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dwysakowicz pushed a commit to branch release-1.14
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The following commit(s) were added to refs/heads/release-1.14 by this push:
new 30d7200 [release] Create 1.14 release-notes
30d7200 is described below
commit 30d7200c96eefb7728018d0550dad2d96f1d7472
Author: Dawid Wysakowicz <dwysakow...@apache.org>
AuthorDate: Tue Sep 7 11:58:18 2021 +0200
[release] Create 1.14 release-notes
This closes #17182
---
docs/content.zh/_index.md | 2 +-
docs/content.zh/release-notes/flink-1.14.md | 473 ++++++++++++++++++++++++++++
docs/content/_index.md | 2 +-
docs/content/release-notes/flink-1.14.md | 473 ++++++++++++++++++++++++++++
4 files changed, 948 insertions(+), 2 deletions(-)
diff --git a/docs/content.zh/_index.md b/docs/content.zh/_index.md
index 782b36a..edf9d57 100644
--- a/docs/content.zh/_index.md
+++ b/docs/content.zh/_index.md
@@ -85,7 +85,7 @@ under the License.
For some reason Hugo will only allow linking to the
release notes if there is a leading '/' and file extension.
-->
-请参阅 [Flink 1.12]({{< ref "/release-notes/flink-1.12.md" >}})，[Flink 1.11]({{<
ref "/release-notes/flink-1.11.md" >}})，[Flink 1.10]({{< ref
"/release-notes/flink-1.10.md" >}})，[Flink 1.9]({{< ref
"/release-notes/flink-1.9.md" >}})，[Flink 1.8]({{< ref
"/release-notes/flink-1.8.md" >}})，或者 [Flink 1.7]({{< ref
"/release-notes/flink-1.7.md" >}}) 的发行说明。
+请参阅 [Flink 1.14]({{< ref "/release-notes/flink-1.14.md" >}}), [Flink 1.13]({{<
ref "/release-notes/flink-1.13.md" >}}), [Flink 1.12]({{< ref
"/release-notes/flink-1.12.md" >}})，[Flink 1.11]({{< ref
"/release-notes/flink-1.11.md" >}})，[Flink 1.10]({{< ref
"/release-notes/flink-1.10.md" >}})，[Flink 1.9]({{< ref
"/release-notes/flink-1.9.md" >}})，[Flink 1.8]({{< ref
"/release-notes/flink-1.8.md" >}})，或者 [Flink 1.7]({{< ref
"/release-notes/flink-1.7.md" >}}) 的发行说明。
{{< /columns >}}
diff --git a/docs/content.zh/release-notes/flink-1.14.md
b/docs/content.zh/release-notes/flink-1.14.md
new file mode 100644
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--- /dev/null
+++ b/docs/content.zh/release-notes/flink-1.14.md
@@ -0,0 +1,473 @@
+---
+title: "Release Notes - Flink 1.14"
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+# Release Notes - Flink 1.14
+
+These release notes discuss important aspects, such as configuration,
behavior, or dependencies,
+that changed between Flink 1.13 and Flink 1.14. Please read these notes
carefully if you are
+planning to upgrade your Flink version to 1.14.
+
+### Known Issues
+
+#### Memory leak with Pulsar connector on Java 11
+
+Netty, that Pulsar client uses underneath, allocates memory differently on
Java 11 and 8. With Java
+11 it will allocate memory from the pool of Java Direct Memory and is affected
by the
+MaxDirectMemory limit. Current Pulsar client has no configuration for
controlling the memory limits,
+which can lead to OOM(s).
+
+Users are advised to use Pulsar connector with Java 8 or overprovision memory
for Flink. See
+[here]({{< ref "docs/deployment/memory/mem_setup_tm" >}}) for documentation
how to configure memory
+for Flink. The proper solution can be tracked in
[FLINK-24302](https://issues.apache.org/jira/browse/FLINK-24302).
+
+### Table & SQL
+
+#### Use pipeline name consistently across DataStream API and Table API
+
+##### [FLINK-23646](https://issues.apache.org/jira/browse/FLINK-23646)
+
+The default job name for DataStream API programs in batch mode has changed
from `"Flink Streaming Job"` to
+`"Flink Batch Job"`. A custom name can be set with config option
`pipeline.name`.
+
+#### Propagate unique keys for fromChangelogStream
+
+##### [FLINK-24033](https://issues.apache.org/jira/browse/FLINK-24033)
+
+Compared to 1.13.2, `StreamTableEnvironment.fromChangelogStream` might produce
a different stream
+because primary keys were not properly considered before.
+
+#### Support new type inference for Table#flatMap
+
+##### [FLINK-16769](https://issues.apache.org/jira/browse/FLINK-16769)
+
+`Table.flatMap()` supports the new type system now. Users are requested to
upgrade their functions.
+
+#### Add Scala implicit conversions for new API methods
+
+##### [FLINK-22590](https://issues.apache.org/jira/browse/FLINK-22590)
+
+The Scala implicits that convert between DataStream API and Table API have
been updated to the new
+methods of FLIP-136.
+
+The changes might require an update of pipelines that used `toTable` or
implicit conversions from
+`Table` to `DataStream[Row]`.
+
+#### Remove YAML environment file support in SQL Client
+
+##### [FLINK-22540](https://issues.apache.org/jira/browse/FLINK-22540)
+
+The sql-client-defaults.yaml YAML file was deprecated in 1.13 release and now
it is completely removed
+in this release. As an alternative, you can use the `-i` startup option to
execute an initialization SQL
+file to setup the SQL Client session. The initialization SQL file can use
Flink DDLs to
+define available catalogs, table sources and sinks, user-defined functions,
and other properties
+required for execution and deployment.
+
+See more:
https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/sqlclient/#initialize-session-using-sql-files
+
+#### Remove the legacy planner code base
+
+##### [FLINK-22864](https://issues.apache.org/jira/browse/FLINK-22864)
+
+The old Table/SQL planner has been removed. BatchTableEnvironment and DataSet
API interop with Table
+API are not supported anymore. Use the unified TableEnvironment for batch and
stream processing with
+the new planner or the DataStream API in batch execution mode.
+
+Users are encouraged to update their pipelines. Otherwise Flink 1.13 is the
last version that offers
+the old functionality.
+
+#### Remove "blink" suffix from table modules
+
+##### [FLINK-22879](https://issues.apache.org/jira/browse/FLINK-22879)
+
+The following Maven modules have been renamed:
+* flink-table-planner-blink -> flink-table-planner
+* flink-table-runtime-blink -> flink-table-runtime
+* flink-table-uber-blink ->flink-table-uber
+
+It might be required to update job JAR dependencies. Note that
+flink-table-planner and flink-table-uber used to contain the legacy planner
before Flink 1.14 and
+now contain the only officially supported planner (i.e. previously known as
'Blink' planner).
+
+#### Remove BatchTableEnvironment and related API classes
+
+##### [FLINK-22877](https://issues.apache.org/jira/browse/FLINK-22877)
+
+Due to the removal of BatchTableEnvironment, BatchTableSource and
BatchTableSink have been removed
+as well. Use DynamicTableSource and DynamicTableSink instead. They support the
old InputFormat and
+OutputFormat interfaces as runtime providers if necessary.
+
+#### Remove TableEnvironment#connect
+
+##### [FLINK-23063](https://issues.apache.org/jira/browse/FLINK-23063)
+
+The deprecated `TableEnvironment#connect()` method has been removed. Use the
+new `TableEnvironment#createTemporaryTable(String, TableDescriptor)` to create
tables
+programatically. Please note that this method only supports sources and sinks
that comply with
+FLIP-95. This is also indicated by the new property design
`'connector'='kafka'` instead of `'connector.type'='kafka'`.
+
+#### Deprecate toAppendStream and toRetractStream
+
+##### [FLINK-23330](https://issues.apache.org/jira/browse/FLINK-23330)
+
+The outdated variants of
`StreamTableEnvironment.{fromDataStream|toAppendStream|toRetractStream)`
+have been deprecated. Use the `(from|to)(Data|Changelog)Stream` alternatives
introduced in 1.13.
+
+#### Remove old connectors and formats stack around descriptors
+
+##### [FLINK-23513](https://issues.apache.org/jira/browse/FLINK-23513)
+
+The legacy versions of the SQL Kafka connector and SQL Elasticsearch connector
have been removed
+together with their corresponding legacy formats. DDL or descriptors that
still use `'connector.type='` or
+`'format.type='` options need to be updated to the new connector and formats
available via the `'connector='` option.
+
+#### Drop BatchTableSource/Sink HBaseTableSource/Sink and related classes
+
+##### [FLINK-22623](https://issues.apache.org/jira/browse/FLINK-22623)
+
+The HBaseTableSource/Sink and related classes including various
HBaseInputFormats and
+HBaseSinkFunction have been removed. It is possible to read via Table & SQL
API and convert the
+Table to DataStream API (or vice versa) if necessary. DataSet API is not
supported anymore.
+
+#### Drop BatchTableSource ParquetTableSource and related classes
+
+##### [FLINK-22622](https://issues.apache.org/jira/browse/FLINK-22622)
+
+The ParquetTableSource and related classes including various
ParquetInputFormats have been removed.
+Use the filesystem connector with a Parquet format as a replacement. It is
possible to read via
+Table & SQL API and convert the Table to DataStream API if necessary. DataSet
API is not supported
+anymore.
+
+#### Drop BatchTableSource OrcTableSource and related classes
+
+##### [FLINK-22620](https://issues.apache.org/jira/browse/FLINK-22620)
+
+The OrcTableSource and related classes (including OrcInputFormat) have been
removed. Use the
+filesystem connector with an ORC format as a replacement. It is possible to
read via Table & SQL API
+and convert the Table to DataStream API if necessary. DataSet API is not
supported anymore.
+
+#### Drop usages of BatchTableEnvironment and old planner in Python
+
+##### [FLINK-22619](https://issues.apache.org/jira/browse/FLINK-22619)
+
+The Python API does not offer a dedicated BatchTableEnvironment anymore.
Instead, users can switch
+to the unified TableEnvironment for both batch and stream processing. Only the
Blink planner (the
+only remaining planner in 1.14) is supported.
+
+#### Migrate ModuleFactory to the new factory stack
+
+##### [FLINK-23720](https://issues.apache.org/jira/browse/FLINK-23720)
+
+The `LOAD/UNLOAD MODULE` architecture for table modules has been updated to
the new factory stack of
+FLIP-95. Users of this feature should update their `ModuleFactory`
implementations.
+
+#### Migrate Table API to new KafkaSink
+
+##### [FLINK-23639](https://issues.apache.org/jira/browse/FLINK-23639)
+
+Table API/SQL write to Kafka with the new `KafkaSink`. When migrating from a
query writing to Kafka
+in exactly-once mode from an earlier Flink version, make sure to terminate the
old application with
+stop-with-savepoint to avoid lingering Kafka transactions. To run in
exactly-once processing mode,
+the sink needs a user-configured and unique transaction prefix, such that
transactions of different
+applications do not interfere with each other.
+
+### DataStream API
+
+#### Allow @TypeInfo annotation on POJO field declarations
+
+##### [FLINK-12141](https://issues.apache.org/jira/browse/FLINK-12141)
+
+`@TypeInfo` annotations can now also be used on POJO fields which, for
example, can help to define
+custom serializers for third-party classes that can otherwise not be annotated
themselves.
+
+#### Clarify SourceFunction#cancel() contract about interrupting
+
+##### [FLINK-23527](https://issues.apache.org/jira/browse/FLINK-23527)
+
+Contract of the `SourceFunction#cancel()` method with respect to interruptions
has been clarified:
+- source itself shouldn’t be interrupting the source thread
+- interrupt shouldn’t be expected in the clean cancellation case
+
+#### Expose a consistent GlobalDataExchangeMode
+
+##### [FLINK-23402](https://issues.apache.org/jira/browse/FLINK-23402)
+
+The default DataStream API shuffle mode for batch executions has been changed
to blocking exchanges
+for all edges of the stream graph. A new option `execution.batch-shuffle-mode`
allows to change it
+to pipelined behavior if necessary.
+
+### Python API
+
+#### Support loopback mode to allow Python UDF worker and client reuse the
same Python VM
+
+##### [FLINK-21222](https://issues.apache.org/jira/browse/FLINK-21222)
+
+Instead of launching a separate Python process, it will reuse the Python
process of the client side
+to execute Python UDFs if you are running jobs locally. This makes it easy for
Python UDFs
+debugging.
+
+#### Support Python UDF chaining in Python DataStream API
+
+##### [FLINK-22913](https://issues.apache.org/jira/browse/FLINK-22913)
+
+The job graph of Python DataStream API jobs may be different from before as
the Python functions
+will be chained as much as possible to optimize the performance. You could
disable Python functions
+chaining by setting `python.operator-chaining.enabled` as `false` explicitly.
+
+### Connectors
+
+#### Implement FLIP-179: Expose Standardized Operator Metrics
+
+##### [FLINK-23652](https://issues.apache.org/jira/browse/FLINK-23652)
+
+Connectors using unified Source and Sink interface expose certain standardized
metrics
+automatically.
+
+#### Port KafkaSink to new Unified Sink API (FLIP-143)
+
+##### [FLINK-22902](https://issues.apache.org/jira/browse/FLINK-22902)
+
+`KafkaSink` supersedes `FlinkKafkaProducer` and provides efficient
exactly-once and at-least-once
+writing with the new unified sink interface supporting both batch and
streaming mode of DataStream
+API. To upgrade, please stop with savepoint. To run in exactly-once processing
mode, `KafkaSink`
+needs a user-configured and unique transaction prefix, such that transactions
of different
+applications do not interfere with each other.
+
+#### Deprecate FlinkKafkaConsumer
+
+##### [FLINK-24055](https://issues.apache.org/jira/browse/FLINK-24055)
+
+`FlinkKafkaConsumer` has been deprecated in favor of `KafkaSource`. To upgrade
to the new version,
+please store the offsets in Kafka with `setCommitOffsetsOnCheckpoints` in the
+old `FlinkKafkaConsumer` and then stop with a savepoint. When resuming from
the savepoint, please
+use `setStartingOffsets(OffsetsInitializer.committedOffsets())` in the new
`KafkaSourceBuilder` to
+transfer the offsets to the new source.
+
+#### InputStatus should not contain END_OF_RECOVERY
+
+##### [FLINK-23474](https://issues.apache.org/jira/browse/FLINK-23474)
+
+`InputStatus.END_OF_RECOVERY` was removed. It was an internal flag that should
never be returned from
+SourceReaders. Returning that value in earlier versions might lead to
misbehaviour.
+
+#### Connector-base exposes dependency to flink-core.
+
+##### [FLINK-22964](https://issues.apache.org/jira/browse/FLINK-22964)
+
+Connectors do not transitively hold a reference to `flink-core` anymore. That
means that a fat jar
+with a connector does not include `flink-core` with this fix.
+
+### Runtime & Coordination
+
+#### Increase akka.ask.timeout for tests using the MiniCluster
+
+##### [FLINK-23906](https://issues.apache.org/jira/browse/FLINK-23906)
+
+The default `akka.ask.timeout` used by the `MiniCluster` has been increased to
5 minutes. If you
+want to use a smaller value, then you have to set it explicitly in the passed
configuration.
+
+The change is due to the fact that messages cannot get lost in a
single-process minicluster, so this
+timeout (which otherwise helps to detect message loss in distributed setups)
has no benefit here.
+
+The increased timeout reduces the number of false-positive timeouts, for
example during heavy tests
+on loaded CI/CD workers or during debugging.
+
+#### The node IP obtained in NodePort mode is a VIP
+
+##### [FLINK-23507](https://issues.apache.org/jira/browse/FLINK-23507)
+
+When using `kubernetes.rest-service.exposed.type=NodePort`, connection string
for Rest gateway is
+now correctly constructed in form of `<nodeIp>:<nodePort>` instead of
+`<kubernetesApiServerUrl>:<nodePort>`. This may be a breaking change for some
users.
+
+This also introduces a new config option
`kubernetes.rest-service.exposed.node-port-address-type` that
+lets you select `<nodeIp>` from a desired range.
+
+#### Timeout heartbeat if the heartbeat target is no longer reachable
+
+##### [FLINK-23209](https://issues.apache.org/jira/browse/FLINK-23209)
+
+Flink now supports to detect dead TaskManagers via the number of consecutive
failed heartbeat RPCs.
+The threshold until a TaskManager is marked as unreachable can be configured
+via `heartbeat.rpc-failure-threshold`. This can speed up the detection of dead
TaskManagers
+significantly.
+
+#### RPCs fail faster when target is unreachable
+
+##### [FLINK-23202](https://issues.apache.org/jira/browse/FLINK-23202)
+
+The same way Flink detects unreachable heartbeat targets faster, Flink now
also immediately fails
+RPCs where the target is known by the OS to be unreachable on a network level,
instead of waiting
+for a timeout (`akka.ask.timeout`).
+
+One effect this are faster task failovers, because cancelling tasks on a dead
TaskExecutor no longer
+gets delayed by the RPC timeout.
+
+If this faster failover is a problem in certain setups (which might rely on
the fast that external
+systems hit timeouts), we recommend configuring the application's restart
strategy with a restart
+delay.
+
+#### Changes in accounting of IOExceptions when triggering checkpoints on
JobManager
+
+##### [FLINK-23189](https://issues.apache.org/jira/browse/FLINK-23189)
+
+In previous versions, IOExceptions thrown from the JobManager, would not fail
the entire Job. We
+changed the way we bookkeep those exceptions and now they do increase the
number of checkpoint
+failures.
+
+The number of tolerable checkpoint failures can be adjusted or disabled via:
+`org.apache.flink.streaming.api.environment.CheckpointConfig#setTolerableCheckpointFailureNumber`
(
+which is set to 0 by default).
+
+#### Refine ShuffleMaster lifecycle management for pluggable shuffle service
framework
+
+##### [FLINK-22910](https://issues.apache.org/jira/browse/FLINK-22910)
+
+We improved the ShuffleMaster interface by adding some lifecycle methods,
including open, close,
+registerJob and unregisterJob. Besides, the ShuffleMaster now becomes a
cluster level service which
+can be shared by multiple jobs. This is a breaking change to the pluggable
shuffle service framework
+and the customized shuffle plugin needs to adapt to the new interface
accordingly.
+
+#### Group job specific ZooKeeper HA services under common jobs/<JobID> zNode
+
+##### [FLINK-22636](https://issues.apache.org/jira/browse/FLINK-22636)
+
+The ZooKeeper job-specific HA services are now grouped under a zNode with the
respective `JobID`.
+Moreover, the config options `high-availability.zookeeper.path.latch`
+, `high-availability.zookeeper.path.leader`,
`high-availability.zookeeper.path.checkpoints`
+and `high-availability.zookeeper.path.checkpoint-counter` have been removed
and, thus, have no
+longer an effect.
+
+#### Fallback value for taskmanager.slot.timeout
+
+##### [FLINK-22002](https://issues.apache.org/jira/browse/FLINK-22002)
+
+The config option `taskmanager.slot.timeout` falls now back to
`akka.ask.timeout` if no value has
+been configured. Previously the default value for `taskmanager.slot.timeout`
was `10 s`.
+
+#### DuplicateJobSubmissionException after JobManager failover
+
+##### [FLINK-21928](https://issues.apache.org/jira/browse/FLINK-21928)
+
+The fix for this problem only works if the ApplicationMode is used with a
single job submission and
+if the user code does not access the `JobExecutionResult`. If any of these
conditions is violated,
+then Flink cannot guarantee that the whole Flink application is executed.
+
+Additionally, it is still required that the user cleans up the corresponding
HA entries for the
+running jobs registry because these entries won't be reliably cleaned up when
encountering the
+situation described by FLINK-21928.
+
+#### Zookeeper node under leader and leaderlatch is not deleted after job
finished
+
+##### [FLINK-20695](https://issues.apache.org/jira/browse/FLINK-20695)
+
+The `HighAvailabilityServices` have received a new method `cleanupJobData`
which can be implemented
+in order to clean up job related HA data after a given job has terminated.
+
+#### Optimize scheduler performance for large-scale jobs
+
+##### [FLINK-21110](https://issues.apache.org/jira/browse/FLINK-21110)
+
+The performance of scheduler has been improved to reduce the time of execution
graph creation,
+task deployment and task failover. This improvement is significant to large
scale jobs which
+currently may spend minutes on the processes mentioned above. This improvement
also helps to avoid
+cases that the job manager main thread gets blocked for too long and leads to
heartbeat timeout.
+
+### Checkpoints
+
+#### The semantic of alignmentTimeout configuration has changed the meaning
+
+##### [FLINK-23041](https://issues.apache.org/jira/browse/FLINK-23041)
+
+The semantic of alignmentTimeout configuration has changed the meaning and now
it's measured as the
+time between the start of a checkpoint(on the checkpoint coordinator) and the
time when the
+checkpoint barrier is received by a task.
+
+#### Disabled unaligned checkpoints for BROADCAST exchanges
+
+##### [FLINK-22815](https://issues.apache.org/jira/browse/FLINK-22815)
+
+Broadcast partitioning can not work with unaligned checkpointing. There are no
guarantees that
+records are consumed at the same rate in all channels. This can result in some
tasks applying state
+changes corresponding to a certain broadcasted event while others don't. In
turn upon restore, it
+may lead to an inconsistent state.
+
+#### DefaultCompletedCheckpointStore drops unrecoverable checkpoints silently
+
+##### [FLINK-22502](https://issues.apache.org/jira/browse/FLINK-22502)
+
+On recovery, if a failure occurs during retrieval of a checkpoint, the job is
restarted (instead of
+skipping the checkpoint in some circumstances). This prevents potential
consistency violations.
+
+#### Removal of CompletedCheckpointRecover#recover() method.
+
+##### [FLINK-22483](https://issues.apache.org/jira/browse/FLINK-22483)
+
+Flink no longer re-loads checkpoint metadata from the external storage before
restoring the task
+state after the failover (except when the JobManager fails over / changes
leadership). This results
+in less external I/O and faster failover.
+
+Please note that this changes a public interfaces around
`CompletedCheckpointStore`, that we allow
+overriding by providing custom implementation of HA Services.
+
+#### Removal of the deprecated CheckpointConfig#setPreferCheckpointForRecovery
method.
+
+##### [FLINK-20427](https://issues.apache.org/jira/browse/FLINK-20427)
+
+Deprecated method CheckpointConfig#setPreferCheckpointForRecovery was removed,
because preferring
+older checkpoints over newer savepoints for recovery can lead to data loss.
+
+### Dependency upgrades
+
+#### Bump up RocksDb version to 6.20.3
+
+##### [FLINK-14482](https://issues.apache.org/jira/browse/FLINK-14482)
+
+RocksDB has been upgraded to 6.20.3. Compared to the previous version, the new
version contains lots
+of bug fixes, ARM platform supported, musl library supported, and more
attractive features. However,
+the new version can entail at most 8% performance regression according to our
tests.
+
+See the corresponding ticket for more information.
+
+#### Support configuration of the RocksDB info logging via configuration
+
+##### [FLINK-23812](https://issues.apache.org/jira/browse/FLINK-23812)
+
+With RocksDB bumped to 6.20.3 (FLINK-14482), you can now also configure a
rolling info logging
+strategy by configuring it accordingly via newly added
state.backend.rocksdb.log.* settings. This
+can be helpful for debugging RocksDB (performance) issues in containerized
environments where the
+local data dir is volatile but the logs should be retained on a separate
volume mount.
+
+#### Make flink-runtime scala-free
+
+##### [FLINK-14105](https://issues.apache.org/jira/browse/FLINK-14105)
+
+Flink's akka dependency is now loaded with a separate classloader and no
longer accessible from the
+outside.
+
+Resulting from that various modules, most prominently flink-runtime, no longer
have a
+scala suffix in their artifactId.
+
+#### Drop Mesos support
+
+##### [FLINK-23118](https://issues.apache.org/jira/browse/FLINK-23118)
+
+The support for Apache Mesos has been removed.
diff --git a/docs/content/_index.md b/docs/content/_index.md
index a610e74..038d804 100644
--- a/docs/content/_index.md
+++ b/docs/content/_index.md
@@ -85,7 +85,7 @@ Release notes cover important changes between Flink versions.
Please read them c
For some reason Hugo will only allow linking to the
release notes if there is a leading '/' and file extension.
-->
-See the release notes for [Flink 1.13]({{< ref "/release-notes/flink-1.13.md"
>}}), [Flink 1.12]({{< ref "/release-notes/flink-1.12.md" >}}), [Flink
1.11]({{< ref "/release-notes/flink-1.11.md" >}}), [Flink 1.10]({{< ref
"/release-notes/flink-1.10.md" >}}), [Flink 1.9]({{< ref
"/release-notes/flink-1.9.md" >}}), [Flink 1.8]({{< ref
"/release-notes/flink-1.8.md" >}}), or [Flink 1.7]({{< ref
"/release-notes/flink-1.7.md" >}}).
+See the release notes for [Flink 1.14]({{< ref "/release-notes/flink-1.14.md"
>}}), [Flink 1.13]({{< ref "/release-notes/flink-1.13.md" >}}), [Flink
1.12]({{< ref "/release-notes/flink-1.12.md" >}}), [Flink 1.11]({{< ref
"/release-notes/flink-1.11.md" >}}), [Flink 1.10]({{< ref
"/release-notes/flink-1.10.md" >}}), [Flink 1.9]({{< ref
"/release-notes/flink-1.9.md" >}}), [Flink 1.8]({{< ref
"/release-notes/flink-1.8.md" >}}), or [Flink 1.7]({{< ref
"/release-notes/flink-1.7.md" >}}).
{{< /columns >}}
diff --git a/docs/content/release-notes/flink-1.14.md
b/docs/content/release-notes/flink-1.14.md
new file mode 100644
index 0000000..b015737
--- /dev/null
+++ b/docs/content/release-notes/flink-1.14.md
@@ -0,0 +1,473 @@
+---
+title: "Release Notes - Flink 1.14"
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+-->
+
+# Release Notes - Flink 1.14
+
+These release notes discuss important aspects, such as configuration,
behavior, or dependencies,
+that changed between Flink 1.13 and Flink 1.14. Please read these notes
carefully if you are
+planning to upgrade your Flink version to 1.14.
+
+### Known Issues
+
+#### Memory leak with Pulsar connector on Java 11
+
+Netty, that Pulsar client uses underneath, allocates memory differently on
Java 11 and 8. With Java
+11 it will allocate memory from the pool of Java Direct Memory and is affected
by the
+MaxDirectMemory limit. Current Pulsar client has no configuration for
controlling the memory limits,
+which can lead to OOM(s).
+
+Users are advised to use Pulsar connector with Java 8 or overprovision memory
for Flink. See
+[here]({{< ref "docs/deployment/memory/mem_setup_tm" >}}) for documentation
how to configure memory
+for Flink. The proper solution can be tracked in
[FLINK-24302](https://issues.apache.org/jira/browse/FLINK-24302).
+
+### Table & SQL
+
+#### Use pipeline name consistently across DataStream API and Table API
+
+##### [FLINK-23646](https://issues.apache.org/jira/browse/FLINK-23646)
+
+The default job name for DataStream API programs in batch mode has changed
from `"Flink Streaming Job"` to
+`"Flink Batch Job"`. A custom name can be set with config option
`pipeline.name`.
+
+#### Propagate unique keys for fromChangelogStream
+
+##### [FLINK-24033](https://issues.apache.org/jira/browse/FLINK-24033)
+
+Compared to 1.13.2, `StreamTableEnvironment.fromChangelogStream` might produce
a different stream
+because primary keys were not properly considered before.
+
+#### Support new type inference for Table#flatMap
+
+##### [FLINK-16769](https://issues.apache.org/jira/browse/FLINK-16769)
+
+`Table.flatMap()` supports the new type system now. Users are requested to
upgrade their functions.
+
+#### Add Scala implicit conversions for new API methods
+
+##### [FLINK-22590](https://issues.apache.org/jira/browse/FLINK-22590)
+
+The Scala implicits that convert between DataStream API and Table API have
been updated to the new
+methods of FLIP-136.
+
+The changes might require an update of pipelines that used `toTable` or
implicit conversions from
+`Table` to `DataStream[Row]`.
+
+#### Remove YAML environment file support in SQL Client
+
+##### [FLINK-22540](https://issues.apache.org/jira/browse/FLINK-22540)
+
+The sql-client-defaults.yaml YAML file was deprecated in 1.13 release and now
it is completely removed
+in this release. As an alternative, you can use the `-i` startup option to
execute an initialization SQL
+file to setup the SQL Client session. The initialization SQL file can use
Flink DDLs to
+define available catalogs, table sources and sinks, user-defined functions,
and other properties
+required for execution and deployment.
+
+See more:
https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/sqlclient/#initialize-session-using-sql-files
+
+#### Remove the legacy planner code base
+
+##### [FLINK-22864](https://issues.apache.org/jira/browse/FLINK-22864)
+
+The old Table/SQL planner has been removed. BatchTableEnvironment and DataSet
API interop with Table
+API are not supported anymore. Use the unified TableEnvironment for batch and
stream processing with
+the new planner or the DataStream API in batch execution mode.
+
+Users are encouraged to update their pipelines. Otherwise Flink 1.13 is the
last version that offers
+the old functionality.
+
+#### Remove "blink" suffix from table modules
+
+##### [FLINK-22879](https://issues.apache.org/jira/browse/FLINK-22879)
+
+The following Maven modules have been renamed:
+* flink-table-planner-blink -> flink-table-planner
+* flink-table-runtime-blink -> flink-table-runtime
+* flink-table-uber-blink ->flink-table-uber
+
+It might be required to update job JAR dependencies. Note that
+flink-table-planner and flink-table-uber used to contain the legacy planner
before Flink 1.14 and
+now contain the only officially supported planner (i.e. previously known as
'Blink' planner).
+
+#### Remove BatchTableEnvironment and related API classes
+
+##### [FLINK-22877](https://issues.apache.org/jira/browse/FLINK-22877)
+
+Due to the removal of BatchTableEnvironment, BatchTableSource and
BatchTableSink have been removed
+as well. Use DynamicTableSource and DynamicTableSink instead. They support the
old InputFormat and
+OutputFormat interfaces as runtime providers if necessary.
+
+#### Remove TableEnvironment#connect
+
+##### [FLINK-23063](https://issues.apache.org/jira/browse/FLINK-23063)
+
+The deprecated `TableEnvironment#connect()` method has been removed. Use the
+new `TableEnvironment#createTemporaryTable(String, TableDescriptor)` to create
tables
+programatically. Please note that this method only supports sources and sinks
that comply with
+FLIP-95. This is also indicated by the new property design
`'connector'='kafka'` instead of `'connector.type'='kafka'`.
+
+#### Deprecate toAppendStream and toRetractStream
+
+##### [FLINK-23330](https://issues.apache.org/jira/browse/FLINK-23330)
+
+The outdated variants of
`StreamTableEnvironment.{fromDataStream|toAppendStream|toRetractStream)`
+have been deprecated. Use the `(from|to)(Data|Changelog)Stream` alternatives
introduced in 1.13.
+
+#### Remove old connectors and formats stack around descriptors
+
+##### [FLINK-23513](https://issues.apache.org/jira/browse/FLINK-23513)
+
+The legacy versions of the SQL Kafka connector and SQL Elasticsearch connector
have been removed
+together with their corresponding legacy formats. DDL or descriptors that
still use `'connector.type='` or
+`'format.type='` options need to be updated to the new connector and formats
available via the `'connector='` option.
+
+#### Drop BatchTableSource/Sink HBaseTableSource/Sink and related classes
+
+##### [FLINK-22623](https://issues.apache.org/jira/browse/FLINK-22623)
+
+The HBaseTableSource/Sink and related classes including various
HBaseInputFormats and
+HBaseSinkFunction have been removed. It is possible to read via Table & SQL
API and convert the
+Table to DataStream API (or vice versa) if necessary. DataSet API is not
supported anymore.
+
+#### Drop BatchTableSource ParquetTableSource and related classes
+
+##### [FLINK-22622](https://issues.apache.org/jira/browse/FLINK-22622)
+
+The ParquetTableSource and related classes including various
ParquetInputFormats have been removed.
+Use the filesystem connector with a Parquet format as a replacement. It is
possible to read via
+Table & SQL API and convert the Table to DataStream API if necessary. DataSet
API is not supported
+anymore.
+
+#### Drop BatchTableSource OrcTableSource and related classes
+
+##### [FLINK-22620](https://issues.apache.org/jira/browse/FLINK-22620)
+
+The OrcTableSource and related classes (including OrcInputFormat) have been
removed. Use the
+filesystem connector with an ORC format as a replacement. It is possible to
read via Table & SQL API
+and convert the Table to DataStream API if necessary. DataSet API is not
supported anymore.
+
+#### Drop usages of BatchTableEnvironment and old planner in Python
+
+##### [FLINK-22619](https://issues.apache.org/jira/browse/FLINK-22619)
+
+The Python API does not offer a dedicated BatchTableEnvironment anymore.
Instead, users can switch
+to the unified TableEnvironment for both batch and stream processing. Only the
Blink planner (the
+only remaining planner in 1.14) is supported.
+
+#### Migrate ModuleFactory to the new factory stack
+
+##### [FLINK-23720](https://issues.apache.org/jira/browse/FLINK-23720)
+
+The `LOAD/UNLOAD MODULE` architecture for table modules has been updated to
the new factory stack of
+FLIP-95. Users of this feature should update their `ModuleFactory`
implementations.
+
+#### Migrate Table API to new KafkaSink
+
+##### [FLINK-23639](https://issues.apache.org/jira/browse/FLINK-23639)
+
+Table API/SQL write to Kafka with the new `KafkaSink`. When migrating from a
query writing to Kafka
+in exactly-once mode from an earlier Flink version, make sure to terminate the
old application with
+stop-with-savepoint to avoid lingering Kafka transactions. To run in
exactly-once processing mode,
+the sink needs a user-configured and unique transaction prefix, such that
transactions of different
+applications do not interfere with each other.
+
+### DataStream API
+
+#### Allow @TypeInfo annotation on POJO field declarations
+
+##### [FLINK-12141](https://issues.apache.org/jira/browse/FLINK-12141)
+
+`@TypeInfo` annotations can now also be used on POJO fields which, for
example, can help to define
+custom serializers for third-party classes that can otherwise not be annotated
themselves.
+
+#### Clarify SourceFunction#cancel() contract about interrupting
+
+##### [FLINK-23527](https://issues.apache.org/jira/browse/FLINK-23527)
+
+Contract of the `SourceFunction#cancel()` method with respect to interruptions
has been clarified:
+- source itself shouldn’t be interrupting the source thread
+- interrupt shouldn’t be expected in the clean cancellation case
+
+#### Expose a consistent GlobalDataExchangeMode
+
+##### [FLINK-23402](https://issues.apache.org/jira/browse/FLINK-23402)
+
+The default DataStream API shuffle mode for batch executions has been changed
to blocking exchanges
+for all edges of the stream graph. A new option `execution.batch-shuffle-mode`
allows to change it
+to pipelined behavior if necessary.
+
+### Python API
+
+#### Support loopback mode to allow Python UDF worker and client reuse the
same Python VM
+
+##### [FLINK-21222](https://issues.apache.org/jira/browse/FLINK-21222)
+
+Instead of launching a separate Python process, it will reuse the Python
process of the client side
+to execute Python UDFs if you are running jobs locally. This makes it easy for
Python UDFs
+debugging.
+
+#### Support Python UDF chaining in Python DataStream API
+
+##### [FLINK-22913](https://issues.apache.org/jira/browse/FLINK-22913)
+
+The job graph of Python DataStream API jobs may be different from before as
the Python functions
+will be chained as much as possible to optimize the performance. You could
disable Python functions
+chaining by setting `python.operator-chaining.enabled` as `false` explicitly.
+
+### Connectors
+
+#### Implement FLIP-179: Expose Standardized Operator Metrics
+
+##### [FLINK-23652](https://issues.apache.org/jira/browse/FLINK-23652)
+
+Connectors using unified Source and Sink interface expose certain standardized
metrics
+automatically.
+
+#### Port KafkaSink to new Unified Sink API (FLIP-143)
+
+##### [FLINK-22902](https://issues.apache.org/jira/browse/FLINK-22902)
+
+`KafkaSink` supersedes `FlinkKafkaProducer` and provides efficient
exactly-once and at-least-once
+writing with the new unified sink interface supporting both batch and
streaming mode of DataStream
+API. To upgrade, please stop with savepoint. To run in exactly-once processing
mode, `KafkaSink`
+needs a user-configured and unique transaction prefix, such that transactions
of different
+applications do not interfere with each other.
+
+#### Deprecate FlinkKafkaConsumer
+
+##### [FLINK-24055](https://issues.apache.org/jira/browse/FLINK-24055)
+
+`FlinkKafkaConsumer` has been deprecated in favor of `KafkaSource`. To upgrade
to the new version,
+please store the offsets in Kafka with `setCommitOffsetsOnCheckpoints` in the
+old `FlinkKafkaConsumer` and then stop with a savepoint. When resuming from
the savepoint, please
+use `setStartingOffsets(OffsetsInitializer.committedOffsets())` in the new
`KafkaSourceBuilder` to
+transfer the offsets to the new source.
+
+#### InputStatus should not contain END_OF_RECOVERY
+
+##### [FLINK-23474](https://issues.apache.org/jira/browse/FLINK-23474)
+
+`InputStatus.END_OF_RECOVERY` was removed. It was an internal flag that should
never be returned from
+SourceReaders. Returning that value in earlier versions might lead to
misbehaviour.
+
+#### Connector-base exposes dependency to flink-core.
+
+##### [FLINK-22964](https://issues.apache.org/jira/browse/FLINK-22964)
+
+Connectors do not transitively hold a reference to `flink-core` anymore. That
means that a fat jar
+with a connector does not include `flink-core` with this fix.
+
+### Runtime & Coordination
+
+#### Increase akka.ask.timeout for tests using the MiniCluster
+
+##### [FLINK-23906](https://issues.apache.org/jira/browse/FLINK-23906)
+
+The default `akka.ask.timeout` used by the `MiniCluster` has been increased to
5 minutes. If you
+want to use a smaller value, then you have to set it explicitly in the passed
configuration.
+
+The change is due to the fact that messages cannot get lost in a
single-process minicluster, so this
+timeout (which otherwise helps to detect message loss in distributed setups)
has no benefit here.
+
+The increased timeout reduces the number of false-positive timeouts, for
example during heavy tests
+on loaded CI/CD workers or during debugging.
+
+#### The node IP obtained in NodePort mode is a VIP
+
+##### [FLINK-23507](https://issues.apache.org/jira/browse/FLINK-23507)
+
+When using `kubernetes.rest-service.exposed.type=NodePort`, connection string
for Rest gateway is
+now correctly constructed in form of `<nodeIp>:<nodePort>` instead of
+`<kubernetesApiServerUrl>:<nodePort>`. This may be a breaking change for some
users.
+
+This also introduces a new config option
`kubernetes.rest-service.exposed.node-port-address-type` that
+lets you select `<nodeIp>` from a desired range.
+
+#### Timeout heartbeat if the heartbeat target is no longer reachable
+
+##### [FLINK-23209](https://issues.apache.org/jira/browse/FLINK-23209)
+
+Flink now supports to detect dead TaskManagers via the number of consecutive
failed heartbeat RPCs.
+The threshold until a TaskManager is marked as unreachable can be configured
+via `heartbeat.rpc-failure-threshold`. This can speed up the detection of dead
TaskManagers
+significantly.
+
+#### RPCs fail faster when target is unreachable
+
+##### [FLINK-23202](https://issues.apache.org/jira/browse/FLINK-23202)
+
+The same way Flink detects unreachable heartbeat targets faster, Flink now
also immediately fails
+RPCs where the target is known by the OS to be unreachable on a network level,
instead of waiting
+for a timeout (`akka.ask.timeout`).
+
+One effect this are faster task failovers, because cancelling tasks on a dead
TaskExecutor no longer
+gets delayed by the RPC timeout.
+
+If this faster failover is a problem in certain setups (which might rely on
the fast that external
+systems hit timeouts), we recommend configuring the application's restart
strategy with a restart
+delay.
+
+#### Changes in accounting of IOExceptions when triggering checkpoints on
JobManager
+
+##### [FLINK-23189](https://issues.apache.org/jira/browse/FLINK-23189)
+
+In previous versions, IOExceptions thrown from the JobManager, would not fail
the entire Job. We
+changed the way we bookkeep those exceptions and now they do increase the
number of checkpoint
+failures.
+
+The number of tolerable checkpoint failures can be adjusted or disabled via:
+`org.apache.flink.streaming.api.environment.CheckpointConfig#setTolerableCheckpointFailureNumber`
(
+which is set to 0 by default).
+
+#### Refine ShuffleMaster lifecycle management for pluggable shuffle service
framework
+
+##### [FLINK-22910](https://issues.apache.org/jira/browse/FLINK-22910)
+
+We improved the ShuffleMaster interface by adding some lifecycle methods,
including open, close,
+registerJob and unregisterJob. Besides, the ShuffleMaster now becomes a
cluster level service which
+can be shared by multiple jobs. This is a breaking change to the pluggable
shuffle service framework
+and the customized shuffle plugin needs to adapt to the new interface
accordingly.
+
+#### Group job specific ZooKeeper HA services under common jobs/<JobID> zNode
+
+##### [FLINK-22636](https://issues.apache.org/jira/browse/FLINK-22636)
+
+The ZooKeeper job-specific HA services are now grouped under a zNode with the
respective `JobID`.
+Moreover, the config options `high-availability.zookeeper.path.latch`
+, `high-availability.zookeeper.path.leader`,
`high-availability.zookeeper.path.checkpoints`
+and `high-availability.zookeeper.path.checkpoint-counter` have been removed
and, thus, have no
+longer an effect.
+
+#### Fallback value for taskmanager.slot.timeout
+
+##### [FLINK-22002](https://issues.apache.org/jira/browse/FLINK-22002)
+
+The config option `taskmanager.slot.timeout` falls now back to
`akka.ask.timeout` if no value has
+been configured. Previously the default value for `taskmanager.slot.timeout`
was `10 s`.
+
+#### DuplicateJobSubmissionException after JobManager failover
+
+##### [FLINK-21928](https://issues.apache.org/jira/browse/FLINK-21928)
+
+The fix for this problem only works if the ApplicationMode is used with a
single job submission and
+if the user code does not access the `JobExecutionResult`. If any of these
conditions is violated,
+then Flink cannot guarantee that the whole Flink application is executed.
+
+Additionally, it is still required that the user cleans up the corresponding
HA entries for the
+running jobs registry because these entries won't be reliably cleaned up when
encountering the
+situation described by FLINK-21928.
+
+#### Zookeeper node under leader and leaderlatch is not deleted after job
finished
+
+##### [FLINK-20695](https://issues.apache.org/jira/browse/FLINK-20695)
+
+The `HighAvailabilityServices` have received a new method `cleanupJobData`
which can be implemented
+in order to clean up job related HA data after a given job has terminated.
+
+#### Optimize scheduler performance for large-scale jobs
+
+##### [FLINK-21110](https://issues.apache.org/jira/browse/FLINK-21110)
+
+The performance of scheduler has been improved to reduce the time of execution
graph creation,
+task deployment and task failover. This improvement is significant to large
scale jobs which
+currently may spend minutes on the processes mentioned above. This improvement
also helps to avoid
+cases that the job manager main thread gets blocked for too long and leads to
heartbeat timeout.
+
+### Checkpoints
+
+#### The semantic of alignmentTimeout configuration has changed the meaning
+
+##### [FLINK-23041](https://issues.apache.org/jira/browse/FLINK-23041)
+
+The semantic of alignmentTimeout configuration has changed the meaning and now
it's measured as the
+time between the start of a checkpoint(on the checkpoint coordinator) and the
time when the
+checkpoint barrier is received by a task.
+
+#### Disabled unaligned checkpoints for BROADCAST exchanges
+
+##### [FLINK-22815](https://issues.apache.org/jira/browse/FLINK-22815)
+
+Broadcast partitioning can not work with unaligned checkpointing. There are no
guarantees that
+records are consumed at the same rate in all channels. This can result in some
tasks applying state
+changes corresponding to a certain broadcasted event while others don't. In
turn upon restore, it
+may lead to an inconsistent state.
+
+#### DefaultCompletedCheckpointStore drops unrecoverable checkpoints silently
+
+##### [FLINK-22502](https://issues.apache.org/jira/browse/FLINK-22502)
+
+On recovery, if a failure occurs during retrieval of a checkpoint, the job is
restarted (instead of
+skipping the checkpoint in some circumstances). This prevents potential
consistency violations.
+
+#### Removal of CompletedCheckpointRecover#recover() method.
+
+##### [FLINK-22483](https://issues.apache.org/jira/browse/FLINK-22483)
+
+Flink no longer re-loads checkpoint metadata from the external storage before
restoring the task
+state after the failover (except when the JobManager fails over / changes
leadership). This results
+in less external I/O and faster failover.
+
+Please note that this changes a public interfaces around
`CompletedCheckpointStore`, that we allow
+overriding by providing custom implementation of HA Services.
+
+#### Removal of the deprecated CheckpointConfig#setPreferCheckpointForRecovery
method.
+
+##### [FLINK-20427](https://issues.apache.org/jira/browse/FLINK-20427)
+
+Deprecated method CheckpointConfig#setPreferCheckpointForRecovery was removed,
because preferring
+older checkpoints over newer savepoints for recovery can lead to data loss.
+
+### Dependency upgrades
+
+#### Bump up RocksDb version to 6.20.3
+
+##### [FLINK-14482](https://issues.apache.org/jira/browse/FLINK-14482)
+
+RocksDB has been upgraded to 6.20.3. Compared to the previous version, the new
version contains lots
+of bug fixes, ARM platform supported, musl library supported, and more
attractive features. However,
+the new version can entail at most 8% performance regression according to our
tests.
+
+See the corresponding ticket for more information.
+
+#### Support configuration of the RocksDB info logging via configuration
+
+##### [FLINK-23812](https://issues.apache.org/jira/browse/FLINK-23812)
+
+With RocksDB bumped to 6.20.3 (FLINK-14482), you can now also configure a
rolling info logging
+strategy by configuring it accordingly via newly added
state.backend.rocksdb.log.* settings. This
+can be helpful for debugging RocksDB (performance) issues in containerized
environments where the
+local data dir is volatile but the logs should be retained on a separate
volume mount.
+
+#### Make flink-runtime scala-free
+
+##### [FLINK-14105](https://issues.apache.org/jira/browse/FLINK-14105)
+
+Flink's akka dependency is now loaded with a separate classloader and no
longer accessible from the
+outside.
+
+Resulting from that various modules, most prominently flink-runtime, no longer
have a
+scala suffix in their artifactId.
+
+#### Drop Mesos support
+
+##### [FLINK-23118](https://issues.apache.org/jira/browse/FLINK-23118)
+
+The support for Apache Mesos has been removed.
