This is an automated email from the ASF dual-hosted git repository.
knaufk pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/flink-web.git
commit 4ed5d4caf7c089145384157915b3ee071e424a47
Author: Konstantin Knauf <kkn...@confluent.io>
AuthorDate: Fri Oct 6 13:59:05 2023 +0200
add announcement blog post for Flink 1.18
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+---
+authors:
+- JingGe:
+ name: "Jing Ge"
+ twitter: jingengineer
+- KonstantinKnauf:
+ name: "Konstantin Knauf"
+ twitter: snntrable
+- SergeyNuyanzin:
+ name: "Sergey Nuyanzin"
+ twitter: uckamello
+- QingshengRen:
+ name: "Qingsheng Ren"
+ twitter: renqstuite
+date: "2023-10-24T22:00:00Z"
+subtitle: ""
+title: Announcing the Release of Apache Flink 1.18
+aliases:
+- /news/2023/10/10/release-1.18.0.html
+---
+
+The Apache Flink PMC is pleased to announce the release of Apache Flink
1.18.0. As usual, we are looking at a packed
+release with a wide variety of improvements and new features. Overall, 174
people contributed to this release completing
+18 FLIPS and 700+ issues. Thank you!
+
+Let's dive into the highlights.
+
+# Towards a Streaming Lakehouse
+
+## Flink SQL Improvements
+
+### Introduce Flink JDBC Driver For SQL Gateway
+
+Flink 1.18 comes with a JDBC Driver for the Flink SQL Gateway. So, you can now
use any SQL Client that supports JDBC to
+interact with your tables via Flink SQL. Here is an example using
[SQLLine](https://julianhyde.github.io/sqlline/manual.html).
+
+```shell
+sqlline> !connect jdbc:flink://localhost:8083
+```
+
+```shell
+sqlline version 1.12.0
+sqlline> !connect jdbc:flink://localhost:8083
+Enter username for jdbc:flink://localhost:8083:
+Enter password for jdbc:flink://localhost:8083:
+0: jdbc:flink://localhost:8083> CREATE TABLE T(
+. . . . . . . . . . . . . . .)> a INT,
+. . . . . . . . . . . . . . .)> b VARCHAR(10)
+. . . . . . . . . . . . . . .)> ) WITH (
+. . . . . . . . . . . . . . .)> 'connector' = 'filesystem',
+. . . . . . . . . . . . . . .)> 'path' = 'file:///tmp/T.csv',
+. . . . . . . . . . . . . . .)> 'format' = 'csv'
+. . . . . . . . . . . . . . .)> );
+No rows affected (0.122 seconds)
+0: jdbc:flink://localhost:8083> INSERT INTO T VALUES (1, 'Hi'), (2, 'Hello');
++----------------------------------+
+| job id |
++----------------------------------+
+| fbade1ab4450fc57ebd5269fdf60dcfd |
++----------------------------------+
+1 row selected (1.282 seconds)
+0: jdbc:flink://localhost:8083> SELECT * FROM T;
++---+-------+
+| a | b |
++---+-------+
+| 1 | Hi |
+| 2 | Hello |
++---+-------+
+2 rows selected (1.955 seconds)
+0: jdbc:flink://localhost:8083>
+```
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/jdbcdriver/)
+* [FLIP-293: Introduce Flink Jdbc Driver For SQL
Gateway](https://cwiki.apache.org/confluence/display/FLINK/FLIP-293%3A+Introduce+Flink+Jdbc+Driver+For+Sql+Gateway)
+
+
+### Stored Procedure Support for Flink Connectors
+
+Stored procedures have been an indispensable tool in traditional databases,
+offering a convenient way to encapsulate complex logic for data manipulation
+and administrative tasks. They also offer the potential for enhanced
+performance, since they can trigger the handling of data operations directly
+within an external database. Other popular data systems like Trino and Iceberg
+automate and simplify common maintenance tasks into small sets of procedures,
+which greatly reduces users' administrative burden.
+
+This new update primarily targets developers of Flink connectors, who can now
+predefine custom stored procedures into connectors via the Catalog interface.
+The primary benefit to users is that connector-specific tasks that previously
+may have required writing custom Flink code can now be replaced with simple
+calls that encapsulate, standardize, and potentially optimize the underlying
+operations. Users can execute procedures using the familiar `CALL` syntax, and
+discover a connector's available procedures with `SHOW PROCEDURES`. Stored
+procedures within connectors improves the extensibility of Flink's SQL and
+Table APIs, and should unlock smoother data access and management for users.
+
+Users can use `CALL` to directly call built-in stored procedures provided by
their catalog. For the built-in
+stored procedures in catalog, please refer to the documentation of the
corresponding catalog. For example, when using
+the Apache Paimon catalog, you can use a stored procedure to trigger
compaction for a table.
+
+```sql
+CREATE TABLE `paimon`.`default`.`T` (
+id BIGINT PRIMARY KEY NOT ENFORCED,
+dt STRING, -- format 'yyyy-MM-dd'
+v STRING
+);
+
+-- use catalog before call procedures
+USE CATALOG `paimon`;
+
+-- compact the whole table using call statement
+CALL sys.compact('default.T');
+```
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/procedures/)
+* [FLIP-311: Support Call Stored
Procedure](https://cwiki.apache.org/confluence/display/FLINK/FLIP-311%3A+Support+Call+Stored+Procedure)
+
+### Extended DDL Support
+
+From this release onwards, Flink supports
+
+- `REPLACE TABLE AS SELECT`
+- `CREATE OR REPLACE TABLE AS SELECT`
+
+and both these commands and previously supported `CREATE TABLE AS` can now
support atomicity provided the underlying
+connector also supports this.
+
+Moreover, Apache Flink now supports TRUNCATE TABLE in batch execution mode.
Same as before, the underlying connector needs
+to implement and provide this capability
+
+And, finally, we have also implemented support for adding, dropping and
listing partitions via
+
+- `ALTER TABLE ADD PARTITION`
+- `ALTER TABLE DROP PARTITION`
+- `SHOW PARTITIONS`
+
+**More Information**
+- [Documentation on
TRUNCATE](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/sql/truncate/)
+- [Documentation on CREATE OR
REPLACE](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/sql/create/#create-or-replace-table)
+- [Documentation on ALTER
TABLE](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/sql/alter/#alter-table)
+- [FLIP-302: Support TRUNCATE TABLE statement in batch
mode](https://cwiki.apache.org/confluence/display/FLINK/FLIP-302%3A+Support+TRUNCATE+TABLE+statement+in+batch+mode)
+- [FLIP-303: Support REPLACE TABLE AS SELECT
statement](https://cwiki.apache.org/confluence/display/FLINK/FLIP-303%3A+Support+REPLACE+TABLE+AS+SELECT+statement)
+- [FLIP-305: Support atomic for CREATE TABLE AS SELECT(CTAS)
statement](https://cwiki.apache.org/confluence/display/FLINK/FLIP-305%3A+Support+atomic+for+CREATE+TABLE+AS+SELECT%28CTAS%29+statement)
+
+### Time Traveling
+
+Flink supports the time travel SQL syntax for querying historical versions of
data that allows users to specify a point
+in time and retrieve the data and schema of a table as it appeared at that
time. With time travel, users can easily
+analyze and compare historical versions of data.
+
+For example, a user can query a table at a specified point in time with the
following statement;
+
+```sql
+-- Query the table `tb` for data on November 11, 2022
+SELECT * FROM tb FOR SYSTEM_TIME AS OF TIMESTAMP '2022-11-11 00:00:00';
+```
+
+**More information**
+-
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/sql/queries/time-travel/)
+- [FLIP-308: Support Time
Travel](https://cwiki.apache.org/confluence/display/FLINK/FLIP-308%3A+Support+Time+Travel)
+
+## Streaming Execution Improvements
+
+### Support Operator-Level State TTL in Table API & SQL
+
+Starting from Flink 1.18, Table API and SQL users can set state time-to-live
(TTL) individually for stateful operators.
+This means that for scenarios like stream regular joins, users can now set
different TTLs for the left and right
+streams. In previous versions, state expiration could only be controlled at
the pipeline level using the configuration
+`table.exec.state.ttl`. With the introduction of operator-level state
retention, users can now optimize resource
+usage according to their specific requirements.
+
+**More Information**
+-
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/concepts/overview/#configure-operator-level-state-ttl)
+- [FLIP-292: Enhance COMPILED PLAN to support operator-level state TTL
configuration](https://cwiki.apache.org/confluence/display/FLINK/FLIP-292%3A+Enhance+COMPILED+PLAN+to+support+operator-level+state+TTL+configuration)
+
+### Watermark Alignment and Idleness Detection in SQL
+
+You can now configure [watermark
alignment](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/datastream/event-time/generating_watermarks/#watermark-alignment)
+and [source idleness
timeouts](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/datastream/event-time/generating_watermarks/#dealing-with-idle-sources)
+in [pure SQL via
hints](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/concepts/time_attributes/#advanced-watermark-features).
Previously, these features were only available in the DataStream API.
+
+**More Information**
+-
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/table/sql/queries/hints/)
+- [FLIP-296: Extend watermark-related features for
SQL](https://cwiki.apache.org/confluence/display/FLINK/FLIP-296%3A+Extend+watermark-related+features+for+SQL)
+
+## Batch Execution Improvements
+
+### Hybrid Shuffle supports Remote Storage
+
+Hybrid Shuffle supports storing the shuffle data in remote storage. The remote
storage path can be configured with the
+option `taskmanager.network.hybrid-shuffle.remote.path`. Hybrid Shuffle uses
less network memory than before by
+decoupling the memory usage from the number of parallelisms, improving the
stability and ease of use.
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/ops/batch/batch_shuffle/#hybrid-shuffle)
+* [FLIP-301: Hybrid Shuffle supports Remote
Storage](https://cwiki.apache.org/confluence/display/FLINK/FLIP-301%3A+Hybrid+Shuffle+supports+Remote+Storage)
+
+### Performance Improvements & TPC-DS Benchmark
+
+In previous releases, the community worked extensively to improve Flink's
batch processing performance, which has led to
+significant improvements. In this release cycle, community contributors
continued to put significant effort into
+further improving Flink's batch performance.
+
+#### Runtime Filter for Flink SQL
+
+Runtime filter is a common method for optimizing Join performance. It is
designed to dynamically generate filter
+conditions for certain Join queries at runtime to reduce the amount of scanned
or shuffled data, avoid unnecessary I/O
+and network transmission, and speed up the query. We introduced runtime
filters in Flink 1.18, and verified its
+effectiveness through the TPC-DS benchmark, and observed up to 3x speedup for
some queries by enabling this feature.
+
+#### Operator Fusion Codegen for Flink SQL
+
+Operator Fusion Codegen improves the execution performance of a query by
fusing an operator DAG into a single optimized
+operator that eliminates virtual function calls, leverages CPU registers for
intermediate data and reduces the
+instruction cache miss. As a general technical optimization, we verified its
effectiveness through TPC-DS, and
+only some batch operators (Calc, HashAgg, and HashJoin) completed fusion
codegen support in version 1.18, getting
+significant performance gains on some query.
+
+Note that both features are disabled by default in Flink 1.18 and the
Community is looking for feedback by users before
+enabling them by default. They can be enabled by using
`table.optimizer.runtime-filter.enabled` and
+`table.exec.operator-fusion-codegen.enabled` respectively.
+
+Since Flink 1.16, the Apache Flink Community has been continuously tracking
the performance of its batch engine via the
+TPC-DS benchmarking framework. After significant improvements in Flink 1.17
(dynamic join-reordering,
+dynamic local aggregations), the two improvements described in the previous
sections (operator fusion, runtime filters)
+lead to 14% performance improvement compared to Flink 1.17, a 54% performance
improvement compared to Flink 1.16 on a
+10T dataset for partitioned tables.
+
+<div style="text-align: center;">
+<img src="/img/blog/2023-10-24-release-1.18.0/tpc-ds-benchmark.png"
style="width:90%;margin:15px">
+</div>
+
+**More Information**
+* [FLIP-324: Introduce Runtime Filter for Flink Batch
Jobs](https://cwiki.apache.org/confluence/display/FLINK/FLIP-324%3A+Introduce+Runtime+Filter+for+Flink+Batch+Jobs)
+* [FLIP-315: Support Operator Fusion Codegen for Flink
SQL](https://cwiki.apache.org/confluence/display/FLINK/FLIP-315+Support+Operator+Fusion+Codegen+for+Flink+SQL)
+* [Benchmarking repository](https://github.com/ververica/flink-sql-benchmark)
+
+# Towards Cloud-Native Elasticity
+
+Elasticity describes the ability of a system to adapt to workload changes in a
non-disruptive, ideally automatic manner.
+It is a defining characteristic of cloud-native systems and for long-running
streaming workloads it is particularly
+important. As such, elasticity improvements are an area of continuous
investment in the Apache Flink community.
+Recent initiatives include the Kubernetes
+[Autoscaler](https://nightlies.apache.org/flink/flink-kubernetes-operator-docs-release-1.6/docs/custom-resource/autoscaler/),
+numerous improvements to rescaling performance and last but not least
+the [Adaptive
Scheduler](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/deployment/elastic_scaling/#adaptive-scheduler).
+
+The Adaptive Scheduler was first introduced in Flink 1.15 and constitutes a
centerpiece of a fully-elastic
+Apache Flink deployment. At its core, it allows jobs to change their resource
requirements and parallelism during
+runtime. In addition, it also adapts to the available resources in the cluster
by only rescaling once the cluster can
+satisfy the minimum required resources of the job.
+
+Until Flink 1.18, the adaptive scheduler was primarily used in
+[Reactive
Mode](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/deployment/elastic_scaling/#reactive-mode),
+which meant that a single job by design would always use all the available
resources in the cluster.
+Please see [this blog
post](https://flink.apache.org/2021/05/06/scaling-flink-automatically-with-reactive-mode/)
on
+how to autoscale Flink Jobs in Reactive Mode using a Horizontal Pod Autoscaler
on Kubernetes.
+
+With Flink 1.18 the adaptive scheduler becomes much more powerful and more
widely applicable and is on a trajectory to
+becoming the default scheduler for streaming workloads on Apache Flink.
+
+## Dynamic Fine-Grained Rescaling via REST API
+
+Despite the underlying capabilities of the Adaptive Scheduler, the ability to
change the resource requirements of a
+Job during runtime has not yet been exposed to the end user directly. This
changes in Flink 1.18. You can now change
+the parallelism of any individual task of your job via the Flink Web UI and
REST API while the job is running.
+
+{{< youtube B1NVDTazsZY >}}
+
+Under the hood, Apache Flink performs a regular rescaling operation as soon as
the required resources for the new
+parallelism have been acquired. The rescaling operation is not based on a
Savepoint, but on an ordinary, periodic
+checkpoint, which means it does not introduce any additional snapshot. As you
can see in the video above, the rescaling
+operation already happens nearly instantaneously and with a very short
downtime for jobs with small state size.
+
+In conjunction with the
+[backpressure
monitor](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/ops/monitoring/back_pressure/)
+of the Apache Flink Web UI, it is now easier than ever to find and maintain an
efficient, backpressure-free parallelism
+for each of the tasks:
+
+- If a task is very busy (red), you increase the parallelism.
+- If a task is mostly idle (blue), you decrease the parallelism.
+
+<div style="text-align: center;">
+<img src="/img/blog/2023-10-24-release-1.18.0/backpressure_monitor.png"
style="width:90%;margin:15px">
+</div>
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/deployment/elastic_scaling/#adaptive-scheduler)
+* [FLIP-291: Externalized Declarative Resource
Management](https://cwiki.apache.org/confluence/display/FLINK/FLIP-291%3A+Externalized+Declarative+Resource+Management)
+
+## Faster Rescaling with RocksDB
+
+The rescaling times when using RocksDB Statebackend with incremental
checkpoints have been improved about 30% in the 99th quantile.
+
+We increased the potential for parallel download from just downloading state
handles in parallel to downloading individual files in parallel.
+
+Furthermore, we deactivated write-ahead-logging for batch-inserting into the
temporary RocksDB instances we use for rescaling.
+
+<div style="text-align: center;">
+<img src="/img/blog/2023-10-24-release-1.18.0/rescaling_performance.png"
style="width:90%;margin:15px">
+</div>
+
+**More Information**
+* [FLINK-32326](https://issues.apache.org/jira/browse/FLINK-32326)
+* [FLINK-32345](https://issues.apache.org/jira/browse/FLINK-32345)
+
+# Support for Java 17
+
+Java 17 was released in 2021 and is the latest long-term support (LTS) release
of Java with an end-of-life in 2029.
+So, it was about time that Apache Flink added support for it. What does this
mean concretely? As of Flink 1.18, you can
+now run Apache Flink on Java 17 and the [official Docker
repository](https://hub.docker.com/_/flink) includes an image
+based on Java 17.
+
+ docker pull flink:1.18.0-java17
+
+If your cluster runs on Java 17, this of course, also allows you to use Java
17 features in your user programs and to
+compile it to a target version of Java 17.
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/deployment/java_compatibility/)
+* [FLINK-15736](https://issues.apache.org/jira/browse/FLINK-15736)
+
+# Others Improvements
+
+## Production-Ready Watermark Alignment
+
+Supported as “Beta” since Flink 1.16 and Flink 1.17,
+[watermark
alignment](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/datastream/event-time/generating_watermarks/#watermark-alignment)
+has been thoroughly tested at scale in the real world. Over that time the
community has collected and addressed
+bugs and performance issues as they were discovered. With the resolution of
these issues, we are now happy to recommend
+watermark alignment for general use.
+
+**More Information**
+* [FLINK-32548](https://issues.apache.org/jira/browse/FLINK-32548)
+
+## Pluggable Failure Handling
+
+Apache Flink serves as the foundation for numerous stream processing platforms
at companies like Apple, Netflix or Uber. It is also the basis for various
commercial stream processing services. Therefore, its ability to easily
integrate into the wider ecosystem of these internal as well as vendor
platforms becomes increasingly important. The catalog modification listener and
pluggable failure handlers fall into this category of improvements.
+
+**More Information**
+*
[Documentation](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/deployment/advanced/failure_enrichers/)
+* [FLIP-304: Pluggable Failure
Enrichers](https://cwiki.apache.org/confluence/display/FLINK/FLIP-304%3A+Pluggable+Failure+Enrichers)
+
+## SQL Client Quality of Life Improvements
+
+In 1.18 the SQL Client received a collection of usability improvements:
+* The SQL Client is now more colorful with the ability to enable SQL syntax
highlighting and switching among 7 different
+color schemes
+* It is now easier to edit and navigate through very large queries.
+* It is now possible to turn line numbers off and on.
+
+More Information
+* [FLIP-189: SQL Client Usability
Improvements](https://cwiki.apache.org/confluence/display/FLINK/FLIP-189%3A+SQL+Client+Usability+Improvements)
+
+## Apache Pekko instead of Akka
+
+A year ago, [Lightbend
announced](https://flink.apache.org/2022/09/08/regarding-akkas-licensing-change/)
changing the
+license of future versions of Akka (2.7+) from Apache 2.0 to BSL. It was also
announced that Akka 2.6, the version that
+Apache Flink uses, would receive security updates and critical bug fixes until
September of 2023. As September 2023 was
+approaching, we decided to switch from Akka to [Apache
Pekko](https://pekko.apache.org/) (incubating).
+Apache Pekko (incubating) is a fork of Akka 2.6.x, prior to the Akka project’s
adoption of the Business Source License.
+Pekko recently released Apache Pekko 1.0.1-incubating, which enabled us to
already use it in Flink 1.18 - just in time.
+While our mid-term plan is to drop the dependency on Akka or Pekko altogether
+(see [FLINK-29281](https://issues.apache.org/jira/browse/FLINK-29281)), the
switch to Pekko presents a good short-term
+solution and ensures that the Apache Pekko and Apache Flink Community can
address critical bug fixes and security
+vulnerabilities throughout our software supply chain.
+
+**More Information**
+* [FLINK-32468](https://issues.apache.org/jira/browse/FLINK-32468)
+
+## Calcite Upgrade(s)
+
+In Apache Flink 1.18, Apache Calcite was gradually upgraded from 1.29 to 1.32.
The immediate benefit of these upgrades
+are bug fixes, a smarter optimizer and performance improvements. On a parser
level, it now allows joins to be
+grouped into trees using parentheses (mentioned in SQL-92) e.g. `SELECT * FROM
a JOIN (b JOIN c ON b.x = c.x) ON a.y = c.y`
+also see [CALCITE-35](https://issues.apache.org/jira/browse/CALCITE-35). In
addition, the upgrade to Calcite 1.31+ has
+unblocked the support of Session Windows via Table-Valued Functions (see
+[CALCITE-4865](https://issues.apache.org/jira/browse/CALCITE-4865),
+[FLINK-24024](https://issues.apache.org/jira/browse/FLINK-24024)) and as a
corollary the deprecation of the legacy
+group window aggregations.
+Due to [CALCITE-4861](https://issues.apache.org/jira/browse/CALCITE-4861)
Flink's casting behavior has slightly changed.
+Some corner cases might behave differently now: For example, casting from
FLOAT/DOUBLE 9234567891.12 to INT/BIGINT has
+now Java behavior for overflows.
+
+**More Information**
+* [FLINK-27998](https://issues.apache.org/jira/browse/FLINK-27998)
+* [FLINK-28744](https://issues.apache.org/jira/browse/FLINK-28744)
+* [FLINK-29319](https://issues.apache.org/jira/browse/FLINK-29319)
+
+# Important Deprecations
+
+In preparation for the release of Flink 2.0 next year, the community has
decided to officially deprecate multiple APIs
+that were approaching end of life for a while.
+
+*
[``SourceFunction``](https://github.com/apache/flink/blob/master/flink-streaming-java/src/main/java/org/apache/flink/streaming/api/functions/source/SourceFunction.java)
+ is now officially deprecated and will be dropped in Flink 2.0. If you are
still using a connector that is built on
+ top of ``SourceFunction`` please migrate it to
+
[``Source``](https://github.com/apache/flink/blob/master/flink-core/src/main/java/org/apache/flink/api/connector/source/Source.java).
+ ``SinkFunction`` is not officially deprecated, but it is also approaching
end-of-life and will be superseded by
+ ``SinkV2``.
+* [Queryable
State](https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/dev/datastream/fault-tolerance/queryable_state/)
+ is now officially deprecated and will be dropped in Flink 2.0.
+* [The DataSet
API](https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/dev/dataset/overview/)
is now
+ officially deprecated. Users are recommended to migrate to the DataStream
API with
+ [execution
mode](https://nightlies.apache.org/flink/flink-docs-release-1.18/docs/dev/datastream/execution_mode/)
``BATCH``.
+
+# Upgrade Notes
+
+The Flink community tries to ensure that upgrades are as seamless as possible.
+However, certain changes may require users to make adjustments to certain parts
+of the program when upgrading to version 1.18. Please refer to the
+[release
notes](https://nightlies.apache.org/flink/flink-docs-release-1.18/release-notes/flink-1.18/)
+for a comprehensive list of adjustments to make and issues to check during the
+upgrading process.
+
+
+# List of Contributors
+
+The Apache Flink community would like to express gratitude to all the
+contributors who made this release possible:
+
+Aitozi,
+Akinfolami Akin-Alamu,
+Alain Brown,
+Aleksandr Pilipenko,
+Alexander Fedulov,
+Anton Kalashnikov,
+Archit Goyal,
+Bangui Dunn,
+Benchao Li,
+BoYiZhang,
+Chesnay Schepler,
+Chris Nauroth,
+Colten Pilgreen,
+Danny Cranmer,
+David Christle,
+David Moravek,
+Dawid Wysakowicz,
+Deepyaman Datta,
+Dian Fu,
+Dian Qi,
+Dong Lin,
+Eric Xiao,
+Etienne Chauchot,
+Feng Jin,
+Ferenc Csaky,
+Fruzsina Nagy,
+Gabor Somogyi,
+Gunnar Morling,
+Gyula Fora,
+HaiYang Chen,
+Hang Ruan,
+Hangxiang Yu,
+Hanyu Zheng,
+Hong Liang Teoh,
+Hongshun Wang,
+Huston,
+Jacky Lau,
+James Hughes,
+Jane Chan,
+Jark Wu,
+Jayadeep Jayaraman,
+Jia Liu,
+JiangXin,
+Joao Boto,
+Junrui Lee,
+Juntao Hu,
+K.I. (Dennis) Jung,
+Kaiqi Dong,
+L,
+Leomax_Sun,
+Leonard Xu,
+Licho,
+Lijie Wang,
+Liu Jiangang,
+Lyn Zhang,
+Maomao Min,
+Martijn Visser,
+Marton Balassi,
+Mason Chen,
+Matthew de Detrich,
+Matthias Pohl,
+Min,
+Mingliang Liu,
+Mohsen Rezaei,
+Mrart,
+Mulavar,
+Nicholas Jiang,
+Nicolas Fraison,
+Noah,
+Panagiotis Garefalakis,
+Patrick Lucas,
+Paul Lin,
+Peter Vary,
+Piotr Nowojski,
+Qingsheng Ren,
+Ran Tao,
+Rich Bowen,
+Robert Metzger,
+Roc Marshal,
+Roman Khachatryan,
+Ron,
+Rui Fan,
+Ryan Skraba,
+Samrat002,
+Sergey Nuyanzin,
+Sergio Morales,
+Shammon FY,
+ShammonFY,
+Shengkai,
+Shuiqiang Chen,
+Stefan Richter,
+Tartarus0zm,
+Timo Walther,
+Tzu-Li (Gordon) Tai,
+Venkata krishnan Sowrirajan,
+Wang FeiFan,
+Weihua Hu,
+Weijie Guo,
+Wencong Liu,
+Xiaogang Zhou,
+Xintong Song,
+XuShuai,
+Yanfei Lei,
+Yu Chen,
+Yubin Li,
+Yun Gao,
+Yun Tang,
+Yuxin Tan,
+Zakelly,
+Zhanghao Chen,
+ZhengYiWeng,
+Zhu Zhu,
+archzi,
+baiwuchang,
+cailiuyang,
+chenyuzhi,
+darenwkt,
+dongwoo kim,
+eason.qin,
+felixzh,
+fengli,
+frankeshi,
+fredia,
+godfrey he,
+haishui,
+hehuiyuan,
+huangxingbo,
+jiangxin,
+jiaoqingbo,
+jinfeng,
+jingge,
+kevin.cyj,
+kristoffSC,
+leixin,
+leiyanfei,
+liming.1018,
+lincoln lee,
+lincoln.lil,
+liujiangang,
+liuyongvs,
+luoyuxia,
+maigeiye,
+mas-chen,
+novakov-alexey,
+oleksandr.nitavskyi,
+pegasas,
+sammieliu,
+shammon,
+shammon FY,
+shuiqiangchen,
+slfan1989,
+sunxia,
+tison,
+tsreaper,
+wangfeifan,
+wangkang,
+whjshj,
+wuqqq,
+xiangyu0xf,
+xincheng.ljr,
+xmzhou,
+xuyu,
+xzw,
+yuanweining,
+yuchengxin,
+yunfengzhou-hub,
+yunhong,
+yuxia Luo,
+yuxiqian,
+zekai-li,
+zhangmang,
+zhengyunhong.zyh,
+zzzzzzzs,
+沈嘉琦
\ No newline at end of file
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