infoverload commented on a change in pull request #468:
URL: https://github.com/apache/flink-web/pull/468#discussion_r716741399
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File path: _posts/2021-09-21-release-1.14.0.md
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@@ -0,0 +1,274 @@
+---
+layout: post
+title: "Apache Flink 1.14.0 Release Announcement"
+date: 2021-09-21T08:00:00.000Z
+categories: news
+authors:
+- joemoe:
+ name: "Johannes Moser"
+
+excerpt: The Apache Flink community is excited to announce the release of
Flink 1.14.0! Around xxx contributors worked on over xxxx issues to TODO.
+---
+
+Just a couple of days ago the Apache Software Foundation announced its annual
report and Apache
+Flink was again in the Top 5 of the most active projects in all relevant
categories. This remarkable
+activity is also reflected in this new 1.14.0 release. Once again, more than
200 contributors worked on
+over 1,000 issues. We are proud of how this community is consistently moving
the project forward.
+
+The release brings many cool improvements, from SQL to connectors,
checkpointing, and PyFlink.
+A big area of changes in this release is the integrated streaming & batch
experience. We believe
+that unbounded stream processing goes hand-in-hand with bounded- and batch
processing tasks in practice.
+Data exploration when developing new applications, bootstrapping state for new
applications, training
+models to be applied in the streaming application, re-processing data after
fixes/upgrades, and many
+other use cases require processing historic data from various sources next to
the streaming data.
+
+In Flink 1.14, we finally made it possible to **mix bounded and unbounded
streams in an application**;
+Flink can now take checkpoints of applications that is partially running and
partially finished (some
+operators reached the end of the bounded inputs). Additionally, **bounded
streams now take a final checkpoint**
+when reaching their end to ensure smooth committing of results in sinks.
+The **batch execution mode now works for programs that mix DataStream &
Table/SQL** (previously only
+pure Table/SQL or DataStream programs). The unified Source and Sink APIs have
made strides ahead,
+we started **consolidating the connector ecosystem around the unified APIs**,
and added a **hybrid source**
+that can bridge between multiple storage systems, like start reading old data
from S3 and switch over
+to Kafka later.
+
+Furthermore, this release takes another step in our initiative of making Flink
more self-tuning and
+to require less Stream-Processor-specific knowledge to operate. We started
that initiative in the previous
+release with [Reactive
Scaling](/news/2021/05/03/release-1.13.0.html#reactive-scaling) and are now
+adding **automatic network memory tuning** (*a.k.a. Buffer Debloating*). This
feature speeds up checkpoints
+under load without sacrificing performance or increasing checkpoint size, by
continuously adjusting the
+network buffering to ensure best throughput while having minimal in-flight
data. See the
+[Buffer Debloating section](#buffer-debloating) for details.
Review comment:
```suggestion
In Flink 1.14, it is now possible to **mix bounded and unbounded streams in
an application**.
To achieve this, Flink now takes checkpoints of applications that are
partially running and partially finished (when some operators reach the end of
the bounded inputs). Additionally, **bounded streams now take a final
checkpoint** when reaching their end to ensure smooth result commits to sinks.
The **batch execution mode now works for programs that use a mixture of the
DataStream API and the Table & SQL APIs**.
The unified Data Source and Data Sink APIs have also been greatly improved.
We started **consolidating the connector ecosystem around the unified APIs**
and added a **hybrid source** that can bridge multiple storage systems. You
can now do things like read old data from Amazon S3 and then switch over to
Apache Kafka.
In addition, this release furthers our initiative in making Flink more
self-tuning and user-friendly. We aim to bring Flink to a wider audience and
make it easier to operate without necessarily requiring a lot of knowledge in
stream processing. We started this initiative in the previous release with
[reactive scaling](/news/2021/05/03/release-1.13.0.html#reactive-scaling) and
are now adding **automatic network memory tuning** (*a.k.a. Buffer
Debloating*). This feature speeds up checkpoints under high load without
sacrificing performance or increasing checkpoint size by continuously adjusting
network buffers to ensure the best throughput while having minimal in-flight
data. See the [Buffer Debloating section](#buffer-debloating) for more details.
```
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