensions
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Show HN: Sayiir – A simple durable workflow engine (Rust core, Python/Node.js)
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Show HN: Sayiir – Durable, simple, workflow engine in Rust, no replay
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Sayiir — a durable workflow engine in Rust, simple, with Python & Node.js bindings
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A look at SurrealQL and how it differs from PostgreSQL
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A note from our sponsor - SaaSHub
www.saashub.com | 19 Aug 2026
Index
What are some of the best open-source Distributed projects in Rust? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | surrealdb | 32,896 |
| 2 | toydb | 7,269 |
| 3 | greptimedb | 6,547 |
| 4 | Daft | 5,713 |
| 5 | bastion | 2,908 |
| 6 | wasmCloud | 2,410 |
| 7 | datafusion-ballista | 2,107 |
| 8 | corrosion | 1,830 |
| 9 | kameo | 1,370 |
| 10 | nanocl | 983 |
| 11 | Coerce-rs | 746 |
| 12 | git-dit | 464 |
| 13 | elfo | 405 |
| 14 | riteraft | 333 |
| 15 | pyrsia | 303 |
| 16 | nodedb | 189 |
| 17 | erl_dist | 179 |
| 18 | TDN | 140 |
| 19 | chamomile | 124 |
| 20 | paxakos | 116 |
| 21 | Cherry-Node | 74 |
| 22 | sayiir | 69 |
| 23 | solomon-db | 52 |
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