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The following commit(s) were added to refs/heads/asf-site by this push:
new de9ecda fixup! Rebuild website
de9ecda is described below
commit de9ecdae9b5851e7a3a0b1cd334a5f060e259126
Author: Piotr Nowojski <[email protected]>
AuthorDate: Tue Jul 7 15:54:56 2020 +0200
fixup! Rebuild website
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+<!DOCTYPE html>
+<html lang="en">
+ <head>
+ <meta charset="utf-8">
+ <meta http-equiv="X-UA-Compatible" content="IE=edge">
+ <meta name="viewport" content="width=device-width, initial-scale=1">
+ <!-- The above 3 meta tags *must* come first in the head; any other head
content must come *after* these tags -->
+ <title>Apache Flink: Apache Flink 1.11.0 Release Announcement</title>
+ <link rel="shortcut icon" href="/favicon.ico" type="image/x-icon">
+ <link rel="icon" href="/favicon.ico" type="image/x-icon">
+
+ <!-- Bootstrap -->
+ <link rel="stylesheet"
href="https://maxcdn.bootstrapcdn.com/bootstrap/3.4.1/css/bootstrap.min.css";>
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+
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title="Apache Flink Blog: RSS feed" />
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+ <img alt="Apache Flink" src="/img/flink-header-logo.svg"
width="147px" height="73px">
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+ <div class="collapse navbar-collapse"
id="bs-example-navbar-collapse-1">
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+ <!-- First menu section explains visitors what Flink is -->
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+ <!-- What is Stream Processing? -->
+ <!--
+ <li><a href="/streamprocessing1.html">What is Stream
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+
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+ <li><a href="/flink-architecture.html">What is Apache
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+ .smalllinks:link {
+ display: inline-block !important; background: none;
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+ }
+ </style>
+
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+ <div class="col-sm-9">
+ <div class="row-fluid">
+ <div class="col-sm-12">
+ <div class="row">
+ <h1>Apache Flink 1.11.0 Release Announcement</h1>
+ <p><i></i></p>
+
+ <article>
+ <p>06 Jul 2020 Marta Paes (<a
href="https://twitter.com/morsapaes";>@morsapaes</a>)</p>
+
+<p>The Apache Flink community is proud to announce the release of Flink
1.11.0! More than 200 contributors worked on over 1.3k issues to bring
significant improvements to usability as well as new features to Flink users
across the whole API stack. Some highlights that we’re particularly excited
about are:</p>
+
+<ul>
+ <li>
+ <p>The core engine is introducing <strong>unaligned checkpoints</strong>,
a major change to Flink’s fault tolerance mechanism that improves checkpointing
performance under heavy backpressure.</p>
+ </li>
+ <li>
+ <p>A <strong>new Source API</strong> that simplifies the implementation of
(custom) sources by unifying batch and streaming execution, as well as
offloading internals such as event-time handling, watermark generation or
idleness detection to Flink.</p>
+ </li>
+ <li>
+ <p>Flink SQL is introducing <strong>Support for Change Data Capture
(CDC)</strong> to easily consume and interpret database changelogs from tools
like Debezium. The renewed <strong>FileSystem Connector</strong> also expands
the set of use cases and formats supported in the Table API/SQL, enabling
scenarios like streaming data directly from Kafka to Hive.</p>
+ </li>
+ <li>
+ <p>Multiple performance optimizations to PyFlink, including support for
<strong>vectorized User-defined Functions (Pandas UDFs)</strong>. This improves
interoperability with libraries like Pandas and NumPy, making Flink more
powerful for data science and ML workloads.</p>
+ </li>
+</ul>
+
+<p>Read on for all major new features and improvements, important changes to
be aware of and what to expect moving forward!</p>
+
+<div class="page-toc">
+<ul id="markdown-toc">
+ <li><a href="#new-features-and-improvements"
id="markdown-toc-new-features-and-improvements">New Features and
Improvements</a> <ul>
+ <li><a href="#unaligned-checkpoints-beta"
id="markdown-toc-unaligned-checkpoints-beta">Unaligned Checkpoints
(Beta)</a></li>
+ <li><a href="#unified-watermark-generators"
id="markdown-toc-unified-watermark-generators">Unified Watermark
Generators</a></li>
+ <li><a href="#new-data-source-api-beta"
id="markdown-toc-new-data-source-api-beta">New Data Source API (Beta)</a></li>
+ <li><a href="#application-mode-deployments"
id="markdown-toc-application-mode-deployments">Application Mode
Deployments</a></li>
+ <li><a href="#other-improvements"
id="markdown-toc-other-improvements">Other Improvements</a></li>
+ <li><a href="#table-apisql-support-for-change-data-capture-cdc"
id="markdown-toc-table-apisql-support-for-change-data-capture-cdc">Table
API/SQL: Support for Change Data Capture (CDC)</a></li>
+ <li><a href="#table-apisql-jdbc-catalog-interface-and-postgres-catalog"
id="markdown-toc-table-apisql-jdbc-catalog-interface-and-postgres-catalog">Table
API/SQL: JDBC Catalog Interface and Postgres Catalog</a></li>
+ <li><a
href="#table-apisql-filesystem-connector-with-support-for-avro-orc-and-parquet"
id="markdown-toc-table-apisql-filesystem-connector-with-support-for-avro-orc-and-parquet">Table
API/SQL: FileSystem Connector with Support for Avro, ORC and Parquet</a></li>
+ <li><a href="#table-apisql-support-for-python-udfs"
id="markdown-toc-table-apisql-support-for-python-udfs">Table API/SQL: Support
for Python UDFs</a></li>
+ <li><a href="#other-improvements-to-the-table-apisql"
id="markdown-toc-other-improvements-to-the-table-apisql">Other Improvements to
the Table API/SQL</a></li>
+ <li><a href="#pyflink-support-for-pandas-udfs"
id="markdown-toc-pyflink-support-for-pandas-udfs">PyFlink: Support for Pandas
UDFs</a></li>
+ <li><a href="#other-improvements-to-pyflink"
id="markdown-toc-other-improvements-to-pyflink">Other Improvements to
PyFlink</a></li>
+ </ul>
+ </li>
+ <li><a href="#important-changes"
id="markdown-toc-important-changes">Important Changes</a></li>
+ <li><a href="#release-notes" id="markdown-toc-release-notes">Release
Notes</a></li>
+ <li><a href="#list-of-contributors"
id="markdown-toc-list-of-contributors">List of Contributors</a></li>
+</ul>
+
+</div>
+
+<p>The binary distribution and source artifacts are now available on the
updated <a href="/downloads.html">Downloads page</a> of the Flink website, and
the most recent distribution of PyFlink is available on <a
href="https://pypi.org/project/apache-flink/";>PyPI</a>. Please review the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/release-notes/flink-1.11.html";>release
notes</a> carefully, and check the complete <a
href="https://issues.apache.org/jira/secure/Release [...]
+
+<p>We encourage you to download the release and share your feedback with the
community through the <a
href="https://flink.apache.org/community.html#mailing-lists";>Flink mailing
lists</a> or <a
href="https://issues.apache.org/jira/projects/FLINK/summary";>JIRA</a>.</p>
+
+<h2 id="new-features-and-improvements">New Features and Improvements</h2>
+
+<h3 id="unaligned-checkpoints-beta">Unaligned Checkpoints (Beta)</h3>
+
+<p>Triggering a checkpoint in Flink will cause a <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/internals/stream_checkpointing.html#barriers";>checkpoint
barrier</a> to flow from the sources of your topology all the way towards the
sinks. For operators that receive more than one input stream, the barriers
flowing through each channel need to be aligned before the operator can
snapshot its state and forward the checkpoint barrier — typically, this
alignment will take [...]
+
+<ul>
+ <li>
+ <p>Checkpoint barriers will flow much slower through backpressured
channels, effectively blocking the remaining channels and their upstream
operators during checkpointing;</p>
+ </li>
+ <li>
+ <p>Slow checkpoint barrier propagation leads to longer checkpointing times
and can, worst case, result in little to no progress in the application.</p>
+ </li>
+</ul>
+
+<p>To improve the performance of checkpointing under backpressure scenarios,
the community is rolling out the first iteration of unaligned checkpoints (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-76%3A+Unaligned+Checkpoints";>FLIP-76</a>)
with Flink 1.11. Compared to the original checkpointing mechanism (Fig. 1),
this approach doesn’t wait for barrier alignment across input channels, instead
allowing barriers to overtake in-flight records (i.e., data stored in buffers)
[...]
+
+<div style="line-height:60%;">
+ <br />
+</div>
+
+<div class="row">
+ <div class="col-lg-6">
+ <div class="text-center">
+ <figure>
+ <img src="/img/blog/2020-07-06-release-1.11.0/image1.gif"
width="600px" alt="Aligned Checkpoints" />
+ <br /><br />
+ <figcaption><i><b>Fig.1:</b> Aligned
Checkpoints</i></figcaption>
+ </figure>
+ </div>
+ </div>
+ <div class="col-lg-6">
+ <div class="text-center">
+ <figure>
+ <img src="/img/blog/2020-07-06-release-1.11.0/image2.png"
width="600px" alt="Unaligned Checkpoints" />
+ <br /><br />
+ <figcaption><i><b>Fig.2:</b> Unaligned
Checkpoints</i></figcaption>
+ </figure>
+ </div>
+ </div>
+</div>
+
+<div style="line-height:150%;">
+ <br />
+</div>
+
+<p>Because in-flight records have to be persisted as part of the snapshot,
unaligned checkpoints will lead to increased checkpoints sizes. On the upside,
<strong>checkpointing times are heavily reduced</strong>, so users will see
more progress (even in unstable environments) as more up-to-date checkpoints
will lighten the recovery process. You can learn more about the current
limitations of unaligned checkpoints in the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11 [...]
+
+<p>As with any beta feature, we appreciate early feedback that you might want
to share with the community after giving unaligned checkpoints a try!</p>
+
+<p><span class="label label-info">Info</span> To enable this feature, you need
to configure the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/api/java/org/apache/flink/streaming/api/environment/CheckpointConfig.html";><code>enableUnalignedCheckpoints</code></a>
option in your <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/stream/state/checkpointing.html#enabling-and-configuring-checkpointing";>checkpoint
config</a>. Please note that unalig [...]
+
+<h3 id="unified-watermark-generators">Unified Watermark Generators</h3>
+
+<p>So far, watermark generation (prev. also called <em>assignment</em>) relied
on two different interfaces: <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/api/java/org/apache/flink/streaming/api/functions/AssignerWithPunctuatedWatermarks.html";><code>AssignerWithPunctuatedWatermarks</code></a>
and <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/api/java/org/apache/flink/streaming/api/functions/AssignerWithPeriodicWatermarks.html";><code>Assigner
[...]
+
+<p>This gives users more control over watermark emission and simplifies the
implementation of new connectors that need to support watermark assignment and
timestamp extraction at the source (see <em><a
href="#new-data-source-api-beta">New Data Source API</a></em>). Multiple <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11//dev/event_timestamps_watermarks.html#introduction-to-watermark-strategies";>strategies
for watermarking</a> are available out-of-the-box as conveni [...]
+
+<p><strong>Support for Watermark Idleness Detection</strong></p>
+
+<p>The <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/api/java/org/apache/flink/api/common/eventtime/WatermarkStrategy.html#withIdleness-java.time.Duration-";><code>WatermarkStrategy.withIdleness()</code></a>
method allows you to mark a stream as idle if no events arrive within a
configured time (i.e. a timeout duration), which in turn allows handling event
time skew properly and preventing idle partitions from holding back the event
time progress of the entire appl [...]
+
+<p><span class="label label-info">Note</span> <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-126%3A+Unify+%28and+separate%29+Watermark+Assigners";>FLIP-126</a>
introduces no breaking changes, but we recommend that users give preference to
the new <code>WatermarkGenerator</code> interface moving forward, in
preparation for the deprecation of the legacy watermark assigners in future
releases.</p>
+
+<h3 id="new-data-source-api-beta">New Data Source API (Beta)</h3>
+
+<p>Up to this point, writing a production-grade source connector for Flink was
a non-trivial task that required users to be somewhat familiar with Flink
internals and account for implementation details like event time assignment,
watermark generation or idleness detection in their code. Flink 1.11 introduces
a new Data Source API (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-27%3A+Refactor+Source+Interface";>FLIP-27</a>)
to overcome these limitations, as well as the nee [...]
+
+<center>
+ <figure>
+ <img src="/img/blog/2020-07-06-release-1.11.0/image3.png" width="600px"
alt="Data Source API" />
+ </figure>
+</center>
+
+<div style="line-height:150%;">
+ <br />
+</div>
+
+<p>Separating the work of split discovery and the actual reading of the
consumed data (i.e. the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/stream/sources.html#data-source-concepts";><em>splits</em></a>)
in different components — resp. the <code>SplitEnumerator</code> and
<code>SourceReader</code> — allows mixing and matching different enumeration
strategies and split readers.</p>
+
+<p>As an example, the existing Kafka connector has multiple strategies for
partition discovery that are intermingled with the rest of the code. With the
new interfaces in place, it would only need a single reader implementation and
there could be several split enumerators for the different partition discovery
strategies.</p>
+
+<p><strong>Batch and Streaming Unification</strong></p>
+
+<p>Source connectors implemented using the Data Source API will be able to
work both as a bounded (<em>batch</em>) and unbounded (<em>streaming</em>)
source. The difference between both cases is minimal: for bounded input, the
<code>SplitEnumerator</code> will generate a fixed set of splits and each split
is finite; for unbounded input, either the splits are not finite or the
<code>SplitEnumerator</code> keeps generating new splits.</p>
+
+<p><strong>Implicit Watermark and Event Time Handling</strong></p>
+
+<p>The <code>TimestampAssigner</code> and <code>WatermarkGenerator</code> run
transparently as part of the <code>SourceReader</code> component, so users also
don’t have to implement any timestamp extraction or watermark generation
code.</p>
+
+<p><span class="label label-info">Note</span> The existing source connectors
have not yet been reimplemented using the Data Source API — this is planned for
upcoming releases. If you’re looking to implement a new source, please refer to
the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/stream/sources.html#data-sources";>Data
Source documentation</a> and <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/stream/sources.html#the-data-source
[...]
+
+<h3 id="application-mode-deployments">Application Mode Deployments</h3>
+
+<p>Prior to Flink 1.11, jobs in a Flink application could either be submitted
to a long-running <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/concepts/flink-architecture.html#flink-session-cluster";>Flink
Session Cluster</a> (<em>session mode</em>) or a dedicated <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/concepts/flink-architecture.html#flink-job-cluster";>Flink
Job Cluster</a> (<em>job mode</em>). For both these modes, the <code>main()</
[...]
+
+<p>From this release on, Flink gets an additional deployment mode: <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/ops/deployment/#application-mode";>Application
Mode</a> (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-85+Flink+Application+Mode";>FLIP-85</a>);
where the <code>main()</code> method runs on the cluster, rather than the
client. The job submission becomes a one-step process: you package your
application logic and dependencies into an execu [...]
+
+<p>In Flink 1.11, the community worked to already support <em>application
mode</em> in Kubernetes (<a
href="https://issues.apache.org/jira/browse/FLINK-10934";>FLINK-10934</a>).</p>
+
+<h3 id="other-improvements">Other Improvements</h3>
+
+<p><strong>Unified Memory Configuration for JobManagers (<a
href="https://jira.apache.org/jira/browse/FLINK-16614";>FLIP-116</a>)</strong></p>
+
+<p>Following the work started in Flink 1.10 to improve memory management and
configuration, this release introduces a new memory model that aligns the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/ops/memory/mem_setup_master.html";>JobManagers’
configuration options</a> and terminology with that introduced in <a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-49%3A+Unified+Memory+Configuration+for+TaskExecutors";>FLIP-49</a>
for TaskManagers. This affec [...]
+
+<p><span class="label label-danger">Attention</span> Reusing a previous Flink
configuration without any adjustments can result in differently computed memory
parameters for the JVM and, as a result, performance changes or even failures.
Make sure to check the <a
href="https://ci.apache.org/projects/flink/flink-docs-master/ops/memory/mem_migration.html#migrate-job-manager-memory-configuration";>migration
guide</a> if you’re planning to update to the latest version.</p>
+
+<p><strong>Improvements to the Flink WebUI (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-75%3A+Flink+Web+UI+Improvement+Proposal";>FLIP-75</a>)</strong></p>
+
+<p>In Flink 1.11, the community kicked off a series of improvements to the
Flink WebUI. The first to roll out are better TaskManager and JobManager log
display (<a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427143";>FLIP-103</a>),
as well as a new thread dump utility (<a
href="https://issues.apache.org/jira/browse/FLINK-14816";>FLINK-14816</a>). Some
additional work planned for upcoming releases includes better backpressure
detection, more flexible and config [...]
+
+<p><strong>Docker Image Unification (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-111%3A+Docker+image+unification";>FLIP-111</a>)</strong></p>
+
+<p>With this release, all Docker-related resources have been consolidated into
<a href="https://github.com/apache/flink-docker";>apache/flink-docker</a> and
the entry point script has been extended to allow users to run the default
Docker image in different modes without the need to create a custom image. The
<a
href="https://ci.apache.org/projects/flink/flink-docs-master/ops/deployment/docker.html#customize-flink-image";>updated
documentation</a> describes in detail how to use and customi [...]
+
+<hr />
+
+<h3 id="table-apisql-support-for-change-data-capture-cdc">Table API/SQL:
Support for Change Data Capture (CDC)</h3>
+
+<p>Change Data Capture (CDC) has become a popular pattern to capture committed
changes from a database and propagate those changes to downstream consumers,
for example to keep multiple datastores in sync and avoid common pitfalls such
as <a href="https://thorben-janssen.com/dual-writes/";>dual writes</a>. Being
able to easily ingest and interpret these changelogs into the Table API/SQL has
been a highly demanded feature in the Flink community — and it’s now possible
with Flink 1.11.</p>
+
+<p>To extend the scope of the Table API/SQL to use cases like CDC, Flink 1.11
introduces new table source and sink interfaces with <strong>changelog
mode</strong> (see <em><a href="#other-improvements-to-the-table-apisql">New
TableSource and TableSink Interfaces</a></em>) and support for the <a
href="https://debezium.io/";>Debezium</a> and <a
href="https://github.com/alibaba/canal";>Canal</a> formats (<a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=147427289";>FLIP
[...]
+
+<center>
+ <figure>
+ <img src="/img/blog/2020-07-06-release-1.11.0/image4.png" width="500px"
alt="CDC" />
+ </figure>
+</center>
+
+<div style="line-height:150%;">
+ <br />
+</div>
+
+<p>Users have to specify either <code>“format=debezium-json”</code> or
<code>“format=canal-json”</code> in their <code>CREATE TABLE</code> statement
to consume changelogs using SQL DDL.</p>
+
+<div class="highlight"><pre><code class="language-sql"><span
class="k">CREATE</span> <span class="k">TABLE</span> <span
class="n">my_table</span> <span class="p">(</span>
+ <span class="p">...</span>
+<span class="p">)</span> <span class="k">WITH</span> <span class="p">(</span>
+ <span class="s1">'connector'</span><span class="o">=</span><span
class="s1">'...'</span><span class="p">,</span> <span class="c1">--
e.g. 'kafka'</span>
+ <span class="s1">'format'</span><span class="o">=</span><span
class="s1">'debezium-json'</span><span class="p">,</span>
+ <span class="s1">'debezium-json.schema-include'</span><span
class="o">=</span><span class="s1">'true'</span> <span class="c1">--
default: false (Debezium can be configured to include or exclude the message
schema)</span>
+ <span class="s1">'debezium-json.ignore-parse-errors'</span><span
class="o">=</span><span class="s1">'true'</span> <span class="c1">--
default: false</span>
+<span class="p">);</span></code></pre></div>
+
+<p>Flink 1.11 only supports Kafka as a changelog source out-of-the-box and
JSON-encoded changelogs, with Avro (Debezium) and Protobuf (Canal) planned for
future releases. There are also plans to support MySQL binlogs and Kafka
compacted topics as sources, as well as to extend changelog support to batch
execution.</p>
+
+<p><span class="label label-danger">Attention</span> There is a known issue
(<a href="https://issues.apache.org/jira/browse/FLINK-18461";>FLINK-18461</a>)
that prevents changelog sources from being used to write to upsert sinks (e.g.
MySQL, HBase, Elasticsearch). This will be fixed in the next patch release
(1.11.1).</p>
+
+<h3 id="table-apisql-jdbc-catalog-interface-and-postgres-catalog">Table
API/SQL: JDBC Catalog Interface and Postgres Catalog</h3>
+
+<p>Flink 1.11 introduces a generic JDBC catalog interface (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-93%3A+JDBC+catalog+and+Postgres+catalog";>FLIP-93</a>)
that enables users of the Table API/SQL to <strong>derive table schemas
automatically</strong> from connections to relational databases over <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/connect.html#jdbc-connector";>JDBC</a>.
This eliminates the previous need for manual schema def [...]
+
+<p>The first implementation, rolling out with the new release, is the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/catalogs.html#postgrescatalog";>Postgres
catalog</a>.</p>
+
+<h3
id="table-apisql-filesystem-connector-with-support-for-avro-orc-and-parquet">Table
API/SQL: FileSystem Connector with Support for Avro, ORC and Parquet</h3>
+
+<p>To improve the user experience for end-to-end streaming ETL use cases, the
Flink community worked on a new FileSystem Connector for the Table API/SQL (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-115%3A+Filesystem+connector+in+Table";>FLIP-115</a>).
The implementation is based on Flink’s <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/ops/filesystems/index.html";>FileSystem
abstraction</a> and reuses <a href="https://ci.apache.org/projects/flink/ [...]
+
+<p>This also means that Table API/SQL users can now make use of all formats
already supported by StreamingFileSink, like (Avro) Parquet, as well as the new
formats introduced with this release, like Avro (<a
href="https://issues.apache.org/jira/browse/FLINK-11395";>FLINK-11395</a>) and
Orc (<a
href="https://issues.apache.org/jira/browse/FLINK-10114";>FLINK-10114</a>).</p>
+
+<div class="highlight"><pre><code class="language-sql"><span
class="k">CREATE</span> <span class="k">TABLE</span> <span
class="n">my_table</span> <span class="p">(</span>
+ <span class="n">column_name1</span> <span class="nb">INT</span><span
class="p">,</span>
+ <span class="n">column_name2</span> <span class="n">STRING</span><span
class="p">,</span>
+ <span class="p">...</span>
+ <span class="n">part_name1</span> <span class="nb">INT</span><span
class="p">,</span>
+ <span class="n">part_name2</span> <span class="n">STRING</span>
+<span class="p">)</span> <span class="n">PARTITIONED</span> <span
class="k">BY</span> <span class="p">(</span><span
class="n">part_name1</span><span class="p">,</span> <span
class="n">part_name2</span><span class="p">)</span> <span class="k">WITH</span>
<span class="p">(</span>
+ <span class="s1">'connector'</span> <span class="o">=</span> <span
class="s1">'filesystem'</span><span class="p">,</span>
+ <span class="s1">'path'</span> <span class="o">=</span> <span
class="s1">'file:///path/to/file,</span>
+<span class="s1"> '</span><span class="n">format</span><span
class="s1">' = '</span><span class="p">...</span><span
class="err">'</span><span class="p">,</span> <span class="c1">-- supported
formats: Avro, ORC, Parquet, CSV, JSON </span>
+ <span class="p">...</span>
+<span class="p">);</span></code></pre></div>
+
+<p>The new all-rounder FileSystem Connector transparently handles batch and
streaming execution, provides exactly-once guarantees and has full partition
support, greatly expanding the scope of usage of the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/connect.html#file-system-connector";>legacy
connector</a>. This allows users to easily implement common use cases like
<strong>directly streaming data from Kafka to Hive</strong>.</p>
+
+<p>You can track the upcoming improvements to the FileSystem Connector in <a
href="https://issues.apache.org/jira/browse/FLINK-17778";>FLINK-17778</a>.</p>
+
+<h3 id="table-apisql-support-for-python-udfs">Table API/SQL: Support for
Python UDFs</h3>
+
+<p>Prior to this release, users of the Table API/SQL were limited to defining
UDFs in either Java or Scala. In Flink 1.11, the community worked on expanding
the usage scope of the Python language beyond PyFlink and providing support for
Python UDFs in the SQL DDL syntax (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-106%3A+Support+Python+UDF+in+SQL+Function+DDL";>FLIP-106</a>),
as well as the SQL Client (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-114 [...]
+
+<h3 id="other-improvements-to-the-table-apisql">Other Improvements to the
Table API/SQL</h3>
+
+<p><strong>DDL and DML Compatibility for the Hive Connector (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-123%3A+DDL+and+DML+compatibility+for+Hive+connector";>FLIP-123</a>)</strong></p>
+
+<p>Starting from Flink 1.11, users can write SQL statements directly using
Hive syntax (HiveQL) in the Table API/SQL and the SQL Client. For this purpose,
an additional dialect was introduced and users can now dynamically switch
between Flink (<code>default</code>) and Hive (<code>hive</code>) on a
per-statement basis. For a complete list of supported DDL and DML statements,
check the Hive dialect <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/hive/hive_d
[...]
+
+<p><strong>Extensions and Improvements to the Flink SQL Syntax</strong></p>
+
+<ul>
+ <li>
+ <p>Flink 1.11 introduces the concept of <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/sql/create.html#create-table";>primary
key constraints</a> to leverage runtime optimizations in Flink SQL DDL (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP+87%3A+Primary+key+constraints+in+Table+API";>FLIP-87</a>);</p>
+ </li>
+ <li>
+ <p>View objects are now fully supported in SQL DDL using the
<code>CREATE</code>/<code>ALTER</code>/<code>DROP VIEW</code> statements (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-71%3A+E2E+View+support+in+FLINK+SQL";>FLIP-71</a>);</p>
+ </li>
+ <li>
+ <p>Users can now specify or override table options in their DQL/DML
statements using <a
href="https://ci.apache.org/projects/flink/flink-docs-master/dev/table/sql/hints.html#dynamic-table-options";>dynamic
table options</a> (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-113%3A+Supports+Dynamic+Table+Options+for+Flink+SQL";>FLIP-113</a>).</p>
+ </li>
+ <li>
+ <p>To make connector properties less verbose and improve exception
handling, some key properties have been refactored (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-122%3A+New+Connector+Property+Keys+for+New+Factory";>FLIP-122</a>).
This change does not break compatibility, so users can still use the old
property keys.</p>
+ </li>
+</ul>
+
+<p><strong>New TableSource and TableSink Interfaces (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces";>FLIP-95</a>)</strong></p>
+
+<p>Flink 1.11 introduces new table source and sink interfaces (resp. <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/api/java/org/apache/flink/table/connector/source/DynamicTableSource.html";><code>DynamicTableSource</code></a>
and <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/api/java/org/apache/flink/table/connector/sink/DynamicTableSink.html";><code>DynamicTableSink</code></a>)
that unify batch and streaming execution, provide more efficient [...]
+
+<p><span class="label label-info">Note</span> Although compatibility is not
immediately affected, we recommend that Table API/SQL users update any sources
and sinks to the new interface stack.</p>
+
+<p><strong>Refactored TableEnvironment Interface (<a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=134745878";>FLIP-84</a>)</strong></p>
+
+<p>The semantics to describe similar behaviours in the
<code>TableEnvironment</code> and <code>Table</code> interfaces have diverged
over time, leading to an inconsistent and sometimes unclear user experience. To
improve this and make programming more fluent in the Table API/SQL, Flink 1.11
introduces new methods that unify behaviours like execution triggering (e.g.
<code>executeSql()</code>) and result representation (e.g. <a
href="https://ci.apache.org/projects/flink/flink-docs-release [...]
+
+<p><span class="label label-info">Note</span> The methods deprecated with
FLIP-84 will not be immediately removed, but we recommend that users adopt the
newly introduced methods. For a complete list of new and deprecated methods,
check the “Summary” section of <a
href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=134745878";>FLIP-84</a>.</p>
+
+<p><strong>New Type Inference for Table API UDFs (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-65%3A+New+type+inference+for+Table+API+UDFs";>FLIP-65</a>)</strong></p>
+
+<p>In Flink 1.9, the community started working on a <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/types.html#data-types";>new
data type system</a> for the Table API to improve its compliance with the SQL
standard (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-37%3A+Rework+of+the+Table+API+Type+System";>FLIP-37</a>).
This work is now close to being completed in Flink 1.11, with the exposure of
Table API UDFs to the new type system (scalar [...]
+
+<hr />
+
+<h3 id="pyflink-support-for-pandas-udfs">PyFlink: Support for Pandas UDFs</h3>
+
+<p>Up to this release, Python UDFs in PyFlink only supported scalar values of
standard Python types. This presented some limitations:</p>
+
+<ul>
+ <li>
+ <p>High serialization/deserialization overhead in the process of
transferring data between the JVM and the Python processes;</p>
+ </li>
+ <li>
+ <p>Hard to integrate with common Python libraries for high-performance
numerical processing like pandas and NumPy.</p>
+ </li>
+</ul>
+
+<p>To overcome these limitations, the community introduced support for
(scalar) <strong>vectorized Python UDFs</strong> based on <a
href="https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html";>pandas</a>
in Flink 1.11 (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-97%3A+Support+Scalar+Vectorized+Python+UDF+in+PyFlink";>FLIP-97</a>).
The performance of vectorized UDFs is usually much higher, as the
serialization/deserialization overhead is minimized b [...]
+
+<div class="highlight"><pre><code class="language-python"><span
class="nd">@udf</span><span class="p">(</span><span
class="n">input_types</span><span class="o">=</span><span
class="p">[</span><span class="n">DataTypes</span><span class="o">.</span><span
class="n">BIGINT</span><span class="p">(),</span> <span
class="n">DataTypes</span><span class="o">.</span><span
class="n">BIGINT</span><span class="p">()],</span> <span
class="n">result_type</span><span class="o">=</span><span class="n">D [...]
+<span class="k">def</span> <span class="nf">add</span><span
class="p">(</span><span class="n">i</span><span class="p">,</span> <span
class="n">j</span><span class="p">):</span>
+ <span class="k">return</span> <span class="n">i</span> <span
class="o">+</span> <span class="n">j</span></code></pre></div>
+
+<p>To mark a UDF as a Pandas UDF, you only need to add an extra parameter
<code>udf_type=”pandas”</code> in the udf decorator, as described in the <a
href="https://ci.apache.org/projects/flink/flink-docs-master/dev/table/python/vectorized_python_udfs.html#vectorized-user-defined-functions";>documentation</a>.</p>
+
+<h3 id="other-improvements-to-pyflink">Other Improvements to PyFlink</h3>
+
+<p><strong>Conversion fromPandas/toPandas (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-120%3A+Support+conversion+between+PyFlink+Table+and+Pandas+DataFrame";>FLIP-120</a>)</strong></p>
+
+<p>Arrow is also supported as an optimization to convert between PyFlink
tables and <a
href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html";><code>pandas.DataFrames</code></a>,
enabling users to switch processing engines seamlessly without the need for an
intermediate connector. For examples on how to use the new
<code>fromPandas()</code> and <code>toPandas()</code> methods in PyFlink, check
out the <a href="https://ci.apache.org/projects/flink/flink-doc [...]
+
+<p><strong>Support for User-defined Table Functions (UDTFs) (<a
href="https://jira.apache.org/jira/browse/FLINK-14500";>FLINK-14500</a>)</strong></p>
+
+<p>From Flink 1.11, you can define and register custom <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/dev/table/python/python_udfs.html#table-functions";>UDTFs</a>
in PyFlink. Similar to a Python UDF, a UDTF takes zero, one or multiple scalar
values as input, but can return an arbitrary number of rows as output instead
of a single value.</p>
+
+<p><strong>Cython Performance Optimization for UDFs (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-121%3A+Support+Cython+Optimizing+Python+User+Defined+Function";>FLIP-121</a>)</strong></p>
+
+<p><a
href="https://cython.readthedocs.io/en/latest/src/quickstart/cythonize.html";>Cython</a>
is a compiled superset of the Python language that is often used to improve
the performance of large-scale numeric processing in Python, as it optimizes
execution to machine code-level speed and pairs well with popular C-based
libraries like NumPy. From Flink 1.11, you can build <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/flinkDev/building.html#build-pyflink";>PyFlink
wi [...]
+
+<p><strong>User-defined Metrics in Python UDFs (<a
href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-112%3A+Support+User-Defined+Metrics+in++Python+UDF";>FLIP-112</a>)</strong></p>
+
+<p>To make it easier for users to monitor and debug the execution of Python
UDFs, PyFlink now allows gathering and exposing metrics to external systems, as
well as defining user scopes and variables. You can access the metrics system
from a UDF by calling <code>function_context.get_metric_group()</code> in the
open method, as described in the <a
href="https://ci.apache.org/projects/flink/flink-docs-master/dev/table/python/metrics.html#registering-metrics";>documentation</a>.</p>
+
+<hr />
+
+<h2 id="important-changes">Important Changes</h2>
+
+<ul>
+ <li>
+ <p>[<a
href="https://jira.apache.org/jira/browse/FLINK-17339";>FLINK-17339</a>] The
Blink planner is the <strong>default</strong> in the Table API/SQL starting
from Flink 1.11. This was already the case for the SQL Client since Flink 1.10.
The old Flink planner is still supported, but not actively developed.</p>
+ </li>
+ <li>
+ <p>[<a
href="https://issues.apache.org/jira/browse/FLINK-5763";>FLINK-5763</a>]
Savepoints now contain all their state inside a single directory (both metadata
and program state). This makes it straightforward to figure out which files
make up the state of a savepoint and allows users to <strong>relocate
savepoints</strong> by simply moving a directory.</p>
+ </li>
+ <li>
+ <p>[<a
href="https://issues.apache.org/jira/browse/FLINK-16408";>FLINK-16408</a>] To
reduce pressure on the JVM metaspace, the user code class loader is being
reused by a <code>TaskExecutor</code> as long as there is at least a single
slot allocated for the respective job. This changes Flink’s recovery behaviour
slightly, so that it will not reload static fields.</p>
+ </li>
+ <li>
+ <p>[<a
href="https://issues.apache.org/jira/browse/FLINK-11086";>FLINK-11086</a>] Flink
now supports Hadoop versions above <strong>Hadoop 3.0.0</strong>. Note that the
Flink project does not provide any updated “flink-shaded-hadoop-*” jars. Users
need to provide Hadoop dependencies through the <code>HADOOP_CLASSPATH</code>
environment variable (recommended) or the lib/ folder.</p>
+ </li>
+ <li>
+ <p>[<a
href="https://issues.apache.org/jira/browse/FLINK-16963";>FLINK-16963</a>] All
<code>MetricReporters</code> that come with Flink have been converted to
plugins. These should no longer be placed into <code>/lib</code> (which may
result in dependency conflicts), but
<code>/plugins/<some_directory></code> instead.</p>
+ </li>
+ <li>
+ <p>[<a
href="https://issues.apache.org/jira/browse/FLINK-12639";>FLINK-12639</a>] The
Flink <strong>documentation</strong> is undergoing some
<strong>rework</strong>, so you might notice that the navigation and
organization of content look slightly different starting from Flink 1.11.</p>
+ </li>
+</ul>
+
+<h2 id="release-notes">Release Notes</h2>
+
+<p>Please review the <a
href="https://ci.apache.org/projects/flink/flink-docs-release-1.11/release-notes/flink-1.11.html";>release
notes</a> carefully for a detailed list of changes and new features if you
plan to upgrade your setup to Flink 1.11. This version is API-compatible with
previous 1.x releases for APIs annotated with the @Public annotation.</p>
+
+<h2 id="list-of-contributors">List of Contributors</h2>
+
+<p>The Apache Flink community would like to thank all the 200+ contributors
that have made this release possible:</p>
+
+<p>Aitozi, Alexander Fedulov, Alexey Trenikhin, Aljoscha Krettek, Andrey
Zagrebin, Arvid Heise, Ayush Saxena, Bairos, Bartosz Krasinski, Benchao Li,
Benoit Hanotte, Benoît Paris, Bhagavan Das, Canbin Zheng, Cedric Chen, Chesnay
Schepler, Colm O hEigeartaigh, Congxian Qiu, CrazyTomatoOo, Danish Amjad, Danny
Chan, David Anderson, Dawid Wysakowicz, Dian Fu, Dominik Wosiński, Echo Lee,
Ethan Marsh, Etienne Chauchot, Fabian Hueske, Fabian Paul, Flavio Pompermaier,
Gao Yun, Gary Yao, Ghildiyal [...]
+
+ </article>
+ </div>
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