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Posted to commits@arrow.apache.org by gi...@apache.org on 2021/08/20 13:59:53 UTC
[arrow-site] branch asf-site updated: Updating built site (build
343e8d6dcfb923ebfb53cf6b22c2e2ab0ef9dad4)
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github-bot pushed a commit to branch asf-site
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The following commit(s) were added to refs/heads/asf-site by this push:
new 38d88f6 Updating built site (build 343e8d6dcfb923ebfb53cf6b22c2e2ab0ef9dad4)
38d88f6 is described below
commit 38d88f69a5b8755ee2367693437ec64c16c41208
Author: Andy Grove <ag...@apache.org>
AuthorDate: Fri Aug 20 13:59:43 2021 +0000
Updating built site (build 343e8d6dcfb923ebfb53cf6b22c2e2ab0ef9dad4)
---
blog/2021/08/18/ballista-0.5.0/index.html | 318 +++++++++++++++
blog/2021/08/18/datafusion-5.0.0/index.html | 349 +++++++++++++++++
blog/index.html | 30 ++
docs/c_glib/index.html | 4 +-
feed.xml | 586 ++++++++--------------------
img/2021-08-18-datafusion500perf.png | Bin 0 -> 21245 bytes
release/0.1.0.html | 4 +-
release/0.10.0.html | 4 +-
release/0.11.0.html | 4 +-
release/0.11.1.html | 4 +-
release/0.12.0.html | 4 +-
release/0.13.0.html | 4 +-
release/0.14.0.html | 4 +-
release/0.14.1.html | 4 +-
release/0.15.0.html | 4 +-
release/0.15.1.html | 4 +-
release/0.16.0.html | 4 +-
release/0.17.0.html | 4 +-
release/0.17.1.html | 4 +-
release/0.2.0.html | 4 +-
release/0.3.0.html | 4 +-
release/0.4.0.html | 4 +-
release/0.4.1.html | 4 +-
release/0.5.0.html | 4 +-
release/0.6.0.html | 4 +-
release/0.7.0.html | 4 +-
release/0.7.1.html | 4 +-
release/0.8.0.html | 4 +-
release/0.9.0.html | 4 +-
release/1.0.0.html | 4 +-
release/1.0.1.html | 4 +-
release/2.0.0.html | 4 +-
release/3.0.0.html | 4 +-
release/4.0.0.html | 4 +-
release/4.0.1.html | 4 +-
release/5.0.0.html | 4 +-
release/index.html | 4 +-
37 files changed, 916 insertions(+), 495 deletions(-)
diff --git a/blog/2021/08/18/ballista-0.5.0/index.html b/blog/2021/08/18/ballista-0.5.0/index.html
new file mode 100644
index 0000000..f34eb3e
--- /dev/null
+++ b/blog/2021/08/18/ballista-0.5.0/index.html
@@ -0,0 +1,318 @@
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+<meta name="description" content="Ballista extends DataFusion to provide support for distributed queries. This is the first release of Ballista since the project was donated to the Apache Arrow project and includes 80 commits from 11 contributors. git shortlog -sn 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust/executor ballista/rust/scheduler 27 Andy Grove 15 Jiayu Liu 12 Andrew Lamb 8 Ximo Guanter 6 Daniël Heres 5 QP Hou 2 Jorge Leitao 1 Javier Goday 1 K.I. (Dennis) [...]
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+ <div class="container p-4 pt-5">
+ <div class="col-md-8 mx-auto">
+ <main role="main" class="pb-5">
+
+<h1>
+ Apache Arrow Ballista 0.5.0 Release
+</h1>
+<hr class="mt-4 mb-3">
+
+
+
+<p class="mb-4 pb-1">
+ <span class="badge badge-secondary">Published</span>
+ <span class="published mr-3">
+ 18 Aug 2021
+ </span>
+ <br />
+ <span class="badge badge-secondary">By</span>
+
+ <a class="mr-3" href="https://arrow.apache.org">The Apache Arrow PMC (pmc) </a>
+
+
+
+</p>
+
+
+ <!--
+
+-->
+
+<p>Ballista extends DataFusion to provide support for distributed queries. This is the first release of Ballista since
+the project was <a href="https://arrow.apache.org/blog/2021/04/12/ballista-donation/">donated</a> to the Apache Arrow project
+and includes 80 commits from 11 contributors.</p>
+
+<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>git shortlog -sn 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust/executor ballista/rust/scheduler
+ 27 Andy Grove
+ 15 Jiayu Liu
+ 12 Andrew Lamb
+ 8 Ximo Guanter
+ 6 Daniël Heres
+ 5 QP Hou
+ 2 Jorge Leitao
+ 1 Javier Goday
+ 1 K.I. (Dennis) Jung
+ 1 Mike Seddon
+ 1 sathis
+</code></pre></div></div>
+
+<!--
+$ git log --pretty=oneline 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust/executor ballista/rust/scheduler ballista-examples/ | wc -l
+80
+-->
+
+<p>The release notes below are not exhaustive and only expose selected highlights of the release. Many other bug fixes
+and improvements have been made: we refer you to the <a href="https://github.com/apache/arrow-datafusion/blob/5.0.0/ballista/CHANGELOG.md">complete changelog</a>.</p>
+
+<h1 id="performance-and-scalability">Performance and Scalability</h1>
+
+<p>Ballista is now capable of running complex SQL queries at scale and supports scalable distributed joins. We have been
+benchmarking using individual queries from the TPC-H benchmark at scale factors up to 1000 (1 TB). When running against
+CSV files, performance is generally very close to DataFusion, and significantly faster in some cases due to the fact
+that the scheduler limits the number of concurrent tasks that run at any given time. Performance against large Parquet
+datasets is currently non ideal due to some issues (<a href="https://github.com/apache/arrow-datafusion/issues/867">#867</a>,
+<a href="https://github.com/apache/arrow-datafusion/issues/868">#868</a>) that we hope to resolve for the next release.</p>
+
+<h1 id="new-features">New Features</h1>
+
+<p>The main new features in this release are:</p>
+
+<ul>
+ <li>Ballista queries can now be executed by calling DataFrame.collect()</li>
+ <li>The shuffle mechanism has been re-implemented</li>
+ <li>Distributed hash-partitioned joins are now supported</li>
+ <li>Keda autoscaling is supported</li>
+</ul>
+
+<p>To get started with Ballista, refer to the <a href="https://docs.rs/ballista/0.5.0/ballista/">crate documentation</a>.</p>
+
+<p>Now that the basic functionality is in place, the focus for the next release will be to improve the performance and
+scalability as well as improving the documentation.</p>
+
+<h1 id="how-to-get-involved">How to Get Involved</h1>
+
+<p>If you are interested in contributing to Ballista, we would love to have you! You
+can help by trying out Ballista on some of your own data and projects and filing bug reports and helping to
+improve the documentation, or contribute to the documentation, tests or code. A list of open issues suitable for
+beginners is <a href="https://github.com/apache/arrow-datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>
+and the full list is <a href="https://github.com/apache/arrow-datafusion/issues">here</a>.</p>
+
+ </main>
+ </div>
+
+ <hr/>
+<footer class="footer">
+ <div class="row">
+ <div class="col-md-9">
+ <p>Apache Arrow, Arrow, Apache, the Apache feather logo, and the Apache Arrow project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p>
+ <p>© 2016-2021 The Apache Software Foundation</p>
+ </div>
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diff --git a/blog/2021/08/18/datafusion-5.0.0/index.html b/blog/2021/08/18/datafusion-5.0.0/index.html
new file mode 100644
index 0000000..114e22c
--- /dev/null
+++ b/blog/2021/08/18/datafusion-5.0.0/index.html
@@ -0,0 +1,349 @@
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+ <meta name="viewport" content="width=device-width, initial-scale=1">
+ <!-- The above meta tags *must* come first in the head; any other head content must come *after* these tags -->
+
+ <title>Apache Arrow DataFusion 5.0.0 Release | Apache Arrow</title>
+
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+<meta property="og:locale" content="en_US" />
+<meta name="description" content="The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work and includes 211 commits from the following 31 distinct contributors. $ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples 61 Jiayu Liu 47 Andrew Lamb 27 Daniël Heres 13 QP Hou 13 Andy Grove 4 Javier Goday 4 sathis 3 Ruan Pearce-Authers 3 Raphael Taylor-Davies 3 Jorge Leitao 3 Cui Wenzheng 3 Mike Seddon 3 Edd Robins [...]
+<meta property="og:description" content="The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work and includes 211 commits from the following 31 distinct contributors. $ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples 61 Jiayu Liu 47 Andrew Lamb 27 Daniël Heres 13 QP Hou 13 Andy Grove 4 Javier Goday 4 sathis 3 Ruan Pearce-Authers 3 Raphael Taylor-Davies 3 Jorge Leitao 3 Cui Wenzheng 3 Mike Seddon 3 Edd [...]
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+ id="navbarDropdownASF" role="button" data-toggle="dropdown"
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+ </div>
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+ </div><!-- /.navbar-collapse -->
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+
+ </header>
+
+ <div class="container p-4 pt-5">
+ <div class="col-md-8 mx-auto">
+ <main role="main" class="pb-5">
+
+<h1>
+ Apache Arrow DataFusion 5.0.0 Release
+</h1>
+<hr class="mt-4 mb-3">
+
+
+
+<p class="mb-4 pb-1">
+ <span class="badge badge-secondary">Published</span>
+ <span class="published mr-3">
+ 18 Aug 2021
+ </span>
+ <br />
+ <span class="badge badge-secondary">By</span>
+
+ <a class="mr-3" href="https://arrow.apache.org">The Apache Arrow PMC (pmc) </a>
+
+
+
+</p>
+
+
+ <!--
+
+-->
+
+<p>The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work
+and includes 211 commits from the following 31 distinct contributors.</p>
+
+<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>$ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples
+ 61 Jiayu Liu
+ 47 Andrew Lamb
+ 27 Daniël Heres
+ 13 QP Hou
+ 13 Andy Grove
+ 4 Javier Goday
+ 4 sathis
+ 3 Ruan Pearce-Authers
+ 3 Raphael Taylor-Davies
+ 3 Jorge Leitao
+ 3 Cui Wenzheng
+ 3 Mike Seddon
+ 3 Edd Robinson
+ 2 思维
+ 2 Liang-Chi Hsieh
+ 2 Michael Lu
+ 2 Parth Sarthy
+ 2 Patrick More
+ 2 Rich
+ 1 Charlie Evans
+ 1 Gang Liao
+ 1 Agata Naomichi
+ 1 Ritchie Vink
+ 1 Evan Chan
+ 1 Ruihang Xia
+ 1 Todd Treece
+ 1 Yichen Wang
+ 1 baishen
+ 1 Nga Tran
+ 1 rdettai
+ 1 Marco Neumann
+</code></pre></div></div>
+
+<!--
+$ git log --pretty=oneline 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples | wc -l
+ 211
+-->
+
+<p>The release notes below are not exhaustive and only expose selected highlights of the release. Many other bug fixes
+and improvements have been made: we refer you to the complete
+<a href="https://github.com/apache/arrow-datafusion/blob/5.0.0/datafusion/CHANGELOG.md">changelog</a>.</p>
+
+<h1 id="performance">Performance</h1>
+
+<p>There have been numerous performance improvements in this release. The following chart shows the relative
+performance of individual TPC-H queries compared to the previous release.</p>
+
+<p><i>TPC-H @ scale factor 100, in parquet format. Concurrency 24.</i></p>
+
+<p><img src="/img/2021-08-18-datafusion500perf.png" /></p>
+
+<p>We also extended support for more TPC-H queries: q7, q8, q9 and q13 are running successfully in DataFusion 5.0.</p>
+
+<h1 id="new-features">New Features</h1>
+
+<ul>
+ <li>Initial support for SQL-99 Analytics (WINDOW functions)</li>
+ <li>Improved JOIN support: cross join, semi-join, anti join, and fixes to null handling</li>
+ <li>Improved EXPLAIN support</li>
+ <li>Initial implementation of metrics in the physical plan</li>
+ <li>Support for SELECT DISTINCT</li>
+ <li>Support for Json and NDJson formatted inputs</li>
+ <li>Query column with relations</li>
+ <li>Added more datetime related functions: <code class="language-plaintext highlighter-rouge">now</code>, <code class="language-plaintext highlighter-rouge">date_trunc</code>, <code class="language-plaintext highlighter-rouge">to_timestamp_millis</code>, <code class="language-plaintext highlighter-rouge">to_timestamp_micros</code>, <code class="language-plaintext highlighter-rouge">to_timestamp_seconds</code></li>
+ <li>Streaming Dataframe.collect</li>
+ <li>Support table column aliases</li>
+ <li>Answer count(*), min() and max() queries using only statistics</li>
+ <li>Non-equi-join filters in JOIN conditions</li>
+ <li>Modulus operation</li>
+ <li>Support group by column positions</li>
+ <li>Added constant folding query optimizer</li>
+ <li>Hash partitioned aggregation</li>
+ <li>Added <code class="language-plaintext highlighter-rouge">random</code> SQL function</li>
+ <li>Implemented count distinct for floats and dictionary types</li>
+ <li>Re-exported arrow and parquet crates in Datafusion</li>
+ <li>General row group pruning logic that’s agnostic to storage format</li>
+</ul>
+
+<h1 id="how-to-get-involved">How to Get Involved</h1>
+
+<p>If you are interested in contributing to DataFusion, we would love to have you! You
+can help by trying out DataFusion on some of your own data and projects and filing bug reports and helping to
+improve the documentation, or contribute to the documentation, tests or code. A list of open issues suitable for
+beginners is <a href="https://github.com/apache/arrow-datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>
+and the full list is <a href="https://github.com/apache/arrow-datafusion/issues">here</a>.</p>
+
+ </main>
+ </div>
+
+ <hr/>
+<footer class="footer">
+ <div class="row">
+ <div class="col-md-9">
+ <p>Apache Arrow, Arrow, Apache, the Apache feather logo, and the Apache Arrow project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p>
+ <p>© 2016-2021 The Apache Software Foundation</p>
+ </div>
+ <div class="col-md-3">
+ <a class="d-sm-none d-md-inline pr-2" href="https://www.apache.org/events/current-event.html">
+ <img src="https://www.apache.org/events/current-event-234x60.png"/>
+ </a>
+ </div>
+ </div>
+</footer>
+
+ </div>
+</body>
+</html>
diff --git a/blog/index.html b/blog/index.html
index 6f1ecb6..4fabc26 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -215,6 +215,36 @@
<p>
<h3>
+ <a href="/blog/2021/08/18/datafusion-5.0.0/">Apache Arrow DataFusion 5.0.0 Release</a>
+ </h3>
+
+ <p>
+ <span class="blog-list-date">
+ 18 August 2021
+ </span>
+ </p>
+ The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work and includes 211 commits from the following 31 distinct contributors. $ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples 61 Jiayu Liu 47 Andrew Lamb 27 Daniël Heres 13 QP Hou 13...
+ </p>
+
+
+
+ <p>
+ <h3>
+ <a href="/blog/2021/08/18/ballista-0.5.0/">Apache Arrow Ballista 0.5.0 Release</a>
+ </h3>
+
+ <p>
+ <span class="blog-list-date">
+ 18 August 2021
+ </span>
+ </p>
+ Ballista extends DataFusion to provide support for distributed queries. This is the first release of Ballista since the project was donated to the Apache Arrow project and includes 80 commits from 11 contributors. git shortlog -sn 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust/executor ballista/rust/scheduler 27 Andy Grove 15 Jiayu Liu 12 Andrew Lamb...
+ </p>
+
+
+
+ <p>
+ <h3>
<a href="/blog/2021/07/29/5.0.0-release/">Apache Arrow 5.0.0 Release</a>
</h3>
diff --git a/docs/c_glib/index.html b/docs/c_glib/index.html
index 8453160..6f84be5 100644
--- a/docs/c_glib/index.html
+++ b/docs/c_glib/index.html
@@ -20,13 +20,13 @@
<meta property="og:site_name" content="Apache Arrow" />
<meta property="og:image" content="https://arrow.apache.org/img/arrow.png" />
<meta property="og:type" content="article" />
-<meta property="article:published_time" content="2021-08-04T10:20:53-04:00" />
+<meta property="article:published_time" content="2021-08-20T09:59:22-04:00" />
<meta name="twitter:card" content="summary_large_image" />
<meta property="twitter:image" content="https://arrow.apache.org/img/arrow.png" />
<meta property="twitter:title" content="Apache Arrow GLib (C)" />
<meta name="twitter:site" content="@ApacheArrow" />
<script type="application/ld+json">
-{"datePublished":"2021-08-04T10:20:53-04:00","headline":"Apache Arrow GLib (C)","dateModified":"2021-08-04T10:20:53-04:00","mainEntityOfPage":{"@type":"WebPage","@id":"https://arrow.apache.org/docs/c_glib/"},"description":"Apache Arrow GLib (C) Apache Arrow GLib is a wrapper library for Apache Arrow C++. Apache Arrow GLib provides C API. Apache Arrow GLib supports GObject Introspection. It means that you can create language bindings at runtime or compile time automatically. API reference [...]
+{"datePublished":"2021-08-20T09:59:22-04:00","headline":"Apache Arrow GLib (C)","dateModified":"2021-08-20T09:59:22-04:00","mainEntityOfPage":{"@type":"WebPage","@id":"https://arrow.apache.org/docs/c_glib/"},"description":"Apache Arrow GLib (C) Apache Arrow GLib is a wrapper library for Apache Arrow C++. Apache Arrow GLib provides C API. Apache Arrow GLib supports GObject Introspection. It means that you can create language bindings at runtime or compile time automatically. API reference [...]
<!-- End Jekyll SEO tag -->
diff --git a/feed.xml b/feed.xml
index 28357e9..3f0d9d6 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,4 +1,157 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.2.0">Jekyll</generator><link href="https://arrow.apache.org/feed.xml" rel="self" type="application/atom+xml" /><link href="https://arrow.apache.org/" rel="alternate" type="text/html" /><updated>2021-08-04T10:20:53-04:00</updated><id>https://arrow.apache.org/feed.xml</id><title type="html">Apache Arrow</title><subtitle>Apache Arrow is a cross-language developm [...]
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.2.0">Jekyll</generator><link href="https://arrow.apache.org/feed.xml" rel="self" type="application/atom+xml" /><link href="https://arrow.apache.org/" rel="alternate" type="text/html" /><updated>2021-08-20T09:59:22-04:00</updated><id>https://arrow.apache.org/feed.xml</id><title type="html">Apache Arrow</title><subtitle>Apache Arrow is a cross-language developm [...]
+
+-->
+
+<p>Ballista extends DataFusion to provide support for distributed queries. This is the first release of Ballista since
+the project was <a href="https://arrow.apache.org/blog/2021/04/12/ballista-donation/">donated</a> to the Apache Arrow project
+and includes 80 commits from 11 contributors.</p>
+
+<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>git shortlog -sn 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust/executor ballista/rust/scheduler
+ 27 Andy Grove
+ 15 Jiayu Liu
+ 12 Andrew Lamb
+ 8 Ximo Guanter
+ 6 Daniël Heres
+ 5 QP Hou
+ 2 Jorge Leitao
+ 1 Javier Goday
+ 1 K.I. (Dennis) Jung
+ 1 Mike Seddon
+ 1 sathis
+</code></pre></div></div>
+
+<!--
+$ git log --pretty=oneline 4.0.0..5.0.0 ballista/rust/client ballista/rust/core ballista/rust/executor ballista/rust/scheduler ballista-examples/ | wc -l
+80
+-->
+
+<p>The release notes below are not exhaustive and only expose selected highlights of the release. Many other bug fixes
+and improvements have been made: we refer you to the <a href="https://github.com/apache/arrow-datafusion/blob/5.0.0/ballista/CHANGELOG.md">complete changelog</a>.</p>
+
+<h1 id="performance-and-scalability">Performance and Scalability</h1>
+
+<p>Ballista is now capable of running complex SQL queries at scale and supports scalable distributed joins. We have been
+benchmarking using individual queries from the TPC-H benchmark at scale factors up to 1000 (1 TB). When running against
+CSV files, performance is generally very close to DataFusion, and significantly faster in some cases due to the fact
+that the scheduler limits the number of concurrent tasks that run at any given time. Performance against large Parquet
+datasets is currently non ideal due to some issues (<a href="https://github.com/apache/arrow-datafusion/issues/867">#867</a>,
+<a href="https://github.com/apache/arrow-datafusion/issues/868">#868</a>) that we hope to resolve for the next release.</p>
+
+<h1 id="new-features">New Features</h1>
+
+<p>The main new features in this release are:</p>
+
+<ul>
+ <li>Ballista queries can now be executed by calling DataFrame.collect()</li>
+ <li>The shuffle mechanism has been re-implemented</li>
+ <li>Distributed hash-partitioned joins are now supported</li>
+ <li>Keda autoscaling is supported</li>
+</ul>
+
+<p>To get started with Ballista, refer to the <a href="https://docs.rs/ballista/0.5.0/ballista/">crate documentation</a>.</p>
+
+<p>Now that the basic functionality is in place, the focus for the next release will be to improve the performance and
+scalability as well as improving the documentation.</p>
+
+<h1 id="how-to-get-involved">How to Get Involved</h1>
+
+<p>If you are interested in contributing to Ballista, we would love to have you! You
+can help by trying out Ballista on some of your own data and projects and filing bug reports and helping to
+improve the documentation, or contribute to the documentation, tests or code. A list of open issues suitable for
+beginners is <a href="https://github.com/apache/arrow-datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>
+and the full list is <a href="https://github.com/apache/arrow-datafusion/issues">here</a>.</p></content><author><name>pmc</name></author><category term="release" /><summary type="html">Ballista extends DataFusion to provide support for distributed queries. This is the first release of Ballista since the project was donated to the Apache Arrow project and includes 80 commits from 11 contributors. git shortlog -sn 4.0.0..5.0.0 ballista/rust/client ballista/rust/ [...]
+
+-->
+
+<p>The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work
+and includes 211 commits from the following 31 distinct contributors.</p>
+
+<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>$ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples
+ 61 Jiayu Liu
+ 47 Andrew Lamb
+ 27 Daniël Heres
+ 13 QP Hou
+ 13 Andy Grove
+ 4 Javier Goday
+ 4 sathis
+ 3 Ruan Pearce-Authers
+ 3 Raphael Taylor-Davies
+ 3 Jorge Leitao
+ 3 Cui Wenzheng
+ 3 Mike Seddon
+ 3 Edd Robinson
+ 2 思维
+ 2 Liang-Chi Hsieh
+ 2 Michael Lu
+ 2 Parth Sarthy
+ 2 Patrick More
+ 2 Rich
+ 1 Charlie Evans
+ 1 Gang Liao
+ 1 Agata Naomichi
+ 1 Ritchie Vink
+ 1 Evan Chan
+ 1 Ruihang Xia
+ 1 Todd Treece
+ 1 Yichen Wang
+ 1 baishen
+ 1 Nga Tran
+ 1 rdettai
+ 1 Marco Neumann
+</code></pre></div></div>
+
+<!--
+$ git log --pretty=oneline 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples | wc -l
+ 211
+-->
+
+<p>The release notes below are not exhaustive and only expose selected highlights of the release. Many other bug fixes
+and improvements have been made: we refer you to the complete
+<a href="https://github.com/apache/arrow-datafusion/blob/5.0.0/datafusion/CHANGELOG.md">changelog</a>.</p>
+
+<h1 id="performance">Performance</h1>
+
+<p>There have been numerous performance improvements in this release. The following chart shows the relative
+performance of individual TPC-H queries compared to the previous release.</p>
+
+<p><i>TPC-H @ scale factor 100, in parquet format. Concurrency 24.</i></p>
+
+<p><img src="/img/2021-08-18-datafusion500perf.png" /></p>
+
+<p>We also extended support for more TPC-H queries: q7, q8, q9 and q13 are running successfully in DataFusion 5.0.</p>
+
+<h1 id="new-features">New Features</h1>
+
+<ul>
+ <li>Initial support for SQL-99 Analytics (WINDOW functions)</li>
+ <li>Improved JOIN support: cross join, semi-join, anti join, and fixes to null handling</li>
+ <li>Improved EXPLAIN support</li>
+ <li>Initial implementation of metrics in the physical plan</li>
+ <li>Support for SELECT DISTINCT</li>
+ <li>Support for Json and NDJson formatted inputs</li>
+ <li>Query column with relations</li>
+ <li>Added more datetime related functions: <code class="language-plaintext highlighter-rouge">now</code>, <code class="language-plaintext highlighter-rouge">date_trunc</code>, <code class="language-plaintext highlighter-rouge">to_timestamp_millis</code>, <code class="language-plaintext highlighter-rouge">to_timestamp_micros</code>, <code class="language-plaintext highlighter-rouge" [...]
+ <li>Streaming Dataframe.collect</li>
+ <li>Support table column aliases</li>
+ <li>Answer count(*), min() and max() queries using only statistics</li>
+ <li>Non-equi-join filters in JOIN conditions</li>
+ <li>Modulus operation</li>
+ <li>Support group by column positions</li>
+ <li>Added constant folding query optimizer</li>
+ <li>Hash partitioned aggregation</li>
+ <li>Added <code class="language-plaintext highlighter-rouge">random</code> SQL function</li>
+ <li>Implemented count distinct for floats and dictionary types</li>
+ <li>Re-exported arrow and parquet crates in Datafusion</li>
+ <li>General row group pruning logic that’s agnostic to storage format</li>
+</ul>
+
+<h1 id="how-to-get-involved">How to Get Involved</h1>
+
+<p>If you are interested in contributing to DataFusion, we would love to have you! You
+can help by trying out DataFusion on some of your own data and projects and filing bug reports and helping to
+improve the documentation, or contribute to the documentation, tests or code. A list of open issues suitable for
+beginners is <a href="https://github.com/apache/arrow-datafusion/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">here</a>
+and the full list is <a href="https://github.com/apache/arrow-datafusion/issues">here</a>.</p></content><author><name>pmc</name></author><category term="release" /><summary type="html">The Apache Arrow team is pleased to announce the DataFusion 5.0.0 release. This covers 4 months of development work and includes 211 commits from the following 31 distinct contributors. $ git shortlog -sn 4.0.0..5.0.0 datafusion datafusion-cli datafusion-examples 61 Jiayu Liu 47 [...]
-->
@@ -1331,433 +1484,4 @@ language implementations <a href="https://issues.apache.org/jira/browse/
suitable for beginners <a href="https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20resolution%20%3D%20Unresolved%20AND%20component%20in%20(Rust%2C%20%22Rust%20-%20DataFusion%22)%20AND%20labels%20%3D%20beginner">here</a> and the full list <a href="https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20resolution%20%3D%20Unresolved%20AND%20component%20in%20(Rust%2C%20%22Rust%20-%20DataFusion%22)%20ORDER%20BY%20updated%20 [...]
<p>Other ways to get involved include trying out Arrow on some of your data and filing bug reports, and helping to
-improve the documentation.</p></content><author><name>pmc</name></author><category term="release" /><summary type="html">Apache Arrow 2.0.0 is a significant release for the Apache Arrow project in general (release notes), and the Rust subproject in particular, with almost 200 issues resolved by 15 contributors. In this blog post, we will go through the main changes affecting core Arrow, Parquet support, and DataFusion query engine. The full list of resolved issues can be found here [...]
-
--->
-
-<p>The Apache Arrow team is pleased to announce the 2.0.0 release. This covers
-over 3 months of development work and includes <a href="https://issues.apache.org/jira/issues/?jql=project%20%3D%20ARROW%20AND%20status%20%3D%20Resolved%20AND%20fixVersion%20%3D%202.0.0"><strong>511 resolved issues</strong></a>
-from <a href="/release/2.0.0.html#contributors"><strong>81 distinct contributors</strong></a>. See the Install Page to learn how to
-get the libraries for your platform.</p>
-
-<p>The release notes below are not exhaustive and only expose selected highlights
-of the release. Many other bugfixes and improvements have been made: we refer
-you to the <a href="/release/2.0.0.html">complete changelog</a>.</p>
-
-<h2 id="community">Community</h2>
-
-<p>Since the 1.0.0 release, <a href="https://github.com/jorgecarleitao">Jorge Leitão</a> has been added as a committer. Thank
-you for your contributions!</p>
-
-<h2 id="columnar-format">Columnar Format</h2>
-
-<p>As this is the first major release since 1.0.0, we remind everyone that we have
-moved to a “split” versioning system where the Library version (which is now
-2.0.0) will now evolve separate from the Format version (which is still
-1.0.0). Major releases of the libraries may contain non-backward-compatible API
-changes, but they will not contain any incompatible format changes. See the
-<a href="http://arrow.apache.org/docs/format/Versioning.html">Versioning and Stability</a> page in the documentation for more.</p>
-
-<p>The columnar format metadata has been updated to permit 256-bit decimal values
-in addition to 128-bit decimals. This change is backward and forward
-compatible.</p>
-
-<h2 id="arrow-flight-rpc-notes">Arrow Flight RPC notes</h2>
-
-<p>For Arrow Flight, 2.0.0 mostly brings bugfixes. In Java, some memory leaks in
-<code class="language-plaintext highlighter-rouge">FlightStream</code> and <code class="language-plaintext highlighter-rouge">DoPut</code> have been addressed. In C++ and Python, a deadlock
-has been fixed in an edge case. Additionally, when supported by gRPC, TLS
-verification can be disabled.</p>
-
-<h2 id="c-notes">C++ notes</h2>
-
-<p>Parquet reading now fully supports round trip of arbitrarily nested data,
-including extension types with a nested storage type. In the process, several
-bugs in writing nested data and FixedSizeList were fixed. If writing data with
-these type we recommend upgrading to this release and validating old data as
-there is potential data loss.</p>
-
-<p>Datasets can now be written with partitions, including these features:</p>
-
-<ul>
- <li>Writing to Parquet, including control over accumulation of statistics for
-individual columns.</li>
- <li>Writing to IPC/Feather, including body buffer compression.</li>
- <li>Basenames of written files can be specified with a string template, allowing
-non-colliding writes into the same partitioned dataset.</li>
-</ul>
-
-<p>Other notable features in the release include</p>
-
-<ul>
- <li>Compute kernels for standard deviation, variance, and mode</li>
- <li>Improvements to S3 support, including automatic region detection</li>
- <li>CSV reading now parses Date type and creating Dictionary types</li>
-</ul>
-
-<h2 id="c-notes-1">C# notes</h2>
-
-<p>The .NET package has added a number of new features this release.</p>
-
-<p>Full support for <code class="language-plaintext highlighter-rouge">Struct</code> types.</p>
-
-<p>Synchronous write APIs for <code class="language-plaintext highlighter-rouge">ArrowStreamWriter</code> and <code class="language-plaintext highlighter-rouge">ArrowFileWriter</code>. These are
-complimentary to the existing async write APIs, and can be used in situations
-where the async APIs can’t be used.</p>
-
-<p>The ability to use <code class="language-plaintext highlighter-rouge">DateTime</code> instances with <code class="language-plaintext highlighter-rouge">Date32Array</code> and <code class="language-plaintext highlighter-rouge">Date64Array</code>.</p>
-
-<h2 id="java-notes">Java notes</h2>
-
-<p>The Java package has supported a number of new features. Users can validate
-vectors in a wider range of aspects, if they are willing to take more time. In
-dictionary encoding, dictionary indices can be expressed as unsigned integers.
-A framework for data compression has been setup for IPC.</p>
-
-<p>The calculation for vector capacity has been simplified, so users should
-experience notable performance improvements for various ‘setSafe’ methods.</p>
-
-<p>Bugs for JDBC adapters, sort algorithms, and ComplexCopier have been resolved
-to make them more usable.</p>
-
-<h2 id="javascript-notes">JavaScript notes</h2>
-
-<p>Upgrades Arrow’s build to use TypeScript 3.9, fixing generated <code class="language-plaintext highlighter-rouge">.d.ts</code> typings.</p>
-
-<h2 id="python-notes">Python notes</h2>
-
-<p>Parquet reading now supports round trip of arbitrarily nested data. Several bug
-fixes for writing nested data and FixedSizeList. If writing data with these
-type we recommend validating old data (there is potential some data loss) and
-upgrade to 2.0.</p>
-
-<p>Extension types with a nested storage type now round trip through Parquet.</p>
-
-<p>The <code class="language-plaintext highlighter-rouge">pyarrow.filesystem</code> submodule is deprecated in favor of new filesystem
-implementations in <code class="language-plaintext highlighter-rouge">pyarrow.fs</code>.</p>
-
-<p>The custom serialization functionality (<code class="language-plaintext highlighter-rouge">pyarrow.serialize()</code>,
-<code class="language-plaintext highlighter-rouge">pyarrow.deserialize()</code>, etc) is deprecated. Those functions provided a
-Python-specific (not cross-language) serialization format which were not
-compatible with the standardized Arrow (IPC) serialization format. For
-arbitrary objects, you can use the standard library <code class="language-plaintext highlighter-rouge">pickle</code> functionality
-instead. For pyarrow objects, you can use the IPC serialization format through
-the <code class="language-plaintext highlighter-rouge">pyarrow.ipc</code> module, as explained above.</p>
-
-<p>The <code class="language-plaintext highlighter-rouge">pyarrow.compute</code> module now has a complete coverage of the available C++
-compute kernels in the python API. Several new kernels have been added.</p>
-
-<p>The <code class="language-plaintext highlighter-rouge">pyarrow.dataset</code> module was further improved. In addition to reading, it
-is now also possible to write partitioned datasets (with <code class="language-plaintext highlighter-rouge">write_dataset()</code>).</p>
-
-<p>The Arrow &lt;-&gt; Python conversion code was refactored, fixing several bugs and
-corner cases.</p>
-
-<p>Conversion of an array of <code class="language-plaintext highlighter-rouge">pyarrow.MapType</code> to Pandas has been added.</p>
-
-<p>Conversion of timezone aware datetimes to and/from pyarrow arrays including
-pandas now round-trip preserving timezone. To use the old behavior (e.g. for
-spark) set the environment variable PYARROW_IGNORE_TIMEZONE to a truthy value
-(i.e. <code class="language-plaintext highlighter-rouge">PYARROW_IGNORE_TIMEZONE=1</code>)</p>
-
-<h2 id="r-notes">R notes</h2>
-
-<p>Highlights of the R release include</p>
-
-<ul>
- <li>Writing multi-file datasets with partitioning to Parquet or Feather</li>
- <li>Reading and writing directly to AWS S3, both individual files and multi-file
-datasets</li>
- <li>Bindings for Flight which use reticulate</li>
-</ul>
-
-<p>In addition, the R package benefits from the various improvements in the C++
-library listed above, including the ability to read and write Parquet files
-with nested struct and list types.</p>
-
-<p>For more on what’s in the 2.0.0 R package, see the <a href="/docs/r/news/">R changelog</a>.</p>
-
-<h2 id="ruby-and-c-glib-notes">Ruby and C GLib notes</h2>
-
-<h3 id="ruby">Ruby</h3>
-
-<p>In Ruby binding, <code class="language-plaintext highlighter-rouge">Arrow::Table#save</code> uses the number of rows as the
-<code class="language-plaintext highlighter-rouge">chunk_size</code> parameter by default when the table is saved in a Parquet file.</p>
-
-<h3 id="c-glib">C GLib</h3>
-
-<p>The GLib binding newly supports <code class="language-plaintext highlighter-rouge">GArrowStringDictionaryArrayBuilder</code> and
-<code class="language-plaintext highlighter-rouge">GArrowBinaryDictionaryArrayBuilder</code>.</p>
-
-<p>Moreover the GLib binding supports new accessors of <code class="language-plaintext highlighter-rouge">GArrowListArray</code> and
-<code class="language-plaintext highlighter-rouge">GArrowLargeListArray</code>. They are <code class="language-plaintext highlighter-rouge">get_values</code>, <code class="language-plaintext highlighter-rouge">get_value_offset</code>,
-<code class="language-plaintext highlighter-rouge">get_value_length</code>, and <code class="language-plaintext highlighter-rouge">get_value_offsets</code>.</p>
-
-<h2 id="rust-notes">Rust notes</h2>
-
-<p>Due to the high volume of activity in the Rust subproject in this release,
-we’re writing a separate blog post dedicated to those changes.</p></content><author><name>pmc</name></author><category term="release" /><summary type="html">The Apache Arrow team is pleased to announce the 2.0.0 release. This covers over 3 months of development work and includes 511 resolved issues from 81 distinct contributors. See the Install Page to learn how to get the libraries for your platform. The release notes below are not exhaustive and only expose selected highlights of [...]
-
--->
-
-<p>Over the last four and a half years, we’ve worked to build a
-“batteries-included” development platform for high-performance analytics
-applications in C++. As the scope of the project has grown, we have sometimes
-taken on additional library dependencies to support a wide variety of systems
-and data processing tasks.</p>
-
-<p>While these dependencies give us leverage on hard problems, in some cases they
-have added complexity for projects that depend on Arrow. Some projects have thus
-been concerned about depending on the Arrow C++ library, particularly if their
-use of the Arrow library’s features is limited. Indeed, in the earlier stages of
-the Arrow project development, dependency management issues did cause problems
-for early adopters.</p>
-
-<p>We want developers to trust that they can use and depend on our libraries, and
-that doing so doesn’t add a burden for their own project maintenance or for
-their users. Over the last year, we have undertaken a number of significant
-projects to accommodate the different ways that people want to depend on Arrow
-C++. We’ve aimed to make the build process simple by default, without requiring
-special environment setup, yet also highly configurable for those who need to
-specialize. This includes a <em>zero-dependency option</em> for projects that wish to use
-the Arrow C++ core but take on no transitive dependencies. We’ve also worked to
-make builds faster and more compact, even as we continue to add new
-functionality.</p>
-
-<p>This post covers many of the efforts we’ve made, both in the C++ libraries and
-in the Arrow Python and R packages that depend on them. Compared to a year ago,
-the build experience is much more reliable on a wider range of platforms,
-requires fewer dependencies, and yields smaller package sizes.</p>
-
-<h2 id="minimal-default-build-options">Minimal default build options</h2>
-
-<p>One rough edge for people using Arrow as a dependency was that many optional
-project components were enabled in the build by default, thus requiring any
-extra dependencies of those optional components. Rather than expecting users to
-disable optional components one by one, we have made the default for all
-optional components to be <code class="language-plaintext highlighter-rouge">OFF</code> so that the default configuration is a
-dependency-free minimal core build.</p>
-
-<p>The only third-party library enabled by default is
-<a href="http://jemalloc.net/">jemalloc</a>, the project’s recommended memory allocator
-(except on Windows, where it is also disabled). Given that Arrow applications
-often process large volumes of data, we have found additionally that using
-memory allocators provided by projects like jemalloc and
-<a href="https://microsoft.github.io/mimalloc/">mimalloc</a> yield significantly better
-performance over the default system allocators. Even so, this can also be
-disabled if desired.</p>
-
-<p>To demonstrate a minimal build, we have provided a
-<a href="https://github.com/apache/arrow/blob/master/cpp/examples/minimal_build/Dockerfile">Dockerfile</a>
-which can be used to build the project requiring only CMake and a C++ compiler
-with zero dependencies. Additionally, we have included an
-<a href="https://github.com/apache/arrow/tree/master/cpp/examples/minimal_build">example</a>
-of including Arrow as an external project dependency in another CMake project.</p>
-
-<h2 id="flexible-dependency-configuration-in-cmake">Flexible dependency configuration in CMake</h2>
-
-<p>As part of improving our CMake-based build system, we have made the
-configuration of build dependencies both flexible and consistent for different
-users’ needs. In some cases, developers want Arrow to build against dependencies
-provided by an external package manager, such as apt in Debian-based Linux
-distributions. In other cases, developers may want to avoid any quirks of system
-libraries and build all dependencies together with the Arrow build.</p>
-
-<p>For each package, the <code class="language-plaintext highlighter-rouge">${Library}_SOURCE</code> CMake option can be set to one of three
-values:</p>
-
-<ul>
- <li><code class="language-plaintext highlighter-rouge">SYSTEM</code>, when the dependency is to be provided externally (such as by a Linux distribution or Homebrew)</li>
- <li><code class="language-plaintext highlighter-rouge">BUNDLED</code>, when you want the dependency to be built from source while building Arrow, and then statically-linked with the resulting libraries</li>
- <li><code class="language-plaintext highlighter-rouge">AUTO</code>, which tries the <code class="language-plaintext highlighter-rouge">SYSTEM</code> approach but falls back on <code class="language-plaintext highlighter-rouge">BUNDLED</code> if the dependency cannot be located</li>
-</ul>
-
-<p>We additionally have provided <code class="language-plaintext highlighter-rouge">CONDA</code> and <code class="language-plaintext highlighter-rouge">BREW</code> source types for the common
-scenarios when developers are using the conda or Homebrew package managers.
-These dependency sources can be configured on an individual dependency basis or
-globally using the <code class="language-plaintext highlighter-rouge">ARROW_DEPENDENCY_SOURCE</code> CMake option. <code class="language-plaintext highlighter-rouge">AUTO</code> is default,
-which enables builds to be faster by using pre-built system libraries where
-possible but still succeed even if all dependencies are not available on the
-system.</p>
-
-<h2 id="reduced-external-dependencies">Reduced external dependencies</h2>
-
-<p>Another area of focus was to audit our dependencies. We went through and found
-places where we could drop external dependencies without losing useful
-functionality and without having to rewrite a lot or copy too much code into our
-codebase.</p>
-
-<p>We have eliminated Boost as a dependency of the core Arrow library, and in other
-components (Gandiva, Parquet, etc.), the use of Boost has been greatly reduced.
-In addition, when building Boost “bundled” in the Arrow build, we stripped down
-the Boost package we download to the minimum needed, cutting out 90 percent of
-the download size.</p>
-
-<p>We vendored a few small dependencies, such as the double-conversion and
-uriparser libraries, so that they do not need to be downloaded and built
-separately.</p>
-
-<p>We also compiled the Flatbuffers and Thrift definitions (which are needed to
-implement the Arrow and Parquet formats, respectively) and checked in the
-resulting C++ code to the Arrow repository. This means that Flatbuffers is no
-longer a build or runtime dependency of Arrow, and we only need the Thrift C++
-library, not the Thrift compiler, which has additional dependencies on flex and
-bison.</p>
-
-<h2 id="c-library-size-reductions">C++ library size reductions</h2>
-
-<p>As the C++ codebase grows in size, so too does compilation times and the amount
-of binary code generated by the C++ compiler. Over the last several months, we
-have begun analyzing the Arrow libraries both compile times and generated code
-sizes. This has yielded both significant size reductions (more than 30 percent
-code size reduction since 0.17.0). We have also restructured header files to
-avoid including unneeded header files, thus lightening the load on C++ compilers
-and improving compilation times.</p>
-
-<h2 id="python-wheels">Python wheels</h2>
-
-<p>The expectation for binary wheel packages on the Python Package Index (PyPI) is
-that they are self-contained and have no external dependencies except on other
-Python packages. Additionally, each user of pyarrow may need different things
-from the project. Some users just want to read Parquet files and convert them to
-pandas data frames while others want to use
-<a href="/blog/2019/10/13/introducing-arrow-flight/">Flight</a> for
-moving around large datasets. Thus, the “pyarrow” wheel has from the beginning
-of the project been a fairly comprehensive build including as many optional
-components as is practical for us to maintain.</p>
-
-<p>A comprehensive wheel package has some downsides: most notably for users, it is large.
-Additionally, through a snafu relating to C++ shared libraries, for several
-releases the wheel packages would create two copies of each C++ library on disk,
-resulting in double the amount of disk usage. This has caused problems for
-people using pyarrow in space-constrained environments like AWS Lambda.</p>
-
-<p>In the 1.0.0 release, we have implemented some changes that have reduced the
-size of the wheels (both in <code class="language-plaintext highlighter-rouge">.whl</code> form and installed on disk) by about 75 percent:</p>
-
-<ul>
- <li>Working around the problems resulting in two copies of each shared library being created in the site-packages directory.</li>
- <li>Disabling Gandiva, which required the LLVM runtime, the largest statically-linked dependency. Gandiva is still available to conda users now–it’s just not included in the wheels–and we hope to package it as a separate <code class="language-plaintext highlighter-rouge">pyarrow-llvm</code> package in the future.</li>
- <li>Reducing the size of the C++ shared libraries as discussed above</li>
-</ul>
-
-<p>Now pyarrow is about the size of NumPy and thus much easier for Python projects
-to take on as a hard dependency without worrying about large on-disk size.</p>
-
-<p>Looking ahead, we have discussed <a href="https://issues.apache.org/jira/browse/ARROW-8518">strategies</a>
-for breaking up pyarrow into multiple wheel packages, sort of a “hub and spoke”
-model where some optional pieces are installed as separate wheels so people only
-needing some “core” functionality only have to install a small package. This
-would be a significant project, though, so for now we’ve focused on improvements
-to the comprehensive wheel package.</p>
-
-<h2 id="r-packaging">R packaging</h2>
-
-<p>Packaging Arrow for R involves similar challenges to Python wheels, though the
-technical details are unique. Like how <code class="language-plaintext highlighter-rouge">pip install pyarrow</code> should just work
-everywhere, so should <code class="language-plaintext highlighter-rouge">install.packages("arrow")</code> in R, and we have invested
-significant effort to get there. Because the R package depends on a C++ library
-in active development, this is not trivial, particularly for all of the
-combinations of C++ compilers and standard libraries on Linux.</p>
-
-<p>In the initial CRAN release last year, version 0.14.1, only Windows and macOS
-binary packages worked out of the box. For Linux, you had to install the C++
-library separately, before installing the R package. While Python wheels contain
-binary libraries even on Linux, CRAN only hosts source packages that must be
-compiled on the user’s machine at install time. This led to an experience that
-was less than ideal for Linux users.</p>
-
-<p>Starting in version 0.16, a source package installation on Linux handles its C++
-dependencies automatically. By default, the package executes a <a href="https://github.com/apache/arrow/blob/master/r/inst/build_arrow_static.sh">bundled
-script</a>
-that downloads and builds the Arrow C++ library with no system dependencies
-beyond what R requires. On many common Linux distributions and versions, this
-can be sped up by
-<a href="/docs/r/articles/install.html">setting an environment variable</a>
-to download a prebuilt static C++ library for inclusion in the package.</p>
-
-<p>To accompany these improvements and to ensure that they succeeded on a wide
-range of platforms, we added
-<a href="https://github.com/apache/arrow/blob/bebcc5db3cc2890a9c53ebd53bc60863ae5ebb49/dev/tasks/tasks.yml#L1704-L1785">extensive</a>
-<a href="https://github.com/ursa-labs/arrow-r-nightly/blob/master/.github/workflows/test-binary.yml">nightly builds</a>
-to our continuous integration system. These are also easily extensible–all we
-need is a Docker image containing R, and we can plug new environments into our
-regular nightly testing.</p>
-
-<p>Since then, we’ve continued to improve the installation experience and look for
-ways to reduce build time and package size. The C++ library improvements
-discussed above help the R package since most installations of the R package
-either build or otherwise include the C++ library. Within the R package itself,
-we’ve looked for ways to include just what is needed and nothing more. These
-efforts have resulted in smaller downloads and installed package sizes. From
-0.17.1 to 1.0.0, installed library sizes for macOS and Windows CRAN binaries are
-down 10 percent, and the prebuilt static C++ libraries for Linux are 33 percent
-smaller compared to 0.16.0, despite the addition of many new features.</p>
-
-<!-- macOS build 0.17.1:
-
-checking installed package size ... NOTE
- installed size is 38.1Mb
- sub-directories of 1Mb or more:
- R 3.2Mb
- libs 34.5Mb
-
-autobrew libs on master: 8.9mb
-
-macOS 1.0.0
-checking installed package size ... NOTE
- installed size is 35.0Mb
- sub-directories of 1Mb or more:
- R 3.2Mb
- libs 31.3Mb
-
-windows 0.17.1:
-checking installed package size ... NOTE
- installed size is 27.9Mb
- sub-directories of 1Mb or more:
- R 3.2Mb
- libs 24.3Mb
-
-windows libs on 1.0.0:
-checking installed package size ... NOTE
- installed size is 24.9Mb
- sub-directories of 1Mb or more:
- R 3.2Mb
- libs 21.2Mb
-
-ubuntu-18.04 libarrow binaries:
-0.16.0.2 18.84 MB
-0.17.0 12.81 MB
-1.0.0 12.45 MB -->
-
-<h2 id="c-interface">C Interface</h2>
-
-<p>Finally, we have observed that some projects may wish to produce or consume a
-subset of the Arrow format and do not want to take on any additional code
-dependencies. There are also scenarios where two libraries need to share
-in-memory Arrow data structures but are unable to depend on a common Arrow
-library such as the reference C++ implementation. To address these use cases, we
-designed the <a href="/docs/format/CDataInterface.html">C Interface</a>
-to provide a lightweight way to exchange Arrow data at the C level without any
-memory copying.</p>
-
-<p>When using the C interface, a developer populates simple C data structures that
-contain the schema (data type) information about an Arrow data structure and the
-addresses of the pieces of memory that constitute the data. This permits
-libraries to be plugged together easily in-memory without any shared code
-(except the C interface structure definitions). Most programming languages have
-the ability to manipulate C structures and so this interface can even be used
-without having to write or compile C code. We have used the C interface to
-<a href="/docs/r/articles/python.html">transfer data structures between Python and R</a>
-in-memory using <code class="language-plaintext highlighter-rouge">reticulate</code>.</p>
-
-<p>One exciting use case for the Arrow C interface is to add Arrow import and
-export to database driver libraries which often contain a C API.</p>
-
-<h2 id="looking-ahead">Looking ahead</h2>
-
-<p>As the project grows, we will continue working to make the build process as
-fast and reliable as possible. If you see ways we can improve it further, or if
-you run into trouble, please bring it up on our
-<a href="https://arrow.apache.org/community/#mailing-lists">mailing list</a> or
-<a href="https://issues.apache.org/jira/browse/ARROW">report an issue</a>.</p></content><author><name>pmc</name></author><category term="application" /><summary type="html">Over the last four and a half years, we’ve worked to build a “batteries-included” development platform for high-performance analytics applications in C++. As the scope of the project has grown, we have sometimes taken on additional library dependencies to support a wide variety of systems a [...]
\ No newline at end of file
+improve the documentation.</p></content><author><name>pmc</name></author><category term="release" /><summary type="html">Apache Arrow 2.0.0 is a significant release for the Apache Arrow project in general (release notes), and the Rust subproject in particular, with almost 200 issues resolved by 15 contributors. In this blog post, we will go through the main changes affecting core Arrow, Parquet support, and DataFusion query engine. The full list of resolved issues can be found here [...]
\ No newline at end of file
diff --git a/img/2021-08-18-datafusion500perf.png b/img/2021-08-18-datafusion500perf.png
new file mode 100644
index 0000000..4406473
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+<meta property="article:published_time" content="2021-08-20T09:59:22-04:00" />
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index bc24eb1..3891d62 100644
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index 3630be2..f9ba172 100644
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