Lioran’s PUT timing trace is designed to show where a request spends time: receiving the body, writing and hashing it, syncing data, promoting the staged file, or persisting metadata. That breakdown can tell an operator what to investigate next, but it does not by itself prove a root cause or show that a change improved performance.
What the PUT timing trace reports
In its article on PUT timing, Lioran describes an opt-in trace controlled by BASTION_TRACE_PUT_TIMINGS. The article says values such as 1, true, and yes enable it, and that the setting is cached atomically. These are claims about the implementation as described by Lioran, not independently verified source-code findings. See Lioran’s timing article.
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The article separates timings into streaming work and the outer PUT operation:
- Streaming: receive duration, write duration, SHA-256 duration, flush duration, fsync duration, and total stream duration.
- Outer PUT: close, directory creation (
mkdir), rename, metadata, and total duration.
This is useful because a single total PUT duration hides whether time was spent handling the request body, doing file operations, or persisting metadata. The field names and behavior above are those the article reports.
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How to interpret a slow stage
A dominant stage narrows the search; it does not identify one cause. For example, receive duration can include multiple parts of the request path rather than measuring only the client’s raw upload speed.
If receiving the body dominates
Investigate the client’s upload rate and the path between client and server, including network conditions, TLS, reverse-proxy buffering, and request-body handling. Compare traces with the same client, object sizes, and network path before concluding that the storage engine is responsible.
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If fsync dominates
Look at device latency, filesystem behavior, virtualization, durability settings, and write-cache behavior. A long sync stage indicates where to investigate; it does not alone establish whether the cause is the device, configuration, or surrounding workload.
If metadata time rises under load
Examine the metadata path and its workload. Relevant areas include RocksDB compaction and write-ahead-log behavior, block-cache pressure, write stalls, disk contention, and concurrent metadata operations. The timing article identifies these as investigation targets, not as proven causes in a measured incident.
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If SHA-256 appears costly
The article says hashing is calculated incrementally while streaming. Measure its actual duration under a representative workload before treating hashing as a bottleneck or proposing a checksum change.
How the described PUT flow fits together
A companion Lioran article describes the V1 pre-alpha implementation as storing object bytes on the filesystem and metadata in RocksDB. Its reported sequence is:
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- Validate the request and check capacity and quota.
- Create a staging file, then stream the request body while hashing it.
- Flush the file and, when configured, fsync it.
- Check capacity and quota again.
- Promote the staged file by renaming it.
- Write metadata to RocksDB; if that write fails, the article says the implementation attempts to remove the promoted physical file.
This sequence helps explain why stream timings and outer PUT timings cover different work. It is the author’s account of the implementation, not an independently confirmed code review. The repository URL named in the companion article could not be fetched, so the ordering and failure behavior should be treated as reported rather than independently verified. See the companion article on Lioran S3 V1 Pre-Alpha.
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Timing values shown in Lioran’s article are illustrative diagnostic cases, not published benchmark results. The article does not provide a reproducible throughput or latency result. A trace can suggest where time went in a particular run; it cannot establish that a new version, setting, or hardware change is faster without a comparable repeat measurement.
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- Record the workload and environment for the run, including object sizes, concurrency, durability mode, machine and storage, filesystem, operating system, deployment type, network path, reverse proxy, TLS, and software commit or version.
- Identify which stage dominates, then form one specific hypothesis about that stage.
- Change one variable at a time and rerun with a comparable workload and environment.
- Compare stage timings as well as total duration or throughput. If important conditions differ, report those differences rather than treating the runs as directly comparable.
These details matter because headline throughput alone can conceal a changed workload or deployment path. Lioran’s own benchmark checklist calls for the same context, including CPU, RAM, disk, filesystem, OS, container or native execution, network topology, proxy, TLS, object-size distribution, concurrency, durability mode, and software version.
What the timing article establishes—and what it does not
The article provides a framework for separating stages in a PUT and using the largest duration to choose the next investigation. It does not supply independent verification of the named timing fields or implementation flow, nor does it demonstrate a performance improvement or establish a universal bottleneck. Treat the trace as a diagnostic instrument: useful for directing measurement, but not a substitute for controlled comparisons or source verification.
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