This is an automated email from the ASF dual-hosted git repository.
dwysakowicz pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git
commit 672b57a73162fe69277eb08977e3d5f7868affe8
Author: Stephan Ewen <se...@apache.org>
AuthorDate: Fri Sep 10 09:02:07 2021 +0200
Add Apache Flink release 1.14.0 Blog post and update links and scripts.
This closes #468
Co-authored-by: Joe Moser <j...@ververica.com>
Co-authored-by: Daisy T <dais...@gmx.com>
---
_config.yml | 61 ++--
_posts/2021-09-29-release-1.14.0.md | 359 +++++++++++++++++++++
.../buffer_debloating.png | Bin 0 -> 141847 bytes
.../fine_grained_resource_management.png | Bin 0 -> 94168 bytes
.../fine_grained_resource_management.svg | 261 +++++++++++++++
.../2021-09-25-release-1.14.0/hybrid_source.png | Bin 0 -> 61706 bytes
q/gradle-quickstart.sh | 2 +-
q/quickstart-SNAPSHOT.sh | 2 +-
q/quickstart-scala-SNAPSHOT.sh | 2 +-
q/quickstart-scala.sh | 2 +-
q/quickstart.sh | 2 +-
q/sbt-quickstart.sh | 4 +-
12 files changed, 655 insertions(+), 40 deletions(-)
diff --git a/_config.yml b/_config.yml
index d2f9029..254f0a7 100644
--- a/_config.yml
+++ b/_config.yml
@@ -56,6 +56,27 @@ FLINK_STATEFUN_GITHUB_REPO_NAME: flink-statefun
flink_releases:
-
+ version_short: "1.14"
+ binary_release:
+ name: "Apache Flink 1.14.0"
+ scala_211:
+ id: "1140-download_211"
+ url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.14.0/flink-1.14.0-bin-scala_2.11.tgz";
+ asc_url:
"https://downloads.apache.org/flink/flink-1.14.0/flink-1.14.0-bin-scala_2.11.tgz.asc";
+ sha512_url:
"https://downloads.apache.org/flink/flink-1.14.0/flink-1.14.0-bin-scala_2.11.tgz.sha512";
+ scala_212:
+ id: "1132-download_212"
+ url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.14.0/flink-1.14.0-bin-scala_2.12.tgz";
+ asc_url:
"https://downloads.apache.org/flink/flink-1.14.0/flink-1.14.0-bin-scala_2.12.tgz.asc";
+ sha512_url:
"https://downloads.apache.org/flink/flink-1.14.0/flink-1.14.0-bin-scala_2.12.tgz.sha512";
+ source_release:
+ name: "Apache Flink 1.14.0"
+ id: "1132-download-source"
+ url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.14.0/flink-1.14.0-src.tgz";
+ asc_url:
"https://downloads.apache.org/flink/flink-1.14.0/flink-1.14.0-src.tgz.asc";
+ sha512_url:
"https://downloads.apache.org/flink/flink-1.14.0/flink-1.14.0-src.tgz.sha512";
+ release_notes_url:
"https://nightlies.apache.org/flink/flink-docs-release-1.14/release-notes/flink-1.14";
+ -
version_short: "1.13"
binary_release:
name: "Apache Flink 1.13.2"
@@ -106,36 +127,6 @@ flink_releases:
url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-avro/1.12.5/flink-avro-1.12.5.jar
asc_url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-avro/1.12.5/flink-avro-1.12.5.jar.asc
sha_url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-avro/1.12.5/flink-avro-1.12.5.jar.sha1
- -
- version_short: "1.11"
- binary_release:
- name: "Apache Flink 1.11.4"
- scala_211:
- id: "1114-download_211"
- url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.11.tgz";
- asc_url:
"https://downloads.apache.org/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.11.tgz.asc";
- sha512_url:
"https://downloads.apache.org/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.11.tgz.sha512";
- scala_212:
- id: "1114-download_212"
- url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.12.tgz";
- asc_url:
"https://downloads.apache.org/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.12.tgz.asc";
- sha512_url:
"https://downloads.apache.org/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.12.tgz.sha512";
- source_release:
- name: "Apache Flink 1.11.4"
- id: "1114-download-source"
- url:
"https://www.apache.org/dyn/closer.lua/flink/flink-1.11.4/flink-1.11.4-src.tgz";
- asc_url:
"https://downloads.apache.org/flink/flink-1.11.4/flink-1.11.4-src.tgz.asc";
- sha512_url:
"https://downloads.apache.org/flink/flink-1.11.4/flink-1.11.4-src.tgz.sha512";
- release_notes_url:
"https://nightlies.apache.org/flink/flink-docs-release-1.11/release-notes/flink-1.11.html";
- optional_components:
- -
- name: "Avro SQL Format"
- category: "SQL Formats"
- scala_dependent: false
- id: 1114-sql-format-avro
- url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-avro/1.11.4/flink-avro-1.11.4.jar
- asc_url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-avro/1.11.4/flink-avro-1.11.4.jar.asc
- sha_url:
https://repo.maven.apache.org/maven2/org/apache/flink/flink-avro/1.11.4/flink-avro-1.11.4.jar.sha1
flink_statefun_releases:
-
@@ -184,6 +175,10 @@ component_releases:
release_archive:
flink:
-
+ version_short: "1.14"
+ version_long: 1.14.0
+ release_date: 2021-09-29
+ -
version_short: "1.13"
version_long: 1.13.2
release_date: 2021-08-02
@@ -533,10 +528,10 @@ release_archive:
# Version numbers used in the text for stable and snapshot versions,
# e.g. "Documentation for {{ site.stable }".
-stable: "1.13"
-snapshot: "1.14"
+stable: "1.14"
+snapshot: "1.15"
-docs-stable: "https://nightlies.apache.org/flink/flink-docs-release-1.13";
+docs-stable: "https://nightlies.apache.org/flink/flink-docs-release-1.14";
docs-snapshot: "https://nightlies.apache.org/flink/flink-docs-master";
docs-statefun-stable:
"https://nightlies.apache.org/flink/flink-statefun-docs-release-3.1";
diff --git a/_posts/2021-09-29-release-1.14.0.md
b/_posts/2021-09-29-release-1.14.0.md
new file mode 100644
index 0000000..af93b52
--- /dev/null
+++ b/_posts/2021-09-29-release-1.14.0.md
@@ -0,0 +1,359 @@
+---
+layout: post
+title: "Apache Flink 1.14.0 Release Announcement"
+date: 2021-09-29T08:00:00.000Z
+categories: news
+authors:
+- stephan:
+ name: "Stephan Ewen"
+ twitter: "StephanEwen"
+- joemoe:
+ name: "Johannes Moser"
+
+excerpt: The Apache Flink community is excited to announce the release of
Flink 1.14.0! More than 200 contributor worked on over 1,000 issues. The
release brings exciting new features like a more seamless streaming/batch
integration, automatic network memory tuning, a hybrid source to switch data
streams between storgage systems (e.g., Kafka/S3), Fine-grained resource
management, PyFlink performance and debugging enhancements, and a Pulsar
connector.
+
+---
+
+The Apache Software Foundation recently released its annual report and Apache
Flink once again made
+it on the list of the top 5 most active projects! This remarkable
+activity also shows in the 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.
+
+This release brings many new features and improvements in areas such as the
SQL API, more connector support, checkpointing, and PyFlink.
+A major area of changes in this release is the integrated streaming & batch
experience. We believe
+that, in practice, unbounded stream processing goes hand-in-hand with bounded-
and batch processing tasks in practice,
+because many use cases require processing historic data from various sources
alongside streaming data.
+Examples are data exploration when developing new applications, bootstrapping
state for new applications, training
+models to be applied in a streaming application, re-processing data after
fixes/upgrades, and .
+
+In Flink 1.14, we finally made it possible to **mix bounded and unbounded
streams in an application**:
+Flink now supports taking checkpoints of applications that are 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 supports programs that use a mixture of the
DataStream API and the SQL/Table API**
+(previously only pure Table/SQL or DataStream programs).
+
+The unified Source and Sink APIs have gotten an update, and we started
**consolidating the connector ecosystem around the unified APIs**. We added a
new **hybrid source** can bridge between 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
+easier to operate, without necessarily requiring a lot of
Stream-Processor-specific knowledge.
+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 while maintaining high
throughput and without
+increasing checkpoint size. The mechanism continuously adjusts the network
buffers to ensure the best
+throughput while having minimal in-flight data. See the [Buffer Debloating
section](#buffer-debloating)
+for more 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** and are
+**removing the active integration with Apache Mesos**.
+
+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 %}
+
+# The Unified Batch and Stream Processing Experience
+
+One of Flink's unique characteristics is how it integrates stream- and batch
processing,
+using unified APIs and a runtime that supports multiple execution paradigms.
+
+As motivated in the introduction, we believe that stream- and batch processing
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 brings
+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 create checkpoints when
all tasks in an application's
+DAG were running. This meant that applications using both bounded and
unbounded data sources 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 of the processing, when some tasks
finished. Without checkpoints, the
+latest output data was not committed, resulting in lingering data for
exactly-once sinks.
+
+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 opt-in tradition for big and new
features,
+this is not activated by default in Flink 1.14. We expect it to become the
default mode in the next release.
+
+Background: While the batch execution mode is often the preferrable way to run
applications over bounded streams,
+there are various reasons to use streaming execution mode over bounded
streams. For example, the sink being used
+might only support streaming execution (i.e. Kafka sink) or you may want to
exploit the streaming-inherent
+quasi-ordering-by-time in your application, such as motivated by the [Kappa+
Architecture](https://youtu.be/4qSlsYogALo?t=666).
+
+## Batch Execution for mixed DataStream and Table/SQL Applications
+
+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
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.
+Under the hood, Flink builds a dataflow DAG mixing 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 details.
+
+## Hybrid Source
+
+The new [Hybrid
Source](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/datastream/hybridsource/)
+produces a combined stream from multiple sources, by reading those sources one
after the other,
+seamlessly switching over from one source to the other.
+
+The motivating use case for the Hybrid Source was to read streams from tiered
storage setups as if there was one
+stream that spans all tiers. For example, new data may land in Kafa and is
eventually
+migrated to S3 (typically in compressed columnar format, for cost efficiency
and performance).
+The Hybrid Source can read this as one contiguous logical stream, starting
with the the historic data on S3
+and transitioning over to the more recent data in Kafka.
+
+<figure style="align-content: center">
+ <img src="{{ site.baseurl
}}/img/blog/2021-09-25-release-1.14.0/hybrid_source.png" style="display: block;
margin-left: auto; margin-right: auto; width: 600px"/>
+</figure>
+
+We believe that this is an exciting step in realizing the full promise of logs
and the *Kappa Architecture.*
+Even if older parts of an event log are physically migrated to different
storage
+(for reasons such as cost, better compression, faster reads) you can still
treat and process it as one
+contiguous log.
+
+Flink 1.14 adds the core functionality of the Hybrid Source. Over the next
releases, we expect to add more
+utilities and patterns for typical switching strategies.
+
+## Consolidating Sources and Sink
+
+With the new unified (streaming/batch) source and sink APIs now being stable,
we started the
+big effort to consolidate all connectors around those APIs. At the same time,
we are
+better aligning connectors between DataStream and SQL/Table API. First are the
*Kafka* and
+*File* Soures and Sinks for the DataStream API.
+
+The result of this effort (that we expect to span at least 1-2 futher
releases) will be a much
+smoother and more consistent experience for Flink users when connecting to
external systems.
+
+
+# Improvements to Operations
+
+## Buffer debloating
+
+*Buffer Debloating* is a new technology in Flink that minimizes checkpoint
latency and cost.
+It does so by automatically tuning the usage of network memory to ensure high
throughput,
+while minimizing the amount of in-flight data.
+
+Apache Flink buffers a certain amount of data in its network stack to be able
to utilize the
+bandwidth of fast networks. A Flink application running with high throughput
uses some (or
+all) of that memory. Aligned checkpoints flow with the data through the
network buffers in milliseconds.
+
+When a Flink application becomes (temporarily) backpressured (for example when
being backpressured
+by an external system, or when hitting skewed records), this typically results
in a lot more data in
+the network buffers than is necessary utilize enough network bandwith for the
application's current throughput
+(which is lowered due to backpressure). There is even an adverse effect: more
buffered data means
+the checkpoints need to do more work. Aligned checkpoint barriers need to wait
for more data to be
+processed, unaligned checkpoints need to persist more in-flight data.
+
+This is where *Buffer Debloating* comes into play: It changes the network
stack from keeping up to X bytes of data
+to keeping data that is worth X milliseconds of receiver computing time. With
the default setting
+of 1000 milliseconds, that means the network stack will buffer as much data as
the receiving task can
+process in 1000 milliseconds. These values are constantly measured and
adjusted, so the system keeps
+this characteristic even under varying conditions. As a result, Flink can now
provide
+stable and predictable alignment times for aligned checkpoints under
backpressure, and can vastly
+reduce the amount of in-flight data stored in unaliged checkpoints under
backpressure.
+
+<figure style="align-content: center">
+ <img src="{{ site.baseurl
}}/img/blog/2021-09-25-release-1.14.0/buffer_debloating.png" style="display:
block; margin-left: auto; margin-right: auto; width: 600px"/>
+</figure>
+
+Buffer Deloating acts as a complementary feature, or even alternative, to
unaligned checkpoints.
+Checkout the
[documentation](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/deployment/memory/network_mem_tuning/#the-buffer-debloating-mechanism)
+to see how to activate this feature.
+
+## Fine-grained Resource Management
+
+*Fine-grained resource management* is an advanced new feature that increases
the resource
+utilization of large shared clusters.
+
+Flink clusters execute various data processing workloads. Different data
processing steps typically need
+different resources such as compute resources and memory. For example, most
`map()` functions are fairly
+lightweight, but large windows with long retention can benefit from lots of
memory.
+By default, Flink manages resources in coarse-grained units called *slots*,
which are slices
+of a TaskManager's resources. Streaming pipelines fill a slot with one parallel
+subtask of each operator, so each slot holds a pipeline of subtasks.
+Through *'slot sharing groups'*, users can influence how subtasks are assigned
to slots.
+
+With fine-grained resource management, TaskManager slots can now be
dynamically sized.
+Transformations and operators specify what resource profiles they would like
(CPU size,
+memory pools, disk space) and Flink's Resource Manager and TaskManagers slice
off that specific
+part of a TaskManager's total resources. You can think of it as a minimal
lightweight resource orchestration
+layer within Flink. The figure below illustrates the difference between the
current default mode of shared
+fixed-size slots and the new fine-grained resource management feature.
+
+<figure style="align-content: center">
+ <img src="{{ site.baseurl
}}/img/blog/2021-09-25-release-1.14.0/fine_grained_resource_management.svg"
style="display: block; margin-left: auto; margin-right: auto; width: 600px"/>
+</figure>
+
+You may be wondering why we implement such a feature in Flink, when we also
integrate with full-fledged
+resource orchestration frameworks like Kubernetes or YARN. There are several
situations where the additional
+resource management layer within Flink significantly increases the resource
utilization:
+
+ - For many small slots, the overhead of dedicated TaskManagers is very high
(JVM overhead, Flink control data structures).
+ Slot-sharing implicitly works around this by sharing the slots between all
operator types, which means
+ sharing resources between lightweight operators (which need small slots)
and heavyweight operators (which need large slots).
+ However, this only works well when all operators share the same
parallelism, which is not aways optiomal.
+ Furthermore, certain operators work better when run in isolation (for
example ML training operators
+ that need dedicated GPU resources).
+ - Kubernetes and YARN often take quite some time to fulfill requests,
especially on loaded clusters.
+ For many batch jobs, efficiency gets lost while waiting for the requests
to be fulfilled.
+
+So when should you use this feature? For most streaming and batch jobs the
default resource management mechanism
+are perfectly suitable. Fine-grained resourced management can help you
increase resource efficiency if you have either long-running
+streaming jobs, or fast batch jobs, where different stages have different
resource requirements, and you may
+have already tuned the parallelism of different operators to different values.
+
+Alibaba's internal Flink-based platform has used this mechanism for some time
now and the resource utilization
+of the cluster has significantly increased.
+
+Please refer to the [Fine-grained Resource Management
documentation](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/deployment/finegrained_resource/)
+for details on how to use this feature.
+
+
+# Connectors
+
+## Connector Metrics
+
+Metrics for connectors have been standardized in this release (see
[FLIP-33](https://cwiki.apache.org/confluence/display/FLINK/FLIP-33%3A+Standardize+Connector+Metrics)).
+The community will gradually pull metrics through all connectors, as we rework
them
+onto the new unified APIs over the next releases. In Flink 1.14, we cover the
Kafka connector
+and (partially) the FileSystem connectors.
+
+Connectors are the entry and exit points for data in a Flink job. If a job is
not running as
+expected, the connector telemetry is among the first parts to be checked. We
believe this will become
+a nice improvement when operating Flink applications in production.
+
+## Pulsar Connector
+
+In this release, Flink added the [Apache Pulsar](https://pulsar.apache.org/)
connector.
+The Pulsar connector reads data from Pulsar topics and supports both streaming
and batch execution modes.
+With the support of the transaction functionality (introduced in Pulsar
2.8.0), the Pulsar connector provides
+exactly-once delivery semantic to ensure that a message is delivered exactly
once to the consumer,
+even if a producer retries sending the message.
+
+To support the different message-ordering and scaling requirements of
different use cases, the Pulsar
+source connector exposes four subscription types:
+ -
[Exclusive](https://pulsar.apache.org/docs/en/concepts-messaging/#exclusive)
+ - [Shared](https://pulsar.apache.org/docs/en/concepts-messaging/#shared)
+ - [Failover](https://pulsar.apache.org/docs/en/concepts-messaging/#failover)
+ -
[Key-Shared](https://pulsar.apache.org/docs/en/concepts-messaging/#key_shared)
+
+The connector currently supports the DataStream API. Table API/SQL bindings
are expected to be
+contributed in a future release. For details about how to use the Pulsar
connector, see
+[Apache Pulsar
Connector](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/datastream/pulsar/#apache-pulsar-connector).
+
+
+# PyFlink
+
+## Performance Improvements through Chaining
+
+Similar to how the Java APIs chain transformation functions/operators within a
task to avoid
+serialization overhead, PyFlink now chains Python functions. In PyFlink's
case, the
+chaining not only eliminates serialization overhead, but also reduces RPC
round trips
+between the Java and Python processes. This provides a significant
+boost to PyFlink's overall performance.
+
+Python function chaining was already available for Python UDFs used in the
Table API & SQL.
+In Flink 1.14, chaining is also exploited for the cPython functions in Python
DataStream API.
+
+## Loopback Mode for Debugging
+
+Python functions are normally executed in a separate Python process next to
Flink's JVM.
+This architecture makes it difficult to debug Python code.
+
+PyFlink 1.14 introduces a *loopback mode*, which is activated by default for
local deployments.
+In this mode, user-defined Python functions will be executed in the Python
process of the client,
+which is the entry point process that starts the PyFlink program and contains
the DataStream API and
+Table API code that builds the dataflow DAG. Users can now easily debug their
Python functions
+by setting breakpoints in their IDEs when launching a PyFlink job locally.
+
+## Miscellaneous Improvements
+
+There are also many other improvements to PyFlink, such as support for
executing
+jobs in YARN application mode and support for compressed tgz files as Python
archives.
+Check out the [Python API
documentation](https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/python/overview/)
+for more details.
+
+
+# Goodbye Legacy SQL Engine and Mesos Support
+
+Maintaining an open source project also means sometimes saying good-bye to
some beloved features.
+
+When we added the Blink SQL Engine to Flink more than two years ago, it was
clear that it would
+eventually replace the previous SQL engine. Blink was faster and more
feature-complete.
+For a year now, Blink has been the default SQL engine. With Flink 1.14 we
finally remove all
+code from the previous SQL engine. This allowed us to drop many outdated
interfaces and reduce
+confusion for users about which interfaces to use when implementing custom
connectors or functions.
+It will also help us in the future to make faster changes to the SQL engine.
+
+The active integration with Apache Mesos was also removed, because we saw
little interest by
+users in this feature and we could not gather enough contributors willing to
help maintaining this
+part of the system. Flink 1.14 can no longer run on Mesos without the help of
projects like Marathon,
+and the Flink Resource Manager can no longer request and release resources
from Mesos for workloads
+with changing changing resource requirements.
+
+
+# Upgrade Notes
+
+While we aim to make upgrades as smooth as possible, some of the changes
require users
+to adjust some parts of the program when upgrading to Apache Flink 1.14.
+Please take a look at the [release
notes](https://nightlies.apache.org/flink/flink-docs-release-1.14/release-notes/flink-1.14/)
+for a list of adjustemnts to make and issues to check during upgrades.
+
+
+# List of Contributors
+
+The Apache Flink community would like to thank each one of the contributors
that have made this
+release possible:
+
+adavis9592, Ada Wong, aidenma, Aitozi, Ankush Khanna, anton, Anton
Kalashnikov, Arvid Heise, Ashwin
+Kolhatkar, Authuir, bgeng777, Brian Zhou, camile.sing, caoyingjie, Cemre
Mengu, chennuo, Chesnay
+Schepler, chuixue, CodeCooker17, comsir, Daisy T, Danny Cranmer, David
Anderson, David Moravek,
+Dawid Wysakowicz, dbgp2021, Dian Fu, Dong Lin, Edmondsky, Elphas Toringepi,
Emre Kartoglu, ericliuk,
+Eron Wright, est08zw, Etienne Chauchot, Fabian Paul, fangliang, fangyue1,
fengli, Francesco
+Guardiani, FuyaoLi2017, fuyli, Gabor Somogyi, gaoyajun02, Gen Luo,
gentlewangyu, GitHub, godfrey he,
+godfreyhe, gongzhongqiang, Guokuai Huang, GuoWei Ma, Gyula Fora, hackergin,
hameizi, Hang Ruan, Han
+Wei, hapihu, hehuiyuan, hstdream, Huachao Mao, HuangXiao, huangxingbo,
huxixiang, Ingo Bürk,
+Jacklee, Jan Brusch, Jane, Jane Chan, Jark Wu, JasonLee, Jiajie Zhong,
Jiangjie (Becket) Qin,
+Jianzhang Chen, Jiayi Liao, Jing, Jingsong Lee, JingsongLi, Jing Zhang,
jinxing64, junfan.zhang, Jun
+Qin, Jun Zhang, kanata163, Kevin Bohinski, kevin.cyj, Kevin Fan, Kurt Young,
kylewang, Lars
+Bachmann, lbb, LB Yu, LB-Yu, LeeJiangchuan, Leeviiii, leiyanfei, Leonard Xu,
LightGHLi, Lijie Wang,
+liliwei, lincoln lee, Linyu, liuyanpunk, lixiaobao14, luoyuxia, Lyn Zhang,
lys0716, MaChengLong,
+mans2singh, Marios Trivyzas, martijnvisser, Matthias Pohl, Mayi, mayue.fight,
Michael Li, Michal
+Ciesielczyk, Mika, Mika Naylor, MikuSugar, movesan, Mulan, Nico Kruber,
Nicolas Raga, Nicolaus
+Weidner, paul8263, Paul Lin, pierre xiong, Piotr Nowojski, Qingsheng Ren,
Rainie Li, Robert Metzger,
+Roc Marshal, Roman, Roman Khachatryan, Rui Li, sammieliu, sasukerui, Senbin
Lin, Senhong Liu, Serhat
+Soydan, Seth Wiesman, sharkdtu, Shengkai, Shen Zhu, shizhengchao, Shuo Cheng,
shuo.cs, simenliuxing,
+sjwiesman, Srinivasulu Punuru, Stefan Gloutnikov, SteNicholas, Stephan Ewen,
sujun, sv3ndk, Svend
+Vanderveken, syhily, Tartarus0zm, Terry Wang, Thesharing, Thomas Weise,
tiegen, Till Rohrmann, Timo
+Walther, tison, Tony Wei, trushev, tsreaper, TsReaper, Tzu-Li (Gordon) Tai,
wangfeifan, wangwei1025,
+wangxianghu, wangyang0918, weizheng92, Wenhao Ji, Wenlong Lyu, wenqiao,
WilliamSong11, wuren,
+wysstartgo, Xintong Song, yanchenyun, yangminghua, yangqu, Yang Wang, Yangyang
ZHANG, Yangze Guo,
+Yao Zhang, yfhanfei, yiksanchan, Yik San Chan, Yi Tang, yljee, Youngwoo Kim,
Yuan Mei, Yubin Li,
+Yufan Sheng, yulei0824, Yun Gao, Yun Tang, yuxia Luo, Zakelly, zhang chaoming,
zhangjunfan,
+zhangmang, zhangzhengqi3, zhao_wei_nan, zhaown, zhaoxing, ZhiJie Yang, Zhilong
Hong, Zhiwen Sun, Zhu
+Zhu, zlzhang0122, zoran, Zor X. LIU, zoucao, Zsombor Chikan, 子扬, 莫辞
\ No newline at end of file
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fill="black" x="5.394016" y="15">B</tspan>
+ </text>
+ </g>
+ <g id="Graphic_182">
+ <rect x="987.0148" y="933.8964" width="161.5748" height="207.77474"
fill="white"/>
+ <rect x="987.0148" y="933.8964" width="161.5748" height="207.77474"
stroke="gray" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ <text transform="translate(992.0148 938.8964)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" font-weight="400"
fill="black" x="0" y="15">TaskManager</tspan>
+ </text>
+ </g>
+ <g id="Group_179">
+ <g id="Graphic_181">
+ <rect x="1000.8337" y="964.3689" width="60.94488" height="165.96371"
fill="#ccc"/>
+ <rect x="1000.8337" y="964.3689" width="60.94488" height="165.96371"
stroke="gray" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ </g>
+ <g id="Graphic_180">
+ <rect x="1073.8258" y="964.3689" width="60.94488" height="165.96371"
fill="#ccc"/>
+ <rect x="1073.8258" y="964.3689" width="60.94488" height="165.96371"
stroke="gray" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ </g>
+ </g>
+ <g id="Graphic_178">
+ <circle cx="1030.9518" cy="996.9673" r="19.8425515714737"
fill="#ffffc0"/>
+ <circle cx="1030.9518" cy="996.9673" r="19.8425515714737"
stroke="black" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ <text transform="translate(1020.0778 987.7433)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" font-weight="400"
fill="black" x="5.690016" y="15">A</tspan>
+ </text>
+ </g>
+ <g id="Graphic_177">
+ <circle cx="1105.3613" cy="996.9673" r="19.8425515714737"
fill="#ffffc0"/>
+ <circle cx="1105.3613" cy="996.9673" r="19.8425515714737"
stroke="black" stroke-linecap="round" stroke-linejoin="round" stroke-width="1"/>
+ <text transform="translate(1094.4873 987.7433)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" font-weight="400"
fill="black" x="5.690016" y="15">A</tspan>
+ </text>
+ </g>
+ <g id="Graphic_183">
+ <text transform="translate(880.0463 910.4484)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" font-weight="400"
fill="black" x="5115908e-19" y="15">Coarse-Grained (Share Slot)</tspan>
+ </text>
+ </g>
+ <g id="Graphic_185">
+ <text transform="translate(564.90426 910.4484)" fill="black">
+ <tspan font-family="Helvetica Neue" font-size="16" font-weight="400"
fill="black" x="4405365e-19" y="15">Fine-Grained</tspan>
+ </text>
+ </g>
+ </g>
+ </g>
+</svg>
diff --git a/img/blog/2021-09-25-release-1.14.0/hybrid_source.png
b/img/blog/2021-09-25-release-1.14.0/hybrid_source.png
new file mode 100644
index 0000000..c395e5f
Binary files /dev/null and
b/img/blog/2021-09-25-release-1.14.0/hybrid_source.png differ
diff --git a/q/gradle-quickstart.sh b/q/gradle-quickstart.sh
index efbb33a..d3eba9a 100755
--- a/q/gradle-quickstart.sh
+++ b/q/gradle-quickstart.sh
@@ -41,7 +41,7 @@ function mkPackage() {
defaultProjectName="quickstart"
defaultOrganization="org.myorg.quickstart"
defaultVersion="0.1-SNAPSHOT"
-defaultFlinkVersion="${1:-1.13.2}"
+defaultFlinkVersion="${1:-1.14.0}"
defaultScalaBinaryVersion="${2:-2.11}"
echo "This script creates a Flink project using Java and Gradle."
diff --git a/q/quickstart-SNAPSHOT.sh b/q/quickstart-SNAPSHOT.sh
index e1fb2e2..5701d77 100755
--- a/q/quickstart-SNAPSHOT.sh
+++ b/q/quickstart-SNAPSHOT.sh
@@ -24,7 +24,7 @@ PACKAGE=quickstart
mvn org.apache.maven.plugins:maven-archetype-plugin:2.4:generate \
-DarchetypeGroupId=org.apache.flink
\
-DarchetypeArtifactId=flink-quickstart-java
\
- -DarchetypeVersion=${1:-1.14-SNAPSHOT}
\
+ -DarchetypeVersion=${1:-1.15-SNAPSHOT}
\
-DgroupId=org.myorg.quickstart
\
-DartifactId=$PACKAGE
\
-Dversion=0.1
\
diff --git a/q/quickstart-scala-SNAPSHOT.sh b/q/quickstart-scala-SNAPSHOT.sh
index f751811..eba658c 100755
--- a/q/quickstart-scala-SNAPSHOT.sh
+++ b/q/quickstart-scala-SNAPSHOT.sh
@@ -24,7 +24,7 @@ PACKAGE=quickstart
mvn org.apache.maven.plugins:maven-archetype-plugin:2.4:generate \
-DarchetypeGroupId=org.apache.flink
\
-DarchetypeArtifactId=flink-quickstart-scala
\
- -DarchetypeVersion=${1:-1.14-SNAPSHOT}
\
+ -DarchetypeVersion=${1:-1.15-SNAPSHOT}
\
-DgroupId=org.myorg.quickstart
\
-DartifactId=$PACKAGE
\
-Dversion=0.1
\
diff --git a/q/quickstart-scala.sh b/q/quickstart-scala.sh
index 2eea1b3..11a5dcd 100755
--- a/q/quickstart-scala.sh
+++ b/q/quickstart-scala.sh
@@ -24,7 +24,7 @@ PACKAGE=quickstart
mvn archetype:generate
\
-DarchetypeGroupId=org.apache.flink \
-DarchetypeArtifactId=flink-quickstart-scala \
- -DarchetypeVersion=${1:-1.13.2}
\
+ -DarchetypeVersion=${1:-1.14.0}
\
-DgroupId=org.myorg.quickstart \
-DartifactId=$PACKAGE
\
-Dversion=0.1
\
diff --git a/q/quickstart.sh b/q/quickstart.sh
index 018a58f..0bf3c47 100755
--- a/q/quickstart.sh
+++ b/q/quickstart.sh
@@ -24,7 +24,7 @@ PACKAGE=quickstart
mvn archetype:generate
\
-DarchetypeGroupId=org.apache.flink \
-DarchetypeArtifactId=flink-quickstart-java \
- -DarchetypeVersion=${1:-1.13.2}
\
+ -DarchetypeVersion=${1:-1.14.0}
\
-DgroupId=org.myorg.quickstart \
-DartifactId=$PACKAGE
\
-Dversion=0.1
\
diff --git a/q/sbt-quickstart.sh b/q/sbt-quickstart.sh
index cf03a60..78a4c24 100755
--- a/q/sbt-quickstart.sh
+++ b/q/sbt-quickstart.sh
@@ -42,7 +42,7 @@ defaultProjectName="Flink Project"
defaultOrganization="org.example"
defaultVersion="0.1-SNAPSHOT"
defaultScalaVersion="2.12.7"
-defaultFlinkVersion="1.13.2"
+defaultFlinkVersion="1.14.0"
echo "This script creates a Flink project using Scala and SBT."
@@ -105,7 +105,7 @@ organization := \"org.example\"
ThisBuild / scalaVersion := \"2.11.12\"
-val flinkVersion = \"1.13.2\"
+val flinkVersion = \"1.14.0\"
val flinkDependencies = Seq(
\"org.apache.flink\" %% \"flink-scala\" % flinkVersion % \"provided\",
