davidradl commented on code in PR #680: URL: https://github.com/apache/flink-web/pull/680#discussion_r1371371189
########## docs/content/posts/2023-10-10-release-1.18.0.md: ########## @@ -0,0 +1,572 @@ +--- +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, 174 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-stable/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 Procedure Support for Flink Connectors + +Stored procedures have been an indispensable tool in traditional databases, +offering a convenient way to encapsulate complex logic for data manipulation +and administrative tasks. They also offer the potential for enhanced +performance, since they can trigger the handling of data operations directly +within an external database. Other popular data systems like Trino and Iceberg +automate and simplify common maintenance tasks into small sets of procedures, +which greatly reduces users' administrative burden. + +This new update primarily targets developers of Flink connectors, who can now +predefine custom stored procedures into connectors via the Catalog interface. +The primary benefit to users is that connector-specific tasks that previously +may have required writing custom Flink code can now be replaced with simple +calls that encapsulate, standardize, and potentially optimize the underlying +operations. Users can execute procedures using the familiar `CALL` syntax, and +discover a connector's available procedures with `SHOW PROCEDURES`. Stored +procedures within connectors improves the extensibility of Flink's SQL and +Table APIs, and should unlock smoother data access and management for users. + +**More Information** +* [Documentation](https://nightlies.apache.org/flink/flink-docs-stable/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 also supports this. + +Moreover, Apache Flink now supports TRUNCATE TABLE in batch execution mode. Same as before, the underlying connector needs +to implement and provide this capability + +And, finally, we have also implemented 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-stable/docs/dev/table/sql/truncate/) +- [Documentation on CREATE OR REPLACE](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/table/sql/create/#create-or-replace-table) +- [Documentation on ALTER TABLE](https://nightlies.apache.org/flink/flink-docs-stable/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 Traveling + +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-stable/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 in Table API & SQL + +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-stable/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](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/concepts/time_attributes/#advanced-watermark-features). Previously, these features were only available in the DataStream API. + +**More Information** +- [Documentation](https://nightlies.apache.org/flink/flink-docs-stable/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. + +**More Information** +* [Documentation](https://nightlies.apache.org/flink/flink-docs-stable/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 worked extensively to improve Flink's batch processing performance, which has led to +significant improvements. In this release cycle, community contributors continued to put significant effort into +further improving Flink's batch performance. + +#### Runtime Filter for Flink SQL + +Runtime filter is a common method for optimizing 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 (Calc, HashAgg, and HashJoin) 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 by using `table.optimizer.runtime-filter.enabled` and `table.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 (operator fusion, runtime filters) +lead to 14% performance improvement compared to Flink 1.17, a 54% 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) +* [Benchmarking repository](https://github.com/ververica/flink-sql-benchmark) + +# 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-stable/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-stable/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 has 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 does not introduce any additional snapshot. As you can see in the video above, the rescaling +operation already 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-stable/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) +* [FLIP-291: Externalized Declarative Resource Management](https://cwiki.apache.org/confluence/display/FLINK/FLIP-291%3A+Externalized+Declarative+Resource+Management) + +## Faster Rescaling with RocksDB + +The rescaling times when using RocksDB Statebackend with incremental checkpoints have been improved about 30% in the 99th quantile. + +We increased the potential for parallel download from just downloading state handles in parallel to downloading individual files in parallel. + +Furthermore, we deactivated write-ahead-logging for batch-inserting into the temporary RocksDB instances we use for rescaling. + +<div style="text-align: center;"> +<img src="/img/blog/2023-10-10-release-1.18.0/rescaling_performance.png" style="width:90%;margin:15px"> +</div> + +**More Information** +* [FLINK-32326](https://issues.apache.org/jira/browse/FLINK-32326) +* [FLINK-32345](https://issues.apache.org/jira/browse/FLINK-32345) + +# Support for Java 17 + +Java 17 was released in 2021 and is the latest long-term support (LTS) release of Java with an end-of-life in 2029. +So, it was about time that Apache Flink added support for it. What does this mean concretely? As of Flink 1.18, you can +now run Apache Flink on Java 17 and the [official Docker repository](https://hub.docker.com/_/flink) includes an image +based on Java 17. + + docker pull flink:1.18.0-java17 + +If your cluster runs on Java 17, this of course, also allows you to use Java 17 features in your user programs and to +compile it to a target version of Java 17. + +**More Information** +* [Documentation](https://nightlies.apache.org/flink/flink-docs-stable/docs/deployment/java_compatibility/) +* [FLINK-15736](https://issues.apache.org/jira/browse/FLINK-15736) + +# Others Improvements + +## Production-Ready Watermark Alignment + +Supported as “Beta” since Flink 1.16 and Flink 1.17, +[watermark alignment](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/#watermark-alignment) Review Comment: I think the link should be https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/#watermark-alignment-_beta_ -- This is an automated message from the Apache Git Service. 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