[hotfix][docs] fix formatting of release notes

docs/content.zh/release-notes/flink-1.12.md

@@ -22,16 +22,14 @@ under the License.
 
 # Release Notes - Flink 1.12
 
-
 These release notes discuss important aspects, such as configuration, behavior,
 or dependencies, that changed between Flink 1.11 and Flink 1.12. Please read
 these notes carefully if you are planning to upgrade your Flink version to 1.12.
 
-
-
 ### Known Issues
 
-#### Unaligned checkpoint recovery may lead to corrupted data stream [FLINK-20654](https://issues.apache.org/jira/browse/FLINK-20654)
+#### Unaligned checkpoint recovery may lead to corrupted data stream 
+##### [FLINK-20654](https://issues.apache.org/jira/browse/FLINK-20654)
 
 Using unaligned checkpoints in Flink 1.12.0 combined with two/multiple inputs tasks or with union inputs for single input tasks can result in corrupted state.
 
@@ -43,19 +41,22 @@ A late barrier of a canceled checkpoint may lead to buffers being not written in
 
 ### APIs
 
-#### Remove deprecated methods in ExecutionConfig [FLINK-19084](https://issues.apache.org/jira/browse/FLINK-19084)
+#### Remove deprecated methods in ExecutionConfig 
+##### [FLINK-19084](https://issues.apache.org/jira/browse/FLINK-19084)
 
 Deprecated method `ExecutionConfig#isLatencyTrackingEnabled` was removed, you can use `ExecutionConfig#getLatencyTrackingInterval` instead. 
 
 Deprecated and methods without effect were removed: `ExecutionConfig#enable/disableSysoutLogging`, `ExecutionConfig#set/isFailTaskOnCheckpointError`.
 
 Removed `-q` flag from cli. The option had no effect. 
 
-#### Remove deprecated RuntimeContext#getAllAccumulators [FLINK-19032](https://issues.apache.org/jira/browse/FLINK-19032)
+#### Remove deprecated RuntimeContext#getAllAccumulators 
+##### [FLINK-19032](https://issues.apache.org/jira/browse/FLINK-19032)
 
 The deprecated method `RuntimeContext#getAllAccumulators` was removed. Please use `RuntimeContext#getAccumulator` instead. 
 
-#### Deprecated CheckpointConfig#setPreferCheckpointForRecovery due to risk of data loss [FLINK-20441](https://issues.apache.org/jira/browse/FLINK-20441)
+#### Deprecated CheckpointConfig#setPreferCheckpointForRecovery due to risk of data loss 
+##### [FLINK-20441](https://issues.apache.org/jira/browse/FLINK-20441)
 
 The `CheckpointConfig#setPreferCheckpointForRecovery` method has been deprecated, because preferring older checkpoints over newer savepoints for recovery can lead to data loss.
 
@@ -91,73 +92,88 @@ The `CheckpointConfig#setPreferCheckpointForRecovery` method has been deprecated
 
   The long deprecated `(Windowed)DataStream#fold` was removed in 1.12. Please use other operations such as e.g. `(Windowed)DataStream#reduce` that perform better in distributed systems. 
 
-#### Extend CompositeTypeSerializerSnapshot to allow composite serializers to signal migration based on outer configuration [FLINK-17520](https://issues.apache.org/jira/browse/FLINK-17520)
+#### Extend CompositeTypeSerializerSnapshot to allow composite serializers to signal migration based on outer configuration 
+##### [FLINK-17520](https://issues.apache.org/jira/browse/FLINK-17520)
 
 The `boolean isOuterSnapshotCompatible(TypeSerializer)` on the `CompositeTypeSerializerSnapshot` class has been deprecated, in favor of a new `OuterSchemaCompatibility#resolveOuterSchemaCompatibility(TypeSerializer)` method. Please implement that instead. Compared to the old method, the new method allows composite serializers to signal state schema migration based on outer schema and configuration. 
 
-#### Bump Scala Macros Version to 2.1.1 [FLINK-19278](https://issues.apache.org/jira/browse/FLINK-19278)
+#### Bump Scala Macros Version to 2.1.1 
+##### [FLINK-19278](https://issues.apache.org/jira/browse/FLINK-19278)
 
 Flink now relies on Scala Macros 2.1.1. This means that we no longer support Scala < 2.11.11. 
 
 ### SQL
 
-#### Use new type inference for SQL DDL of aggregate functions [FLINK-18901](https://issues.apache.org/jira/browse/FLINK-18901)
+#### Use new type inference for SQL DDL of aggregate functions 
+##### [FLINK-18901](https://issues.apache.org/jira/browse/FLINK-18901)
 
 The `CREATE FUNCTION` DDL for aggregate functions uses the new type inference now. It might be necessary to update existing implementations to the new reflective type extraction logic. Use `StreamTableEnvironment.registerFunction` for the old stack. 
 
-#### Update parser module for FLIP-107 [FLINK-19273](https://issues.apache.org/jira/browse/FLINK-19273)
+#### Update parser module for FLIP-107 
+##### [FLINK-19273](https://issues.apache.org/jira/browse/FLINK-19273)
 
 The term `METADATA` is a reserved keyword now. Use backticks to escape column names and other identifiers with this name. 
 
-#### Update internal aggregate functions to new type system [FLINK-18809](https://issues.apache.org/jira/browse/FLINK-18809)
+#### Update internal aggregate functions to new type system 
+##### [FLINK-18809](https://issues.apache.org/jira/browse/FLINK-18809)
 
 SQL queries that use the `COLLECT` function might need to be updated to the new type system. 
 
 ### Connectors and Formats
 
-#### Remove Kafka 0.10.x and 0.11.x connectors [FLINK-19152](https://issues.apache.org/jira/browse/FLINK-19152)
+#### Remove Kafka 0.10.x and 0.11.x connectors 
+##### [FLINK-19152](https://issues.apache.org/jira/browse/FLINK-19152)
 
 In Flink 1.12 we removed the Kafka 0.10.x and 0.11.x connectors. Please use the universal Kafka connector which works with any Kafka cluster version after 0.10.2.x. 
 
 Please refer to the [documentation]({{< ref "docs/connectors/datastream/kafka" >}}) to learn about how to upgrade the Flink Kafka Connector version. 
 
-#### Csv Serialization schema contains line delimiter [FLINK-19868](https://issues.apache.org/jira/browse/FLINK-19868)
+#### Csv Serialization schema contains line delimiter 
+##### [FLINK-19868](https://issues.apache.org/jira/browse/FLINK-19868)
 
 The `csv.line-delimiter` option has been removed from CSV format. Because the line delimiter should be defined by the connector instead of format. If users have been using this option in previous Flink version, they should alter such table to remove this option when upgrading to Flink 1.12. There should not much users using this option. 
 
-#### Upgrade to Kafka Schema Registry Client 5.5.0 [FLINK-18546](https://issues.apache.org/jira/browse/FLINK-18546)
+#### Upgrade to Kafka Schema Registry Client 5.5.0 
+##### [FLINK-18546](https://issues.apache.org/jira/browse/FLINK-18546)
 
 The `flink-avro-confluent-schema-registry` module is no longer provided as a fat-jar. You should include its dependencies in your job's fat-jar. Sql-client users can use flink-sql-avro-confluent-schema-registry fat jar. 
 
-#### Upgrade to Avro version 1.10.0 from 1.8.2 [FLINK-18192](https://issues.apache.org/jira/browse/FLINK-18192)
+#### Upgrade to Avro version 1.10.0 from 1.8.2 
+##### [FLINK-18192](https://issues.apache.org/jira/browse/FLINK-18192)
 
 The default version of Avro in flink-avro module was upgraded to 1.10. If for some reason you need an older version (you have Avro coming from Hadoop, or you use classes generated from an older Avro version), please explicitly downgrade the Avro version in your project. 
 
 NOTE: We observed a decreased performance of the Avro 1.10 version compared to 1.8.2. If you are concerned with the performance and you are fine working with an older version of Avro, consider downgrading the Avro version. 
 
-#### Create an uber jar when packaging flink-avro for SQL Client [FLINK-18802](https://issues.apache.org/jira/browse/FLINK-18802)
+#### Create an uber jar when packaging flink-avro for SQL Client 
+##### [FLINK-18802](https://issues.apache.org/jira/browse/FLINK-18802)
 
 The SQL Client jar was renamed to flink-sql-avro-{{< version >}}.jar, previously flink-avro-{{< version >}}-sql-jar.jar. Moreover it is no longer needed to add Avro dependencies manually. 
 
 ### Deployment
 
-#### Default log4j configuration rolls logs after reaching 100 megabytes [FLINK-8357](https://issues.apache.org/jira/browse/FLINK-8357)
+#### Default log4j configuration rolls logs after reaching 100 megabytes 
+##### [FLINK-8357](https://issues.apache.org/jira/browse/FLINK-8357)
 
 The default log4j configuration has changed: Besides the existing rolling of log files on startup of Flink, they also roll once they've reached a size of 100MB. Flink keeps a total of 10 log files, effectively limiting the total size of the log directory to 1GB (per Flink service logging to that directory).
 
-#### Use jemalloc by default in the Flink docker image [FLINK-19125](https://issues.apache.org/jira/browse/FLINK-19125)
+#### Use jemalloc by default in the Flink docker image 
+##### [FLINK-19125](https://issues.apache.org/jira/browse/FLINK-19125)
 
 jemalloc is adopted as the default memory allocator in Flink's docker image to reduce issues with memory fragmentation. Users can roll back to using glibc by passing the 'disable-jemalloc' flag to the `docker-entrypoint.sh` script. For more details, please refer to the [Flink on Docker documentation]({{< ref "docs/deployment/resource-providers/standalone/docker" >}}). 
 
-#### Upgrade Mesos version to 1.7 [FLINK-19783](https://issues.apache.org/jira/browse/FLINK-19783)
+#### Upgrade Mesos version to 1.7 
+##### [FLINK-19783](https://issues.apache.org/jira/browse/FLINK-19783)
 
 The Mesos dependency has been bumped from 1.0.1 to 1.7.0. 
 
-#### Send SIGKILL if Flink process doesn't stop after a timeout [FLINK-17470](https://issues.apache.org/jira/browse/FLINK-17470)
+#### Send SIGKILL if Flink process doesn't stop after a timeout 
+##### [FLINK-17470](https://issues.apache.org/jira/browse/FLINK-17470)
 
 In Flink 1.12 we changed the behavior of the standalone scripts to issue a SIGKILL if a SIGTERM did not succeed in shutting down a Flink process. 
 
-#### Introduce non-blocking job submission [FLINK-16866](https://issues.apache.org/jira/browse/FLINK-16866)
+#### Introduce non-blocking job submission 
+##### [FLINK-16866](https://issues.apache.org/jira/browse/FLINK-16866)
 
 The semantics of submitting a job have slightly changed. The submission call returns almost immediately, with the job being in a new `INITIALIZING` state. Operations such as triggering a savepoint or retrieving the full job details are not available while the job is in that state. 
 
@@ -169,10 +185,9 @@ Once the JobManager for that job has been created, the job is in `CREATED` state
 
 The configuration `python.fn-execution.buffer.memory.size` and `python.fn-execution.framework.memory.size` have been removed and so will not take effect any more. Besides, the default value of `python.fn-execution.memory.managed` has been changed to true and so managed memory will be used by default for Python workers. In cases where Python UDFs are used together with the RocksDB state backend in streaming or built-in batch algorithms in batch, the user can control how managed memory should be shared between data processing (RocksDB state backend or batch algorithms) and Python, by overwriting [managed memory consumer weights]({{< ref "docs/deployment/memory/mem_setup_tm" >}}#consumer-weights). 
 
-#### FLIP-119 Pipelined Region Scheduling [FLINK-16430](https://issues.apache.org/jira/browse/FLINK-16430)
+#### FLIP-119 Pipelined Region Scheduling 
+##### [FLINK-16430](https://issues.apache.org/jira/browse/FLINK-16430)
 
 Beginning from Flink 1.12, jobs will be scheduled in the unit of pipelined regions. A pipelined region is a set of pipelined connected tasks. This means that, for streaming jobs which consist of multiple regions, it no longer waits for all tasks to acquire slots before starting to deploy tasks. Instead, any region can be deployed once it has acquired enough slots for within tasks. For batch jobs, tasks will not be assigned slots and get deployed individually. Instead, a task will be deployed together with all other tasks in the same region, once the region has acquired enough slots. 
 
 The old scheduler can be enabled using the `jobmanager.scheduler.scheduling-strategy: legacy` setting.
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