pingzh commented on code in PR #205:
URL: https://github.com/apache/datafusion-site/pull/205#discussion_r4197798158
##########
content/blog/2026-10-01-datafusion-comet-1.1.0.md:
##########
@@ -0,0 +1,602 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.1.0 Release
+date: 2026-10-01
+author: pmc
+categories: [subprojects]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+[TOC]
+
+The Apache DataFusion PMC is pleased to announce version 1.1.0 of the
[Comet](https://datafusion.apache.org/comet/) subproject.
+
+Comet is an accelerator for Apache Spark that translates Spark physical plans
to DataFusion physical plans for
+improved performance and efficiency without requiring any code changes.
+
+This release covers roughly seven weeks of development since 1.0.0: 398
commits from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.1/docs/source/changelog/1.1.0.md
+
+The highlights:
+
+- **Native Iceberg writes** (experimental): Comet can write Iceberg data files
with iceberg-rust instead of
+ iceberg-java.
+- **Memory management**: Comet now measures the native memory its pools don't
track, logs it on every executor,
+ and fixes several long-standing pool accounting bugs.
+- **Native shuffle over Apache Celeborn**: Comet's side is in place, but it
needs a Celeborn client API that no
+ released Celeborn version provides yet.
Review Comment:
This makes both the native implementation and the current fallback with
released clients explicit.
```suggestion
- **Native shuffle over Apache Celeborn**: 1.1.0 includes the native writer
and reader, but released Celeborn
clients currently fall back to ordinary Spark/Celeborn shuffle.
```
##########
content/blog/2026-10-01-datafusion-comet-1.1.0.md:
##########
@@ -0,0 +1,602 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.1.0 Release
+date: 2026-10-01
+author: pmc
+categories: [subprojects]
+---
+
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements. See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+[TOC]
+
+The Apache DataFusion PMC is pleased to announce version 1.1.0 of the
[Comet](https://datafusion.apache.org/comet/) subproject.
+
+Comet is an accelerator for Apache Spark that translates Spark physical plans
to DataFusion physical plans for
+improved performance and efficiency without requiring any code changes.
+
+This release covers roughly seven weeks of development since 1.0.0: 398
commits from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.1/docs/source/changelog/1.1.0.md
+
+The highlights:
+
+- **Native Iceberg writes** (experimental): Comet can write Iceberg data files
with iceberg-rust instead of
+ iceberg-java.
+- **Memory management**: Comet now measures the native memory its pools don't
track, logs it on every executor,
+ and fixes several long-standing pool accounting bugs.
+- **Native shuffle over Apache Celeborn**: Comet's side is in place, but it
needs a Celeborn client API that no
+ released Celeborn version provides yet.
+- **Native Parquet writes on Spark 4.0+** (experimental), now built on Spark's
own write path.
+
+## Native Iceberg Writes (Experimental)
+
+Until now, Comet accelerated Iceberg reads but left writes to the JVM. In
1.1.0, Comet can write Iceberg data
+files natively using [iceberg-rust](https://github.com/apache/iceberg-rust).
The feature is **experimental and
+disabled by default**, and it only engages under fairly strict conditions.
+
+### Splitting the write operator
+
+Spark writes an Iceberg table with a single operator that writes the data
files, writes the metadata, commits,
+and validates against the catalog. Because file writing is bundled with the
metadata and commit steps, there was
+no separate piece for Comet to replace.
+
+Setting `spark.comet.write.iceberg.splitOperator.enabled=true` splits eligible
Iceberg writes into two operators:
+
+1. **`IcebergWrite`** writes data files on the executors and returns each
task's commit message.
+2. **`IcebergCommit`** collects the commit messages on the driver and performs
the normal Iceberg commit, once.
+
+With only the split enabled, iceberg-java still writes the files; the native
writer, described next, replaces
+that step. The split covers `INSERT INTO` and DataFrame `append`, static and
dynamic `INSERT OVERWRITE`, and
+copy-on-write `DELETE`, `UPDATE`, and `MERGE`, on every supported Spark
version. Merge-on-read writes are left
+alone. When Comet can't split a write (an unrecognized write class, CTAS on
Spark 3.4, or a write that needs
+Spark's commit coordinator), it plans the write exactly as if Comet weren't
there.
+
+### Writing Parquet natively
+
+The native writer requires the split. Also setting
`spark.comet.iceberg.write.enabled=true` hands each task's
+Parquet writing to iceberg-rust. The driver passes the native writer
everything it needs: schema, partition spec,
+data location, Parquet settings, writer mode, and object-store configuration.
Each task writes its files and
+returns their metadata as an in-memory Iceberg manifest.
+
+From there, iceberg-java takes over again. The JVM reads the manifest,
recomputes each file's metrics from its
+Parquet footer, and builds the same commit message the JVM writer would have
produced. Snapshots, manifest
+lists, commit validation, and retries all work as before.
+
+The native writer consumes Arrow batches from a Comet operator, so the query
feeding the write must run in Comet
+too. Writes from a local relation, such as `INSERT ... VALUES`, also need
+`spark.comet.exec.localTableScan.enabled=true`.
+
+To see which path a write took, check the physical plan: a native write shows
`CometIcebergWrite` under
+`IcebergCommit`, and a write that fell back shows `IcebergWrite`.
+
+### Matching iceberg-java
+
+The native writer aims to write the same table iceberg-java would, down to the
metadata. Manifest metrics drive
+pruning for every future reader, so rather than trust the native writer's
numbers, Comet recomputes them with
+iceberg-java's own code. Parity tests write the same rows through both writers
and compare the committed counts
+and bounds. The cost is one small footer read per file.
+
+Eligibility is an allowlist. A write goes native only if its whole
configuration matches the documented set of
+supported settings. Anything else, including properties added by future
Iceberg versions, falls back to
+iceberg-java and reports why in Comet's extended `EXPLAIN` output. Encryption,
object-storage layout, custom
+location providers, bloom filters, and unrecognized `parquet.*` properties all
fall back. For a partitioned
+table, Iceberg asks Spark to cluster and sort rows by partition value, such as
`bucket(16, id)` or `days(ts)`,
+before writing. That step must also run natively, which it now can because
Iceberg's system functions have
+native implementations.
+
+Comet decides all of this at planning time, including checking that every
iceberg-java class it relies on is
+where it expects. An Iceberg release that moves one falls back instead of
failing tasks mid-write. CI also runs
+Apache Iceberg's own Spark test suites, for Iceberg 1.8.1 through 1.11.0, with
the native writer enabled.
+
+### Failure handling
+
+Only successful tasks' commit messages are committed. A failed task deletes
the files it wrote, as iceberg-java
+does, and a failed job commits nothing and deletes the completed tasks' files.
Retries can't collide, because
+each attempt's ID is part of its file names. Anything cleanup misses is
invisible to readers and is removed by
+Iceberg's normal `remove_orphan_files` maintenance.
+
+### Known differences
+
+Files written by parquet-rs, the Rust Parquet library iceberg-rust uses,
differ from those written by
+parquet-java (formerly parquet-mr), which iceberg-java uses. Enabling the
feature accepts these differences.
+Most are cosmetic, such as footer metadata, `created_by`, and page encoding
labels. Two are worth knowing about:
+
+- **Files won't split at the same points.** Both writers check file size every
1000 rows, but they estimate it
+ differently, so they can roll over to a new file at different rows.
+- **High-cardinality columns keep a dictionary page.** parquet-java drops
dictionary encoding early when it isn't
+ saving space, while parquet-rs keeps it up to
`write.parquet.dict-size-bytes`. Results are the same, but
+ selective reads of native-written files fetch more bytes
+ ([#6114](https://github.com/apache/datafusion-comet/issues/6114)).
+
+The [Iceberg Writes guide] lists every supported setting and every difference.
Please try it on a non-production
+table and tell us how it goes. Feedback from real workloads is what this
feature needs before it loses the
+experimental label.
+
+Thanks to [@jordepic] for designing and implementing the split-operator plan,
write detection, and the native
+writer, and to [@andygrove] for the fidelity and failure-handling work, with
contributions from
+[@zhangfengcdt], [@snmvaughan], [@liupoyi-1031], and [@0lai0], and reviews
from [@sunchao], [@comphead],
+[@unikdahal], and [@mbutrovich]. Related PRs: [#4658], [#5298], [#5361],
[#5663], [#5780].
+
+[Iceberg Writes guide]:
https://datafusion.apache.org/comet/user-guide/latest/iceberg-writes.html
+
+## Memory Management
+
+A recurring problem for Comet users has been executors killed by the cluster
manager (on Kubernetes,
+`ExecutorLostFailure` with exit code 137) even though Comet stayed within its
memory pool. 1.1.0 explains why
+this happens, lets you measure it, and fixes pool bugs that made it worse.
+
+### Where the memory goes
+
+Comet's native operators allocate from the Rust heap but charge their
reservations against Spark's off-heap
+pool, sized by `spark.memory.offHeap.size`. Operators only reserve memory for
data they deliberately hold onto:
+sort buffers, hash join build sides, aggregation state, and buffered shuffle
partitions.
+
+Plenty of memory is never reserved: working memory in expression kernels,
decompression buffers, Parquet reader
+state, object store buffers, JVM-side Arrow buffers, and allocator overhead.
All of it has to fit in
+`spark.executor.memoryOverhead`, and until now there was no way to see how
much of it there was.
+
+<img
+src="/blog/images/comet-1.1.0/comet-executor-memory.svg"
+width="100%"
+class="img-fluid"
+alt="The executor container holds the JVM heap, the off-heap memory pool, and
the memory overhead. Spark and Comet share the off-heap pool, where Comet's
sorts, joins, aggregations, and shuffles reserve memory. The memory overhead
holds the JVM's own overhead plus Comet's native memory that the pool does not
track."
+/>
+
+### Measuring used memory
+
+Comet now wraps its native allocator in a counter that tracks every byte
allocated and not yet freed. It only
+observes and never rejects an allocation. Arrow buffers the JVM imports from
native code are now tracked
+separately, so they aren't counted twice.
+
+Each executor uses the counter to log its native memory usage every 10 seconds
while Comet runs:
+
+```
+Comet native memory usage: allocated 5412.3 MiB, reserved 3890.0 MiB (16
native plans, 8 memory pools)
+```
+
+`reserved` is what the pools track, and the container already has room for it.
`allocated` is everything
+Comet's native code holds. The difference is what has to fit in
`spark.executor.memoryOverhead`, next to the
+JVM's own non-heap memory. To size the overhead, run a representative
workload, take the largest difference from
+the log, add it to the overhead you had before Comet, and leave a margin.
Setting
+`spark.comet.memory.logInterval=1s` for that run makes it less likely to miss
a short peak.
+
+The executor also warns when its native memory looks larger than the container
allows, and the
+[tuning guide] walks through sizing with examples. One gotcha: setting
`spark.executor.memoryOverhead`
+_replaces_ the value Spark derives from `spark.executor.memoryOverheadFactor`
instead of adding to it, so on a
+large executor it can shrink the container. For large executors, raise the
factor instead.
+
+[tuning guide]:
https://datafusion.apache.org/comet/user-guide/latest/tuning/memory.html
+
+### Memory pool fixes
+
+Comet's off-heap memory comes from one of two pools. `fair_unified`, the
default, caps each operator at an even
+share of the task's memory. `greedy_unified` gives memory to operators first
come, first served. When Spark
+grants an operator less memory than it asked for (a partial grant), the
operator spills.
+
+- **`fair_unified` limited a whole task to one operator's share.** Since
0.15.0, the pool compared all of a
+ task's reservations against a single operator's share, and each new operator
shrank the limit for the rest.
+ Each operator now gets its own share, so tasks with several operators can
use more memory before spilling.
+- **A partial grant from Spark no longer panics the task.** DataFusion
sometimes has to record memory that
+ already exists, such as a spilled batch being read back, and both pools
panicked when Spark granted less than
+ they asked for. They now track the shortfall and repay it before returning
memory to Spark.
+- **Leaks on failure paths.** A plan that failed during setup or teardown
could leak its task's memory pool, and
+ a failed Arrow import leaked the vectors it had already imported.
+- **Configuration units.** 1.0.0 misread three size settings, including
`spark.memory.offHeap.size` written as
+ a bare number. See [Upgrading to 1.1.0](#upgrading-to-110) for what changes.
+- **Less log noise when spilling.** 1.0.0 logged a warning and a memory dump
for every partial grant, so a
+ spilling query could log hundreds. Partial grants now log at DEBUG, and the
dump is gone because it could
+ deadlock the task.
+- **Metrics.** Spills from native sorts, sort-merge joins, and aggregates now
count toward Spark's Spill (Disk)
+ task metric in every stage. The native shuffle writer reports Spill (Disk)
and Spill (Memory) separately, where
+ 1.0.0 put the same number in both. Native aggregates with grouping keys also
show spill counts, bytes, and rows
+ in the SQL tab.
+
+`spark.comet.exec.memoryPool.fraction` is deprecated. It was meant to leave
room for untracked memory, but Spark
+hands out the whole pool anyway, so it never did. Size
`spark.executor.memoryOverhead` instead.
+
+### On-heap mode
+
+On-heap mode exists so that Spark's and Iceberg's test suites can run against
Comet; production runs off-heap.
+Its memory accounting didn't protect anything, because native memory isn't on
the JVM heap, so 1.1.0 removes that
+accounting, along with six of the nine pool types and several testing-only
settings. Outside tests, Comet now requires
+off-heap memory however it's enabled, including when
`CometSparkSessionExtensions` is registered directly, and
+disables itself with a warning otherwise.
+
+Contributors can find more detail in the new [memory management guide].
+
+Thanks to [@andygrove] for driving this work, [@peterxcli] for the memory pool
lifecycle and shuffle spill
+accounting fixes, [@ywskycn] for reporting native memory usage to Spark,
[@1fanwang] for the Arrow import leak
+fix, and [@sunchao] for the native aggregate spill metrics, with reviews from
[@sunchao],
+[@comphead], and [@mbutrovich]. Related PRs: [#5934], [#6162], [#6048],
[#6128], [#6205], [#6066], [#5494].
+
+[memory management guide]:
https://datafusion.apache.org/comet/contributor-guide/memory_management.html
+
+## More Iceberg Improvements
+
+- **V3 deletion vectors** are applied on native scans.
+- **Iceberg system functions** (`bucket`, `truncate`, `years`, `months`,
`days`, and `hours`) run natively.
+- **Scan planning metrics and scan time** appear in the Spark UI for native
Iceberg scans.
+- **A wrong-results fix.** A filter such as `bucket(4, id) = 2` combined with
another predicate was pushed to
+ the native scan as `id = 2`, returning too few rows.
+- Tables partitioned by an unknown transform can be read natively, and `IS
NULL` / `IS NOT NULL` checks on list
+ and map columns no longer force a fallback.
+
+Thanks to [@mbutrovich] for deletion vector support, [@parthchandra] for the
scan metrics, [@ErikBPF] for the
+null-check fix, and [@andygrove] for the native system functions and residual
fix, with reviews from
+[@sunchao], [@rich7420], [@unikdahal], and [@jordepic]. Related PRs: [#5853],
[#5638], [#6027], [#6154].
+
+## Remote Shuffle with Celeborn
+
+1.1.0 adds Comet's side of native shuffle over Apache Celeborn: map tasks push
Comet's Arrow data straight to
+Celeborn, and reducers read it back natively. In 1.0.0, Celeborn users always
got ordinary Spark shuffle.
+
+It does not run with a released Celeborn yet. Native shuffle needs Celeborn to
report reliably when an in-flight
+push has completed, and no released client does, including 0.7.0, the latest
release. Those clients keep ordinary
+shuffle even when native shuffle is requested. Once a Celeborn release
provides that API, set
+`spark.shuffle.manager` to
`org.apache.spark.sql.comet.execution.shuffle.CometCelebornShuffleManager` and
+`spark.comet.shuffle.mode=native`; the default `auto` mode keeps ordinary
shuffle. Native shuffle over Celeborn
+doesn't support `spark.io.encryption.enabled=true`, and Celeborn isn't bundled
with Comet. The
+[Celeborn tuning guide] has the full requirements.
Review Comment:
The current blocker is Comet's push-completion compatibility checks. The
follow-up discussion in
[#6523](https://github.com/apache/datafusion-comet/issues/6523#issuecomment-6019598622)
considers changes to those checks as well as a supported completion API, so I
would avoid implying that a future Celeborn release alone will enable the
native path. This also makes clear that other Comet operators can still be
accelerated.
```suggestion
Native Celeborn shuffle is unavailable with released Celeborn 0.6.x and
0.7.x clients in Comet 1.1.0 because
these clients do not pass Comet's push-completion compatibility checks. Set
`spark.shuffle.manager` to
`org.apache.spark.sql.comet.execution.shuffle.CometCelebornShuffleManager`
to use Comet alongside
ordinary Spark/Celeborn shuffle; Comet can still accelerate other operators.
These clients retain
ordinary Spark/Celeborn shuffle even when `spark.comet.shuffle.mode=native`
is requested, and the
default `auto` mode also retains ordinary shuffle. Follow-up work to enable
native shuffle with
released clients is tracked in
[#6523](https://github.com/apache/datafusion-comet/issues/6523).
Native shuffle over Celeborn does not support
`spark.io.encryption.enabled=true`, and Celeborn
is not bundled with Comet. The [Celeborn tuning guide] has the full
requirements.
```
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