wesm commented on a change in pull request #100:
URL: https://github.com/apache/arrow-site/pull/100#discussion_r611259754
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File path: _posts/2021-04-12-ballista-donation.md
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+---
+layout: post
+title: "Ballista: A Distributed Scheduler for Apache Arrow"
+description: "We are excited to announce that Ballista has been donated to the
Apache Arrow project. Ballista is a distributed scheduler for the Rust
implementation of Apache Arrow."
+date: "2021-04-12 00:00:00 -0600"
+author: agrove
+categories: [application]
+---
+<!--
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+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+
+We are excited to announce that
[Ballista](https://github.com/apache/arrow/tree/master/rust/ballista) has been
donated
+to the Apache Arrow project.
+
+Ballista is a distributed compute platform primarily implemented in Rust, and
powered by Apache Arrow. It is built
+on an architecture that allows other programming languages (such as Python,
C++, and Java) to be supported as
+first-class citizens without paying a penalty for serialization costs.
+
+The foundational technologies in Ballista are:
+
+- [Apache Arrow](https://arrow.apache.org/) memory model and compute kernels
for efficient processing of data.
+- [Apache Arrow Flight
Protocol](https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight/)
for efficient
+ data transfer between processes.
+- [Google Protocol Buffers](https://developers.google.com/protocol-buffers)
for serializing query plans.
+- [Docker](https://www.docker.com/) for packaging up executors along with
user-defined code.
+
+Ballista can be deployed as a standalone cluster and also supports
[Kubernetes](https://kubernetes.io/). In either
+case, the scheduler can be configured to use [etcd](https://etcd.io/) as a
backing store to (eventually) provide
+redundancy in the case of a scheduler failing.
+
+## Status
+
+The Ballista project is at an early stage of development. However, it is
capable of running complex analytics queries
+in a distributed cluster with reasonable performance.
+
+The following chart shows the current performance of Ballista for a number of
TPC-H queries at scale factor 100. The
+current performance at this scale is similar to Apache Spark, and better in
some cases. More work is now needed to
+make Ballista scale with larger data sets.
+
+
+
+_Benchmarks were executed on a 24 core desktop with 64 GB RAM and NVMe drives.
The 100 GB dataset was converted to
+Parquet and repartitioned with 8 partitions._
Review comment:
To be gracious, you might point out that these benchmarks haven't been
peer reviewed and you look forward to working with the Spark community to
create realistic and reproducible benchmarks, since the benchmarks are more
about learning (so you can make the software better) rather than trying to show
"which is better".
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