knaufk commented on code in PR #680: URL: https://github.com/apache/flink-web/pull/680#discussion_r1356975072
########## docs/content/posts/2023-10-10-release-1.18.0.md: ########## @@ -0,0 +1,542 @@ +--- +authors: +- JingGe: + name: "Jing Ge" + twitter: jingengineer +- KonstantinKnauf: + name: "Konstantin Knauf" + twitter: snntrable +- SergeyNuyanzin: + name: "Sergey Nuyanzin" + twitter: uckamello +- QingshengRen: + name: "Qingsheng Ren" + twitter: renqstuite +date: "2023-10-10T08:00:00Z" +subtitle: "" +title: Announcing the Release of Apache Flink 1.18 +aliases: +- /news/2023/10/10/release-1.18.0.html +--- + +The Apache Flink PMC is pleased to announce the release of Apache Flink 1.18.0. As usual, we are looking at a packed +release with a wide variety of improvements and new features. Overall, 176 people contributed to this release completing +18 FLIPS and 700+ issues. Thank you! + +Let's dive into the highlights. + +# Towards a Streaming Lakehouse + +## Flink SQL Improvements + +### Introduce Flink JDBC Driver For Sql Gateway + +Flink 1.18 comes with a JDBC Driver for the Flink SQL Gateway. So, you can now use any SQL Client that supports JDBC to +interact with your tables via Flink SQL. Here is an example using [SQLLine](https://julianhyde.github.io/sqlline/manual.html). + +```shell +sqlline> !connect jdbc:flink://localhost:8083 +``` + +```shell +sqlline version 1.12.0 +sqlline> !connect jdbc:flink://localhost:8083 +Enter username for jdbc:flink://localhost:8083: +Enter password for jdbc:flink://localhost:8083: +0: jdbc:flink://localhost:8083> CREATE TABLE T( +. . . . . . . . . . . . . . .)> a INT, +. . . . . . . . . . . . . . .)> b VARCHAR(10) +. . . . . . . . . . . . . . .)> ) WITH ( +. . . . . . . . . . . . . . .)> 'connector' = 'filesystem', +. . . . . . . . . . . . . . .)> 'path' = 'file:///tmp/T.csv', +. . . . . . . . . . . . . . .)> 'format' = 'csv' +. . . . . . . . . . . . . . .)> ); +No rows affected (0.122 seconds) +0: jdbc:flink://localhost:8083> INSERT INTO T VALUES (1, 'Hi'), (2, 'Hello'); ++----------------------------------+ +| job id | ++----------------------------------+ +| fbade1ab4450fc57ebd5269fdf60dcfd | ++----------------------------------+ +1 row selected (1.282 seconds) +0: jdbc:flink://localhost:8083> SELECT * FROM T; ++---+-------+ +| a | b | ++---+-------+ +| 1 | Hi | +| 2 | Hello | ++---+-------+ +2 rows selected (1.955 seconds) +0: jdbc:flink://localhost:8083> +``` + +**More Information** +* [Documentation](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/jdbcdriver/) +* [FLIP-293: Introduce Flink Jdbc Driver For Sql Gateway](https://cwiki.apache.org/confluence/display/FLINK/FLIP-293%3A+Introduce+Flink+Jdbc+Driver+For+Sql+Gateway) + + +### Stored Procedures + +Stored Procedures provide a convenient way to encapsulate complex logic to perform data manipulation or administrative +tasks in Apache Flink itself. Therefore， Flink introduces the support for calling stored procedures. +Flink now allows catalog developers to develop their own built-in stored procedures and then enables users to call these +predefined stored procedures. + +**More Information** +* [Documentation](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/procedures/) +* [FLIP-311: Support Call Stored Procedure](https://cwiki.apache.org/confluence/display/FLINK/FLIP-311%3A+Support+Call+Stored+Procedure) + +### Extended DDL Support + +From this release onwards, Flink supports + +- `REPLACE TABLE AS SELECT` +- `CREATE OR REPLACE TABLE AS SELECT` + +and both these commands and previously supported `CREATE TABLE AS` can now support atomicity provided the underlying +connector supports this. + +Moreover, Apache Flink now supports TRUNCATE TABLE in batch execution mode. As before, the underlying connector needs +to implement and provide this capability + +And, finally, we have also added support for adding, dropping and listing partitions via + +- `ALTER TABLE ADD PARTITION` +- `ALTER TABLE DROP PARTITION` +- `SHOW PARTITIONS` + +**More Information** +- [Documentation on TRUNCATE](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/truncate/) +- [Documentation on CREATE OR REPLACE](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/create/#create-or-replace-table) +- [Documentation on ALTER TABLE](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/alter/#alter-table) +- [FLIP-302: Support TRUNCATE TABLE statement in batch mode](https://cwiki.apache.org/confluence/display/FLINK/FLIP-302%3A+Support+TRUNCATE+TABLE+statement+in+batch+mode) +- [FLIP-303: Support REPLACE TABLE AS SELECT statement](https://cwiki.apache.org/confluence/display/FLINK/FLIP-303%3A+Support+REPLACE+TABLE+AS+SELECT+statement) +- [FLIP-305: Support atomic for CREATE TABLE AS SELECT(CTAS) statement](https://cwiki.apache.org/confluence/display/FLINK/FLIP-305%3A+Support+atomic+for+CREATE+TABLE+AS+SELECT%28CTAS%29+statement) + +### Time Travelling + +Flink supports the time travel SQL syntax for querying historical versions of data that allows users to specify a point +in time and retrieve the data and schema of a table as it appeared at that time. With time travel, users can easily +analyze and compare historical versions of data. + +**More information** +- [Documentation](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/time-travel/) +- [FLIP-308: Support Time Travel](https://cwiki.apache.org/confluence/display/FLINK/FLIP-308%3A+Support+Time+Travel) + +## Streaming Execution Improvements + +### Support Operator-Level State TTL + +Starting from Flink 1.18, Table API and SQL users can set state time-to-live (TTL) individually for stateful operators. +This means that for scenarios like stream regular joins, users can now set different TTLs for the left and right +streams. In previous versions, state expiration could only be controlled at the pipeline level using the configuration +`table.exec.state.ttl`. With the introduction of operator-level state retention, users can now optimize resource +usage according to their specific requirements. + +**More Information** +- [Documentation](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/concepts/overview/#configure-operator-level-state-ttl) +- [FLIP-292: Enhance COMPILED PLAN to support operator-level state TTL configuration](https://cwiki.apache.org/confluence/display/FLINK/FLIP-292%3A+Enhance+COMPILED+PLAN+to+support+operator-level+state+TTL+configuration) + +### Watermark Alignment and Idleness Detection in SQL + +You can now configure [watermark alignment](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/#watermark-alignment) +and [source idleness timeouts](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/#dealing-with-idle-sources) +in pure SQL via hints. Previously, these features were only available in the DataStream API. + +**More Information** +- [Documentation](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/hints/) +- [FLIP-296: Extend watermark-related features for SQL](https://cwiki.apache.org/confluence/display/FLINK/FLIP-296%3A+Extend+watermark-related+features+for+SQL) + +## Batch Execution Improvements + +### Hybrid Shuffle supports Remote Storage + +Hybrid Shuffle supports storing the shuffle data in remote storage. The remote storage path can be configured with the +option `taskmanager.network.hybrid-shuffle.remote.path`. Hybrid Shuffle uses less network memory than before by +decoupling the memory usage from the number of parallelisms, improving the stability and ease of use. + +For more detailed +information, please refer to + +**More Information** +* [Documentation](https://nightlies.apache.org/flink/flink-docs-master/docs/ops/batch/batch\_shuffle/#hybrid-shuffle) +* [FLIP-301: Hybrid Shuffle supports Remote Storage](https://cwiki.apache.org/confluence/display/FLINK/FLIP-301%3A+Hybrid+Shuffle+supports+Remote+Storage) + + +### Performance Improvements & TPC-DS Benchmark + +In previous releases, the community has done a lot of work to improve batch processing performance, which has led to +significant improvements. In this release cycle, community contributors have continued to put a lot of effort into +improving batch performance. + +#### Runtime Filter for Flink SQL + +Runtime filter is a common optimization to improve join performance. It is designed to dynamically generate filter +conditions for certain Join queries at runtime to reduce the amount of scanned or shuffled data, avoid unnecessary I/O +and network transmission, and speed up the query. We introduced runtime filters in Flink 1.18, and verified its +effectiveness through the TPC-DS benchmark, and observed up to 3x speedup for some queries by enabling this feature. + +#### Operator Fusion Codegen for Flink SQL + +Operator Fusion Codegen improves the execution performance of a query by fusing an operator DAG into a single optimized +operator that eliminates virtual function calls, leverages CPU registers for intermediate data and reduces the +instruction cache miss. As a general technical optimization, we verified its effectiveness through TPC-DS, and +only some batch operators completed fusion codegen support in version 1.18, getting significant performance gains on +some query. + +Note that both features are considered experimental and disabled by default in Flink 1.18. +They can be enabled using `table.optimizer.runtime-filter.enabled` and `able.exec.operator-fusion-codegen.enabled` +respectively. + +Since Flink 1.16, the Apache Flink Community has been continuously tracking the performance of its batch engine via the +TPC-DS benchmarking framework. After significant improvements in Flink 1.17 (dynamic join-reordering, +dynamic local aggregations), the two improvements described in the previous sections lead to 12% performance improvement +compared to Flink 1.17 , a 35% performance improvement compared to Flink 1.16 on a 10T dataset for partitioned tables. + +<div style="text-align: center;"> +<img src="/img/blog/2023-10-10-release-1.18.0/tpc-ds-benchmark.png" style="width:90%;margin:15px"> +</div> + +**More Information** +* [FLIP-324: Introduce Runtime Filter for Flink Batch Jobs](https://cwiki.apache.org/confluence/display/FLINK/FLIP-324%3A+Introduce+Runtime+Filter+for+Flink+Batch+Jobs) +* [FLIP-315: Support Operator Fusion Codegen for Flink SQL](https://cwiki.apache.org/confluence/display/FLINK/FLIP-315+Support+Operator+Fusion+Codegen+for+Flink+SQL) + +# Towards Cloud-Native Elasticity + +Elasticity describes the ability of a system to adapt to workload changes in a non-disruptive ideally automatic manner. +It is a defining characteristic of cloud-native systems and for long-running streaming workloads it is particularly +important. As such elasticity improvements are an area of continuous investment in the Apache Flink community. +Recent initiatives include the Kubernetes +[Autoscaler](https://nightlies.apache.org/flink/flink-kubernetes-operator-docs-release-1.6/docs/custom-resource/autoscaler/), +numerous improvements to rescaling performance and last but not least +the [Adaptive Scheduler](https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/deployment/elastic_scaling/#adaptive-scheduler). + +The Adaptive Scheduler was first introduced in Flink 1.15 and constitutes a centerpiece of a fully-elastic +Apache Flink deployment. At its core, it allows jobs to change their resource requirements and parallelism during +runtime. In addition, it also adapts to the available resources in the cluster by only rescaling once the cluster can +satisfy the minimum required resources of the job. + +Until Flink 1.18, the adaptive scheduler was primarily used in +[Reactive Mode](https://nightlies.apache.org/flink/flink-docs-master/docs/deployment/elastic_scaling/#reactive-mode), which meant that a single job by design would always use all the available resources in the cluster. Please see [this blog post](https://flink.apache.org/2021/05/06/scaling-flink-automatically-with-reactive-mode/) on how to autoscale Flink Jobs in Reactive Mode using a Horizontal Pod Autoscaler on Kubernetes. + +With Flink 1.18 the adaptive scheduler becomes much more powerful and more widely applicable and is on a trajectory to becoming the default scheduler for streaming workloads on Apache Flink. + + +## Dynamic Fine-Grained Rescaling via REST API + +Despite the underlying capabilities of the Adaptive Scheduler, the ability to change the resource requirements of a +Job during runtime had not yet been exposed to the end user directly. This changes in Flink 1.18. You can now change +the parallelism of any individual task of your job via the Flink Web UI and REST API while the job is running. + +{{< youtube B1NVDTazsZY >}} + +Under the hood, Apache Flink performs a regular rescaling operation as soon as the required resources for the new +parallelism have been acquired. The rescaling operation is not based on a Savepoint, but on an ordinary, periodic +checkpoint, which means it doesn’t introduce any additional snapshot. As you can see in the video above, the rescaling +operation happens nearly instantaneously and with a very short downtime for jobs with small state size. + +In conjunction with the +[backpressure monitor](https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/ops/monitoring/back_pressure/) +of the Apache Flink Web UI, it is now easier than ever to find and maintain an efficient, backpressure-free parallelism +for each of the tasks: + +- If a task is very busy (red), you increase the parallelism. +- If a task is mostly idle (blue), you decrease the parallelism. + +<div style="text-align: center;"> +<img src="/img/blog/2023-10-10-release-1.18.0/backpressure_monitor.png" style="width:90%;margin:15px"> +</div> + +**More Information** +* [Documentation](missing) Review Comment: Yes! @dmvk Where are we on this? -- 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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