andygrove commented on code in PR #120:
URL: https://github.com/apache/datafusion-site/pull/120#discussion_r2448598621
##########
content/blog/2025-10-21-datafusion-comet-0.11.0.md:
##########
@@ -0,0 +1,172 @@
+---
+layout: post
+title: Apache DataFusion Comet 0.11.0 Release
+date: 2025-10-21
+author: pmc
+categories: [subprojects]
+---
+
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+{% endcomment %}
+-->
+
+[TOC]
+
+The Apache DataFusion PMC is pleased to announce version 0.11.0 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 approximately five weeks of development work and is the
result of merging 131 PRs from 15
+contributors. See the [change log] for more information.
+
+[change log]:
https://github.com/apache/datafusion-comet/blob/main/dev/changelog/0.11.0.md
+
+## Release Highlights
+
+### Parquet Modular Encryption Support
+
+Spark supports Parquet Modular Encryption to independently encrypt column
values and metadata. Furthermore, Spark supports custom encryption factories
for users to provide their own key-management service (KMS) implementations.
Thanks to [a](https://github.com/apache/arrow-rs/issues/7278)
[number](https://github.com/apache/datafusion/issues/15216)
[of](https://github.com/apache/datafusion/pull/16351)
[contributions](https://github.com/apache/datafusion/pull/16779) in upstream
DataFusion and arrow-rs, Comet now [supports Parquet Modular Encryption with
Spark KMS](https://github.com/apache/datafusion-comet/pull/2447) for native
readers, enabling secure reading of encrypted Parquet files in production
environments.
+
+### Improved Memory Management
+
+Comet 0.11.0 introduces significant improvements to memory management, making
it easier to deploy and more resilient to out-of-memory conditions:
+
+- **Changed default memory pool**: The default off-heap memory pool has been
[changed from `greedy_unified` to
`fair_unified`](https://github.com/apache/datafusion-comet/pull/2526),
providing better memory fairness across operations
+- **Off-heap deployment recommended**: To simplify configuration and improve
performance, Comet now expects to be deployed with Spark's off-heap memory
configuration. [On-heap memory is still
available](https://github.com/apache/datafusion-comet/pull/2554) for
development and debugging, but is not recommended for deployment
+- **Better disk management**: The [DiskManager `max_temp_directory_size` is
now configurable](https://github.com/apache/datafusion-comet/pull/2479) for
better control over temporary disk usage
+- **Enhanced safety**: Memory pool operations now [use checked arithmetic
operations](https://github.com/apache/datafusion-comet/pull/2455) to prevent
overflow issues
+
+These changes make Comet significantly easier to configure and deploy in
production environments.
+
+### Improved Apache Spark 4.0 Support
+
+Comet has improved its support for Apache Spark 4.0.1 with several important
enhancements:
+
+- [Updated support from Spark 4.0.0 to Spark
4.0.1](https://github.com/apache/datafusion-comet/pull/2414)
+- [Spark 4.0 is now included in the release build
script](https://github.com/apache/datafusion-comet/pull/2514)
+- Expanded ANSI mode compatibility with several new implementations:
+ - [ANSI evaluation mode arithmetic
operations](https://github.com/apache/datafusion-comet/pull/2136)
+ - [ANSI mode integral
divide](https://github.com/apache/datafusion-comet/pull/2421)
+ - [ANSI mode rounding
functions](https://github.com/apache/datafusion-comet/pull/2542)
+ - [ANSI mode remainder
function](https://github.com/apache/datafusion-comet/pull/2556)
+
+TODO: Confirm if Spark 4.0 jars are available on Maven Central.
Review Comment:
They are. You can following the link from the installation guide.
https://datafusion.apache.org/comet/user-guide/0.11/installation.html#using-a-published-jar-file
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