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The following commit(s) were added to refs/heads/master by this push:
new 3b00b7f583 HDDS-15801. Add blogs on S3 compatibility and S3
conditional requests (#488)
3b00b7f583 is described below
commit 3b00b7f583763f2f725468dbc8ed19389ea13dbe
Author: Peter Lee <[email protected]>
AuthorDate: Sat Jul 18 07:06:48 2026 +0800
HDDS-15801. Add blogs on S3 compatibility and S3 conditional requests (#488)
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@@ -0,0 +1,163 @@
+---
+title: "Making Apache Ozone’s S3 Compatibility Visible"
+date: 2026-06-27
+authors: ["peterxcli"]
+tags: [Ozone, S3, compatibility]
+---
+
+<!-- cspell:ignore peterxcli Xiangpeng Zstd amz -->
+
+Recently I created
[https://ozone.s3.peterxcli.dev/](https://ozone.s3.peterxcli.dev/), an
automated update dashboard for Apache Ozone S3 compatibility. It runs Ozone
against real S3 compatibility suites, publishes the result every day, and lets
you search test cases, inspect failures, read source snippets, open logs, and
check whether your PR helps the compatibility story. It is fully open source at
[https://github.com/peterxcli/ozone-s3-compatibility](https://github.com/peterxcli/ozone-
[...]
+
+<!-- truncate -->
+
+TL;DR:
+
+- Overall, **7% (50/711)** compatibility improvement at the time of writing.
+- Parquet \+ search-index optimization made the Pages data **11.8x smaller**,
yearly git growth **12x slower**, and search data **14x smaller**.
+- It runs Apache Ozone against
[`ceph/s3-tests`](https://github.com/ceph/s3-tests) and
[`minio/mint`](https://github.com/minio/mint), then publishes a GitHub Pages
report.
+- It turns compatibility from “a few epic tickets and some memory in people’s
heads” into a searchable, linkable, daily signal.
+- For users, it answers “does Ozone support this S3 behavior today?” For
developers, it answers “which exact test is failing, why, and did my PR move
it?”
+
+## Why did I create it?
+
+Before this dashboard was introduced, Apache Ozone had already invested
considerable effort in tracking the compatibility gap through several epic
issues: [Ozone S3 gateway Phase
4](https://issues.apache.org/jira/browse/HDDS-1186), [Ozone S3 gateway (phase
III)](https://issues.apache.org/jira/browse/HDDS-12716), and [S3 API
compatibility improvements and
fixes](https://issues.apache.org/jira/browse/HDDS-8423).
+
+But if you click through those links, you will quickly get lost. They are
useful, but they are not a fresh report of what Apache Ozone’s S3 compatibility
looks like today.
+
+Users don’t know whether Ozone supports a specific S3 behavior. Developers
don’t know whether a feature gap, implementation gap, or corner case is already
handled correctly. And when a compatibility issue is fixed or newly introduced,
there was no fresh source of truth to show that change.
+
+Also, when we develop S3 features, we often look at AWS S3 docs or source code
from other open source S3-compatible systems, then infer how the implementation
should behave and what test cases we should add. That works sometimes, but it
is still mostly guessing. There was no easy harness to continuously test
Ozone’s behavior against existing compatibility suites and make the result easy
to inspect.
+
+So that’s why I wanted to create this dashboard.
+
+## How to Use the Dashboard
+
+I usually think of the dashboard as having a few different modes.
+
+First, start from the latest run summary. The top cards show the current
`s3-tests` and `mint` compatibility rate, eligible case count, pass/fail/error
count, skipped count, and the delta compared with the previous run. This makes
it easy to know whether today’s result is better, worse, or basically
unchanged. One small detail here: the compatibility rate is `passed / (passed +
failed + errored)`.
+
+
+
+Second, use the search box as the main debugging entry point. You can search
by suite, test name, run id, run date, source path, feature name, status, or
failure text. For example, if you are working on object tagging, you can search
for `tagging`; if you are looking at a specific failure from `s3-tests`, you
can paste part of the error message. Search results show the matched fields,
suite, run date, status, feature tags, and a short failure preview.
+
+
+Third, click a search result. The modal shows the full failure detail, related
metadata, a permalink, and the test code. For `s3-tests`, the dashboard can
fetch the upstream source file and extract the Python test function, so you
don’t need to jump between the report, GitHub, and your local checkout just to
understand what the test is asking for.
+
+
+
+Fourth, open the current report detail. The page does not load all case
details upfront. Instead, it loads summary first, then fetches the full run
detail when you open the latest run or an archived run. Inside the run detail,
you can inspect suites, feature summaries, individual failed cases, and log
files.
+
+
+
+
+Fifth, use the trend panel. The top-line trend is useful to see whether the
overall compatibility is moving. The per-feature trend is even more useful
because a single global number can hide what changed. For example, a PR might
only improve object tagging, bucket listing, copy object behavior, or some
conditional-write corner case. Those changes are much easier to reason about
feature by feature.
+
+
+
+Sixth, use archived runs when you need history. Old runs are still there, but
their details are loaded only when opened. This is helpful when you want to
compare a failure today with an older run, or when a PR says “this test used to
fail before the fix.”
+
+
+Finally, the Parquet file inspector is there for people who want to inspect
the published dataset itself. This is mostly for debugging the dashboard and
data model, but it is surprisingly useful when you want to understand what
files were generated, how many rows they contain, and whether the published
data looks sane. I also want to thank Xiangpeng for building the [Parquet
Viewer](https://github.com/XiangpengHao/parquet-viewer), which let us inspect
the parquet file content directly on [...]
+
+
+
+For PR authors, there is also a PR comparison workflow. It can run an Ozone PR
head, compare it with the latest published main run, write a step summary, and
optionally comment back on the Ozone PR. That is the part I like the most:
compatibility is not just a dashboard to look at, but something you can wire
into development review.
+
+## How is this built?
+
+For the very first version, I literally just prompted Codex with `gpt5.4
xhigh`:
+
+:::info Prompt
+I want to create a repo that runs
[https://github.com/ceph/s3-tests](https://github.com/ceph/s3-tests) and
[https://github.com/minio/mint](https://github.com/minio/mint) on GitHub Action
and generate the compatibility report page on GitHub Pages nightly. Make the
page pretty. My desired steps that run in the GitHub Action are: clone the
Ozone repo, pull latest master change, compile, start running cluster, run Mint
and s3-tests to get result, compile result to be the page. We can show th [...]
+:::
+
+It got most of it right on the first try. I still made some follow-ups on the
result, like “attach `s3-tests` and `mint` as submodule” and “use `act` to test
the GitHub Action works locally within Docker”, but those were relatively minor
compared with the first scaffold.
+
+The nightly workflow now does roughly this:
+
+1. Build the Vue/Vite report frontend.
+2. Clone Ozone and the upstream compatibility suites.
+3. Build an Ozone distribution.
+4. Start a packaged Ozone compose cluster.
+5. Run `s3-tests` and Mint against the S3 Gateway.
+6. Normalize the raw output into a run result.
+7. Build the static Pages output, including the historical data.
+8. Publish to `gh-pages` for scheduled runs.
+
+The frontend is Vue 3 \+ Vite. The automation layer is just scripts and GitHub
Actions. The data processing is mostly Python. For the newer version, the
published report data is JSON.
+
+### Optimization
+
+After a few runs, I started to notice that the dashboard was becoming
sluggish. After some inspection, I found a few reasons.
+
+1. First, we originally used one JSON file to represent one run result. Each
latest run was about 0.236 MiB. This sounds small, but the problem is that we
did not lazy-load these files initially. Every time the page refreshed, even if
some files might already be cached by the browser, the TypeScript data
structure still ended up holding too much content from too many files. That was
memory-intensive.
+
+2. Second, the JSON data had a ton of room to compress. The data shape is very
repetitive: `run_id`, `suite_key`, `status`, feature names, source paths, class
names, test names, and similar object keys appear again and again. JSON stores
all of that as repeated text and nested objects. Even if HTTP compression helps
over the network, the browser still has to parse it into JavaScript objects,
and Git still stores changed blobs. This kind of data is much closer to a table
than a document, [...]
+
+3. Finally, the search index data was not normalized correctly. The index
repeated run metadata and case metadata too much, and it grew quickly. For Git
history, this is especially bad because every new run changes a large generated
file again.
+
+So then I basically applied three optimizations: lazy loading, Parquet
compression, and search/index normalization.
+
+1. **Lazy loading.** The page now loads the light catalog/index first. Full
run details are fetched only when you open the latest run or expand an archived
run. Search index loading is also delayed until you actually search, and the
browser can keep the FlexSearch index in IndexedDB. Logs are fetched only when
you open a log modal. Source snippets are fetched only when you open a case.
This makes the first page load much cheaper.
+2. **Compress data as Parquet.** Instead of treating every run as one large
JSON document, the report data is split into Parquet tables: catalog runs,
suites, features, per-run metadata, per-run cases, search rows, log files, and
log lines. The Parquet writer uses Zstd compression and dictionary encoding,
which is a much better fit for repeated values like suite names, statuses,
feature tags, and source paths. The frontend then queries these Parquet files
in the browser through DuckDB-Wasm.
+3. **Index file optimization.** The search rows now contain normalized fields
and a short `detail_preview`, while the full case detail can be hydrated from
the per-run case Parquet file when needed. For JSON fallback, the index and
search data are partitioned into smaller shards. For the normal Parquet path,
the global search index is a compressed Parquet file. The search index also has
a stable `index_id`, so the browser can reuse the IndexedDB cache when the data
has not changed.
+
+The results were dramatic:
+
+1. Parquet data now:
+ 1. search rows: 7.64 MiB, 33,876 rows, growing about 0.142-0.147 MiB/run
+ 2. run case/detail data excluding search: 4.92 MiB across 52 runs, growing
about 0.093 MiB/run
+ 3. catalog/index data: 0.03 MiB now, growing less than 1 KiB/run
+ 4. total Pages data: 12.59 MiB across 52 runs
+ 5. latest run data: 246,696 bytes \= 0.235 MiB
+2. Projection at the current rate:
+ 1. about \+0.236 MiB/run
+ 2. about \+7.1 MiB/month
+ 3. about \+86 MiB/year
+2. Compared with the JSON baseline:
+ 1. current data size: 149.11 MiB \-\> 12.59 MiB, about 11.8x smaller
+ 2. yearly growth: \~1.0 GiB/year \-\> \~86 MiB/year, about 12x slower
+ 3. search data: 107.09 MiB \-\> 7.64 MiB, about 14x smaller
+
+## Impact
+
+Ozone has already improved since the dashboard was released.
+
+
+I don’t want to take credit for every improvement below, but I do think the
dashboard changed the workflow. It gives us concrete failing cases, links that
can be pasted into PRs, and an easy way to check whether the gap is still there
after a patch.
+
+There are already quite a few PRs and designs which aim to fix compatibility
gaps discovered or made easier to discuss by the dashboard:
+
+- Object tagging and bucket tagging:
+ - [HDDS-15258. Include x-amz-tagging-count header in HEAD object tagging
responses](https://github.com/apache/ozone/pull/10265)
+ - [HDDS-15283. GetObjectTagging should return TagSet in sorted order of
key](https://github.com/apache/ozone/pull/10277)
+ - [HDDS-15259. PutObject should treat null tag value for x-amz-tagging
header as empty tag value](https://github.com/apache/ozone/pull/10299)
+ - [HDDS-15510. Implement OM read/write paths for bucket tagging with audit
and metrics](https://github.com/apache/ozone/pull/10498)
+ - [Proposal: S3 Bucket Tagging in Apache
Ozone](https://docs.google.com/document/d/1jq1JOatCrUpD7k3PutfkGrZ7nFC1C69BkMoMWcRWaw4/edit?tab=t.0#heading=h.rs6cprl5cddi)
+- Bucket listing and directory bucket behavior:
+ - [HDDS-15302. Throw S3 InvalidArgument for RequestParameters getInt if
parsing error](https://github.com/apache/ozone/pull/10297)
+ - [HDDS-15303. Enable prefix filter for
OBS](https://github.com/apache/ozone/pull/10298)
+ - [HDDS-15450. Implement ListDirectoryBuckets S3 API for Directory Buckets
support](https://github.com/apache/ozone/pull/10408)
+- Other compatibility-related correctness work:
+ - [HDDS-15515. Support object Content-Type end-to-end in S3
Gateway](https://github.com/apache/ozone/pull/10472)
+
+The most useful effect is not just that the percentage moved. It is that the
compatibility gap became much more concrete. A failed test can now become a
dashboard link, then a JIRA, then a PR, then a nightly result showing whether
it passed.
+
+## Insight
+
+1. The main reason this was adopted by other community members is that I
shared it as soon as it was useful enough for others to try: [\[DISCUSS\]
Nightly Ozone S3 compatibility
report](https://lists.apache.org/thread/2td4jt8r26pwph5lwq3ncbnv97n27d5w). It
was not perfect yet, and I think posting early was important.
+2. This played a role in my promotion to PMC because it made a long-standing
problem visible and gave the community a practical tool to keep improving it.
+3. Not every failed test case means Ozone is wrong. Sometimes the
compatibility suite itself has a questionable expectation. For example, I
opened [ceph/s3-tests\#735](https://github.com/ceph/s3-tests/pull/735) to align
conditional write tests with `If-None-Match: *` semantics. At the time of
writing, it is still open. This is a good reminder that compatibility work is
not only “fix Ozone until all tests pass”; sometimes it is also “debug the
tests and upstream better expectations.”
+4. A dashboard is also a social tool. If the result is not searchable,
linkable, and easy to quote in a PR, it will not be used much. The permalink
part sounds small, but it matters a lot when people discuss failures
asynchronously.
+5. A single compatibility percentage is useful, but it is not enough. The real
value is in the drill-down: which feature changed, which test changed, what
error message changed, and what source code was the test running. Without that,
the percentage is just a vanity number.
+6. LLMs are very good at scaffolding this kind of glue project. But the part
that made it trustworthy was not the first generated code. It was the boring
follow-up: run it locally with `act`, add tests, normalize data, make it
faster, and keep it running every day.
+7. I should have treated the data model as important from the beginning. The
UI can grow quickly, but if the data layout is wrong, the page becomes slow and
the Git history becomes huge. Moving to Parquet later worked, but I probably
should have designed the report as a dataset earlier.
+
+## Conclusion
+
+This dashboard started as a pretty simple idea: run `s3-tests` and Mint
against Apache Ozone every night, then publish a nice report page. But after
using it for a while, I think the more important part is that it turns S3
compatibility into a feedback loop.
+
+Before, compatibility was scattered across JIRA epics, docs, people’s memory,
and occasional manual testing. Now there is a daily report that users can
search, developers can debug, and PR authors can use as evidence. It is still
not perfect, and some failures still need human judgment, but at least we have
a place to look.
+
+If you are using Ozone S3 Gateway, or if you are working on Ozone S3
compatibility, please try the dashboard and the repo. Search for the feature
you care about, click into the failures, and open issues or PRs when something
looks wrong. The more boring and continuous this compatibility work becomes,
the better Ozone will get.
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@@ -0,0 +1,417 @@
+---
+title: "Fast Optimistic Concurrency Control with Apache Ozone S3 Conditional
Requests"
+date: 2026-07-08
+authors: ["peterxcli"]
+tags: [Ozone, S3, concurrency, conditional-requests]
+---
+
+<!-- cspell:ignore peterxcli RDBMS LSN CAS amaliujia Turbopuffer MVCC -->
+
+:::note
+Apache Ozone will support conditional `PutObject`, `GetObject`, `HeadObject`,
`CopyObject`, and `CompleteMultipartUpload` in the upcoming [2.2
release](https://github.com/apache/ozone/releases/tag/ozone-2.2.0-RC0) (RC0 is
currently under a
[vote](https://lists.apache.org/thread/gz567ljydh4ht63h6c9pjfclrbrrr9z7)), and
will add conditional `DeleteObject` and `DeleteObjects` support in 2.3.
+:::
+
+An increasing number of database systems are moving storage to S3 in
shared-everything architectures to reduce cost, dependencies, and operational
complexity. In the Hadoop 🐘 era, we typically used ZooKeeper and HDFS as the
control plane and data plane. Modern systems are moving the control plane to
self-managed consensus groups or RDBMS-backed catalogs, while moving the data
plane onto AWS S3 or S3-compatible storage.
+
+Shared-everything systems usually have two pain points: communication overhead
and coordination. To reduce write latency, systems often use inline data
writes, background flush, and LSN-based union reads. To reduce read latency,
they add multi-layer caches, such as self-managed or OS-managed in-memory
caches and on-disk caches. Coordination is harder: multiple clients may read
the same metadata, make decisions locally, and then try to update the same
object. Without a storage-level compa [...]
+
+Because Apache Ozone exposes S3, HCFS, HttpFS, and Java APIs as part of its
multi-protocol story, conditional requests have become increasingly important.
This work is now nearly complete.
+
+<!-- truncate -->
+
+## TL;DR
+
+Apache Ozone is adding S3 conditional request support for operations such as
`PutObject`, `GetObject`, `HeadObject`, `CopyObject`, `DeleteObject`, and
`CompleteMultipartUpload`. This allows applications to perform an operation
only when the object state matches expected conditions, such as “create this
object only if it does not already exist” or “overwrite this object only if the
current ETag still matches.”
+
+Under the hood, Ozone reuses its atomic rewrite path. For conditional writes,
the S3 Gateway passes the caller’s expected ETag to Ozone Manager (OM). OM
validates it near the metadata write path, stores the matched generation in the
open key, and revalidates it during commit. This keeps the ETag check close to
the place where the object state changes, so Ozone can provide a distributed
CAS-style primitive without adding an extra gateway-side metadata read on the
successful path.
+
+This unlocks safer optimistic concurrency control for data systems built on
top of object storage, including metadata catalogs, WAL-like workflows,
leader-election patterns, queues, model-serving caches, and object-store-backed
databases.
+
+## What is an S3 conditional request?
+
+:::note
+You can use conditional requests to add preconditions to your S3 operations.
To use conditional requests, you add an additional header to your Amazon S3 API
operation. This header specifies a condition that, if not met, will result in
the S3 operation failing.
+
+Source: [Amazon S3 conditional
requests](https://docs.aws.amazon.com/AmazonS3/latest/userguide/conditional-requests.html)
+:::
+
+Conditional requests allow atomic CAS operations on target objects: clients
can coordinate through S3 objects instead of relying on external arbiters.
Using S3 conditional requests is like moving part of the coordination logic
into storage.
+
+AWS also noted that conditional requests [let customers remove workaround code
and simplify their
systems](https://www.allthingsdistributed.com/2025/03/in-s3-simplicity-is-table-stakes.html#:~:text=When%20we%20moved%20S3%20to,similar%20reaction).
The same storage-level feature also [powers S3
Tables](https://www.allthingsdistributed.com/2025/03/in-s3-simplicity-is-table-stakes.html#:~:text=they%20involve%20a%20complex,storage%2Dlevel%20features),
which manages tabular data on S3.
+
+Common conditional request patterns include:
+
+- **Conditional `PUT`**
+ - Use `If-None-Match: *` to create an object only if it does not already
exist.
+ - Use `If-Match: <etag>` to overwrite an object only if the current ETag is
still the one the client observed.
+ - Use case: metadata commits, manifest updates, distributed lock files, and
create-only log chunks.
+- **Conditional `GET`**
+ - Use `If-None-Match: <etag>` or date-based conditions to download the
object only when it has changed.
+ - Use case: cache validation, dynamic model reloading, config refresh, and
avoiding repeated downloads of large objects.
+- **Conditional `HEAD`**
+ - Same idea as conditional `GET`, but metadata-only.
+ - Use case: checking whether an object is still current without fetching the
body.
+- **Conditional `POST` / `CompleteMultipartUpload`**
+ - Multipart upload writes data in parts, but the destination object becomes
visible only when `CompleteMultipartUpload` succeeds.
+ - Conditional completion lets the client say: “complete this large object
only if the destination still does not exist” or “only if the destination still
matches this ETag.”
+ - Use case: large object writes where the final commit still needs
create-only or compare-and-swap semantics.
+- **Conditional `COPY`**
+ - Source-side conditions make sure the source object is the version the
client expects.
+ - Destination-side conditions make sure the copy does not overwrite
unexpected data.
+ - Use case: snapshot promotion, clone/copy workflows, and metadata copy
where both source and destination need validation.
+- **Conditional `DELETE`**
+ - Use `If-Match: <etag>` to delete only if the object is still unchanged.
+ - Use `If-Match: *` to delete only if the object exists.
+ - Use case: preventing a client from deleting an object that another writer
has already replaced.
+
+To see this in practice, let’s look at how modern systems use these APIs.
+
+## Application use cases
+
+Who is this for?
+
+### Dynamic AI model reloading
+
+Advanced inference servers often cache massive model weights on local NVMe
drives. To keep these models updated without constantly re-downloading them
every few minutes, they can use S3 conditional requests. For example, the
inference server can send a `GET` request with `If-None-Match` and the ETag of
the model it already has.
+
+If the ETag still matches, Ozone can return `304 Not Modified`, so the server
keeps using the local cached model and skips the download. If the ETag does not
match, the model has been updated, so the server downloads the newest model and
stores the new ETag for the next check. Thanks to
[@amaliujia](https://github.com/amaliujia) for the example.
+
+
+
+*Figure 1. An inference server uses `If-None-Match` to avoid re-downloading
unchanged model weights.*
+
+### Turbopuffer
+
+Turbopuffer’s object-storage queue is a great example of using a small
metadata object to coordinate larger data work. In the simple version, a pusher
reads `queue.json`, appends a job, and writes it back with CAS. A worker does
the same thing to claim the next job. The CAS write only succeeds if
`queue.json` has not changed since the client read it; otherwise the client
reads the new queue and retries.
+
+This design is simple, but many clients can contend on one queue object.
Turbopuffer improves it by batching updates with group commit, then moving the
object-storage interaction behind a stateless broker. Clients talk to the
broker, the broker performs one group-commit loop, and it only acknowledges
work after the updated queue is durably committed.
+
+
+
+*Figure 2. Turbopuffer’s object-storage queue batches contended CAS updates
behind a broker. Source: [Turbopuffer object storage
queue](https://turbopuffer.com/blog/object-storage-queue).*
+
+### SlateDB
+
+SlateDB is an embedded LSM-tree storage engine built for object storage. It
stores durable state as object-store files: WAL SSTs, compacted SSTs, and
manifest files. Like other LSM engines, it relies on immutable files for most
data movement, but it still needs a safe coordination point to decide which
writer owns the next part of the log.
+
+Conditional object creation is useful here because SlateDB can use object
names as fencing points. Instead of overwriting a shared file, the writer
creates the next manifest or WAL object with `If-None-Match: *`. The write
succeeds only if that object does not already exist.
+
+
+
+*Figure 3. A SlateDB writer uses conditional object creation as a fencing
point for object-store state.*
+
+### Iceberg file catalog
+
+Iceberg is another natural fit for this style of coordination. Iceberg table
state is maintained in metadata files, and each commit creates a new metadata
file. The commit succeeds by atomically swapping the table metadata pointer
from the old metadata file to the new one. If another writer commits first, the
swap fails, and the writer retries against the new table state.
+
+For an Iceberg-style file catalog on object storage, conditional requests can
provide the missing compare-and-swap primitive. The catalog can store a small
pointer object, such as `current.json`, that points to the latest table
metadata file. A writer first writes the new metadata file using a unique name,
then updates the current metadata pointer only if its ETag still matches the
ETag that the writer read.
+
+
+
+*Figure 4. An Iceberg-style file catalog updates a small metadata pointer only
if its ETag still matches.*
+
+### Leader election
+
+Leader election can also be built on top of conditional writes. The basic idea
is to have all nodes compete to create the next lock file, such as
`lock-0000000002.json`, using `If-None-Match: *`. Only one node can create the
file successfully. That node becomes the leader for that epoch; the others
receive a precondition failure and keep watching.
+
+
+
+*Figure 5. Nodes compete to create the next epoch lock file; only one writer
succeeds.*
+
+Leader election by itself is not enough. A paused old leader can come back and
still believe it owns the lock. This is the “zombie leader” problem. The fix is
to use the leader epoch as a fencing token. Every request made by the leader
includes the epoch, and downstream systems reject requests with an older epoch
than the highest one they have already seen.
+
+
+
+*Figure 6. Epoch fencing prevents an old paused leader from writing after a
newer leader takes over.*
+
+The leader should periodically update the lock file it acquired to signal
liveness. Other nodes can poll the lock and check whether the lock was released
or expired by looking at `Last-Modified`, which S3 exposes as standard object
metadata.
+
+### WAL Writes and Reads with OSWALD
+
+OSWALD, the Object Storage Write-Ahead Log Device, shows how to build a WAL
directly on object storage primitives. The design has three object types: a
manifest object, snapshots, and log chunks. The manifest tracks the latest
checkpoint and garbage collection progress; chunks hold the log content.
+
+Appending to the WAL can be done with conditional object creation. A writer
creates the next chunk with `PUT If-None-Match`. If another writer already
created that chunk for the same LSN, the write fails and the writer catches up
by tailing the log. After creating a chunk, the writer also checks the manifest
with `GET If-None-Match` to make sure garbage collection has not moved past its
LSN before acknowledging the write.
+
+
+
+*Figure 7. OSWALD uses manifest, snapshot, and chunk objects to build a WAL on
object storage. Source: [OSWALD](https://github.com/nvartolomei/oswald).*
+
+Now let’s look at how Ozone implements S3 conditional requests efficiently.
+
+## Technical details
+
+To keep performance overhead minimal—thanks to
[Ivan](https://github.com/ivandika3)’s suggestion—we coalesce the “conditional
flag” with the normal client-to-server RPC message, so no additional RPC round
trip is introduced. This fits well with the optimistic concurrency path.
+
+Before diving into that coalescing optimization, let’s look at Ozone’s atomic
rewrite work, which is the foundation reused by S3 conditional requests.
+
+### Atomic rewrite
+
+
+
+*Figure 8. Atomic rewrite create and write phases.*
+
+The client indicates the expected epoch of the target object in the
key-creation request. After Ozone Manager receives the `createKey` request, it
first compares the expected epoch with the live key in the key table. If they
match, OM stores the expected epoch along with the open key record in the open
key table.
+
+At this stage, two clients can both successfully create an open key, so they
can both start streaming file data to the datanode pipeline. Once a client
believes the file data write is finished, it starts committing the key.
+
+:::note
+The client ID in `/vol1/bucket1/key1/{client_id}` is generated by Ozone
Manager. It is a unique, non-human-readable ID that binds to a single
key-creation lifecycle. It is not a persistent client ID. “Session ID” would
probably be a better name, but the diagram uses a meaningful ID for clarity.
+:::
+
+During the commit phase, Ozone Manager compares the expected epoch stored in
the client’s open key record with the live key epoch in the key table in the
same transaction. If they match, OM overwrites the live key with the open key.
If they do not match, OM cleans up the open key table and returns an atomic
rewrite failure for the concurrent conflict.
+
+
+
+*Figure 9. Atomic rewrite commit phase.*
+
+### Coalescing the conditional flag
+
+In normal workloads, and especially under optimistic concurrency control, the
successful path is expected to be common. Therefore, we validate the key’s ETag
metadata at the same time as the key-creation request. Instead of introducing
an additional request to fetch the latest key epoch and then plumbing that
epoch into the atomic rewrite path, we extend atomic rewrite so it can
recognize the expected ETag sent from S3 Gateway, validate it, translate it to
an expected epoch, and store th [...]
+
+> For more information, see the discussion in
[apache/ozone#9334](https://github.com/apache/ozone/pull/9334#discussion_r2558578333).
+
+
+
+*Figure 10. Coalescing the conditional flag into the normal Ozone write path.*
+
+### Implementation by API
+
+The discussion, design, and patches are available in Apache Jira and GitHub:
+
+- Epic ticket: [HDDS-13117](https://issues.apache.org/jira/browse/HDDS-13117)
+- Design:
[`s3-conditional-requests.md`](https://github.com/apache/ozone/blob/master/hadoop-hdds/docs/content/design/s3-conditional-requests.md)
+
+#### PutObject
+
+Ozone Manager enforces conditional writes using a two-phase validation
process, returning specific HTTP error codes depending on when a state mismatch
is detected.
+
+During the initial `createKey` request, if OM determines that the expected
ETag already mismatches the current state of the key, it immediately rejects
the request with `412 Precondition Failed`.
+
+If the initial check passes but a concurrent modification occurs during the
upload, OM detects the discrepancy during the `commitKey` phase. By comparing
the expected epoch stored in the open session against the live key record in
the key table, OM identifies the concurrent update and returns `409 Conflict`.
+
+Related patches:
+
+- [apache/ozone#10182](https://github.com/apache/ozone/pull/10182)
+- [apache/ozone#10023](https://github.com/apache/ozone/pull/10023)
+- [apache/ozone#9815](https://github.com/apache/ozone/pull/9815)
+
+##### `If-None-Match`
+
+We extend the existing atomic rewrite path to support the “generation must
match `0`” condition, which matches GCS semantics. `If-None-Match: *` then
reuses this create-if-absent path.
+
+```mermaid
+sequenceDiagram
+ actor User
+ participant GW as S3 Gateway
+ participant OM as Ozone Manager
+
+ User->>GW: PUT object with If-None-Match = *
+ GW->>OM: createKey(expectedDataGeneration = 0)
+ OM->>OM: Reject if key already exists
+ OM-->>GW: Open key or KEY_ALREADY_EXISTS
+ opt Open key created
+ GW->>OM: commitKey()
+ OM->>OM: Recheck key absence during commit
+ OM-->>GW: Success or generation mismatch
+ end
+ GW-->>User: 200 OK or 412 Precondition Failed
+```
+
+*Figure 11. `PUT If-None-Match` maps create-if-absent semantics to Ozone’s
generation-match path.*
+
+##### `If-Match`
+
+S3 Gateway carries the expected ETag along with the `createKey` request. OM
compares it with the live key, translates it to the expected epoch recognized
by atomic rewrite, and then reuses the existing commit path.
+
+```mermaid
+sequenceDiagram
+ actor User
+ participant GW as S3 Gateway
+ participant OM as Ozone Manager
+
+ User->>GW: PUT object with If-Match ETag
+ Note over GW,OM: No pre-read for current ETag/updateID on optimistic path
+ GW->>OM: createKey(expectedETag = ETag)
+ OM->>OM: Validate expected ETag against live key
+ alt ETag already unmatched
+ OM-->>GW: ETAG_MISMATCH or KEY_NOT_FOUND
+ GW-->>User: 412 Precondition Failed
+ else ETag matches
+ OM->>OM: Derive updateID and persist expected epoch
+ OM-->>GW: Open key with resolved expected epoch
+ GW->>OM: commitKey()
+ OM->>OM: Compare open session epoch with live key record
+ alt Epoch mismatch
+ OM-->>GW: GENERATION_MISMATCH
+ GW-->>User: 409 ConditionalRequestConflict
+ else Epoch matches
+ OM->>OM: Commit successful
+ OM-->>GW: Success
+ GW-->>User: 200 OK
+ end
+ end
+```
+
+*Figure 12. `PUT If-Match` validates the ETag once, stores the matched epoch,
and rechecks that epoch at commit time.*
+
+#### GetObject / HeadObject
+
+For `GetObject` and `HeadObject`, S3 Gateway fetches key metadata from OM and
evaluates the conditional headers against that metadata.
+
+Related patch: [apache/ozone#10031](https://github.com/apache/ozone/pull/10031)
+
+```mermaid
+sequenceDiagram
+ actor User
+ participant GW as S3 Gateway
+ participant OM as Ozone Manager
+
+ User->>GW: GET/HEAD with conditional headers
+ GW->>OM: getS3KeyDetails() or headS3Object()
+ OM-->>GW: ETag, modificationTime, and key info
+ GW->>GW: Evaluate ETag/date precedence rules
+ alt Not modified
+ GW-->>User: 304 Not Modified
+ else Precondition failed
+ GW-->>User: 412 Precondition Failed
+ else Preconditions pass
+ opt GET or ranged GET
+ GW->>OM: getKey() or open data stream
+ OM-->>GW: Object data
+ end
+ GW-->>User: 200 OK or 206 Partial Content
+ end
+```
+
+*Figure 13. `GET` and `HEAD` evaluate conditional headers after retrieving
object metadata from OM.*
+
+#### CopyObject
+
+`CopyObject` is effectively `GetObject` plus `PutObject`, because it must
enforce conditional logic on both the source and destination. During the source
phase, the gateway resolves the source object metadata and evaluates
preconditions against that snapshot before streaming the data. In the
destination phase, the gateway performs a conditional write to the destination,
reusing the atomic write APIs introduced for `PutObject`.
+
+Related patch:
[apache/ozone#10207](https://github.com/apache/ozone/pull/10207). Thanks
[@YutaLin](https://github.com/YutaLin).
+
+```mermaid
+sequenceDiagram
+ actor User
+ participant GW as S3 Gateway
+ participant OM as Ozone Manager
+
+ User->>GW: COPY with source and destination conditions
+ GW->>OM: Get source key details
+ OM-->>GW: Source metadata and bound content stream
+ GW->>GW: Evaluate x-amz-copy-source-if-* headers
+ alt Source precondition failed
+ GW-->>User: 412 Precondition Failed
+ else Source passes
+ GW->>OM: Create destination key with optional conditions
+ OM->>OM: Validate destination state and open key
+ OM-->>GW: Open key or 412-mapped error
+ opt Destination open key created
+ GW->>OM: Commit destination key using source snapshot
+ OM->>OM: Revalidate generation for conditional writes
+ OM-->>GW: Success or 412-mapped error
+ end
+ GW-->>User: 200 OK or 412 Precondition Failed
+ end
+```
+
+*Figure 14. `CopyObject` checks source preconditions first, then reuses
conditional destination writes.*
+
+#### CompleteMultipartUpload
+
+`CompleteMultipartUpload` compares the caller’s expected ETag or
create-if-absent condition with the committed key state.
+
+Related patch:
[apache/ozone#10164](https://github.com/apache/ozone/pull/10164). Thanks
[@YutaLin](https://github.com/YutaLin).
+
+```mermaid
+sequenceDiagram
+ actor User
+ participant GW as S3 Gateway
+ participant OM as Ozone Manager
+
+ User->>GW: CompleteMultipartUpload with optional conditions
+ GW->>GW: Parse conditional write headers
+ GW->>OM: completeMultipartUpload(keyArgs + partsList)
+ OM->>OM: Acquire bucket lock
+ OM->>OM: Load current committed key and MPU state
+ OM->>OM: Validate destination precondition
+ alt Precondition failed
+ OM-->>GW: KEY_ALREADY_EXISTS, KEY_NOT_FOUND, or ETAG_* error
+ GW-->>User: 412 Precondition Failed
+ else Preconditions pass
+ OM->>OM: Validate MPU parts and build final key
+ OM->>OM: Write key table entry and remove MPU state
+ OM-->>GW: Success
+ GW-->>User: 200 OK
+ end
+```
+
+*Figure 15. Conditional `CompleteMultipartUpload` validates the destination
state before publishing the completed object.*
+
+#### DeleteObject
+
+`DeleteObject` compares the caller’s expected ETag with the committed key
state before writing the delete tombstone.
+
+Related patch: [apache/ozone#10511](https://github.com/apache/ozone/pull/10511)
+
+```mermaid
+sequenceDiagram
+ actor User
+ participant GW as S3 Gateway
+ participant OM as Ozone Manager
+
+ User->>GW: DELETE with optional If-Match
+ alt No If-Match
+ GW->>OM: deleteKey()
+ OM->>OM: Apply existing delete semantics
+ OM-->>GW: Success or KEY_NOT_FOUND
+ GW-->>User: 204 No Content
+ else If-Match present
+ GW->>OM: deleteKey(expectedETag or *)
+ OM->>OM: Validate current key under lock and write tombstone
+ OM-->>GW: Success, KEY_NOT_FOUND, or ETAG_* error
+ GW-->>User: 204 No Content or 412 Precondition Failed
+ end
+```
+
+*Figure 16. Conditional `DeleteObject` deletes only when the current object
still matches the expected ETag.*
+
+## Benchmark
+
+The main performance question for conditional writes is whether adding the
precondition makes the common successful path slower than a normal write. In
Ozone, the expected ETag or create-if-absent flag is carried with the normal S3
Gateway to OM request, then validated in OM’s metadata update path. This design
avoids a separate gateway-side metadata read before every conditional write.
+
+To sanity-check that assumption, we ran a small local benchmark against a
Docker Compose Ozone cluster through S3 Gateway. The benchmark used 4 KiB
objects, 15 warmup iterations, and 100 measured iterations per operation. It
compared four operations:
+
+- normal `PUT` creating a new key,
+- `PUT If-None-Match: *` creating a new key,
+- normal `PUT` overwriting an existing key,
+- `PUT If-Match: <etag>` overwriting an existing key.
+
+
+
+*Figure 17. Local benchmark latency for normal `PUT`, `PUT If-None-Match`,
normal overwrite, and `PUT If-Match`.*
+
+In this setup, conditional create was effectively the same as normal create:
the mean latency differed by about 0.12 ms, and the p95 was slightly lower.
Conditional overwrite was also close to normal overwrite: the mean latency was
about 0.35 ms higher, while p95 remained slightly lower and p99 was nearly
identical.
+
+## Conclusion
+
+Object storage has quietly crossed a line. It is no longer just where systems
park their bytes. It has become the place where they coordinate. Metadata
catalogs, write-ahead logs, leader election, job queues, model caches: all of
these need a single point of truth about “who wrote last,” and increasingly
they want to ask that question of the storage layer itself.
+
+By bringing native S3 conditional requests to Apache Ozone without adding an
extra RPC on the happy path, we turn Ozone into a first-class substrate for
optimistic concurrency control. Applications can compare-and-swap directly
against the objects they already store, and retire the external lock services,
catalog databases, and consensus clusters they once needed just to avoid a lost
update.
+
+The result is a simpler architecture: fewer moving parts, one less thing to
operate, and coordination that lives exactly where the data does.
+
+## References
+
+- [Amazon S3 conditional
requests](https://docs.aws.amazon.com/AmazonS3/latest/userguide/conditional-requests.html)
+- [In S3, simplicity is table
stakes](https://www.allthingsdistributed.com/2025/03/in-s3-simplicity-is-table-stakes.html)
+- [Protocols for transactional
usage](https://www.bitsxpages.com/p/protocols-for-transactional-usage)
+- [Leader election with S3 conditional
writes](https://www.morling.dev/blog/leader-election-with-s3-conditional-writes/)
+- [OSWALD writer-writer
conflicts](https://nvartolomei.com/oswald/#writer-writer-conflicts)
+- [OSWALD repository](https://github.com/nvartolomei/oswald/tree/main/p/Oswald)
+- [An MVCC-like columnar table on S3 with constant-time
deletes](https://www.shayon.dev/post/2025/277/an-mvcc-like-columnar-table-on-s3-with-constant-time-deletes/#the-delete-problem-with-immutable-formats)
+- [Turbopuffer object storage
queue](https://turbopuffer.com/blog/object-storage-queue)
+- [SeaweedFS S3 conditional
operations](https://github.com/seaweedfs/seaweedfs/wiki/S3-Conditional-Operations)
+- [Hacker News discussion on S3 conditional
writes](https://news.ycombinator.com/item?id=45493158)
+- [Buffer HA pipelines without
Kafka](https://www.opendata.dev/blog/buffer-ha-pipelines-without-kafka)
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diff --git a/blog/authors.yml b/blog/authors.yml
index c7d2a5fc64..cece1dea55 100644
--- a/blog/authors.yml
+++ b/blog/authors.yml
@@ -21,6 +21,12 @@ apache-ozone-community:
url: https://ozone.apache.org
image_url: /img/ozone-logo.svg
+peterxcli:
+ name: Peter Lee
+ title: Apache Ozone PMC
+ url: https://github.com/peterxcli
+ image_url: https://github.com/peterxcli.png
+
jojochuang:
name: Wei-Chiu Chuang
title: Apache Ozone PMC
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