davidradl commented on code in PR #751: URL: https://github.com/apache/flink-web/pull/751#discussion_r1682470114
########## docs/content/posts/2024-07-15-release-1.20.0.md: ########## @@ -0,0 +1,560 @@ +--- +authors: +- reswqa: + name: "Weijie Guo" + twitter: "WeijieGuo12" +- 1996fanrui: + name: "Rui Fan" + twitter: "1996fanrui" + +date: "2024-07-15T08:00:00Z" +subtitle: "" +title: Announcing the Release of Apache Flink 1.20 +aliases: +- /news/2024/07/15/release-1.20.0.html +--- + +The Apache Flink PMC is pleased to announce the release of Apache Flink 1.20.0. As usual, we are +looking at a packed release with a wide variety of improvements and new features. Overall, 142 +people contributed to this release completing 13 FLIPs and 300+ issues. Thank you! + +Let's dive into the highlights. + +# Standing on the Eve of Apache Flink 2.0 + +Flink 1.0 was released seven years ago. Since several months, the community is actively planning and taking steps towards +the next major release. The new 1.20 release is planned to be the last minor release before Flink 2.0, which is anticipated by the end of 2024. + +Start from Flink 1.19, the community has decided to officially deprecate multiple APIs that were approaching +end of life for a while. In 1.20, we further sorted through all relevant APIs that might need to be replaced +or deprecated to clear the way for the 2.0 release: +- Configuration Improvements: As Flink moves towards version 2.0, we have revisited all runtime & Table API/SQL +configuration options and identified several opportunities to enhance user-friendliness and maintainability. +- Deprecate the Legacy [SinkFunction](https://nightlies.apache.org/flink/flink-docs-release-1.20/api/java/org/apache/flink/streaming/api/functions/sink/SinkFunction.html) API: Since its introduction in Flink 1.12, the [Unified Sink API](https://nightlies.apache.org/flink/flink-docs-release-1.20/api/java/org/apache/flink/api/connector/sink2/Sink.html) +has undergone extensive development and testing. Over multiple release cycles, the API has demonstrated +stability and robustness, aligning with the criteria set forth in [FLIP-197](https://cwiki.apache.org/confluence/display/FLINK/FLIP-197%3A+API+stability+graduation+process) for API stability graduation. +Therefore, we promote the Unified Sink API v2 to `@Public` and deprecate the legacy `SinkFunction` interface. + +It has been seven years since the Flink community's last major release, and we have great expectations for Flink 2.0. +We are planning to release several high-impact features in `2.x`. Some of them are already introduced in Flink 1.20 in MVP (minimum viable product) state and discussed in more detail below. +- Introduce a New Materialized Table for Simplifying Data Pipelines: [FLIP-435](https://cwiki.apache.org/confluence/display/FLINK/FLIP-435%3A+Introduce+a+New+Materialized+Table+for+Simplifying+Data+Pipelines) designed to simplify the development of +data processing pipelines. With dynamic table with uniform SQL statements and freshness, users can define batch +and streaming transformations to data in the same way, accelerate ETL pipeline development, and manage task scheduling +automatically. See below for more details on this exciting feature. +- Unified File Merging Mechanism for Checkpoints: The unified file merging mechanism for checkpointing is introduced to +Flink 1.20 as an MVP 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. + +# Flink SQL Improvements + +## Introduce Materialized Tables + +We introduced Materialized Tables abstraction in Flink SQL, a new table type designed to simplify both batch and stream +data pipelines while providing a consistent development experience. + +Materialized tables are defined with a query and a data freshness specification. The engine automatically derives the table +schema and creates a data refresh pipeline to maintain the query result with the requested freshness. Users are relieved from +the burden of comprehending the concepts and differences between streaming and batch processing, and they do not have to directly +maintain Flink streaming or batch jobs. All operations are done on Materialized tables, which can significantly accelerate ETL pipeline +development. + +Here is an example to create a materialized table that is constantly refreshed with a data freshness of `3` minutes. + +```sql +-- 1. Create table schema and data refresh pipeline +CREATE MATERIALIZED TABLE dwd_orders +( + PRIMARY KEY(ds, id) NOT ENFORCED +) +PARTITIONED BY (ds) +FRESHNESS = INTERVAL '3' MINUTE +AS SELECT + o.ds + o.id, + o.order_number, + o.user_id, +... +FROM + orders as o + LEFT JOIN products FOR SYSTEM_TIME AS OF proctime() AS prod + ON o.product_id = prod.id + LEFT JOIN order_pay AS pay + ON o.id = pay.order_id and o.ds = pay.ds; + +-- 2. Pause the data refresh pipeline +ALTER MATERIALIZED TABLE dwd_orders SUSPEND; + +-- 3. Resume the data refresh pipeline +ALTER MATERIALIZED TABLE dwd_orders RESUME +-- Set table option via WITH clause +WITH( + 'sink.parallesim' = '10' +); + +-- Refresh historical partition manually +ALTER MATERIALIZED TABLE dwd_orders REFRESH PARTITION(ds='20231023'); +``` + +**More Information** +* [FLINK-35187](https://issues.apache.org/jira/browse/FLINK-35187) +* [FLIP-435](https://cwiki.apache.org/confluence/display/FLINK/FLIP-435%3A+Introduce+a+New+Materialized+Table+for+Simplifying+Data+Pipelines) +* [Materialized Table Overview](https://nightlies.apache.org/flink/flink-docs-release-1.20/docs/dev/table/materialized-table/overview/) + + +## Introduce Catalog-Related Syntax + +With the growing adoption of Flink SQL, implementations of Flink's `Catalog` interface play an increasingly important role. Today, Flink features a JDBC and a Hive catalog implementation and other open source projects such as Apache Paimon integrate with this interface as well. + +Now in Flink 1.20, you can use 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. + +```sql +Flink SQL> CREATE CATALOG `cat` WITH ('type'='generic_in_memory', 'default-database'='db'); +[INFO] Execute statement succeeded. + +Flink SQL> SHOW CREATE CATALOG `cat`; ++---------------------------------------------------------------------------------------------+ +| result | ++---------------------------------------------------------------------------------------------+ +| CREATE CATALOG `cat` WITH ( + 'default-database' = 'db', + 'type' = 'generic_in_memory' +) +| ++---------------------------------------------------------------------------------------------+ +1 row in set + +Flink SQL> DESCRIBE CATALOG `cat`; ++-----------+-------------------+ +| info name | info value | ++-----------+-------------------+ +| name | cat | +| type | generic_in_memory | +| comment | | ++-----------+-------------------+ +3 rows in set + +Flink SQL> ALTER CATALOG `cat` SET ('default-database'='new-db'); +[INFO] Execute statement succeeded. + +Flink SQL> SHOW CREATE CATALOG `cat`; ++-------------------------------------------------------------------------------------------------+ +| result | ++-------------------------------------------------------------------------------------------------+ +| CREATE CATALOG `cat` WITH ( + 'default-database' = 'new-db', + 'type' = 'generic_in_memory' +) +| ++-------------------------------------------------------------------------------------------------+ +1 row in set +``` + +**More Information** +* [FLINK-34914](https://issues.apache.org/jira/browse/FLINK-34914) +* [FLIP-436](https://cwiki.apache.org/confluence/display/FLINK/FLIP-436%3A+Introduce+Catalog-related+Syntax) + + +## Add DISTRIBUTED BY Clause + +Many SQL engines expose the concepts of `Partitioning`, `Bucketing`, or `Clustering`. We propose to introduce +the concept of `Bucketing` to Flink. + +Buckets enable load balancing in an external storage system by splitting data into disjoint subsets. It depends +heavily on the semantics of the underlying connector. However, a user can influence the bucketing behavior by +specifying the number of buckets, the bucketing algorithm, and (if the algorithm allows it) the columns which +are used for target bucket calculation. All bucketing components (i.e. bucket number, distribution algorithm, bucket key columns) Review Comment: we have used bucketting algorithm and distribution algorithm, I think they are the same name , I suggest only using one of these names for the algorithm. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. 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