infoverload commented on a change in pull request #468:
URL: https://github.com/apache/flink-web/pull/468#discussion_r716860510
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File path: _posts/2021-09-21-release-1.14.0.md
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+---
+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.
+
+There are many more improvements and new additions throughout various
components, as we discuss below.
+We also had to say goodbye to some features that have been superceded by newer
ones in recent releases,
+most prominently we are **removing the old SQL execution engine**.
+
+We hope you like the new release and we'd be eager to learn about your
experience with it, which yet
+unsolved problems it solves, what new use-cases it unlocks for you.
+
+{% toc %}
+
+# Unified Batch and Stream Processing experience
+
+One of Flink's unique characteristics is how it integrates streaming and batch
processing,
+using common unified APIs, and a runtime that supports multiple execution
paradigms.
+
+As motivated in the introduction, we believe Streaming and Batch always go
hand in hand. This quote from
+a [report on Facebook's streaming
infrastructure](https://research.fb.com/wp-content/uploads/2016/11/realtime_data_processing_at_facebook.pdf)
+echos this sentiment nicely.
+
+> Streaming versus batch processing is not an either/or decision. Originally,
all data warehouse
+> processing at Facebook was batch processing. We began developing Puma and
Swift about five years
+> ago. As we showed in Section [...], using a mix of streaming and batch
processing can speed up
+> long pipelines by hours.
+
+Having both the real-time and the historic computations in the same engine
also ensures consistency
+between semantics and makes results well comparable. Here is an [article by
Alibaba](https://www.ververica.com/blog/apache-flinks-stream-batch-unification-powers-alibabas-11.11-in-2020)
+about unifying business reporting with Apache Flink and getting consistent
reports that way.
+
+While unified streaming & batch are already possible in earlier versions, this
release bring a
+some features that unlock new use cases, as well as a series of
quality-of-life improvements.
+
+## Checkpointing and Bounded Streams
+
+Flink's checkpointing mechanism could originally only draw checkpoints when
all tasks in an application's
+DAG were running. That means that mixed bounded/unbounded applications were
not really possible.
+In addition, applications on bounded inputs that were executed in a streaming
way (not in a batch way)
+stopped checkpointing towards the end, when some tasks finished. Lingering
data for exactly-once
+sinks was the result, because in the absence of checkpoints, the latest output
data was not committed.
+
+With
[FLIP-147](https://cwiki.apache.org/confluence/display/FLINK/FLIP-147%3A+Support+Checkpoints+After+Tasks+Finished)
+Flink now supports checkpoints after tasks are finished, and takes a final
checkpoint at the end of a
+bounded stream, ensuring that all sink data is committed before the job ends
(similar to how
+*stop-with-savepoint* behaves).
+
+To activate this feature, add
`execution.checkpointing.checkpoints-after-tasks-finish.enabled: true`
+to your configuration. Keeping with the tradition of making big new features
opt-in for the first release,
+the feature is not active by default in Flink 1.14. We expect it to become the
default in the next release.
+
+As a side note: Why would we even want to execute applications over bounded
streams in a streaming way,
+rather than in a batch-y way? There can be many reasons, like (a) the sink
that you write to needs to be
+written to in a streaming way (like the Kafka Sink), or (b) that you do want
to exploit the
+streaming-inherent quasi-ordering-by-time, such as in the [Kappa+
Architecture](https://youtu.be/4qSlsYogALo?t=666).
+
+## Mixing Batch DataStream API and Table API/SQL
+
+SQL and the Table API are becoming the default starting points for new
projects. The declarative
+nature and richness of built-in types and operations make it easy to develop
applications fast.
+It is not uncommon, though, for developers to eventually hit the limit of
SQL's expressiveness for
+certain types of event-driven business logic (or hit the point when it becomes
grotesque to express
+that logic in SQL).
+
+At that point, the natural step is to blend in a piece of stateful DataStream
API logic, before
+switching back to SQL again.
+
+In Flink 1.14, bounded batch-executed SQL/Table programs can convert their
intermediate
+Tables to a DataStream, apply some DataSteam API operations, and convert it
back to a Table. Flink builds
+a dataflow DAG mixing declarative optimized SQL execution with batch-executed
DataStream logic.
+Check out the [relevant
docs](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/data_stream_api/#converting-between-datastream-and-table)
for details.
Review comment:
```suggestion
## Mixing the DataStream API and the SQL/Table API
SQL and the Table API are becoming the default starting points for new
projects. The declarative
nature and richness of built-in types and operations make it easy to develop
applications fast.
It is not uncommon, however, for developers to eventually hit the limit of
SQL's expressiveness for
certain types of event-driven business logic (or hit the point when it
becomes tricky to express
that logic in SQL). At that point, the natural step is to blend in some
stateful DataStream API logic before
switching back to SQL again.
In Flink 1.14, bounded batch-executed SQL/Table API programs can convert
their intermediate
Tables to a DataStream, apply some DataStream API operations, and convert it
back to a Table. Under the hood, Flink builds a dataflow DAG that mixes
declarative optimized SQL execution with batch-executed DataStream logic.
Check out the
[documentation](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/data_stream_api/#converting-between-datastream-and-table)
for more details.
```
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