mbutrovich commented on code in PR #210:
URL: https://github.com/apache/datafusion-site/pull/210#discussion_r4447363761
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,525 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
Review Comment:
Could we say once where the performance figures come from, and drop the
attribution from each bullet? "The PR reports" or "in the PR's benchmark"
appears on lines 175, 179, 180, 185, 277, and 319. With the sentence below,
those become plain statements. For example, line 185 becomes "A 1M-row build
side read by 128 tasks on `local[8]` went from 1,321 ms to 684 ms." Line 163
cites an earlier build from an issue, so it keeps its own attribution.
```suggestion
This release covers roughly five weeks of development since 1.1.0: 399
commits from 40 contributors. See the
[change log] for the full list of changes. Unless stated otherwise,
performance figures come from the
benchmarks in the linked PRs.
```
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,525 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.2/docs/source/changelog/1.2.0.md
+
+Comet has always aimed to return the same results as Spark. Much of this cycle
went into the edge cases that
+remained, working through whole categories at a time (timezones,
floating-point `-0.0` and `NaN`, collations)
+and adding tests that keep them fixed.
+
+The highlights:
+
+- **Results that match Spark**: native timestamp results carry the right
timezone label. `-0.0`, `0.0`, and
+ `NaN` follow Spark in hashing, comparison, sorting, aggregation, and array
functions. Decimal `AVG`, nested
+ types, broadcasts, and join order get fixes too.
+- **Memory, spill, and shuffle at scale**: memory returned to Spark sooner,
fewer memory-related task failures,
+ much smaller JVM shuffle memory use with many partitions, and smaller
shuffle files.
+- **Iceberg**: native writes are a documented, opt-in feature with support for
format-version 3 tables, and the
+ native scan reads fewer nested fields and more files at once.
+- **More of the plan runs natively**: existence joins, `SortAggregateExec`,
the `PIVOT` fast path, and
+ HyperLogLog sketch functions, among others.
+- **New places to run**: a native Delta Lake scan (experimental), Arrow Python
UDFs (experimental), and Spark 4.2
+ as a supported version.
+
+## Results That Match Spark
+
+Timestamps and floating point produced the most reports, so we started there,
followed by collations. Individual
+fixes in other areas come after that.
+
Review Comment:
Could we drop this paragraph? Lines 40-42 already name the categories, and
the headings show the order.
```suggestion
```
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,525 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.2/docs/source/changelog/1.2.0.md
+
+Comet has always aimed to return the same results as Spark. Much of this cycle
went into the edge cases that
+remained, working through whole categories at a time (timezones,
floating-point `-0.0` and `NaN`, collations)
+and adding tests that keep them fixed.
+
+The highlights:
+
+- **Results that match Spark**: native timestamp results carry the right
timezone label. `-0.0`, `0.0`, and
+ `NaN` follow Spark in hashing, comparison, sorting, aggregation, and array
functions. Decimal `AVG`, nested
+ types, broadcasts, and join order get fixes too.
+- **Memory, spill, and shuffle at scale**: memory returned to Spark sooner,
fewer memory-related task failures,
+ much smaller JVM shuffle memory use with many partitions, and smaller
shuffle files.
+- **Iceberg**: native writes are a documented, opt-in feature with support for
format-version 3 tables, and the
+ native scan reads fewer nested fields and more files at once.
+- **More of the plan runs natively**: existence joins, `SortAggregateExec`,
the `PIVOT` fast path, and
+ HyperLogLog sketch functions, among others.
+- **New places to run**: a native Delta Lake scan (experimental), Arrow Python
UDFs (experimental), and Spark 4.2
+ as a supported version.
+
+## Results That Match Spark
+
+Timestamps and floating point produced the most reports, so we started there,
followed by collations. Individual
+fixes in other areas come after that.
+
+### Timestamps and timezones
+
+Spark labels a timestamp with the session timezone, and Comet's native code
has to produce the same label, not
+only the same instant. Several native expressions got this wrong. 1.2.0 fixes
them:
+
+- Native timestamp results carry the correct timezone label:
`timestamp_seconds` results are labeled as UTC
+ timestamps ([#6341]), `date_trunc` keeps its input's label ([#6345]), and
`CASE` and `COALESCE` relabel
+ timestamp branches instead of panicking ([#6347]).
Review Comment:
Could the bullet start with the fixes? The paragraph above already says that
native code has to produce the right label.
```suggestion
- `timestamp_seconds` results are labeled as UTC timestamps ([#6341]),
`date_trunc` keeps its input's label
([#6345]), and `CASE` and `COALESCE` relabel timestamp branches instead of
panicking ([#6347]).
```
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,529 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.2/docs/source/changelog/1.2.0.md
+
+Comet has always aimed to return the same results as Spark. Much of this cycle
went into the edge cases that
+remained, working through whole categories at a time (timezones,
floating-point `-0.0` and `NaN`, collations)
+and adding tests that keep them fixed.
+
+The highlights:
+
+- **Results that match Spark**: native timestamp results carry the right
timezone label. `-0.0`, `0.0`, and
+ `NaN` follow Spark in hashing, comparison, sorting, aggregation, and array
functions. Decimal `AVG`, nested
+ types, broadcasts, and join order get fixes too.
+- **Memory, spill, and shuffle at scale**: memory returned to Spark sooner,
fewer memory-related task failures,
+ much smaller JVM shuffle memory use with many partitions, and smaller
shuffle files.
+- **Iceberg**: native writes are a documented, opt-in feature with support for
format-version 3 tables, and the
+ native scan reads fewer nested fields and more files at once.
+- **More of the plan runs natively**: existence joins, `SortAggregateExec`,
the `PIVOT` fast path, and
+ HyperLogLog sketch functions, among others.
+- **New places to run**: a native Delta Lake scan (experimental), Arrow Python
UDFs (experimental), and Spark 4.2
+ as a supported version.
+
+## Results That Match Spark
+
+Timestamps and floating point produced the most reports, so we started there,
followed by collations. Individual
+fixes in other areas come after that.
+
+### Timestamps and timezones
+
+Spark labels a timestamp with the session timezone, and Comet's native code
has to produce the same label, not
+only the same instant. Several native expressions got this wrong. 1.2.0 fixes
them:
+
+- Native timestamp results carry the correct timezone label:
`timestamp_seconds` results are labeled as UTC
+ timestamps ([#6341]), `date_trunc` keeps its input's label ([#6345]), and
`CASE` and `COALESCE` relabel
+ timestamp branches instead of panicking ([#6347]).
+- Session timezone IDs are normalized before they reach native code ([#6351]).
+- `date_trunc` with a format that differs from row to row uses the same kernel
as a literal format ([#6354]).
+- The driver warns when the native and JVM timezone databases differ
([#6353]), because Comet cannot correct
+ for that difference.
+- The test harness now fails a test when a native plan mislabels a timestamp,
so this class of bug can't come
+ back unnoticed ([#6868], [#6930]).
+
+Thanks to [@andygrove] for the timezone sweep and [@LinSimon-901101] for the
label checks. The new
+[timezone handling page] in the contributor guide explains the rules.
+
+[timezone handling page]:
https://datafusion.apache.org/comet/contributor-guide/timezones.html
+
+### Floating point: `-0.0`, `0.0`, and `NaN`
+
+In some operations Spark treats `-0.0` and `0.0` as equal and every `NaN` as
one value; in others it treats them
+as distinct. Which rule applies depends on the operation and the Spark
version, and DataFusion makes different
+choices. The differences are easy to miss in testing and show up as wrong
joins, groups, and sort orders. In
+1.2.0, these follow Spark:
+
+- **Hashing**: `hash`, `xxhash64`, and native shuffle hash `NaN` as Spark does
([#6413]).
+- **Comparison and ordering**: `min`, `max`, `greatest`, and `least`
([#6457]), sort and window keys nested in
+ arrays and structs ([#6475]), and `percentile` for negative `NaN`s ([#6829]).
+- **Array functions**: `array_remove`, `sort_array`, `array_distinct`, and
`array_union` ([#6518], [#6563]), and
+ `arrays_overlap` on the Spark versions where `-0.0` and `0.0` are one value
([#6989]).
+- **Arithmetic**: `signum(-0.0)` ([#6556]) and the zero check in `Remainder`
([#6896]).
+- **Maps**: Comet no longer builds a map natively when its key type holds a
`FLOAT` or `DOUBLE` ([#6992]).
+ Spark finds duplicate keys by value, so `NaN`s with different bits are one
key, and from 4.0 it also
+ normalizes `-0.0` keys to `0.0`. Native code told keys apart by their bits.
+
+The contributor guide now has a [floating point page] that says which Spark
rule each expression follows and how
+to test for it. A sweep suite crosses edge values with every operator context,
so a DataFusion upgrade that
+shifts float behavior will fail a test before it reaches users.
+
+Thanks to [@andygrove] for the float sweep, [@rich7420] for `percentile`, and
[@zhangfengcdt] for `signum`.
+
+[floating point page]:
https://datafusion.apache.org/comet/contributor-guide/floating_point.html
+
+### Collations
+
+Strings with a non-default collation compare differently from ordinary
strings. For collated casts, sort keys, and
+comparisons, Comet now falls back to Spark or runs the expression through
[codegen dispatch] instead of comparing
+bytes ([#5302], [#6206], [#6471], [#6480]). Thanks to [@stantheman0128],
[@LinSimon-901101], and [@viirya].
+
+[codegen dispatch]:
https://datafusion.apache.org/blog/2026/06/20/datafusion-comet-0.17.0/
+
+### Other wrong-results fixes
+
+- **Decimal `AVG`**: results were `NULL` or wrong for sums that overflowed an
intermediate `i128`, and empty and
+ overflowing partial states were mixed up. 1.2.0 repairs the division and the
buffers ([#5420], thanks to
+ [@sunchao]) and falls back to Spark for the cases it cannot yet do correctly
([#7017]).
+- **Variance, covariance, and correlation**: these now update and merge in
Spark's order, so values that are large
+ and close together give Spark's result, and `regr_r2` returns `NULL` or
`DIVIDE_BY_ZERO` where Spark does
+ ([#6076], thanks to [@rich7420]).
+- **Nested values in comparisons and sorts**: `greatest` and `least` compare
struct arguments by position and
+ order nulls inside nested values first ([#6760], [#6761], thanks to
[@viirya]), and native sort keeps Spark's
+ null ordering inside nested values ([#6968], thanks to [@Weijun-H]).
+- **Missing nested Parquet fields**: when every nested field a query asks for
is missing from a Parquet file,
+ Comet could return a non-null struct, array, or map where Spark returns
`NULL`, which changed `IS NULL`, filters,
+ and `count`. Comet now follows Spark's version-dependent rules, and falls
back for nested reads of name-mapped
+ Iceberg tables ([#7085], thanks to [@rich7420]).
+- **Nested binary read as strings**: a nested Parquet `BINARY` value read as a
string that held invalid UTF-8
+ became `NULL`. It is now decoded the way Comet's `CAST(binary AS STRING)`
decodes it, replacing ill-formed bytes
+ with U+FFFD ([#7106], thanks to [@manuzhang]).
+- **Sliced broadcasts**: broadcasts of sliced string, binary, array, or map
batches could return wrong values or
+ fail with `OversizedAllocationException` ([#6900], thanks to [@comphead]).
+- **Identity broadcasts**: a Spark operator that keeps every row of a Comet
broadcast could see the last row of
+ each batch repeated in place of the others. Comet now copies each row
([#7260], thanks to [@pingzh]).
+- **Join output order**: Spark drops a sort above a hash join because the join
keeps its streamed side's order.
+ Comet's native hash join did not keep it for some inputs, so the query could
return unsorted partitions
+ ([#6835], [#6810], thanks to [@dwsmith1983]).
+- **Spark patch releases**: Spark 4.0.5, 4.1.4, and 4.2.1 change what
`sequence` returns when `stop - start`
+ overflows a long, and the result of casting a zero with a large exponent,
such as `'0e79'`, to decimal. Comet
+ follows the behavior of the Spark version it runs on ([#7171], thanks to
[@snmvaughan]).
+- **ANSI mode**: `array_append` now evaluates its item on rows whose array is
`NULL`, so it raises the errors
+ Spark raises, and `corr` raises `DIVIDE_BY_ZERO` ([#7025], [#6830], thanks
to [@kazuyukitanimura], with
+ [@rich7420] on `corr`).
+
+## Memory, Spill, and Shuffle
+
+1.1.0 made Comet's memory use visible. 1.2.0 fixes more of the ways it went
wrong, and makes shuffle cheaper.
+
+### Memory
+
+- **Memory is returned sooner.** Native plans return their memory before the
Spark task ends ([#6261]). When
+ Spark has already dropped a task's memory entry, a failed native memory
request or shuffle page allocation is
+ now retried instead of failing the task ([#6310], [#6403], [#6813]).
+- **Spilling works in more places.** A final aggregate that spilled can now
read its spill files back even when
+ they exceed its memory share ([#6544]). A partial merge aggregate whose
input is sorted on some of its grouping
+ keys spills instead of failing the task ([#7222]). Grouped integer `SUM`
state now counts toward the memory
+ reservation ([#6370]).
+- **Container sizing.** The native memory limit follows how YARN and
Kubernetes size executor containers
+ ([#6375]).
+- **Spill files are compressed with lz4 by default.** The codec is set by
+ `spark.comet.exec.spill.compression.codec` ([#6698]). In the investigation
behind it, [Nested TPC-H] q21 at
+ scale factor 1000 went from 380 s to 250 s, against 367 s for Spark.
+- **The memory usage log is in the Spark event log.** It can be read after the
application ends ([#6401],
+ [#6250], [#6271]).
+
+Thanks to [@andygrove] for most of this work, with [@dwsmith1983] and
[@viirya] on the retry fixes and
+[@comphead] on spill compression and the event log.
+
+### Shuffle and broadcast
+
+- **JVM shuffle with many partitions.** The bypass-merge writer gave every
partition a page of Spark's page size,
+ 32 to 64 MB, as soon as it buffered a row. When a task's memory could not
hold one page per partition, every
+ new page forced a spill. Pages now start small and grow ([#7059]). The PR
reports `REPARTITION(64)` on TPC-H
+ SF2 `orders` going from 266 s and 2.48 GB of shuffle output to 1.1 s and 156
MB, and on `lineitem` from
+ 15.5 minutes to 3.3 s.
+- **Smaller shuffle files.** Shuffle files with string, binary, array, and map
columns are 9% to 40% smaller in
+ the PR's benchmarks, because offsets are now stored as lengths and
compressed with zstd ([#6943]).
+- **Faster spills.** A JVM shuffle spill writes every partition in one native
call ([#7065]). The PR reports this
+ as 21% to 28% faster with 1,000 partitions, and 45% to 49% faster when the
writer spills every 100,000 rows.
+ Spill pointers are now sorted in the sorter's scratch space ([#7125]).
+- **Shuffle reads.** Shuffle blocks are read in 64 KiB pieces ([#6805]).
+- **Broadcast.** Every task that read a Comet broadcast decoded the whole
broadcast for itself. Tasks on an
+ executor now share one decoded copy ([#7016]). In the PR's benchmark, a
1M-row build side read by 128 tasks on
+ `local[8]` went from 1,321 ms to 684 ms.
+
+Thanks to [@viirya] for the spill pointer sort, [@comphead] for the block
reads, and [@andygrove] for the rest.
+
+## Iceberg
+
+### Native writes
+
+Native Iceberg writes stay **off by default** in 1.2.0, as they were in 1.1.0
when they were experimental. We
+considered turning them on, but this is their first release as a documented,
user-facing feature, and we would
+rather users opt in and report what they find first. We plan to revisit the
default in a later release.
Review Comment:
Could this be two sentences? The reason and the plan to revisit fit together.
```suggestion
Native Iceberg writes stay **off by default** in 1.2.0. This is their first
release as a documented feature, and
we want users to opt in and report what they find before we revisit the
default.
```
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,525 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.2/docs/source/changelog/1.2.0.md
+
+Comet has always aimed to return the same results as Spark. Much of this cycle
went into the edge cases that
+remained, working through whole categories at a time (timezones,
floating-point `-0.0` and `NaN`, collations)
+and adding tests that keep them fixed.
+
+The highlights:
+
+- **Results that match Spark**: native timestamp results carry the right
timezone label. `-0.0`, `0.0`, and
+ `NaN` follow Spark in hashing, comparison, sorting, aggregation, and array
functions. Decimal `AVG`, nested
+ types, broadcasts, and join order get fixes too.
Review Comment:
Could this bullet be a short list like the other highlights? The section
below explains each item, so the highlight only needs to name them.
```suggestion
- **Results that match Spark**: timezone labels, `-0.0` and `NaN`,
collations, decimal `AVG`, nested types,
broadcasts, and join order.
```
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,529 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.2/docs/source/changelog/1.2.0.md
+
+Comet has always aimed to return the same results as Spark. Much of this cycle
went into the edge cases that
+remained, working through whole categories at a time (timezones,
floating-point `-0.0` and `NaN`, collations)
+and adding tests that keep them fixed.
+
+The highlights:
+
+- **Results that match Spark**: native timestamp results carry the right
timezone label. `-0.0`, `0.0`, and
+ `NaN` follow Spark in hashing, comparison, sorting, aggregation, and array
functions. Decimal `AVG`, nested
+ types, broadcasts, and join order get fixes too.
+- **Memory, spill, and shuffle at scale**: memory returned to Spark sooner,
fewer memory-related task failures,
+ much smaller JVM shuffle memory use with many partitions, and smaller
shuffle files.
+- **Iceberg**: native writes are a documented, opt-in feature with support for
format-version 3 tables, and the
+ native scan reads fewer nested fields and more files at once.
+- **More of the plan runs natively**: existence joins, `SortAggregateExec`,
the `PIVOT` fast path, and
+ HyperLogLog sketch functions, among others.
+- **New places to run**: a native Delta Lake scan (experimental), Arrow Python
UDFs (experimental), and Spark 4.2
+ as a supported version.
+
+## Results That Match Spark
+
+Timestamps and floating point produced the most reports, so we started there,
followed by collations. Individual
+fixes in other areas come after that.
+
+### Timestamps and timezones
+
+Spark labels a timestamp with the session timezone, and Comet's native code
has to produce the same label, not
+only the same instant. Several native expressions got this wrong. 1.2.0 fixes
them:
+
+- Native timestamp results carry the correct timezone label:
`timestamp_seconds` results are labeled as UTC
+ timestamps ([#6341]), `date_trunc` keeps its input's label ([#6345]), and
`CASE` and `COALESCE` relabel
+ timestamp branches instead of panicking ([#6347]).
+- Session timezone IDs are normalized before they reach native code ([#6351]).
+- `date_trunc` with a format that differs from row to row uses the same kernel
as a literal format ([#6354]).
+- The driver warns when the native and JVM timezone databases differ
([#6353]), because Comet cannot correct
+ for that difference.
+- The test harness now fails a test when a native plan mislabels a timestamp,
so this class of bug can't come
+ back unnoticed ([#6868], [#6930]).
+
+Thanks to [@andygrove] for the timezone sweep and [@LinSimon-901101] for the
label checks. The new
+[timezone handling page] in the contributor guide explains the rules.
+
+[timezone handling page]:
https://datafusion.apache.org/comet/contributor-guide/timezones.html
+
+### Floating point: `-0.0`, `0.0`, and `NaN`
+
+In some operations Spark treats `-0.0` and `0.0` as equal and every `NaN` as
one value; in others it treats them
+as distinct. Which rule applies depends on the operation and the Spark
version, and DataFusion makes different
+choices. The differences are easy to miss in testing and show up as wrong
joins, groups, and sort orders. In
+1.2.0, these follow Spark:
+
+- **Hashing**: `hash`, `xxhash64`, and native shuffle hash `NaN` as Spark does
([#6413]).
+- **Comparison and ordering**: `min`, `max`, `greatest`, and `least`
([#6457]), sort and window keys nested in
+ arrays and structs ([#6475]), and `percentile` for negative `NaN`s ([#6829]).
+- **Array functions**: `array_remove`, `sort_array`, `array_distinct`, and
`array_union` ([#6518], [#6563]), and
+ `arrays_overlap` on the Spark versions where `-0.0` and `0.0` are one value
([#6989]).
+- **Arithmetic**: `signum(-0.0)` ([#6556]) and the zero check in `Remainder`
([#6896]).
+- **Maps**: Comet no longer builds a map natively when its key type holds a
`FLOAT` or `DOUBLE` ([#6992]).
+ Spark finds duplicate keys by value, so `NaN`s with different bits are one
key, and from 4.0 it also
+ normalizes `-0.0` keys to `0.0`. Native code told keys apart by their bits.
+
+The contributor guide now has a [floating point page] that says which Spark
rule each expression follows and how
+to test for it. A sweep suite crosses edge values with every operator context,
so a DataFusion upgrade that
+shifts float behavior will fail a test before it reaches users.
+
+Thanks to [@andygrove] for the float sweep, [@rich7420] for `percentile`, and
[@zhangfengcdt] for `signum`.
+
+[floating point page]:
https://datafusion.apache.org/comet/contributor-guide/floating_point.html
+
+### Collations
+
+Strings with a non-default collation compare differently from ordinary
strings. For collated casts, sort keys, and
+comparisons, Comet now falls back to Spark or runs the expression through
[codegen dispatch] instead of comparing
+bytes ([#5302], [#6206], [#6471], [#6480]). Thanks to [@stantheman0128],
[@LinSimon-901101], and [@viirya].
+
+[codegen dispatch]:
https://datafusion.apache.org/blog/2026/06/20/datafusion-comet-0.17.0/
+
+### Other wrong-results fixes
+
+- **Decimal `AVG`**: results were `NULL` or wrong for sums that overflowed an
intermediate `i128`, and empty and
+ overflowing partial states were mixed up. 1.2.0 repairs the division and the
buffers ([#5420], thanks to
+ [@sunchao]) and falls back to Spark for the cases it cannot yet do correctly
([#7017]).
+- **Variance, covariance, and correlation**: these now update and merge in
Spark's order, so values that are large
+ and close together give Spark's result, and `regr_r2` returns `NULL` or
`DIVIDE_BY_ZERO` where Spark does
+ ([#6076], thanks to [@rich7420]).
+- **Nested values in comparisons and sorts**: `greatest` and `least` compare
struct arguments by position and
+ order nulls inside nested values first ([#6760], [#6761], thanks to
[@viirya]), and native sort keeps Spark's
+ null ordering inside nested values ([#6968], thanks to [@Weijun-H]).
+- **Missing nested Parquet fields**: when every nested field a query asks for
is missing from a Parquet file,
+ Comet could return a non-null struct, array, or map where Spark returns
`NULL`, which changed `IS NULL`, filters,
+ and `count`. Comet now follows Spark's version-dependent rules, and falls
back for nested reads of name-mapped
+ Iceberg tables ([#7085], thanks to [@rich7420]).
+- **Nested binary read as strings**: a nested Parquet `BINARY` value read as a
string that held invalid UTF-8
+ became `NULL`. It is now decoded the way Comet's `CAST(binary AS STRING)`
decodes it, replacing ill-formed bytes
+ with U+FFFD ([#7106], thanks to [@manuzhang]).
+- **Sliced broadcasts**: broadcasts of sliced string, binary, array, or map
batches could return wrong values or
+ fail with `OversizedAllocationException` ([#6900], thanks to [@comphead]).
+- **Identity broadcasts**: a Spark operator that keeps every row of a Comet
broadcast could see the last row of
+ each batch repeated in place of the others. Comet now copies each row
([#7260], thanks to [@pingzh]).
+- **Join output order**: Spark drops a sort above a hash join because the join
keeps its streamed side's order.
+ Comet's native hash join did not keep it for some inputs, so the query could
return unsorted partitions
+ ([#6835], [#6810], thanks to [@dwsmith1983]).
+- **Spark patch releases**: Spark 4.0.5, 4.1.4, and 4.2.1 change what
`sequence` returns when `stop - start`
+ overflows a long, and the result of casting a zero with a large exponent,
such as `'0e79'`, to decimal. Comet
+ follows the behavior of the Spark version it runs on ([#7171], thanks to
[@snmvaughan]).
+- **ANSI mode**: `array_append` now evaluates its item on rows whose array is
`NULL`, so it raises the errors
+ Spark raises, and `corr` raises `DIVIDE_BY_ZERO` ([#7025], [#6830], thanks
to [@kazuyukitanimura], with
+ [@rich7420] on `corr`).
+
+## Memory, Spill, and Shuffle
+
+1.1.0 made Comet's memory use visible. 1.2.0 fixes more of the ways it went
wrong, and makes shuffle cheaper.
+
+### Memory
+
+- **Memory is returned sooner.** Native plans return their memory before the
Spark task ends ([#6261]). When
+ Spark has already dropped a task's memory entry, a failed native memory
request or shuffle page allocation is
+ now retried instead of failing the task ([#6310], [#6403], [#6813]).
+- **Spilling works in more places.** A final aggregate that spilled can now
read its spill files back even when
+ they exceed its memory share ([#6544]). A partial merge aggregate whose
input is sorted on some of its grouping
+ keys spills instead of failing the task ([#7222]). Grouped integer `SUM`
state now counts toward the memory
+ reservation ([#6370]).
+- **Container sizing.** The native memory limit follows how YARN and
Kubernetes size executor containers
+ ([#6375]).
+- **Spill files are compressed with lz4 by default.** The codec is set by
+ `spark.comet.exec.spill.compression.codec` ([#6698]). In the investigation
behind it, [Nested TPC-H] q21 at
+ scale factor 1000 went from 380 s to 250 s, against 367 s for Spark.
+- **The memory usage log is in the Spark event log.** It can be read after the
application ends ([#6401],
+ [#6250], [#6271]).
+
+Thanks to [@andygrove] for most of this work, with [@dwsmith1983] and
[@viirya] on the retry fixes and
+[@comphead] on spill compression and the event log.
+
+### Shuffle and broadcast
+
+- **JVM shuffle with many partitions.** The bypass-merge writer gave every
partition a page of Spark's page size,
+ 32 to 64 MB, as soon as it buffered a row. When a task's memory could not
hold one page per partition, every
+ new page forced a spill. Pages now start small and grow ([#7059]). The PR
reports `REPARTITION(64)` on TPC-H
+ SF2 `orders` going from 266 s and 2.48 GB of shuffle output to 1.1 s and 156
MB, and on `lineitem` from
+ 15.5 minutes to 3.3 s.
+- **Smaller shuffle files.** Shuffle files with string, binary, array, and map
columns are 9% to 40% smaller in
+ the PR's benchmarks, because offsets are now stored as lengths and
compressed with zstd ([#6943]).
+- **Faster spills.** A JVM shuffle spill writes every partition in one native
call ([#7065]). The PR reports this
+ as 21% to 28% faster with 1,000 partitions, and 45% to 49% faster when the
writer spills every 100,000 rows.
+ Spill pointers are now sorted in the sorter's scratch space ([#7125]).
+- **Shuffle reads.** Shuffle blocks are read in 64 KiB pieces ([#6805]).
+- **Broadcast.** Every task that read a Comet broadcast decoded the whole
broadcast for itself. Tasks on an
+ executor now share one decoded copy ([#7016]). In the PR's benchmark, a
1M-row build side read by 128 tasks on
+ `local[8]` went from 1,321 ms to 684 ms.
+
+Thanks to [@viirya] for the spill pointer sort, [@comphead] for the block
reads, and [@andygrove] for the rest.
+
+## Iceberg
+
+### Native writes
+
+Native Iceberg writes stay **off by default** in 1.2.0, as they were in 1.1.0
when they were experimental. We
+considered turning them on, but this is their first release as a documented,
user-facing feature, and we would
+rather users opt in and report what they find first. We plan to revisit the
default in a later release.
+
+To use them, set `spark.comet.write.iceberg.enabled=true`. This replaces the
pair of settings used in 1.1.0, so see
+[Upgrading to 1.2.0](#upgrading-to-120) if you enabled the feature then. What
changed in the writer:
+
+- **Format-version 3 tables**: written natively ([#6672]).
+- **Per-location credentials**: applied to native reads and writes ([#6478]).
+- **Files match iceberg-java more closely**: dictionary encoding is chosen the
way iceberg-java does ([#6241]),
+ partitions past year 262142 match ([#6239]), and float and double identity
partitions are written natively
+ ([#6851]).
+- **Memory**: write buffers are charged to the task's memory pool ([#6247]),
and when the pool refuses a new
+ buffer the writer closes fanout partitions early instead of failing
([#6773]).
+- **Speed**: the writer's dictionary pre-pass is 1.8x to 3.9x faster ([#6937]).
+- **Fallbacks**: more unsupported writes are detected up front and run through
iceberg-java, such as writes with
+ unsupported S3 settings or through a void partition field whose source
column was dropped ([#6441], [#6502],
+ [#6677]).
+
+The [Iceberg guide] lists the supported operations, every fallback, and every
known difference from iceberg-java.
+
+[Iceberg guide]:
https://datafusion.apache.org/comet/user-guide/latest/iceberg.html#writes
+
+### Reads and row-level operations
+
+- The native scan reads only the nested fields Spark keeps ([#6725]) and opens
**two data files at once** by
+ default ([#7132]), which hides the latency of opening files on tables with
many small files or on S3. See
+ [Upgrading to 1.2.0](#upgrading-to-120) for the trade-offs and how to turn
it off.
+- The native scan falls back to Spark when Iceberg reports an output ordering,
since it does not keep that order
+ and Spark would otherwise drop the sort above it ([#7043]).
+- `MERGE INTO` can run natively in `MergeRowsExec` on Spark 3.5+ ([#5318]). It
is off by default; enable it with
+ `spark.comet.exec.mergeRows.enabled`. It also supports `MergeSummary` on
Spark 4.1+ ([#6640]) and Spark 4.2
+ write transactions ([#6587]).
+
+Thanks to [@andygrove] for the writer work, [@snmvaughan] for per-location
credentials, [@unikdahal] for the
+row-level operations, [@mbutrovich] for float and double identity partitions,
[@comphead] for nested-field
+pruning, and [@sam-1112], [@0lai0], and [@parthchandra] for fallback gates.
+
+## More of the Plan Runs Natively
+
+- **On by default**: existence joins ([#4587], thanks to [@coderfender]),
queries without a `FROM` clause
+ (`OneRowRelation`, [#6666]), aggregates with neither grouping keys nor
aggregate functions ([#6899]), and `min`
+ and `max` over strings and binary ([#6927], thanks to [@comphead]).
Broadcast joins whose build side holds an
+ unsupported scan now run natively too ([#6786], thanks to [@parthchandra]),
except that a Text scan on the build
+ side stays in Spark unless `spark.comet.convert.text.enabled` is set,
because converting it replaces bytes that
+ are not valid UTF-8 with U+FFFD ([#7123]).
Review Comment:
Could we split the Text scan exception into its own sentence? This bullet
already has four items before it, and the exception is one long sentence with
two clauses.
```suggestion
unsupported scan now run natively too ([#6786], thanks to
[@parthchandra]). A Text scan on the build side
stays in Spark unless `spark.comet.convert.text.enabled` is set, because
converting it replaces invalid UTF-8
with U+FFFD ([#7123]).
```
##########
content/blog/2026-11-03-datafusion-comet-1.2.0.md:
##########
@@ -0,0 +1,525 @@
+---
+layout: post
+title: Apache DataFusion Comet 1.2.0 Release
+date: 2026-11-03
+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 1.2.0 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 five weeks of development since 1.1.0: 399 commits
from 40 contributors. See the
+[change log] for the full list of changes.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/branch-1.2/docs/source/changelog/1.2.0.md
+
+Comet has always aimed to return the same results as Spark. Much of this cycle
went into the edge cases that
+remained, working through whole categories at a time (timezones,
floating-point `-0.0` and `NaN`, collations)
+and adding tests that keep them fixed.
+
+The highlights:
+
+- **Results that match Spark**: native timestamp results carry the right
timezone label. `-0.0`, `0.0`, and
+ `NaN` follow Spark in hashing, comparison, sorting, aggregation, and array
functions. Decimal `AVG`, nested
+ types, broadcasts, and join order get fixes too.
+- **Memory, spill, and shuffle at scale**: memory returned to Spark sooner,
fewer memory-related task failures,
+ much smaller JVM shuffle memory use with many partitions, and smaller
shuffle files.
+- **Iceberg**: native writes are a documented, opt-in feature with support for
format-version 3 tables, and the
+ native scan reads fewer nested fields and more files at once.
+- **More of the plan runs natively**: existence joins, `SortAggregateExec`,
the `PIVOT` fast path, and
+ HyperLogLog sketch functions, among others.
+- **New places to run**: a native Delta Lake scan (experimental), Arrow Python
UDFs (experimental), and Spark 4.2
+ as a supported version.
+
+## Results That Match Spark
+
+Timestamps and floating point produced the most reports, so we started there,
followed by collations. Individual
+fixes in other areas come after that.
+
+### Timestamps and timezones
+
+Spark labels a timestamp with the session timezone, and Comet's native code
has to produce the same label, not
+only the same instant. Several native expressions got this wrong. 1.2.0 fixes
them:
+
+- Native timestamp results carry the correct timezone label:
`timestamp_seconds` results are labeled as UTC
+ timestamps ([#6341]), `date_trunc` keeps its input's label ([#6345]), and
`CASE` and `COALESCE` relabel
+ timestamp branches instead of panicking ([#6347]).
+- Session timezone IDs are normalized before they reach native code ([#6351]).
+- `date_trunc` with a format that differs from row to row uses the same kernel
as a literal format ([#6354]).
+- The driver warns when the native and JVM timezone databases differ
([#6353]), because Comet cannot correct
+ for that difference.
+- The test harness now fails a test when a native plan mislabels a timestamp,
so this class of bug can't come
+ back unnoticed ([#6868], [#6930]).
+
+Thanks to [@andygrove] for the timezone sweep and [@LinSimon-901101] for the
label checks. The new
+[timezone handling page] in the contributor guide explains the rules.
+
+[timezone handling page]:
https://datafusion.apache.org/comet/contributor-guide/timezones.html
+
+### Floating point: `-0.0`, `0.0`, and `NaN`
+
+In some operations Spark treats `-0.0` and `0.0` as equal and every `NaN` as
one value; in others it treats them
+as distinct. Which rule applies depends on the operation and the Spark
version, and DataFusion makes different
+choices. The differences are easy to miss in testing and show up as wrong
joins, groups, and sort orders. In
+1.2.0, these follow Spark:
Review Comment:
Could we name the two rules once? "In some operations ... in others" and
"Which rule applies depends on the operation" say the same thing. "Easy to miss
in testing" is also covered by the sweep suite paragraph below.
```suggestion
Spark has two rules for these values. Some operations treat `-0.0` and `0.0`
as equal and every `NaN` as one value,
and others keep them distinct. Which rule an operation follows can change
between Spark versions, and DataFusion
makes its own choices. The differences show up as wrong joins, groups, and
sort orders. In 1.2.0, these follow
Spark:
```
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