superdiaodiao commented on code in PR #25091: URL: https://github.com/apache/flink/pull/25091#discussion_r1691112012
########## docs/content.zh/release-notes/flink-1.20.md: ########## @@ -0,0 +1,434 @@ +--- +title: "Release Notes - Flink 1.20" +--- +<!-- +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 1.20 + +These release notes discuss important aspects, such as configuration, behavior or dependencies, +that changed between Flink 1.19 and Flink 1.20. Please read these notes carefully if you are +planning to upgrade your Flink version to 1.20. + +### Checkpoints + +#### Unified File Merging Mechanism for Checkpoints + +##### [FLINK-32070](https://issues.apache.org/jira/browse/FLINK-32070) + +The unified file merging mechanism for checkpointing is introduced to Flink 1.20 as an MVP ("minimum viable product") +feature, which allows scattered small checkpoint files to be written into larger files, reducing the +number of file creations and file deletions and alleviating the pressure of file system metadata management +raised by the file flooding problem during checkpoints. The mechanism can be enabled by setting +`state.checkpoints.file-merging.enabled` to `true`. For more advanced options and principle behind +this feature, please refer to the document of `Checkpointing`. + +#### Reorganize State & Checkpointing & Recovery Configuration + +##### [FLINK-34255](https://issues.apache.org/jira/browse/FLINK-34255) + +Currently, all the options about state and checkpointing are reorganized and categorized by +prefixes as listed below: + +1. execution.checkpointing: all configurations associated with checkpointing and savepoint. +2. execution.state-recovery: all configurations pertinent to state recovery. +3. state.*: all configurations related to the state accessing. + 1. state.backend.*: specific options for individual state backends, such as RocksDB. + 2. state.changelog: configurations for the changelog, as outlined in FLIP-158, including the options for the "Durable Short-term Log" (DSTL). + 3. state.latency-track: configurations related to the latency tracking of state access. + +At the meantime, all the original options scattered everywhere are annotated as `@Deprecated`. + +#### Use common thread pools when transferring RocksDB state files + +##### [FLINK-35501](https://issues.apache.org/jira/browse/FLINK-35501) + +The semantics of `state.backend.rocksdb.checkpoint.transfer.thread.num` changed slightly: +If negative, the common (TM) IO thread pool is used (see `cluster.io-pool.size`) for up/downloading RocksDB files. + +#### Expose RocksDB bloom filter metrics + +##### [FLINK-34386](https://issues.apache.org/jira/browse/FLINK-34386) + +We expose some RocksDB bloom filter metrics to monitor the effectiveness of bloom filter optimization: + +`BLOOM_FILTER_USEFUL`: times bloom filter has avoided file reads. +`BLOOM_FILTER_FULL_POSITIVE`: times bloom FullFilter has not avoided the reads. +`BLOOM_FILTER_FULL_TRUE_POSITIVE`: times bloom FullFilter has not avoided the reads and data actually exist. + +#### Manually Compact Small SST Files + +##### [FLINK-26050](https://issues.apache.org/jira/browse/FLINK-26050) + +In some cases, the number of files produced by RocksDB state backend grows indefinitely.This might +cause task state info (TDD and checkpoint ACK) to exceed RPC message size and fail recovery/checkpoint +in addition to having lots of small files. + +In Flink 1.20, you can manually merge such files in the background using RocksDB API. + +### Runtime & Coordination + +#### Support Job Recovery from JobMaster Failures for Batch Jobs + +##### [FLINK-33892](https://issues.apache.org/jira/browse/FLINK-33892) + +In 1.20, we introduced a batch job recovery mechanism to enable batch jobs to recover as much progress as possible +after a JobMaster failover, avoiding the need to rerun tasks that have already been finished. + +More information about this feature and how to enable it could be found in: https://nightlies.apache.org/flink/flink-docs-master/docs/ops/batch/recovery_from_job_master_failure/ + +#### Extend Curator config option for Zookeeper configuration + +##### [FLINK-33376](https://issues.apache.org/jira/browse/FLINK-33376) + +Adds support for the following curator parameters: +`high-availability.zookeeper.client.authorization` (corresponding curator parameter: `authorization`), +`high-availability.zookeeper.client.max-close-wait` (corresponding curator parameter: `maxCloseWaitMs`), +`high-availability.zookeeper.client.simulated-session-expiration-percent` (corresponding curator parameter: `simulatedSessionExpirationPercent`). + +#### More fine-grained timer processing + +##### [FLINK-20217](https://issues.apache.org/jira/browse/FLINK-20217) + +Firing timers can now be interrupted to speed up checkpointing. Timers that were interrupted by a checkpoint, +will be fired shortly after checkpoint completes. + +By default, this features is disabled. To enabled it please set `execution.checkpointing.unaligned.interruptible-timers.enabled` to `true`. +Currently supported only by all `TableStreamOperators` and `CepOperator`. + +#### Add numFiredTimers and numFiredTimersPerSecond metrics + +##### [FLINK-35065](https://issues.apache.org/jira/browse/FLINK-35065) + +Currently, there is no way of knowing how many timers are being fired by Flink, so it's impossible to distinguish, +even using code profiling, if operator is firing only a couple of heavy timers per second using ~100% of the CPU time, +vs firing thousands of timer per seconds. + +We added the following metrics to address this issue: + +- `numFiredTimers`: total number of fired timers per operator +- `numFiredTimersPerSecond`: per second rate of firing timers per operator + +#### Support EndOfStreamTrigger and isOutputOnlyAfterEndOfStream Operator Attribute to Optimize Task Deployment + +##### [FLINK-34371](https://issues.apache.org/jira/browse/FLINK-34371) + +For operators that only generates outputs after all inputs have been consumed, they are now optimized +to run in blocking mode, and the other operators in the same job will wait to start until these operators +have finished. Such operators include windowing with `GlobalWindows#createWithEndOfStreamTrigger`, +sorting, and etc. + +### SDK + +#### Support Full Partition Processing On Non-keyed DataStream + +##### [FLINK-34543](https://issues.apache.org/jira/browse/FLINK-34543) + +We have introduced some full window processing APIs, allowing collection and processing of all records +in each subtask: +- `mapPartition`: Processes all records using the MapPartitionFunction. +- `sortPartition`: Sort all records by field or key in the full partition window. +- `aggregate`: Aggregate all records in the full partition window. +- `reduce`: Reduce all records in the full partition window. + +### Table SQL / API + +#### Introduce a New Materialized Table for Simplifying Data Pipelines + +##### [FLINK-35187](https://issues.apache.org/jira/browse/FLINK-35187) + +We introduced the Materialized Table in Flink SQL, a new table type designed to simplify both +batch and stream data pipelines while providing a consistent development experience. + +By specifying data freshness and query at creation, the engine automatically derives the schema +and creates a data refresh pipeline to maintain the specified freshness. + +More information about this feature can be found here: [Materialized Table Overview](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/materialized-table/overview/) + +#### Introduce Catalog-related Syntax + +##### [FLINK-34914](https://issues.apache.org/jira/browse/FLINK-34914) + +As the application scenario of `Catalog` expands, which widely applied in services such as JDBC/Hive/Paimon, +`Catalog` plays an increasingly crucial role in Flink. + +FLIP-436 introduces the DQL syntax to obtain detailed metadata from existing catalogs, and the DDL syntax +to modify metadata such as properties or comment in the specified catalog. 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