In Lioran S3’s reported design, RocksDB stores metadata and application state; the filesystem stores object payload bytes. Bucket and object records let the service find data without putting large object contents in the database. The details below describe the project’s V1 pre-alpha implementation, not a production guarantee.
What RocksDB stores—and what it does not
RocksDB is the metadata plane in Lioran S3, not the object-byte store. In his October 1, 2026 architecture article, Swaraj Puppalwar, Founder & CTO of Lioran Group and Lioran Developer Solutions, states: “Object payload/image bytes are NEVER written to RocksDB.” The project assigns the payload to filesystem paths and uses RocksDB for records needed to locate and manage it. Read the project’s RocksDB architecture explanation.
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This separation gives the service two different jobs: database lookups and iteration handle metadata, while filesystem streaming and range-oriented I/O handle large payload transfers. A bucket and key are logical identifiers; the payload’s physical location is an internal path. The project article illustrates a logical object-key encoder based on bucket/key, while describing payload filenames as UUID-based internal paths. That distinction allows the user-facing name and on-disk location to be separate namespaces.
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Which metadata entities have column families?
The project describes a metadata crate with a MetadataStore abstraction. Higher layers can depend on that interface rather than the concrete RocksDB implementation. The article reports column families for the following records, in addition to RocksDB’s default family:
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- Users and access keys
- Buckets and objects
- Uploads
- Video jobs, video shares, and video manifests
- System records
These families reflect more than bucket and object lookup: the service also needs state for credentials, uploads, indexes, and media-related work. The article does not establish that this schema is stable or exhaustive for later versions.
What configuration values does the project report?
The October 1, 2026 project article gives the following implementation settings. They are configuration values—not benchmark results, recommendations, or evidence of a particular throughput or latency.
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| Synchronization | bytes_per_sync and wal_bytes_per_sync: 1 MiB each |
| Object-family write buffers | 64 MiB write buffer; maximum of 4 buffers; minimum of 2 buffers to merge |
| Object-family table settings | 16 KiB block size; 10-bit Bloom filter; LZ4 compression |
The article describes a larger write-buffer setup for object metadata than for several smaller metadata families, alongside shared cache and table options. It publishes no named performance benchmark or adoption statistic for this RocksDB implementation, so the settings should not be read as proof that a workload will be faster or more efficient. The project article lists the column families and configuration.
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A separate project walkthrough describes the write path as a sequence in which data is staged before its final metadata record is committed:
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- Stage and stream the payload. The incoming bytes are written to a file rather than stored in RocksDB.
- Promote the file. The staged payload is moved to its permanent filesystem path.
- Write metadata. The final RocksDB write records the object’s metadata so the service can locate it through its logical bucket and key.
- Attempt cleanup on metadata failure. The walkthrough says the implementation attempts to remove the promoted file if the metadata write fails.
This is the behavior described in the project’s write-path article; it is not proof of crash safety across every interruption, storage failure, or recovery scenario. The article does not establish every durability guarantee that would be needed to assess those cases. See the project’s PUT walkthrough.
How does this compare with Apache Ozone?
Apache Ozone also documents RocksDB-backed metadata for object-store namespace operations, including volumes, buckets, keys, S3-style multipart uploads, snapshots, tenancy, and supporting state. This is a useful architectural parallel, not evidence that the systems share a schema or implementation. Ozone documents its own tables and key formats; the Lioran S3 article describes its own families and logical-to-physical object distinction. Apache Ozone’s official schema documentation provides details of that separate design.
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What maturity and compatibility limits matter?
The project author describes Lioran S3 V1 as pre-alpha. The overview says it exposes a native REST API and does not yet provide a drop-in AWS S3 API compatibility layer. Do not assume that an AWS S3 client or workflow will work unchanged, or treat the architecture description as a stable production contract. The project overview describes its pre-alpha status and API scope.
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