alamb commented on code in PR #479:
URL: https://github.com/apache/arrow-site/pull/479#discussion_r1509481390
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_posts/2024-02-27-comet-donation.md:
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@@ -0,0 +1,106 @@
+---
+layout: post
+title: "Announcing Apache Arrow DataFusion Comet"
+date: "2024-02-27 00:00:00"
+author: pmc
+categories: [release]
+---
+<!--
+{% 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 %}
+-->
+
+# Introduction
+The Apache Arrow PMC is pleased to announce the donation of the [Comet
project],
+a native Spark SQL Accelerator built on [Apache Arrow DataFusion].
+
+Comet is an Apache Spark plugin that uses Apache Arrow DataFusion to
+accelerate Spark workloads. It is designed as a drop-in
+replacement for Spark's JVM based SQL execution engine and offers significant
+performance improvements for some workloads as shown below.
+
+```text
+ ┌─────────────────────────────────────────────────────────────────┐
+ │ │
+ │ ┌──────────┐ ┌────────────┐ ┌────────────┐ ┌────────────┐ │
+ │ │ SQL │ │ Cluster │ │ DAG/Task │ ... │ Executor │ │
+ │ │ Planner │ │ Manager │ │ Scheduler │ │ │ │
+ │ └──────────┘ └────────────┘ └────────────┘ └────────────┘ │
+ │ │ │
+ └─────────────────────────────────────────────────────────────────┘
+ Spark (JVM Based) │
+ ┌ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─
+
+ │
+ ▼
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+Comet Execution ┃ ┃
+Engine ┃ ┌─────────────────────────┐ ┃
+(Native Code) ┃ │ Apache Arrow DataFusion │ ┃
+ ┃ └─────────────────────────┘ ┃
+ ┃ ┃
+ ┃ ┌─────────────────────────┐ ┃
+ ┃ │ Spark Compatible │ ┃
+ ┃ │ Expressions/Operators │ ┃
+ ┃ └─────────────────────────┘ ┃
+ ┃ ┃
+ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+```
+
+**Figure 1**: With Comet, users interact with the same Spark ecosystem, tools
+and APIs such as Spark SQL. Queries still run through Spark's mature and
feature
+rich query optimizer and planner. However, the execution is delegated to Comet,
+which is significantly faster and more resource efficient than the JVM based
+implementation.
+
+[Rust]: https://www.rust-lang.org/
+
+# Background
+
+Comet is one of a growing class of projects that aim to accelerate Spark using
+native columnar engines such as the proprietary [Databricks Photon Engine] and
+the open source [Gluten project] and [Spark RAPIDS].
Review Comment:
Thank you @SChakravorti21 for this suggestion. I agree such background
would help -- maybe some of the other reviewers (some of whom are spark
committers I believe) can offer more specifics and how this relates to mainline
spark.
The [Velox Paper](https://vldb.org/pvldb/vol15/p3372-pedreira.pdf) basically
says the JVM implementation of spark is slow so native columnar execution is
better, but I don't recall it delving into any more detail
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