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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchLioran S3’s described design keeps object records and state in RocksDB while storing the actual object bytes on the filesystem. That split gives metadata operations and large-payload I/O different storage paths. The project author describes the implementation as pre-alpha and single-node, so the architecture should be read as a project-reported design—not as independently verified production behavior.
What the metadata/data-plane split means
Lioran S3, also called Lioran Bastion in project material, is described by its author as a self-hosted object-storage server written primarily in Rust. In the October 1, 2026 project article, the author calls it “V1 Pre-Alpha,” says it exposes a native REST API rather than a drop-in AWS S3 compatibility layer, and says distributed storage is deferred while the single-node engine is developed. These are the project’s own release and capability descriptions.
The architecture separates two responsibilities: RocksDB holds metadata and application state, while the filesystem holds object payload bytes. The author states the intended boundary directly: “Object payload/image bytes are NEVER written to RocksDB.” That is a description of the current implementation, not an independently verified inspection of the code.
| Plane | What it stores or does | Workload it serves |
|---|---|---|
| Metadata plane: RocksDB | Records and state associated with buckets, objects, uploads and multipart work, indexes, and media features such as video jobs, shares, and manifests. | Compact records and indexed lookup or access to application state. |
| Object data plane: filesystem | The bytes making up stored objects. | Streaming writes, range reads, and direct filesystem access. |
The rationale in the project article is that these workloads differ: metadata needs indexed access to relatively compact records, whereas object payloads are large byte streams. The article presents this as the design rationale; it does not supply measurements proving that this arrangement is faster or more durable than alternatives.
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Why does it need RocksDB?
Object storage needs more than a place to put file contents. The service must also track which bucket and key refer to an object, manage upload state, and maintain records used by indexes and media-related operations. The project assigns those records and state to RocksDB, using it as the metadata engine rather than as the container for object bytes.
The RocksDB-focused project article describes column families for users, access keys, buckets, objects, uploads, video jobs, video shares, video manifests, and system data, alongside RocksDB’s default column family. This list indicates the variety of state the author says the engine manages; it should not be mistaken for a general prescription for RocksDB deployments.
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The same October 1, 2026 article reports a shared 64 MiB LRU block cache and a 256 MiB WAL retention bound in the current implementation. Those are reported configuration values, not benchmark results, universal tuning advice, or evidence of a particular capacity or throughput.
Why not put payloads in RocksDB?
In this design, the filesystem is the object data plane. Keeping payload bytes out of RocksDB leaves the database responsible for records and state, while object bytes follow a filesystem path intended to support streaming and range access. The project article argues that this separation suits the different access patterns; it does not establish a performance advantage with comparative tests.
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For uploads, the author describes bounded streaming buffers rather than loading an entire object into memory as one value. That is an implementation description, not a published memory benchmark or a guarantee about every request size and operating condition.
How the described PUT path works
The project’s PUT walkthrough separates filesystem handling from the metadata write. In the author’s account, an upload is staged first, promoted into the object tree, and then represented in RocksDB metadata. The stated intent is that incomplete uploads are not exposed as committed objects.
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- Validate: Check the bucket and object key.
- Check capacity and quota: Confirm the request can proceed under the current limits.
- Stage the payload: Create a staging file, stream the request into it, and compute its hash.
- Flush the staged file: Flush it and optionally call fsync, as described in the walkthrough.
- Recheck capacity and quota: Check these constraints again before promotion.
- Promote the file: Choose an internal final path and rename the staged file into the object tree.
- Write metadata: Store the object’s metadata through the metadata store in RocksDB.
If the metadata write fails after the file has been promoted, the walkthrough says the code attempts to remove that file. This describes a cleanup attempt, not proof that every failure, crash, or concurrent operation is handled safely. The article is not a crash-consistency audit, so it does not establish what happens in every interruption window between filesystem and database operations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this architecture does—and does not—establish
- It establishes the intended storage boundary: object records and state are assigned to RocksDB; object payload bytes are assigned to the filesystem.
- It describes separate write stages: the file is staged and promoted before object metadata is written, with a removal attempt if that metadata write fails.
- It does not establish production readiness: the author labels the current service pre-alpha and single-node.
- It does not establish AWS S3 API compatibility or distributed operation: the project article says the current interface is a native REST API and distributed storage is deferred.
- It does not establish comparative performance or durability: the cited implementation details are configuration and walkthrough descriptions, not independent benchmarks or audits.
The architecture and behavior above are reported in project articles by Swaraj Puppalwar published on DEV Community on October 1, 2026: the Lioran S3 architecture article, the RocksDB metadata-engine article, and the PUT walkthrough. They are first-party descriptions of the project, not independent validation.
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