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What flush and fsync do in the write path
Lioran’s vendor description distinguishes two steps that are easy to conflate. A flush moves buffered application writes toward the operating system. The subsequent staging_file.sync_all().await asks for synchronization across a durability boundary before the streaming helper returns. As Founder and CTO Swaraj Puppalwar puts it, “Flush is not automatically equivalent to stable physical persistence.”
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The call applies while the payload is still held in a staging file, before the later placement and metadata steps. An explicit sync is a stronger request than merely flushing application buffers, but it cannot by itself guarantee what every operating system, filesystem, device, controller, or hardware stack will do. Puppalwar’s explanation is appropriately qualified: “fsync strengthens the contract, but ‘durable storage’ still ultimately depends on the operating system, filesystem, device, controller, and hardware behavior underneath.” (Lioran S3’s strict-versus-balanced explanation)
How a normal PUT is committed
The vendor describes the current normal PUT sequence as follows. It is an account of the V1 Pre-Alpha implementation, not an independently verified guarantee for every possible failure window.
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- Create a staging file and stream the payload into it while computing SHA-256.
- Flush the staging file; in strict mode, call
sync_all().awaitbefore the streaming helper returns. - Close the staging file, recheck capacity and quota, create the final parent directory, and rename the staged file into its final location.
- Write metadata to RocksDB. If that metadata commit fails synchronously, remove the physical file.
The staging step separates incomplete request data from ordinary committed-object visibility. Lioran’s documented design says an interrupted stream should not appear as a normal object returned by GET, multipart parts do not become the final object until completion, and an interrupted replacement is intended to leave the previous committed object intact. These are described design properties, not proof that every combination of process crash, host failure, or storage behavior has been tested. (Lioran S3’s durability and crash-safety description; V1 commit-pipeline failure windows)
Strict and balanced compared
| Question | Strict, as described | Balanced, as described |
|---|---|---|
| Explicit synchronization | Calls sync_all() after flush when the configured mode requires synchronization. |
Skips that explicit per-write sync stage when the configured mode does not require it. |
| Persistence assumption | Makes an explicit synchronization request before the later commit steps. | Relies more on operating-system and filesystem writeback behavior. |
| Performance evidence | Synchronous persistence can add latency; no numerical penalty is published. | The vendor says throughput can improve; no numerical gain is published. |
| Remaining dependencies | Results still depend on the deployed OS, filesystem, device, controller, and hardware. | The same storage stack remains relevant, with different assumptions about power loss. |
These descriptions do not support a universal “safe” versus “unsafe” ranking. They describe different synchronization semantics; which contract suits a workload depends on its failure assumptions and actual storage stack. No controlled crash-test results or quantified mode comparison are reported in the cited materials. (iTechGuides’ explainer, October 4, 2026)
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Why ordering does not make the commit atomic
The filesystem object and RocksDB metadata are separate systems; ordering their writes does not turn them into one atomic transaction. The vendor says a metadata-first order could leave metadata pointing to missing bytes. Its described approach instead stages data, uses filesystem rename semantics where supported, checks metadata, and rolls back the physical file after a synchronous metadata failure.
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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 matchThat strategy addresses particular failure windows but does not remove every recovery concern. For example, an orphaned file can consume disk space even if it is not exposed as a committed object. Puppalwar summarizes the boundary directly: “It does not magically turn filesystem + RocksDB into one atomic transaction.” (V1 commit-pipeline failure windows)
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How to evaluate the modes on your deployment
Record the configuration and timings
The vendor says a useful benchmark records the durability mode, filesystem, disk type, object size, concurrency, reverse proxy, network path, CPU, RAM, and software version. Its timing instrumentation separates receive, write, SHA-256, flush, fsync, close, directory creation, rename, metadata, and total durations. Those measurements can help identify whether a workload is limited by network transfer, disk activity, synchronization, or hashing; the published material supplies no benchmark values or performance conclusions. (mode and timing description; streaming implementation details)
Test failure cases deliberately
On disposable systems, the vendor proposes evaluating process termination during PUT and multipart upload, restart followed by checksum comparison, disk-full and quota conditions, host reboot, and filesystem remount. These are suggested tests, not reported test results. Keep the production configuration and storage stack consistent with the system being evaluated, since the outcome depends on those details.
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Make storage persistent outside the container
Lioran’s deployment guidance says both object data and metadata need persistent storage. A container-only data path is not durable across container removal. A UPS may reduce exposure to external power interruption, but it is not a substitute for an explicit synchronization request or backups, and it does not establish persistence by itself. (Lioran S3 Docker deployment guidance)
What the available evidence establishes—and what it does not
The strict-versus-balanced behavior described here comes from vendor-authored material for the V1 Pre-Alpha path. A secondary explainer dated October 4, 2026, likewise notes the absence of published benchmark numbers and independent validation across hardware, filesystem, and power-loss scenarios. The available sources do not establish a measured throughput percentage, latency figure, failure probability, or survival guarantee across named storage configurations. For a deployment decision, that leaves the practical question of how the selected mode behaves on the exact OS, filesystem, and hardware in use.
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