viirya commented on code in PR #305:
URL: https://github.com/apache/arrow-site/pull/305#discussion_r1103880751
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_posts/2023-01-26-rust-32.0.0.md:
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+---
+layout: post
+title: "January 2023 Rust Apache Arrow Highlights"
+date: "2023-01-26 00:00:00 +0000"
+author: pmc
+categories: [release]
+---
+<!--
+{% comment %}
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+(the "License"); you may not use this file except in compliance with
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+
+Unless required by applicable law or agreed to in writing, software
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+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 %}
+-->
+
+# Introduction
+
+With the recent release of [32.0.0](https://crates.io/crates/arrow/32.0.0) of
the Rust implementation of [Apache Arrow](https://arrow.apache.org/), it seemed
timely to highlight some of the communities work since the [last
update](https://arrow.apache.org/blog/2022/06/16/rust-16.0.0/).
+
+The most recent list of detailed changes can always be found in the
[CHANGELOG](https://github.com/apache/arrow-rs/blob/master/CHANGELOG.md), with
the full historical list available
[here](https://github.com/apache/arrow-rs/blob/master/CHANGELOG-old.md).
+
+# Arrow
+
+[arrow](https://crates.io/crates/arrow) and
[arrow-flight](https://crates.io/crates/arrow-flight) are native Rust
implementations of Apache Arrow. Apache Arrow defines a language-independent
columnar memory format for flat and hierarchical data, organized for efficient
analytic operations on modern hardware like CPUs and GPUs. The Arrow memory
format also supports zero-copy reads for lightning-fast data access without
serialization overhead.
+
+The [Rust language](https://www.rust-lang.org/) offers best in class
performance, memory safety, and the developer productivity of a modern
programming language. These features make Rust an excellent choice for building
modern high performance analytical systems. When combined, Rust and the Apache
Arrow Ecosystem are a compelling toolkit for building the next generation of
systems.
+
+The [repository](https://github.com/apache/arrow-rs) recently passed 1400
stars on github, and the community has been focused on performance and feature
completeness.
+
+
+**Major Highlights**
+
+* **New CSV and JSON Readers**: The CSV and JSON readers have been revamped.
Their performance has more than doubled, and they now support push-driven
parsing facilitating async streaming decode from object storage.
+* **Faster Build Times and Reduced Codegen**: The `arrow` crate has been split
into multiple smaller crates, and large kernels have been moved behind optional
feature flags. These changes allow downstream projects to choose a smaller
dependency footprint and build times, if desired.
+* **Support for Copy-On-Write**: Arrow arrays now support copy-on-write, via
the
[`into_builder`](https://docs.rs/arrow/32.0.0/arrow/array/struct.ArrayData.html#method.into_builder)
methods
+* **Comparable Row Format**: [Much faster multi-column Sorting and
Grouping](https://arrow.apache.org/blog/2022/11/07/multi-column-sorts-in-arrow-rust-part-1/)
is now possible with the the new spillable, comparable
[row-format](https://docs.rs/arrow-row/32.0.0/arrow_row/index.html)
+* **FlightSQL Support**:
[FlightSQL](https://arrow.apache.org/docs/format/FlightSql.html)
[support](https://docs.rs/arrow-flight/32.0.0/arrow_flight/sql/index.html) has
been expanded
+* **Mid-Level Flight Client**: A new
[FlightClient](https://docs.rs/arrow-flight/32.0.0/arrow_flight/client/struct.FlightClient.html)
is available that handles lower level protocol details, and easier to use
[encoding](https://docs.rs/arrow-flight/32.0.0/arrow_flight/encode/struct.FlightDataEncoderBuilder.html)
and
[decoding](https://docs.rs/arrow-flight/32.0.0/arrow_flight/decode/struct.FlightDataDecoder.html)
APIs.
+* **IPC File Compression**: Arrow IPC file
[compression](https://docs.rs/arrow-ipc/32.0.0/arrow_ipc/gen/Message/struct.CompressionType.html)
with ZSTD and LZ4 is now fully supported.
+* **Full Decimal Support**: 256-bit decimals and negative scales can be
created and manipulated using many kernels, such as arithmetic.
+* **Improved Dictionary Support**: Dictionaries are now transparently
supported in most kernels.
+* **Improved Temporal Support**: Timestamps with Timezones and other temporal
types are supported in many more kernels.
+* **Improved Generics**: Improved generics allow writing code generic over all
arrays, or all arrays with the same layout
+* **Downcast Macros**: Various
[helper](https://docs.rs/arrow/32.0.0/arrow/macro.downcast_primitive_array.html)
[macros](https://docs.rs/arrow/32.0.0/arrow/macro.downcast_dictionary_array.html)
are now available to simplify dynamic dispatch to statically typed
implementations.
+
+# Parquet
+
+[Apache Parquet](https://parquet.apache.org/) is an open source,
column-oriented data file format designed for efficient data storage and
retrieval. It provides efficient data compression and encoding schemes with
enhanced performance to handle complex data in bulk. The Apache Parquet
implementation in Rust is one of the [fastest and most sophisticated
](https://arrow.apache.org/blog/2022/12/26/querying-parquet-with-millisecond-latency/)
open source implementations available.
Review Comment:
```suggestion
[Apache Parquet](https://parquet.apache.org/) is an open source,
column-oriented data file format designed for efficient data storage and
retrieval. It provides efficient data compression and encoding schemes with
enhanced performance to handle complex data in bulk. The Apache Parquet
implementation in Rust is one of the [fastest and most
sophisticated](https://arrow.apache.org/blog/2022/12/26/querying-parquet-with-millisecond-latency/)
open source implementations available.
```
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