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dwysakowicz pushed a commit to branch release-1.14
in repository https://gitbox.apache.org/repos/asf/flink.git
The following commit(s) were added to refs/heads/release-1.14 by this push:
new 9bc8502 [release][docs] Clean up 1.14 release notes
9bc8502 is described below
commit 9bc8502d03616543d5fe302d48108abd97151822
Author: Daisy T <[email protected]>
AuthorDate: Thu Sep 30 02:46:56 2021 +0200
[release][docs] Clean up 1.14 release notes
This closes #17392
---
docs/content/release-notes/flink-1.14.md | 216 ++++++++++++++++---------------
1 file changed, 113 insertions(+), 103 deletions(-)
diff --git a/docs/content/release-notes/flink-1.14.md
b/docs/content/release-notes/flink-1.14.md
index b015737..e11e1ba 100644
--- a/docs/content/release-notes/flink-1.14.md
+++ b/docs/content/release-notes/flink-1.14.md
@@ -20,33 +20,41 @@ specific language governing permissions and limitations
under the License.
-->
-# Release Notes - Flink 1.14
+# 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
+### 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,
+Netty, which the Pulsar client uses underneath, allocates memory differently
on Java 11 and Java 8. On Java
+11, it will allocate memory from the pool of Java Direct Memory and is
affected by the
+MaxDirectMemory limit. The current Pulsar client has no configuration options
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).
+Users are advised to use the Pulsar connector with Java 8 or overprovision
memory for Flink. Read the
+[memory setup guide]({{< ref "docs/deployment/memory/mem_setup_tm" >}}) on how
to configure memory
+for Flink and track the proper solution in
[FLINK-24302](https://issues.apache.org/jira/browse/FLINK-24302).
-### Table & SQL
+### Summary of changed dependency names
+
+There are two changes in Flink 1.14 that require updating dependency names
when upgrading from earlier versions.
+
+* The removal of the Blink planner
([FLINK-22879](https://issues.apache.org/jira/browse/FLINK-22879))
+ requires the removal of the `blink` infix.
+* Due to [FLINK-14105](https://issues.apache.org/jira/browse/FLINK-14105), if
you have a dependency on `flink-runtime`, `flink-optimizer` and/or
`flink-queryable-state-runtime`, the Scala suffix (`_2.11`/`_2.12`) needs to be
removed from the `artifactId`.
+
+### Table API & 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`.
+`"Flink Batch Job"`. A custom name can be set with the config option
`pipeline.name`.
#### Propagate unique keys for fromChangelogStream
@@ -66,7 +74,7 @@ because primary keys were not properly considered before.
##### [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.
+methods of
[FLIP-136](https://cwiki.apache.org/confluence/display/FLINK/FLIP-136%3A++Improve+interoperability+between+DataStream+and+Table+API).
The changes might require an update of pipelines that used `toTable` or
implicit conversions from
`Table` to `DataStream[Row]`.
@@ -75,9 +83,9 @@ The changes might require an update of pipelines that used
`toTable` or implicit
##### [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
+The `sql-client-defaults.yaml` file was deprecated in the 1.13 release and is
now completely removed.
+As an alternative, you can use the `-i` startup option to execute an SQL
initialization
+file to set up the SQL Client session. The SQL initialization file can use
Flink DDLs to
define available catalogs, table sources and sinks, user-defined functions,
and other properties
required for execution and deployment.
@@ -87,42 +95,43 @@ See more:
https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/ta
##### [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 old Table/SQL planner has been removed. `BatchTableEnvironment` and
DataSet API interoperability 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
+#### Remove the "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
+* 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).
+`flink-table-planner` and `flink-table-uber` used to contain the legacy
planner before Flink 1.14 and
+now they 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.
+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'`.
+The deprecated `TableEnvironment.connect()` method has been removed. Use the
+new `TableEnvironment.createTemporaryTable(String, TableDescriptor)` to create
tables
+programmatically. Please note that this method only supports sources and sinks
that comply with
+[FLIP-95](https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces).
+This is also indicated by the new property design `'connector'='kafka'`
instead of `'connector.type'='kafka'`.
#### Deprecate toAppendStream and toRetractStream
@@ -143,33 +152,33 @@ together with their corresponding legacy formats. DDL or
descriptors that still
##### [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.
+The `HBaseTableSource/Sink` and related classes including various
`HBaseInputFormats` and
+`HBaseSinkFunction` have been removed. It is possible to read via the Table &
SQL API and convert the
+Table to DataStream API (or vice versa) if necessary. The 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
+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 the
+Table & SQL API and convert the Table to DataStream API if necessary. The
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.
+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 the Table & SQL API
+and convert the Table to DataStream API if necessary. The DataSet API is not
supported anymore.
-#### Drop usages of BatchTableEnvironment and old planner in Python
+#### Drop usages of BatchTableEnvironment and the 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
+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
@@ -177,13 +186,14 @@ only remaining planner in 1.14) is supported.
##### [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.
+[FLIP-95](https://cwiki.apache.org/confluence/display/FLINK/FLIP-95%3A+New+TableSource+and+TableSink+interfaces).
+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
+Table API/SQL now writes 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
@@ -198,47 +208,46 @@ applications do not interfere with each other.
`@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
+#### Clarify SourceFunction#cancel() contract about interruptions
##### [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
+Contract of the `SourceFunction.cancel()` method with respect to interruptions
has been clarified:
+- source itself should not be interrupting the source thread
+- interrupt should not 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
+for all edges of the stream graph. A new option `execution.batch-shuffle-mode`
allows you 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
+#### Support loopback mode to allow Python UDF worker and client to 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.
+Instead of launching a separate Python process, the Python UDF worker will
reuse the Python process of
+the client side when running jobs locally. This makes it easier to debug
Python UDFs.
#### 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.
+
+The job graph of Python DataStream API jobs may be different from before as
Python functions
+will be chained as much as possible to optimize performance. You could disable
Python functions
+chaining by explicitly setting `python.operator-chaining.enabled` as `false`.
### Connectors
-#### Implement FLIP-179: Expose Standardized Operator Metrics
+#### Expose standardized operator metrics (FLIP-179)
##### [FLINK-23652](https://issues.apache.org/jira/browse/FLINK-23652)
-Connectors using unified Source and Sink interface expose certain standardized
metrics
+Connectors using the unified Source and Sink interface will expose certain
standardized metrics
automatically.
#### Port KafkaSink to new Unified Sink API (FLIP-143)
@@ -246,7 +255,7 @@ automatically.
##### [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
+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.
@@ -266,13 +275,13 @@ transfer the offsets to the new source.
##### [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.
+SourceReaders. Returning that value in earlier versions might lead to
misbehavior.
#### 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
+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
@@ -284,18 +293,18 @@ with a connector does not include `flink-core` with this
fix.
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
+The change is due to the fact that messages can not 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
+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
+When using `kubernetes.rest-service.exposed.type=NodePort`, the connection
string for the REST gateway is
+now correctly constructed in the form `<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
@@ -305,7 +314,7 @@ lets you select `<nodeIp>` from a desired range.
##### [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.
+Flink now supports detecting 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.
@@ -318,10 +327,10 @@ The same way Flink detects unreachable heartbeat targets
faster, Flink now also
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
+This creates 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
+If this faster failover is a problem in certain setups (which might rely on
the fact that external
systems hit timeouts), we recommend configuring the application's restart
strategy with a restart
delay.
@@ -329,20 +338,20 @@ delay.
##### [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
+In previous versions, IOExceptions thrown from the JobManager would not fail
the entire Job. We
+changed the way we track 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).
+`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
+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.
@@ -351,17 +360,17 @@ and the customized shuffle plugin needs to adapt to the
new interface accordingl
##### [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.
+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, no
+longer have 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`.
+The config option `taskmanager.slot.timeout` now falls 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
@@ -379,35 +388,35 @@ situation described by FLINK-21928.
##### [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.
+The `HighAvailabilityServices` interface has 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
+The performance of the 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.
+cases when 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
+#### The semantic of alignmentTimeout configuration has changed 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
+The semantic of alignmentTimeout configuration has changed meaning and now it
is 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
+#### Disable 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
+changes corresponding to a certain broadcasted event while others do not. Upon
restore, it
may lead to an inconsistent state.
#### DefaultCompletedCheckpointStore drops unrecoverable checkpoints silently
@@ -417,22 +426,22 @@ may lead to an inconsistent state.
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.
+#### Remove the 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
+Flink no longer reloads checkpoint metadata from the external storage before
restoring the task
+state after 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
+Please note that this changes public interfaces around
`CompletedCheckpointStore`, that we allow
overriding by providing custom implementation of HA Services.
-#### Removal of the deprecated CheckpointConfig#setPreferCheckpointForRecovery
method.
+#### Remove the deprecated CheckpointConfig#setPreferCheckpointForRecovery
method.
##### [FLINK-20427](https://issues.apache.org/jira/browse/FLINK-20427)
-Deprecated method CheckpointConfig#setPreferCheckpointForRecovery was removed,
because preferring
+The deprecated method CheckpointConfig#setPreferCheckpointForRecovery was
removed, because preferring
older checkpoints over newer savepoints for recovery can lead to data loss.
### Dependency upgrades
@@ -441,8 +450,8 @@ older checkpoints over newer savepoints for recovery can
lead to data loss.
##### [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,
+RocksDB has been upgraded to 6.20.3. The new version contains lots
+of bug fixes, ARM platform support, musl library support, 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.
@@ -451,20 +460,21 @@ See the corresponding ticket for more information.
##### [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.
+With RocksDB upgraded to 6.20.3
([FLINK-14482](https://issues.apache.org/jira/browse/FLINK-14482)),
+you can now also configure a rolling info logging strategy by configuring it
accordingly via
+the newly added `state.backend.rocksdb.log.*` settings. This can be helpful
for debugging
+RocksDB (performance) issues in containerized environments where the local
data directory 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
+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.
+As a result, various modules (most prominently, flink-runtime) no longer have a
+scala suffix in their `artifactId`.
#### Drop Mesos support
