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Posted to commits@arrow.apache.org by gi...@apache.org on 2020/02/12 16:30:42 UTC
[arrow-site] branch asf-site updated: Updating built site (build
2365ba238360c022fff9d32f21984d957696d94b)
This is an automated email from the ASF dual-hosted git repository.
github-bot pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/arrow-site.git
The following commit(s) were added to refs/heads/asf-site by this push:
new 7e7c31c Updating built site (build 2365ba238360c022fff9d32f21984d957696d94b)
7e7c31c is described below
commit 7e7c31c1eefa4872946f7a9fe27de7fd5d687ea9
Author: Neal Richardson <ne...@gmail.com>
AuthorDate: Wed Feb 12 16:30:33 2020 +0000
Updating built site (build 2365ba238360c022fff9d32f21984d957696d94b)
---
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release/0.7.1.html | 2 +-
release/0.8.0.html | 2 +-
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release/index.html | 2 +-
61 files changed, 810 insertions(+), 410 deletions(-)
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index 4e8d8fd..4c5199e 100644
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index 49916a5..b646da1 100644
--- a/blog/2019/10/13/introducing-arrow-flight-japanese/index.html
+++ b/blog/2019/10/13/introducing-arrow-flight-japanese/index.html
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index a891355..b82e1ca 100644
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diff --git a/blog/2019/10/06/0.15.0-release/index.html b/blog/2020/02/12/0.16.0-release/index.html
similarity index 50%
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index 4e8d8fd..2f66540 100644
--- a/blog/2019/10/06/0.15.0-release/index.html
+++ b/blog/2020/02/12/0.16.0-release/index.html
@@ -6,29 +6,29 @@
<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 -->
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+ <title>Apache Arrow 0.16.0 Release | Apache Arrow</title>
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@@ -216,8 +216,8 @@
<h1>
- Apache Arrow 0.15.0 Release
- <a href="/blog/2019/10/06/0.15.0-release/" class="permalink" title="Permalink">∞</a>
+ Apache Arrow 0.16.0 Release
+ <a href="/blog/2020/02/12/0.16.0-release/" class="permalink" title="Permalink">∞</a>
</h1>
@@ -225,7 +225,7 @@
<p>
<span class="badge badge-secondary">Published</span>
<span class="published">
- 06 Oct 2019
+ 12 Feb 2020
</span>
<br />
<span class="badge badge-secondary">By</span>
@@ -239,267 +239,246 @@
-->
-<p>The Apache Arrow team is pleased to announce the 0.15.0 release. This covers
-about 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%200.15.0"><strong>687 resolved issues</strong></a>
-from <a href="https://arrow.apache.org/release/0.15.0.html#contributors"><strong>80 distinct contributors</strong></a>. See the Install Page to learn how to
-get the libraries for your platform. The <a href="https://arrow.apache.org/release/0.15.0.html">complete changelog</a> is also
-available.</p>
+<p>The Apache Arrow team is pleased to announce the 0.16.0 release. This covers
+about 4 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%200.16.0"><strong>735 resolved issues</strong></a>
+from <a href="https://arrow.apache.org/release/0.16.0.html#contributors"><strong>99 distinct contributors</strong></a>. See the Install Page to learn how to
+get the libraries for your platform.</p>
-<p>About a third of issues closed (240) were classified as bug fixes, so this
-release brings many stability, memory use, and performance improvements over
-0.14.x. We will discuss some of the language-specific improvements and new
-features below.</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="https://arrow.apache.org/release/0.16.0.html">complete changelog</a>.</p>
<h2 id="new-committers">New committers</h2>
-<p>Since the 0.14.0 release, we’ve added four new committers:</p>
+<p>Since the 0.15.0 release, we’ve added two new committers:</p>
<ul>
- <li><a href="https://github.com/bkietz">Ben Kietzman</a></li>
- <li><a href="https://github.com/lidavidm">David Li</a></li>
- <li><a href="https://github.com/mrkn">Kenta Murata</a></li>
- <li><a href="https://github.com/nealrichardson">Neal Richardson</a></li>
+ <li><a href="https://github.com/eerhardt">Eric Erhardt</a></li>
+ <li><a href="https://github.com/jorisvandenbossche">Joris Van den Bossche</a></li>
</ul>
-<p>In addition, <a href="https://github.com/sbinet">Sebastien Binet</a> and <a href="https://github.com/emkornfield">Micah Kornfield</a> have joined the PMC.</p>
-
<p>Thank you for all your contributions!</p>
<h2 id="columnar-format-notes">Columnar Format Notes</h2>
-<p>The format gets new datatypes : LargeList(ARROW-4810), LargeBinary and
-LargeString (ARROW-750). LargeList is similar to List but with 64-bit
-offsets instead of 32-bit. The same relationship holds for LargeBinary
-and LargeString with respect to Binary and String.</p>
+<p>We still have work to do to complete comprehensive columnar format integration
+testing between the Java and C++ libraries. Once this work is completed, we
+intend to make a 1.0.0 release with <a href="https://arrow.apache.org/docs/format/Versioning.html">forward and backward compatibility
+guarantees</a>.</p>
-<p>Since the last major release, we have also made a significant overhaul of the
-<a href="https://github.com/apache/arrow/blob/master/docs/source/format/Columnar.rst">columnar format documentation</a> to be clearer and easier to follow for
-implementation creators.</p>
+<p>We <a href="https://github.com/apache/arrow/commit/0ddc1f4737c35008cd06be1ee28472ebd7da68e2">clarified some ambiguity</a> on dictionary encoding in the specification.
+Work is on going to implement the features in Arrow libraries.</p>
-<h2 id="upcoming-columnar-format-stability-and-library--format-version-split">Upcoming Columnar Format Stability and Library / Format Version Split</h2>
+<h2 id="arrow-flight-rpc-notes">Arrow Flight RPC notes</h2>
-<p>The Arrow community has decided to make a 1.0.0 release of the project marking
-formal stability of the columnar format and binary protocol, including explicit
-forward and backward compatibility guarantees. You can read about these
-guarantees in the <a href="https://github.com/apache/arrow/blob/master/docs/source/format/Versioning.rst">new documentation page</a> about versioning.</p>
+<p>Flight development work has recently focused on robustness and stability. If
+you are not yet familiar with Flight, read the <a href="https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight/">introductory blog post from
+October</a>.</p>
-<p>Starting with 1.0.0, we will give the columnar format and libraries separate
-version numbers. This will allow the library versions to evolve without
-creating confusion or uncertainty about whether the Arrow columnar format
-remains stable or not.</p>
+<p>We are also discussing adding a “bidirectional RPC” which enables
+request-response workflows requiring both client and server to send data
+streams to be performed a single RPC request.</p>
-<h2 id="columnar-streaming-protocol-change-since-0140">Columnar “Streaming Protocol” Change since 0.14.0</h2>
+<h2 id="c-notes">C++ notes</h2>
-<p>Since 0.14.0 we have modified the IPC “encapsulated message” format to insert 4
-bytes of additional data in the message preamble to ensure that the Flatbuffers
-metadata starts on an aligned offset. By default, IPC streams generated by
-0.15.0 and later will not be readable by library versions 0.14.1 and
-prior. Implementations have offered options to write messages using the now
-“legacy” message format.</p>
+<p>Some work has been done to make the default build configuration of Arrow C++ as
+lean as possible. The Arrow C++ core can now be built without any external
+dependencies other than a new enough C++ compiler (gcc 4.9 or higher). Notably,
+Boost is no longer required. We invested effort to vendor some small essential
+dependencies: Flatbuffers, double-conversion, and uriparser. Many optional
+features requiring external libraries, like compression and GLog integration,
+are now disabled by default. Several subcomponents of the C++ project like the
+filesystem API, CSV, compute, dataset and JSON layers, as well as command-line
+utilities, are now disabled by default. The only toolchain dependency enabled
+by default is jemalloc, the recommended memory allocator, but this can also be
+disabled if desired. For illustration, see the <a href="https://github.com/apache/arrow/blob/master/cpp/examples/minimal_build/build.sh">example minimal build script and
+Dockerfile</a>.</p>
+
+<p>When enabled, the default jemalloc configuration has been tweaked to return
+memory more aggressively to the OS (ARROW-6910, ARROW-6994). We welcome
+feedback from users about our memory allocation configuration and performance
+in applications.</p>
+
+<p>The array validation facilities have been vastly expanded and now exist in
+two flavors: the <code class="highlighter-rouge">Validate</code> method does a light-weight validation that’s
+O(1) in array size, while the potentially O(N) method <code class="highlighter-rouge">ValidateFull</code> does
+thorough data validation (ARROW-6157).</p>
+
+<p>The IO APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7235).</p>
+
+<h3 id="c-csv">C++: CSV</h3>
+
+<p>An option is added to attempt automatic dictionary encoding of string columns
+during reading a CSV file, until a cardinality limit is reached. When
+successful, it can make reading faster and the resulting Arrow data is
+much more memory-efficient (ARROW-3408).</p>
+
+<p>The CSV APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7236).</p>
+
+<h3 id="c-datasets">C++: Datasets</h3>
+
+<p>The 0.16 release introduces the Datasets API to the C++ library, along with
+bindings in Python and R. This API allows you to treat multiple files as a
+single logical dataset entity and make efficient selection queries against it.
+This release includes support for Parquet and Arrow IPC file formats. Factory
+objects allow you to discover files in a directory recursively, inspect the
+schemas in the files, and performs some basic schema unification. You may
+specify how file path segments map to partition, and there is support for
+auto-detecting some partition information, including Hive-style partitioning.
+The Datasets API includes a filter expression syntax as well as column
+selection. These are evaluated with predicate pushdown, and for Parquet,
+evaluation is pushed down to row groups.</p>
-<p>For users who cannot upgrade to version 0.15.0 in all parts of their system,
-such as Apache Spark users, we recommend one of the two routes:</p>
+<h3 id="c-filesystem-layer">C++: Filesystem layer</h3>
-<ul>
- <li>If using pyarrow, set the environment variable <code class="highlighter-rouge">ARROW_PRE_0_15_IPC_FORMAT=1</code>
-when using 0.15.0 and sending data to an old library</li>
- <li>Wait to upgrade all components simultaneously</li>
-</ul>
+<p>An HDFS implementation of the FileSystem class is available (ARROW-6720). We
+plan to deprecate the prior bespoke C++ HDFS class in favor of the standardized
+filesystem API.</p>
-<p>We do not anticipate making this kind of change again in the near future and
-would not have made such a non-forward-compatible change unless we deemed it
-very important.</p>
+<p>The filesystem APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7161).</p>
-<h2 id="arrow-flight-notes">Arrow Flight notes</h2>
+<h3 id="c-ipc">C++: IPC</h3>
-<p>A GetFlightSchema method is added to the Flight RPC protocol (ARROW-6094).
-As the name suggests, it returns the schema for a given Flight descriptor
-on the server. This is useful for cases where the Flight locations are
-not immediately available, depending on the server implementation.</p>
+<p>The Arrow IPC reader is being fuzzed continuously by the <a href="https://google.github.io/oss-fuzz/">OSS-Fuzz</a>
+infrastructure, to detect undesirable behavior on invalid or malicious input.
+Several issues have already been found and fixed.</p>
-<p>Flight implementations for C++ and Java now implement half-closed
-semantics for DoPut (ARROW-6063). The client can close the writing
-end of the stream to signal that it has finished sending the Flight
-data, but still receive the batch-specific response and its associated
-metadata.</p>
+<h3 id="c-parquet">C++: Parquet</h3>
-<h2 id="c-notes">C++ notes</h2>
+<p><a href="https://github.com/apache/parquet-format/blob/master/Encryption.md">Modular encryption</a> is now supported (PARQUET-1300).</p>
-<p>C++ now supports the LargeList, LargeBinary and LargeString datatypes.</p>
+<p>A performance regression when reading a file with a large number of columns has
+been fixed (ARROW-6876, ARROW-7059), as well as several bugs (PARQUET-1766,
+ARROW-6895).</p>
-<p>The Status class gains the ability to carry additional subsystem-specific
-data with it, under the form of an opaque StatusDetail interface (ARROW-4036).
-This allows, for example, to store not only an exception message coming from
-Python but the actual Python exception object, such as to raise it again if
-the Status is propagated back to Python. It can also enable the consumer
-of Status to inspect the subsystem-specific error, such as a finer-grained
-Flight error code.</p>
+<h3 id="c-tensors">C++: Tensors</h3>
-<p>DataType and Schema equality are significantly faster (ARROW-6038).</p>
+<p>CSC sparse matrices are supported (ARROW-4225).</p>
-<p>The Column class is completely removed, as it did not have a strong enough
-motivation for existing between ChunkedArray and RecordBatch / Table
-(ARROW-5893).</p>
+<p>The Tensor APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7420).</p>
-<h3 id="c-parquet">C++: Parquet</h3>
+<h2 id="c-notes-1">C# Notes</h2>
-<p>The 0.15 release includes many improvements to the Apache Parquet C++ internals,
-resulting in greatly improved read and write performance. We described the work
-and published some benchmarks in a <a href="https://arrow.apache.org/blog/2019/09/05/faster-strings-cpp-parquet/">recent blog post</a>.</p>
+<p>There were a number of C# bug fixes this release. Note that the C# library is
+not yet being tested in CI against the other native Arrow implementations
+(integration tests). We are looking for more contributors for the C# project to
+help with this and other new feature development.</p>
-<h3 id="c-csv-reader">C++: CSV reader</h3>
+<h2 id="java-notes">Java notes</h2>
-<p>The CSV reader is now more flexible in terms of how column names are chosen
-(ARROW-6231) and column selection (ARROW-5977).</p>
+<ul>
+ <li>Added prose documentation describing how to work with the Java libraries.</li>
+ <li>Some additional algorithms have been added to the “contrib” algorithms package: multithreaded searching of ValueVectors,</li>
+ <li>The memory modules have been refactored so non-netty allocators can now be used.</li>
+ <li>A new utility for populating ValueVectors more concisely for testing was introduced</li>
+ <li>The “contrib” Avro adapter now supports Avro Logical type conversion to corresponding Arrow type.</li>
+ <li>Various bug fixes across all packages</li>
+</ul>
-<h3 id="c-memory-allocation-layer">C++: Memory Allocation Layer</h3>
+<h2 id="python-notes">Python notes</h2>
-<p>Arrow now has the option to allocate memory using the mimalloc memory
-allocator. jemalloc is still preferred for best performance, but mimalloc
-is a reasonable alternative to the system allocator on Windows where jemalloc
-is not currently supported.</p>
+<p>pyarrow 0.16 will be the last release to support Python 2.7.</p>
-<p>Also, we now expose explicit global functions to get a MemoryPool for each
-of the jemalloc allocator, mimalloc allocator and system allocator (ARROW-6292).</p>
+<p>Python now has bindings for the datasets API (ARROW-6341) as well as the S3
+(ARROW-6655) and HDFS (ARROW-7310) filesystem implementations.</p>
-<p>The vendored jemalloc version is bumped from 4.5.x to 5.2.x (ARROW-6549).
-Performance characteristics may differ on memory allocation-heavy workloads,
-though we did not notice any significant regression on our suite of
-micro-benchmarks (and a multi-threaded benchmark of reading a CSV file
-showed a 25% speedup).</p>
+<p>The Duration (ARROW-5855) and Fixed Size List (ARROW-7261) types are exposed
+in Python.</p>
-<h3 id="c-filesystem-layer">C++: Filesystem layer</h3>
+<p>Sparse tensors can be converted to dense tensors (ARROW-6624). They are
+also interoperable with the <code class="highlighter-rouge">pydata/sparse</code> and <code class="highlighter-rouge">scipy.sparse</code> libraries
+(ARROW-4223, ARROW-4224).</p>
-<p>A FileSystem implementation to access Amazon S3-compatible filesystems is now
-available. It depends on the AWS SDK for C++.</p>
+<p>Pandas extension arrays now are able to roundtrip through Arrow conversion
+(ARROW-2428).</p>
-<h3 id="c-io-layer">C++: I/O layer</h3>
+<p>A memory leak when converting Arrow data to Pandas “object” data has been
+fixed (ARROW-6874).</p>
-<p>Significant improvements were made to the Arrow I/O stack.</p>
+<p>Arrow is now tested against Python 3.8, and we now build manylinux2014 wheels
+for Python 3 (ARROW-7344).</p>
-<ul>
- <li>ARROW-6180: Add RandomAccessFile::GetStream that returns an InputStream over
-a fixed subset of the file.</li>
- <li>ARROW-6381: Improve performance of small writes with BufferOutputStream</li>
- <li>ARROW-2490: Streamline concurrency semantics of InputStream implementations,
-and add debug checks for race conditions between non-thread-safe InputStream
-operations.</li>
- <li>ARROW-6527: Add an OutputStream::Write overload that takes an owned Buffer
-rather than a raw memory area. This allows OutputStream implementations
-to safely implement delayed writing without having to copy the data.</li>
-</ul>
+<h2 id="r-notes">R notes</h2>
-<h3 id="c-tensors">C++: Tensors</h3>
+<p>This release includes a <code class="highlighter-rouge">dplyr</code> interface to Arrow Datasets,
+which let you work efficiently with large, multi-file datasets as a single entity.
+See <a href="https://arrow.apache.org/docs/r/articles/dataset.html"><code class="highlighter-rouge">vignette("dataset", package = "arrow")</code></a> for more.</p>
-<p>There are three improvements of Tensor and SparseTensor in this release.</p>
+<p>Another major area of work in this release was to improve the installation
+experience on Linux. A source package installation (as from CRAN) will now
+handle its C++ dependencies automatically, with no system dependencies beyond what R requires.
+For common Linux distributions and versions, installation will retrieve a prebuilt static
+C++ library for inclusion in the package; where this binary is not available,
+the package executes a bundled script that should build the Arrow C++ library.
+See <a href="https://arrow.apache.org/docs/r/articles/install.html"><code class="highlighter-rouge">vignette("install", package = "arrow")</code></a> for details.</p>
-<ul>
- <li>Add Tensor::Value template function for element access</li>
- <li>Add EqualOptions support in Tensor::Equals function, that allows us to control the way to compare two float tensors</li>
- <li>Add smaller bit-width index supports in SparseTensor</li>
-</ul>
+<p>For more on what’s in the 0.16 R package, see the R <a href="https://arrow.apache.org/docs/r/news/">changelog</a>.</p>
-<h2 id="c-notes-1">C# Notes</h2>
+<h2 id="ruby-and-c-glib-notes">Ruby and C GLib notes</h2>
-<p>We have fixed some bugs causing incompatibilities between C# and other Arrow
-implementations.</p>
+<p>Ruby and C GLib continues to follow the features in the C++ project.</p>
-<h2 id="java-notes">Java notes</h2>
+<h3 id="ruby">Ruby</h3>
+
+<p>Ruby includes the following improvements.</p>
<ul>
- <li>Added an initial version of an Avro adapter</li>
- <li>To improve the JDBC adapter performance, refactored consume data logic and
-implemented an iterator API to prevent loading all data into one vector</li>
- <li>Implemented subField encoding for complex type, now List and Struct vectors
-subField encoding is available</li>
- <li>Implemented visitor API for vector/range/type/approx equals compare</li>
- <li>Performed a lot of optimization and refactoring for DictionaryEncoder,
-supporting all data types and avoiding memory copy via hash table and visitor
-API</li>
- <li>Introduced <a href="https://github.com/google/error-prone">Error Prone</a> into code base to catch more potential errors
-earlier</li>
- <li>Fixed the bug where dictionary entries were required in IPC streams even when
-empty; readers can now also read interleaved messages</li>
+ <li>Improve CSV save performance (ARROW-7474).</li>
+ <li>Add support for saving/loading TSV (ARROW-7454).</li>
+ <li>Add <code class="highlighter-rouge">Arrow::Schema#build_expression</code> to improve building <code class="highlighter-rouge">Gandiva::Expression</code> (ARROW-6619).</li>
</ul>
-<h2 id="python-notes">Python notes</h2>
-
-<p>The FileSystem API, implemented in C++, is now available in Python (ARROW-5494).</p>
-
-<p>The API for extension types has been straightened and definition of
-custom extension types in Python is now more powerful (ARROW-5610).</p>
-
-<p>Sparse tensors are now available in Python (ARROW-4453).</p>
-
-<p>A potential crash when handling Python dates and datetimes was fixed
-(ARROW-6597).</p>
-
-<p>Based on a mailing list discussion, we are looking for help with maintaining
-our Python wheels. Community members have found that the wheels take up a great
-deal of maintenance time, so if you or your organization depend on <code class="highlighter-rouge">pip install
-pyarrow</code> working, we would appreciate your assistance.</p>
-
-<h2 id="ruby-and-c-glib-notes">Ruby and C GLib notes</h2>
+<h3 id="c-glib">C GLib</h3>
-<p>Ruby and C GLib continues to follow the features in the C++ project.
-Ruby includes the following backward incompatible changes.</p>
+<p>C GLib includes the following changes.</p>
<ul>
- <li>Remove Arrow::Struct and use Hash instead.</li>
- <li>Add Arrow::Time for Arrow::Time{32,64}DataType value.</li>
- <li>Arrow::Decimal128Array#get_value returns BigDecimal.</li>
+ <li>Add support for LargeList, LargeBinary, and LargeString (ARROW-6285, ARROW-6286).</li>
+ <li>Add filter and take API for GArrowTable, GArrowChunkedArray, and GArrowRecordBatch (ARROW-7110, ARROW-7111).</li>
+ <li>Add <code class="highlighter-rouge">garrow_table_combine_chunks()</code> API (ARROW-7369).</li>
</ul>
-<p>Ruby improves the performance of Arrow#values.</p>
-
<h2 id="rust-notes">Rust notes</h2>
-<p>A number of core Arrow improvements were made to the Rust library.</p>
+<p>Support for Arrow data types has been improved, with the following array types now supported (ARROW-3690):</p>
<ul>
- <li>Add explicit SIMD vectorization for the divide kernel</li>
- <li>Add a feature to disable SIMD</li>
- <li>Use “if cfg!” pattern</li>
- <li>Optimizations to BooleanBufferBuilder::append_slice</li>
- <li>Implemented Debug trait for List/Struct/BinaryArray</li>
+ <li>Fixed Size List and Fixed Size Binary</li>
+ <li>Adding a String Array for utf-8 strings, and keeping the Binary Array for general binary data</li>
+ <li>Duration and interval arrays.</li>
</ul>
-<p>Improvements related to Rust Parquet and DataFusion are detailed next.</p>
+<p>Initial work on Arrow IPC support has been completed, with readers and writers for streams and files implemented (ARROW-5180).</p>
-<h3 id="rust-parquet">Rust Parquet</h3>
+<h3 id="rust-datafusion">Rust: DataFusion</h3>
-<ul>
- <li>Implement Arrow record reader</li>
- <li>Add converter that is used to convert record reader’s content to arrow primitive array.</li>
-</ul>
+<p>Query execution has been reimplemented with an extensible physical query plan. This allows other projects to add other plans, such as for distributed computing or for specific database servers (ARROW-5227).</p>
-<h3 id="rust-datafusion">Rust DataFusion</h3>
+<p>Added support for writing query results to CSV (ARROW-6274).</p>
-<ul>
- <li>Preview of new query execution engine using an extensible trait-based
-physical execution plan that supports parallel execution using threads</li>
- <li>ExecutionContext now has a register_parquet convenience method for
-registering Parquet data sources</li>
- <li>Fixed bug in type coercion optimizer rule</li>
- <li>TableProvider.scan() now returns a thread-safe BatchIterator</li>
- <li>Remove use of bare trait objects (switched to using dyn syntax)</li>
- <li>Adds casting from unsigned to signed integer data types</li>
-</ul>
+<p>The new Parquet -> Arrow reader is now used to read Parquet files (ARROW-6700).</p>
-<h2 id="r-notes">R notes</h2>
+<p>Various other query improvements have been implemented, especially on grouping and aggregate queries (ARROW-6689).</p>
+
+<h3 id="rust-parquet">Rust: Parquet</h3>
-<p>A major development since the 0.14 release was the arrival of the <code class="highlighter-rouge">arrow</code> R
-package on <a href="https://cran.r-project.org/package=arrow">CRAN</a>. We wrote about this in August on the <a href="https://arrow.apache.org/blog/2019/08/08/r-package-on-cran/">Arrow blog</a>.
-In addition to the package availability on CRAN, we also published
-<a href="https://arrow.apache.org/docs/r">package documentation</a> on the Arrow website.</p>
+<p>The Arrow reader integration has been completed, allowing Parquet files to be
+read into Arrow memory (ARROW-4059).</p>
-<p>The 0.15 R package includes many of the enhancements in the C++ library
-release, such as the Parquet performance improvements and the FileSystem API.
-In addition, there are a number of upgrades that make it easier to read and
-write data, specify types and schema, and interact with Arrow tables and record
-batches in R.</p>
+<h2 id="development-notes">Development notes</h2>
-<p>For more on what’s in the 0.15 R package, see the <a href="http://arrow.apache.org/docs/r/news/">changelog</a>.</p>
+<p>Arrow has moved away from Travis-CI and is now using Github Actions for
+PR-based continuous integration. This new CI configuration relies heavily
+on <code class="highlighter-rouge">docker-compose</code>, making it easier for developers to reproduce builds
+locally, thanks to tremendous work by Krisztián Szűcs (ARROW-7101).</p>
<h2 id="community-discussions-ongoing">Community Discussions Ongoing</h2>
@@ -507,6 +486,20 @@ batches in R.</p>
dev@arrow.apache.org mailing list. We look forward to hearing from the
community there.</p>
+<ul>
+ <li>Mandatory fields in IPC format: to ease input validation, it is being
+<a href="https://mail-archives.apache.org/mod_mbox/arrow-dev/202001.mbox/%3C0dd13489-9221-459a-3560-1426738d3bb4%40python.org%3E">proposed</a> to mark some fields in our Flatbuffers schema “required”.
+Those fields are already semantically required, but are not considered so by
+the generated Flatbuffers verifier. Before accepting this proposal, we need
+to ensure that it does not break binary compatibility with existing valid
+data.</li>
+ <li>The C Data Interface has not yet been formally adopted, though the community
+has reached consensus to move forward after addressing various design
+questions and concerns.</li>
+ <li>Guidelines for the use of “unsafe” in the Rust implementation are being
+<a href="https://mail-archives.apache.org/mod_mbox/arrow-dev/202001.mbox/%3cMN2PR19MB347050FB22046485C0B2525EDD380@MN2PR19MB3470.namprd19.prod.outlook.com%3e">discussed</a>.</li>
+</ul>
+
</main>
diff --git a/blog/index.html b/blog/index.html
index 9bd1789..69e44d4 100644
--- a/blog/index.html
+++ b/blog/index.html
@@ -24,7 +24,7 @@
<meta property="twitter:title" content="Project News and Blog" />
<meta name="twitter:site" content="@ApacheArrow" />
<script type="application/ld+json">
-{"@type":"WebPage","headline":"Project News and Blog","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://arrow.apache.org/img/logo.png"}},"image":"https://arrow.apache.org/img/arrow.png","description":"Updates and announcements about the Apache Arrow project","url":"https://arrow.apache.org/blog/","@context":"https://schema.org"}</script>
+{"@type":"WebPage","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://arrow.apache.org/img/logo.png"}},"headline":"Project News and Blog","image":"https://arrow.apache.org/img/arrow.png","description":"Updates and announcements about the Apache Arrow project","url":"https://arrow.apache.org/blog/","@context":"https://schema.org"}</script>
<!-- End Jekyll SEO tag -->
@@ -219,6 +219,299 @@
<div class="blog-post" style="margin-bottom: 4rem">
<h1>
+ Apache Arrow 0.16.0 Release
+ <a href="/blog/2020/02/12/0.16.0-release/" class="permalink" title="Permalink">∞</a>
+</h1>
+
+
+
+<p>
+ <span class="badge badge-secondary">Published</span>
+ <span class="published">
+ 12 Feb 2020
+ </span>
+ <br />
+ <span class="badge badge-secondary">By</span>
+
+ <a href="https://arrow.apache.org">The Apache Arrow PMC (pmc) </a>
+
+</p>
+
+ <!--
+
+-->
+
+<p>The Apache Arrow team is pleased to announce the 0.16.0 release. This covers
+about 4 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%200.16.0"><strong>735 resolved issues</strong></a>
+from <a href="https://arrow.apache.org/release/0.16.0.html#contributors"><strong>99 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="https://arrow.apache.org/release/0.16.0.html">complete changelog</a>.</p>
+
+<h2 id="new-committers">New committers</h2>
+
+<p>Since the 0.15.0 release, we’ve added two new committers:</p>
+
+<ul>
+ <li><a href="https://github.com/eerhardt">Eric Erhardt</a></li>
+ <li><a href="https://github.com/jorisvandenbossche">Joris Van den Bossche</a></li>
+</ul>
+
+<p>Thank you for all your contributions!</p>
+
+<h2 id="columnar-format-notes">Columnar Format Notes</h2>
+
+<p>We still have work to do to complete comprehensive columnar format integration
+testing between the Java and C++ libraries. Once this work is completed, we
+intend to make a 1.0.0 release with <a href="https://arrow.apache.org/docs/format/Versioning.html">forward and backward compatibility
+guarantees</a>.</p>
+
+<p>We <a href="https://github.com/apache/arrow/commit/0ddc1f4737c35008cd06be1ee28472ebd7da68e2">clarified some ambiguity</a> on dictionary encoding in the specification.
+Work is on going to implement the features in Arrow libraries.</p>
+
+<h2 id="arrow-flight-rpc-notes">Arrow Flight RPC notes</h2>
+
+<p>Flight development work has recently focused on robustness and stability. If
+you are not yet familiar with Flight, read the <a href="https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight/">introductory blog post from
+October</a>.</p>
+
+<p>We are also discussing adding a “bidirectional RPC” which enables
+request-response workflows requiring both client and server to send data
+streams to be performed a single RPC request.</p>
+
+<h2 id="c-notes">C++ notes</h2>
+
+<p>Some work has been done to make the default build configuration of Arrow C++ as
+lean as possible. The Arrow C++ core can now be built without any external
+dependencies other than a new enough C++ compiler (gcc 4.9 or higher). Notably,
+Boost is no longer required. We invested effort to vendor some small essential
+dependencies: Flatbuffers, double-conversion, and uriparser. Many optional
+features requiring external libraries, like compression and GLog integration,
+are now disabled by default. Several subcomponents of the C++ project like the
+filesystem API, CSV, compute, dataset and JSON layers, as well as command-line
+utilities, are now disabled by default. The only toolchain dependency enabled
+by default is jemalloc, the recommended memory allocator, but this can also be
+disabled if desired. For illustration, see the <a href="https://github.com/apache/arrow/blob/master/cpp/examples/minimal_build/build.sh">example minimal build script and
+Dockerfile</a>.</p>
+
+<p>When enabled, the default jemalloc configuration has been tweaked to return
+memory more aggressively to the OS (ARROW-6910, ARROW-6994). We welcome
+feedback from users about our memory allocation configuration and performance
+in applications.</p>
+
+<p>The array validation facilities have been vastly expanded and now exist in
+two flavors: the <code class="highlighter-rouge">Validate</code> method does a light-weight validation that’s
+O(1) in array size, while the potentially O(N) method <code class="highlighter-rouge">ValidateFull</code> does
+thorough data validation (ARROW-6157).</p>
+
+<p>The IO APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7235).</p>
+
+<h3 id="c-csv">C++: CSV</h3>
+
+<p>An option is added to attempt automatic dictionary encoding of string columns
+during reading a CSV file, until a cardinality limit is reached. When
+successful, it can make reading faster and the resulting Arrow data is
+much more memory-efficient (ARROW-3408).</p>
+
+<p>The CSV APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7236).</p>
+
+<h3 id="c-datasets">C++: Datasets</h3>
+
+<p>The 0.16 release introduces the Datasets API to the C++ library, along with
+bindings in Python and R. This API allows you to treat multiple files as a
+single logical dataset entity and make efficient selection queries against it.
+This release includes support for Parquet and Arrow IPC file formats. Factory
+objects allow you to discover files in a directory recursively, inspect the
+schemas in the files, and performs some basic schema unification. You may
+specify how file path segments map to partition, and there is support for
+auto-detecting some partition information, including Hive-style partitioning.
+The Datasets API includes a filter expression syntax as well as column
+selection. These are evaluated with predicate pushdown, and for Parquet,
+evaluation is pushed down to row groups.</p>
+
+<h3 id="c-filesystem-layer">C++: Filesystem layer</h3>
+
+<p>An HDFS implementation of the FileSystem class is available (ARROW-6720). We
+plan to deprecate the prior bespoke C++ HDFS class in favor of the standardized
+filesystem API.</p>
+
+<p>The filesystem APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7161).</p>
+
+<h3 id="c-ipc">C++: IPC</h3>
+
+<p>The Arrow IPC reader is being fuzzed continuously by the <a href="https://google.github.io/oss-fuzz/">OSS-Fuzz</a>
+infrastructure, to detect undesirable behavior on invalid or malicious input.
+Several issues have already been found and fixed.</p>
+
+<h3 id="c-parquet">C++: Parquet</h3>
+
+<p><a href="https://github.com/apache/parquet-format/blob/master/Encryption.md">Modular encryption</a> is now supported (PARQUET-1300).</p>
+
+<p>A performance regression when reading a file with a large number of columns has
+been fixed (ARROW-6876, ARROW-7059), as well as several bugs (PARQUET-1766,
+ARROW-6895).</p>
+
+<h3 id="c-tensors">C++: Tensors</h3>
+
+<p>CSC sparse matrices are supported (ARROW-4225).</p>
+
+<p>The Tensor APIs now use <code class="highlighter-rouge">Result<T></code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7420).</p>
+
+<h2 id="c-notes-1">C# Notes</h2>
+
+<p>There were a number of C# bug fixes this release. Note that the C# library is
+not yet being tested in CI against the other native Arrow implementations
+(integration tests). We are looking for more contributors for the C# project to
+help with this and other new feature development.</p>
+
+<h2 id="java-notes">Java notes</h2>
+
+<ul>
+ <li>Added prose documentation describing how to work with the Java libraries.</li>
+ <li>Some additional algorithms have been added to the “contrib” algorithms package: multithreaded searching of ValueVectors,</li>
+ <li>The memory modules have been refactored so non-netty allocators can now be used.</li>
+ <li>A new utility for populating ValueVectors more concisely for testing was introduced</li>
+ <li>The “contrib” Avro adapter now supports Avro Logical type conversion to corresponding Arrow type.</li>
+ <li>Various bug fixes across all packages</li>
+</ul>
+
+<h2 id="python-notes">Python notes</h2>
+
+<p>pyarrow 0.16 will be the last release to support Python 2.7.</p>
+
+<p>Python now has bindings for the datasets API (ARROW-6341) as well as the S3
+(ARROW-6655) and HDFS (ARROW-7310) filesystem implementations.</p>
+
+<p>The Duration (ARROW-5855) and Fixed Size List (ARROW-7261) types are exposed
+in Python.</p>
+
+<p>Sparse tensors can be converted to dense tensors (ARROW-6624). They are
+also interoperable with the <code class="highlighter-rouge">pydata/sparse</code> and <code class="highlighter-rouge">scipy.sparse</code> libraries
+(ARROW-4223, ARROW-4224).</p>
+
+<p>Pandas extension arrays now are able to roundtrip through Arrow conversion
+(ARROW-2428).</p>
+
+<p>A memory leak when converting Arrow data to Pandas “object” data has been
+fixed (ARROW-6874).</p>
+
+<p>Arrow is now tested against Python 3.8, and we now build manylinux2014 wheels
+for Python 3 (ARROW-7344).</p>
+
+<h2 id="r-notes">R notes</h2>
+
+<p>This release includes a <code class="highlighter-rouge">dplyr</code> interface to Arrow Datasets,
+which let you work efficiently with large, multi-file datasets as a single entity.
+See <a href="https://arrow.apache.org/docs/r/articles/dataset.html"><code class="highlighter-rouge">vignette("dataset", package = "arrow")</code></a> for more.</p>
+
+<p>Another major area of work in this release was to improve the installation
+experience on Linux. A source package installation (as from CRAN) will now
+handle its C++ dependencies automatically, with no system dependencies beyond what R requires.
+For common Linux distributions and versions, installation will retrieve a prebuilt static
+C++ library for inclusion in the package; where this binary is not available,
+the package executes a bundled script that should build the Arrow C++ library.
+See <a href="https://arrow.apache.org/docs/r/articles/install.html"><code class="highlighter-rouge">vignette("install", package = "arrow")</code></a> for details.</p>
+
+<p>For more on what’s in the 0.16 R package, see the R <a href="https://arrow.apache.org/docs/r/news/">changelog</a>.</p>
+
+<h2 id="ruby-and-c-glib-notes">Ruby and C GLib notes</h2>
+
+<p>Ruby and C GLib continues to follow the features in the C++ project.</p>
+
+<h3 id="ruby">Ruby</h3>
+
+<p>Ruby includes the following improvements.</p>
+
+<ul>
+ <li>Improve CSV save performance (ARROW-7474).</li>
+ <li>Add support for saving/loading TSV (ARROW-7454).</li>
+ <li>Add <code class="highlighter-rouge">Arrow::Schema#build_expression</code> to improve building <code class="highlighter-rouge">Gandiva::Expression</code> (ARROW-6619).</li>
+</ul>
+
+<h3 id="c-glib">C GLib</h3>
+
+<p>C GLib includes the following changes.</p>
+
+<ul>
+ <li>Add support for LargeList, LargeBinary, and LargeString (ARROW-6285, ARROW-6286).</li>
+ <li>Add filter and take API for GArrowTable, GArrowChunkedArray, and GArrowRecordBatch (ARROW-7110, ARROW-7111).</li>
+ <li>Add <code class="highlighter-rouge">garrow_table_combine_chunks()</code> API (ARROW-7369).</li>
+</ul>
+
+<h2 id="rust-notes">Rust notes</h2>
+
+<p>Support for Arrow data types has been improved, with the following array types now supported (ARROW-3690):</p>
+
+<ul>
+ <li>Fixed Size List and Fixed Size Binary</li>
+ <li>Adding a String Array for utf-8 strings, and keeping the Binary Array for general binary data</li>
+ <li>Duration and interval arrays.</li>
+</ul>
+
+<p>Initial work on Arrow IPC support has been completed, with readers and writers for streams and files implemented (ARROW-5180).</p>
+
+<h3 id="rust-datafusion">Rust: DataFusion</h3>
+
+<p>Query execution has been reimplemented with an extensible physical query plan. This allows other projects to add other plans, such as for distributed computing or for specific database servers (ARROW-5227).</p>
+
+<p>Added support for writing query results to CSV (ARROW-6274).</p>
+
+<p>The new Parquet -> Arrow reader is now used to read Parquet files (ARROW-6700).</p>
+
+<p>Various other query improvements have been implemented, especially on grouping and aggregate queries (ARROW-6689).</p>
+
+<h3 id="rust-parquet">Rust: Parquet</h3>
+
+<p>The Arrow reader integration has been completed, allowing Parquet files to be
+read into Arrow memory (ARROW-4059).</p>
+
+<h2 id="development-notes">Development notes</h2>
+
+<p>Arrow has moved away from Travis-CI and is now using Github Actions for
+PR-based continuous integration. This new CI configuration relies heavily
+on <code class="highlighter-rouge">docker-compose</code>, making it easier for developers to reproduce builds
+locally, thanks to tremendous work by Krisztián Szűcs (ARROW-7101).</p>
+
+<h2 id="community-discussions-ongoing">Community Discussions Ongoing</h2>
+
+<p>There are a number of active discussions ongoing on the developer
+dev@arrow.apache.org mailing list. We look forward to hearing from the
+community there.</p>
+
+<ul>
+ <li>Mandatory fields in IPC format: to ease input validation, it is being
+<a href="https://mail-archives.apache.org/mod_mbox/arrow-dev/202001.mbox/%3C0dd13489-9221-459a-3560-1426738d3bb4%40python.org%3E">proposed</a> to mark some fields in our Flatbuffers schema “required”.
+Those fields are already semantically required, but are not considered so by
+the generated Flatbuffers verifier. Before accepting this proposal, we need
+to ensure that it does not break binary compatibility with existing valid
+data.</li>
+ <li>The C Data Interface has not yet been formally adopted, though the community
+has reached consensus to move forward after addressing various design
+questions and concerns.</li>
+ <li>Guidelines for the use of “unsafe” in the Rust implementation are being
+<a href="https://mail-archives.apache.org/mod_mbox/arrow-dev/202001.mbox/%3cMN2PR19MB347050FB22046485C0B2525EDD380@MN2PR19MB3470.namprd19.prod.outlook.com%3e">discussed</a>.</li>
+</ul>
+
+
+ </div>
+
+
+
+
+
+ <div class="blog-post" style="margin-bottom: 4rem">
+
+<h1>
Introducing Apache Arrow Flight: A Framework for Fast Data Transport
<a href="/blog/2019/10/13/introducing-arrow-flight/" class="permalink" title="Permalink">∞</a>
</h1>
diff --git a/committers/index.html b/committers/index.html
index f308403..db66de2 100644
--- a/committers/index.html
+++ b/committers/index.html
@@ -24,7 +24,7 @@
<meta property="twitter:title" content="Committers" />
<meta name="twitter:site" content="@ApacheArrow" />
<script type="application/ld+json">
-{"@type":"WebPage","headline":"Committers","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://arrow.apache.org/img/logo.png"}},"image":"https://arrow.apache.org/img/arrow.png","description":"List of project-management committee (PMC) members and committers on the Apache Arrow project.","url":"https://arrow.apache.org/committers/","@context":"https://schema.org"}</script>
+{"@type":"WebPage","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://arrow.apache.org/img/logo.png"}},"headline":"Committers","image":"https://arrow.apache.org/img/arrow.png","description":"List of project-management committee (PMC) members and committers on the Apache Arrow project.","url":"https://arrow.apache.org/committers/","@context":"https://schema.org"}</script>
<!-- End Jekyll SEO tag -->
diff --git a/faq/index.html b/faq/index.html
index 2020d8e..41de42f 100644
--- a/faq/index.html
+++ b/faq/index.html
@@ -24,7 +24,7 @@
<meta property="twitter:title" content="FAQ" />
<meta name="twitter:site" content="@ApacheArrow" />
<script type="application/ld+json">
-{"@type":"WebPage","headline":"FAQ","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://arrow.apache.org/img/logo.png"}},"image":"https://arrow.apache.org/img/arrow.png","description":"Frequently asked questions about the Apache Arrow project","url":"https://arrow.apache.org/faq/","@context":"https://schema.org"}</script>
+{"@type":"WebPage","publisher":{"@type":"Organization","logo":{"@type":"ImageObject","url":"https://arrow.apache.org/img/logo.png"}},"headline":"FAQ","image":"https://arrow.apache.org/img/arrow.png","description":"Frequently asked questions about the Apache Arrow project","url":"https://arrow.apache.org/faq/","@context":"https://schema.org"}</script>
<!-- End Jekyll SEO tag -->
diff --git a/feed.xml b/feed.xml
index eec25a1..988c46b 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1,4 +1,267 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.8.4">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>2020-02-08T20:55:36-05:00</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="3.8.4">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>2020-02-12T11:30:21-05:00</updated><id>https://arrow.apache.org/feed.xml</id><title type="html">Apache Arrow</title><subtitle>Apache Arrow is a cross-language developm [...]
+
+-->
+
+<p>The Apache Arrow team is pleased to announce the 0.16.0 release. This covers
+about 4 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%200.16.0"><strong>735 resolved issues</strong></a>
+from <a href="https://arrow.apache.org/release/0.16.0.html#contributors"><strong>99 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="https://arrow.apache.org/release/0.16.0.html">complete changelog</a>.</p>
+
+<h2 id="new-committers">New committers</h2>
+
+<p>Since the 0.15.0 release, we’ve added two new committers:</p>
+
+<ul>
+ <li><a href="https://github.com/eerhardt">Eric Erhardt</a></li>
+ <li><a href="https://github.com/jorisvandenbossche">Joris Van den Bossche</a></li>
+</ul>
+
+<p>Thank you for all your contributions!</p>
+
+<h2 id="columnar-format-notes">Columnar Format Notes</h2>
+
+<p>We still have work to do to complete comprehensive columnar format integration
+testing between the Java and C++ libraries. Once this work is completed, we
+intend to make a 1.0.0 release with <a href="https://arrow.apache.org/docs/format/Versioning.html">forward and backward compatibility
+guarantees</a>.</p>
+
+<p>We <a href="https://github.com/apache/arrow/commit/0ddc1f4737c35008cd06be1ee28472ebd7da68e2">clarified some ambiguity</a> on dictionary encoding in the specification.
+Work is on going to implement the features in Arrow libraries.</p>
+
+<h2 id="arrow-flight-rpc-notes">Arrow Flight RPC notes</h2>
+
+<p>Flight development work has recently focused on robustness and stability. If
+you are not yet familiar with Flight, read the <a href="https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight/">introductory blog post from
+October</a>.</p>
+
+<p>We are also discussing adding a “bidirectional RPC” which enables
+request-response workflows requiring both client and server to send data
+streams to be performed a single RPC request.</p>
+
+<h2 id="c-notes">C++ notes</h2>
+
+<p>Some work has been done to make the default build configuration of Arrow C++ as
+lean as possible. The Arrow C++ core can now be built without any external
+dependencies other than a new enough C++ compiler (gcc 4.9 or higher). Notably,
+Boost is no longer required. We invested effort to vendor some small essential
+dependencies: Flatbuffers, double-conversion, and uriparser. Many optional
+features requiring external libraries, like compression and GLog integration,
+are now disabled by default. Several subcomponents of the C++ project like the
+filesystem API, CSV, compute, dataset and JSON layers, as well as command-line
+utilities, are now disabled by default. The only toolchain dependency enabled
+by default is jemalloc, the recommended memory allocator, but this can also be
+disabled if desired. For illustration, see the <a href="https://github.com/apache/arrow/blob/master/cpp/examples/minimal_build/build.sh">example minimal build script and
+Dockerfile</a>.</p>
+
+<p>When enabled, the default jemalloc configuration has been tweaked to return
+memory more aggressively to the OS (ARROW-6910, ARROW-6994). We welcome
+feedback from users about our memory allocation configuration and performance
+in applications.</p>
+
+<p>The array validation facilities have been vastly expanded and now exist in
+two flavors: the <code class="highlighter-rouge">Validate</code> method does a light-weight validation that’s
+O(1) in array size, while the potentially O(N) method <code class="highlighter-rouge">ValidateFull</code> does
+thorough data validation (ARROW-6157).</p>
+
+<p>The IO APIs now use <code class="highlighter-rouge">Result&lt;T&gt;</code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7235).</p>
+
+<h3 id="c-csv">C++: CSV</h3>
+
+<p>An option is added to attempt automatic dictionary encoding of string columns
+during reading a CSV file, until a cardinality limit is reached. When
+successful, it can make reading faster and the resulting Arrow data is
+much more memory-efficient (ARROW-3408).</p>
+
+<p>The CSV APIs now use <code class="highlighter-rouge">Result&lt;T&gt;</code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7236).</p>
+
+<h3 id="c-datasets">C++: Datasets</h3>
+
+<p>The 0.16 release introduces the Datasets API to the C++ library, along with
+bindings in Python and R. This API allows you to treat multiple files as a
+single logical dataset entity and make efficient selection queries against it.
+This release includes support for Parquet and Arrow IPC file formats. Factory
+objects allow you to discover files in a directory recursively, inspect the
+schemas in the files, and performs some basic schema unification. You may
+specify how file path segments map to partition, and there is support for
+auto-detecting some partition information, including Hive-style partitioning.
+The Datasets API includes a filter expression syntax as well as column
+selection. These are evaluated with predicate pushdown, and for Parquet,
+evaluation is pushed down to row groups.</p>
+
+<h3 id="c-filesystem-layer">C++: Filesystem layer</h3>
+
+<p>An HDFS implementation of the FileSystem class is available (ARROW-6720). We
+plan to deprecate the prior bespoke C++ HDFS class in favor of the standardized
+filesystem API.</p>
+
+<p>The filesystem APIs now use <code class="highlighter-rouge">Result&lt;T&gt;</code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7161).</p>
+
+<h3 id="c-ipc">C++: IPC</h3>
+
+<p>The Arrow IPC reader is being fuzzed continuously by the <a href="https://google.github.io/oss-fuzz/">OSS-Fuzz</a>
+infrastructure, to detect undesirable behavior on invalid or malicious input.
+Several issues have already been found and fixed.</p>
+
+<h3 id="c-parquet">C++: Parquet</h3>
+
+<p><a href="https://github.com/apache/parquet-format/blob/master/Encryption.md">Modular encryption</a> is now supported (PARQUET-1300).</p>
+
+<p>A performance regression when reading a file with a large number of columns has
+been fixed (ARROW-6876, ARROW-7059), as well as several bugs (PARQUET-1766,
+ARROW-6895).</p>
+
+<h3 id="c-tensors">C++: Tensors</h3>
+
+<p>CSC sparse matrices are supported (ARROW-4225).</p>
+
+<p>The Tensor APIs now use <code class="highlighter-rouge">Result&lt;T&gt;</code> when returning both a Status
+and result value, rather than taking a pointer-out function parameter
+(ARROW-7420).</p>
+
+<h2 id="c-notes-1">C# Notes</h2>
+
+<p>There were a number of C# bug fixes this release. Note that the C# library is
+not yet being tested in CI against the other native Arrow implementations
+(integration tests). We are looking for more contributors for the C# project to
+help with this and other new feature development.</p>
+
+<h2 id="java-notes">Java notes</h2>
+
+<ul>
+ <li>Added prose documentation describing how to work with the Java libraries.</li>
+ <li>Some additional algorithms have been added to the “contrib” algorithms package: multithreaded searching of ValueVectors,</li>
+ <li>The memory modules have been refactored so non-netty allocators can now be used.</li>
+ <li>A new utility for populating ValueVectors more concisely for testing was introduced</li>
+ <li>The “contrib” Avro adapter now supports Avro Logical type conversion to corresponding Arrow type.</li>
+ <li>Various bug fixes across all packages</li>
+</ul>
+
+<h2 id="python-notes">Python notes</h2>
+
+<p>pyarrow 0.16 will be the last release to support Python 2.7.</p>
+
+<p>Python now has bindings for the datasets API (ARROW-6341) as well as the S3
+(ARROW-6655) and HDFS (ARROW-7310) filesystem implementations.</p>
+
+<p>The Duration (ARROW-5855) and Fixed Size List (ARROW-7261) types are exposed
+in Python.</p>
+
+<p>Sparse tensors can be converted to dense tensors (ARROW-6624). They are
+also interoperable with the <code class="highlighter-rouge">pydata/sparse</code> and <code class="highlighter-rouge">scipy.sparse</code> libraries
+(ARROW-4223, ARROW-4224).</p>
+
+<p>Pandas extension arrays now are able to roundtrip through Arrow conversion
+(ARROW-2428).</p>
+
+<p>A memory leak when converting Arrow data to Pandas “object” data has been
+fixed (ARROW-6874).</p>
+
+<p>Arrow is now tested against Python 3.8, and we now build manylinux2014 wheels
+for Python 3 (ARROW-7344).</p>
+
+<h2 id="r-notes">R notes</h2>
+
+<p>This release includes a <code class="highlighter-rouge">dplyr</code> interface to Arrow Datasets,
+which let you work efficiently with large, multi-file datasets as a single entity.
+See <a href="https://arrow.apache.org/docs/r/articles/dataset.html"><code class="highlighter-rouge">vignette("dataset", package = "arrow")</code></a> for more.</p>
+
+<p>Another major area of work in this release was to improve the installation
+experience on Linux. A source package installation (as from CRAN) will now
+handle its C++ dependencies automatically, with no system dependencies beyond what R requires.
+For common Linux distributions and versions, installation will retrieve a prebuilt static
+C++ library for inclusion in the package; where this binary is not available,
+the package executes a bundled script that should build the Arrow C++ library.
+See <a href="https://arrow.apache.org/docs/r/articles/install.html"><code class="highlighter-rouge">vignette("install", package = "arrow")</code></a> for details.</p>
+
+<p>For more on what’s in the 0.16 R package, see the R <a href="https://arrow.apache.org/docs/r/news/">changelog</a>.</p>
+
+<h2 id="ruby-and-c-glib-notes">Ruby and C GLib notes</h2>
+
+<p>Ruby and C GLib continues to follow the features in the C++ project.</p>
+
+<h3 id="ruby">Ruby</h3>
+
+<p>Ruby includes the following improvements.</p>
+
+<ul>
+ <li>Improve CSV save performance (ARROW-7474).</li>
+ <li>Add support for saving/loading TSV (ARROW-7454).</li>
+ <li>Add <code class="highlighter-rouge">Arrow::Schema#build_expression</code> to improve building <code class="highlighter-rouge">Gandiva::Expression</code> (ARROW-6619).</li>
+</ul>
+
+<h3 id="c-glib">C GLib</h3>
+
+<p>C GLib includes the following changes.</p>
+
+<ul>
+ <li>Add support for LargeList, LargeBinary, and LargeString (ARROW-6285, ARROW-6286).</li>
+ <li>Add filter and take API for GArrowTable, GArrowChunkedArray, and GArrowRecordBatch (ARROW-7110, ARROW-7111).</li>
+ <li>Add <code class="highlighter-rouge">garrow_table_combine_chunks()</code> API (ARROW-7369).</li>
+</ul>
+
+<h2 id="rust-notes">Rust notes</h2>
+
+<p>Support for Arrow data types has been improved, with the following array types now supported (ARROW-3690):</p>
+
+<ul>
+ <li>Fixed Size List and Fixed Size Binary</li>
+ <li>Adding a String Array for utf-8 strings, and keeping the Binary Array for general binary data</li>
+ <li>Duration and interval arrays.</li>
+</ul>
+
+<p>Initial work on Arrow IPC support has been completed, with readers and writers for streams and files implemented (ARROW-5180).</p>
+
+<h3 id="rust-datafusion">Rust: DataFusion</h3>
+
+<p>Query execution has been reimplemented with an extensible physical query plan. This allows other projects to add other plans, such as for distributed computing or for specific database servers (ARROW-5227).</p>
+
+<p>Added support for writing query results to CSV (ARROW-6274).</p>
+
+<p>The new Parquet -&gt; Arrow reader is now used to read Parquet files (ARROW-6700).</p>
+
+<p>Various other query improvements have been implemented, especially on grouping and aggregate queries (ARROW-6689).</p>
+
+<h3 id="rust-parquet">Rust: Parquet</h3>
+
+<p>The Arrow reader integration has been completed, allowing Parquet files to be
+read into Arrow memory (ARROW-4059).</p>
+
+<h2 id="development-notes">Development notes</h2>
+
+<p>Arrow has moved away from Travis-CI and is now using Github Actions for
+PR-based continuous integration. This new CI configuration relies heavily
+on <code class="highlighter-rouge">docker-compose</code>, making it easier for developers to reproduce builds
+locally, thanks to tremendous work by Krisztián Szűcs (ARROW-7101).</p>
+
+<h2 id="community-discussions-ongoing">Community Discussions Ongoing</h2>
+
+<p>There are a number of active discussions ongoing on the developer
+dev@arrow.apache.org mailing list. We look forward to hearing from the
+community there.</p>
+
+<ul>
+ <li>Mandatory fields in IPC format: to ease input validation, it is being
+<a href="https://mail-archives.apache.org/mod_mbox/arrow-dev/202001.mbox/%3C0dd13489-9221-459a-3560-1426738d3bb4%40python.org%3E">proposed</a> to mark some fields in our Flatbuffers schema “required”.
+Those fields are already semantically required, but are not considered so by
+the generated Flatbuffers verifier. Before accepting this proposal, we need
+to ensure that it does not break binary compatibility with existing valid
+data.</li>
+ <li>The C Data Interface has not yet been formally adopted, though the community
+has reached consensus to move forward after addressing various design
+questions and concerns.</li>
+ <li>Guidelines for the use of “unsafe” in the Rust implementation are being
+<a href="https://mail-archives.apache.org/mod_mbox/arrow-dev/202001.mbox/%3cMN2PR19MB347050FB22046485C0B2525EDD380@MN2PR19MB3470.namprd19.prod.outlook.com%3e">discussed</a>.</li>
+</ul></content><author><name>pmc</name></author><media:thumbnail xmlns:media="http://search.yahoo.com/mrss/" url="https://arrow.apache.org/img/arrow.png" /><media:content medium="image" url="https://arrow.apache.org/img/arrow.png" xmlns:media="http://search.yahoo.com/mrss/" /></entry><entry><title type="html">Apache Arrow Flightの紹介：高速データトランスポートフレームワーク</title><link href="https://arrow.apache.org/blog/2019/10/13/introducing-arrow-flight-japanese/" rel="alternate" type="text/html" tit [...]
-->
@@ -1759,153 +2022,4 @@ popular libraries.</p></content><author><name>wesm</name></author><media:t
<h2 id="contributors-welcome">Contributors Welcome!</h2>
-<p>If you are excited about being able to use Rust for data science and would like to contribute to this work then there are many ways to get involved. The simplest way to get started is to try out DataFusion against your own data sources and file bug reports for any issues that you find. You could also check out the current <a href="https://cwiki.apache.org/confluence/display/ARROW/Rust+JIRA+Dashboard">list of issues</a> and have a go at fixing one. You can a [...]
-
--->
-
-<p><em><a href="https://github.com/javierluraschi">Javier Luraschi</a> is a software engineer at <a href="https://rstudio.com">RStudio</a></em></p>
-
-<p>Support for Apache Arrow in Apache Spark with R is currently under active
-development in the <a href="https://github.com/rstudio/sparklyr">sparklyr</a> and <a href="https://spark.apache.org/docs/latest/sparkr.html">SparkR</a> projects. This post explores early, yet
-promising, performance improvements achieved when using R with <a href="https://spark.apache.org">Apache Spark</a>,
-Arrow and <code class="highlighter-rouge">sparklyr</code>.</p>
-
-<h1 id="setup">Setup</h1>
-
-<p>Since this work is under active development, install <code class="highlighter-rouge">sparklyr</code> and
-<code class="highlighter-rouge">arrow</code> from GitHub as follows:</p>
-
-<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">devtools</span><span class="o">::</span><span class="n">install_github</span><span class="p">(</span><span class="s2">"apache/arrow"</span><span class="p">,</span><span class="w& [...]
-</span><span class="n">devtools</span><span class="o">::</span><span class="n">install_github</span><span class="p">(</span><span class="s2">"rstudio/sparklyr"</span><span class="p">,</span><span class="w"> </span><span class="n">ref</span><span class="w"> </span><span class [...]
-</span></code></pre></div></div>
-
-<p>In this benchmark, we will use <a href="https://dplyr.tidyverse.org">dplyr</a>, but similar improvements can
-be expected from using <a href="https://cran.r-project.org/package=DBI">DBI</a>, or <a href="https://spark.rstudio.com/reference/#section-spark-dataframes">Spark DataFrames</a> in <code class="highlighter-rouge">sparklyr</code>.
-The local Spark connection and dataframe with 10M numeric rows was
-initialized as follows:</p>
-
-<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">library</span><span class="p">(</span><span class="n">sparklyr</span><span class="p">)</span><span class="w">
-</span><span class="n">library</span><span class="p">(</span><span class="n">dplyr</span><span class="p">)</span><span class="w">
-
-</span><span class="n">sc</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">spark_connect</span><span class="p">(</span><span class="n">master</span><span class="w"> </span><span class="o">=</span><span class="w"> </s [...]
-</span><span class="n">data</span><span class="w"> </span><span class="o">&lt;-</span><span class="w"> </span><span class="n">data.frame</span><span class="p">(</span><span class="n">y</span><span class="w"> </span><span class="o">=</span><span class="w"> </span> [...]
-</span></code></pre></div></div>
-
-<h1 id="copying">Copying</h1>
-
-<p>Currently, copying data to Spark using <code class="highlighter-rouge">sparklyr</code> is performed by persisting
-data on-disk from R and reading it back from Spark. This was meant to be used
-for small datasets since there are better tools to transfer data into
-distributed storage systems. Nevertheless, many users have requested support to
-transfer more data at fast speeds into Spark.</p>
-
-<p>Using <code class="highlighter-rouge">arrow</code> with <code class="highlighter-rouge">sparklyr</code>, we can transfer data directly from R to
-Spark without having to serialize this data in R or persist in disk.</p>
-
-<p>The following example copies 10M rows from R into Spark using <code class="highlighter-rouge">sparklyr</code>
-with and without <code class="highlighter-rouge">arrow</code>, there is close to a 16x improvement using <code class="highlighter-rouge">arrow</code>.</p>
-
-<p>This benchmark uses the <a href="https://CRAN.R-project.org/package=microbenchmark">microbenchmark</a> R package, which runs code
-multiple times, provides stats on total execution time and plots each
-excecution time to understand the distribution over each iteration.</p>
-
-<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">microbenchmark</span><span class="o">::</span><span class="n">microbenchmark</span><span class="p">(</span><span class="w">
- </span><span class="n">setup</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">library</span><span class="p">(</span><span class="n">arrow</span><span class="p">),</span><span class="w">
- </span><span class="n">arrow_on</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="w">
- </span><span class="n">sparklyr_df</span><span class="w"> </span><span class="o">&lt;&lt;-</span><span class="w"> </span><span class="n">copy_to</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span><span class="w"> </span><span class="n" [...]
- </span><span class="n">count</span><span class="p">(</span><span class="n">sparklyr_df</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span><span class="w"> </span><span class="n">collect</span><span class="p">()</span><span class="w">
- </span><span class="p">},</span><span class="w">
- </span><span class="n">arrow_off</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="w">
- </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="s2">"arrow"</span><span class="w"> </span><span class="o">%in%</span><span class="w"> </span><span class="n">.packages</span><span class="p">())</span><span class="w"&g [...]
- </span><span class="n">sparklyr_df</span><span class="w"> </span><span class="o">&lt;&lt;-</span><span class="w"> </span><span class="n">copy_to</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span><span class="w"> </span><span class="n" [...]
- </span><span class="n">count</span><span class="p">(</span><span class="n">sparklyr_df</span><span class="p">)</span><span class="w"> </span><span class="o">%&gt;%</span><span class="w"> </span><span class="n">collect</span><span class="p">()</span><span class="w">
- </span><span class="p">},</span><span class="w">
- </span><span class="n">times</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">10</span><span class="w">
-</span><span class="p">)</span><span class="w"> </span><span class="o">%T&gt;%</span><span class="w"> </span><span class="n">print</span><span class="p">()</span><span class="w"> </span><span class="o">%&gt;%</span><span class="w"> </span><span class="n">ggplot2&l [...]
-</span></code></pre></div></div>
-<div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code> Unit: seconds
- expr min lq mean median uq max neval
- arrow_on 3.011515 4.250025 7.257739 7.273011 8.974331 14.23325 10
- arrow_off 50.051947 68.523081 119.946947 71.898908 138.743419 390.44028 10
-</code></pre></div></div>
-
-<div align="center">
-<img src="/img/arrow-r-spark-copying.png" alt="Copying data with R into Spark with and without Arrow" width="60%" class="img-responsive" />
-</div>
-
-<h1 id="collecting">Collecting</h1>
-
-<p>Similarly, <code class="highlighter-rouge">arrow</code> with <code class="highlighter-rouge">sparklyr</code> can now avoid deserializing data in R
-while collecting data from Spark into R. These improvements are not as
-significant as copying data since, <code class="highlighter-rouge">sparklyr</code> already collects data in
-columnar format.</p>
-
-<p>The following benchmark collects 10M rows from Spark into R and shows that
-<code class="highlighter-rouge">arrow</code> can bring 3x improvements.</p>
-
-<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">microbenchmark</span><span class="o">::</span><span class="n">microbenchmark</span><span class="p">(</span><span class="w">
- </span><span class="n">setup</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">library</span><span class="p">(</span><span class="n">arrow</span><span class="p">),</span><span class="w">
- </span><span class="n">arrow_on</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="w">
- </span><span class="n">collect</span><span class="p">(</span><span class="n">sparklyr_df</span><span class="p">)</span><span class="w">
- </span><span class="p">},</span><span class="w">
- </span><span class="n">arrow_off</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="w">
- </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="s2">"arrow"</span><span class="w"> </span><span class="o">%in%</span><span class="w"> </span><span class="n">.packages</span><span class="p">())</span><span class="w"&g [...]
- </span><span class="n">collect</span><span class="p">(</span><span class="n">sparklyr_df</span><span class="p">)</span><span class="w">
- </span><span class="p">},</span><span class="w">
- </span><span class="n">times</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">10</span><span class="w">
-</span><span class="p">)</span><span class="w"> </span><span class="o">%T&gt;%</span><span class="w"> </span><span class="n">print</span><span class="p">()</span><span class="w"> </span><span class="o">%&gt;%</span><span class="w"> </span><span class="n">ggplot2&l [...]
-</span></code></pre></div></div>
-<div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Unit: seconds
- expr min lq mean median uq max neval
- arrow_on 4.520593 5.609812 6.154509 5.928099 6.217447 9.432221 10
- arrow_off 7.882841 13.358113 16.670708 16.127704 21.051382 24.373331 10
-</code></pre></div></div>
-
-<div align="center">
-<img src="/img/arrow-r-spark-collecting.png" alt="Collecting data with R from Spark with and without Arrow" width="60%" class="img-responsive" />
-</div>
-
-<h1 id="transforming">Transforming</h1>
-
-<p>Today, custom transformations of data using R functions are performed in
-<code class="highlighter-rouge">sparklyr</code> by moving data in row-format from Spark into an R process through a
-socket connection, transferring data in row-format is inefficient since
-multiple data types need to be deserialized over each row, then the data gets
-converted to columnar format (R was originally designed to use columnar data),
-once R finishes this computation, data is again converted to row-format,
-serialized row-by-row and then sent back to Spark over the socket connection.</p>
-
-<p>By adding support for <code class="highlighter-rouge">arrow</code> in <code class="highlighter-rouge">sparklyr</code>, it makes Spark perform the
-row-format to column-format conversion in parallel in Spark. Data
-is then transferred through the socket but no custom serialization takes place.
-All the R process needs to do is copy this data from the socket into its heap,
-transform it and copy it back to the socket connection.</p>
-
-<p>The following example transforms 100K rows with and without <code class="highlighter-rouge">arrow</code> enabled,
-<code class="highlighter-rouge">arrow</code> makes transformation with R functions close to 41x faster.</p>
-
-<div class="language-r highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">microbenchmark</span><span class="o">::</span><span class="n">microbenchmark</span><span class="p">(</span><span class="w">
- </span><span class="n">setup</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">library</span><span class="p">(</span><span class="n">arrow</span><span class="p">),</span><span class="w">
- </span><span class="n">arrow_on</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="w">
- </span><span class="n">sample_n</span><span class="p">(</span><span class="n">sparklyr_df</span><span class="p">,</span><span class="w"> </span><span class="m">10</span><span class="o">^</span><span class="m">5</span><span class="p">)</span><span class="w"> </span& [...]
- </span><span class="p">},</span><span class="w">
- </span><span class="n">arrow_off</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="w">
- </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="s2">"arrow"</span><span class="w"> </span><span class="o">%in%</span><span class="w"> </span><span class="n">.packages</span><span class="p">())</span><span class="w"&g [...]
- </span><span class="n">sample_n</span><span class="p">(</span><span class="n">sparklyr_df</span><span class="p">,</span><span class="w"> </span><span class="m">10</span><span class="o">^</span><span class="m">5</span><span class="p">)</span><span class="w"> </span& [...]
- </span><span class="p">},</span><span class="w">
- </span><span class="n">times</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">10</span><span class="w">
-</span><span class="p">)</span><span class="w"> </span><span class="o">%T&gt;%</span><span class="w"> </span><span class="n">print</span><span class="p">()</span><span class="w"> </span><span class="o">%&gt;%</span><span class="w"> </span><span class="n">ggplot2&l [...]
-</span></code></pre></div></div>
-<div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code>Unit: seconds
- expr min lq mean median uq max neval
- arrow_on 3.881293 4.038376 5.136604 4.772739 5.759082 7.873711 10
- arrow_off 178.605733 183.654887 213.296238 227.182018 233.601885 238.877341 10
-</code></pre></div></div>
-
-<div align="center">
-<img src="/img/arrow-r-spark-transforming.png" alt="Transforming data with R in Spark with and without Arrow" width="60%" class="img-responsive" />
-</div>
-
-<p>Additional benchmarks and fine-tuning parameters can be found under <code class="highlighter-rouge">sparklyr</code>
-<a href="https://github.com/rstudio/sparklyr/pull/1611">/rstudio/sparklyr/pull/1611</a> and <code class="highlighter-rouge">SparkR</code> <a href="https://github.com/apache/spark/pull/22954">/apache/spark/pull/22954</a>. Looking forward to bringing this feature
-to the Spark, Arrow and R communities.</p></content><author><name>Javier Luraschi</name></author><media:thumbnail xmlns:media="http://search.yahoo.com/mrss/" url="https://arrow.apache.org/img/arrow.png" /><media:content medium="image" url="https://arrow.apache.org/img/arrow.png" xmlns:media="http://search.yahoo.com/mrss/" /></entry></feed>
\ No newline at end of file
+<p>If you are excited about being able to use Rust for data science and would like to contribute to this work then there are many ways to get involved. The simplest way to get started is to try out DataFusion against your own data sources and file bug reports for any issues that you find. You could also check out the current <a href="https://cwiki.apache.org/confluence/display/ARROW/Rust+JIRA+Dashboard">list of issues</a> and have a go at fixing one. You can a [...]
\ No newline at end of file
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