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| <h1> |
| <a href="/2023/03/23/announcing-the-release-of-apache-flink-1.17/">Announcing the Release of Apache Flink 1.17</a> |
| </h1> |
| |
| |
| |
| March 23, 2023 - |
| |
| |
| |
| Leonard Xu |
| |
| <a href="https://twitter.com/Leonardxbj">(@Leonardxbj)</a> |
| |
| |
| |
| |
| <p><p>The Apache Flink PMC is pleased to announce Apache Flink release 1.17.0. Apache |
| Flink is the leading stream processing standard, and the concept of unified |
| stream and batch data processing is being successfully adopted in more and more |
| companies. Thanks to our excellent community and contributors, Apache Flink |
| continues to grow as a technology and remains one of the most active projects in |
| the Apache Software Foundation. Flink 1.17 had 172 contributors enthusiastically |
| participating and saw the completion of 7 FLIPs and 600+ issues, bringing many |
| exciting new features and improvements to the community.</p> |
| <h1 id="towards-streaming-warehouses"> |
| Towards Streaming Warehouses |
| <a class="anchor" href="#towards-streaming-warehouses">#</a> |
| </h1> |
| <p>In order to achieve greater efficiency in the realm of <a href="https://www.alibabacloud.com/blog/more-than-computing-a-new-era-led-by-the-warehouse-architecture-of-apache-flink_598821">streaming |
| warehouse</a>, |
| Flink 1.17 contains substantial improvements to both the performance of batch |
| processing and the semantics of streaming processing. These improvements |
| represent a significant stride towards the creation of a more efficient and |
| streamlined data warehouse, capable of processing large quantities of data in |
| real-time.</p> |
| <p>For batch processing, this release includes several new features and |
| improvements:</p> |
| <ul> |
| <li><strong>Streaming Warehouse API:</strong> |
| <a href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=235838061">FLIP-282</a> |
| introduces the new Delete and Update API in Flink SQL which only works in batch |
| mode. External storage systems like Flink Table Store can implement row-level |
| modification via this new API. The ALTER TABLE syntax is enhanced by including the |
| ability to ADD/MODIFY/DROP columns, primary keys, and watermarks, making it |
| easier for users to maintain their table schema.</li> |
| <li><strong>Batch Execution Improvements:</strong> Execution of batch workloads has been |
| significantly improved in Flink 1.17 in terms of performance, stability and |
| usability. Performance wise, a 26% TPC-DS improvement on 10T dataset is achieved |
| with strategy and operator optimizations, such as new join reordering and adaptive |
| local hash aggregation, Hive aggregate functions improvements, and the |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/ops/batch/batch_shuffle/#hybrid-shuffle">Hybrid Shuffle Mode</a> |
| enhancements. Stability wise, |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/deployment/speculative_execution/">Speculative Execution</a> |
| now supports all operators, and the |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/deployment/elastic_scaling/#adaptive-batch-scheduler">Adaptive Batch Scheduler</a> |
| is more robust against data skew. Usability wise, the tuning effort required |
| for batch workloads has been reduced. The is now the default scheduler in |
| batch mode. The Hybrid Shuffle Mode is compatible with Speculative Execution |
| and the Adaptive Batch Scheduler, next to various configuration simplifications.</li> |
| <li><strong>SQL Client/Gateway:</strong> Apache Flink 1.17 introduces the “gateway mode” for |
| SQL Client, allowing users to submit SQL queries to a SQL Gateway for enhanced |
| functionality. Users can use SQL statements to manage job lifecycles, |
| including displaying job information and stopping running jobs. This provides |
| a powerful tool for managing Flink jobs.</li> |
| </ul> |
| <p>For stream processing, the following features and improvements are realized:</p> |
| <ul> |
| <li><strong>Streaming SQL Semantics:</strong> Non-deterministic operations may bring incorrect |
| results or exceptions which is a challenging topic in streaming SQL. Incorrect |
| optimization plans and functional issues have been fixed, and the experimental |
| feature of <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/dev/table/sql/explain/#explaindetails">PLAN_ADVICE</a> |
| is introduced to inform of potential correctness risks and optimization |
| suggestions to SQL users.</li> |
| <li><strong>Checkpoint Improvements:</strong> The generic incremental checkpoint improvements |
| enhance the speed and stability of the checkpoint procedure, and the unaligned |
| checkpoint has improved stability under backpressure and is production-ready |
| in Flink 1.17. Users can manually trigger checkpoints with self-defined |
| checkpoint types while a job is running with the newly introduced REST |
| interface for triggering checkpoints.</li> |
| <li><strong>Watermark Alignment Enhancement:</strong> Efficient watermark processing directly |
| affects the execution efficiency of event time applications. In Flink 1.17, |
| <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-217%3A+Support+watermark+alignment+of+source+splits">FLIP-217</a> |
| introduces an improvement to watermark alignment by aligning data emission |
| across splits within a source operator. This improvement results in more |
| efficient coordination of watermark progress in the source, which in turn |
| mitigates excessive buffering by downstream operators and enhances the overall |
| efficiency of steaming job execution.</li> |
| <li><strong>StateBackend Upgrade:</strong> The updated version of |
| <a href="https://github.com/ververica/frocksdb">FRocksDB</a> to 6.20.3-ververica-2.0 |
| brings improvements to RocksDBStateBackend like sharing memory between slots, |
| and now supports Apple Silicon chipsets like the Mac M1.</li> |
| </ul> |
| <h1 id="batch-processing"> |
| Batch processing |
| <a class="anchor" href="#batch-processing">#</a> |
| </h1> |
| <p>As a unified stream and batch data processing engine, Flink stands out |
| particularly in the field of stream processing. In order to improve its batch |
| processing capabilities, the community contributors put in a lot of effort into |
| improving Flink’s batch performance and ecosystem in version 1.17. This makes it |
| easier for users to build a streaming warehouse based on Flink.</p> |
| <h2 id="speculative-execution"> |
| Speculative Execution |
| <a class="anchor" href="#speculative-execution">#</a> |
| </h2> |
| <p><a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/deployment/speculative_execution/">Speculative Execution</a> |
| for sinks is now supported. Previously, Speculative |
| Execution was not enabled for sinks to avoid instability or incorrect results. |
| In Flink 1.17, the context of sinks is improved so that sinks, including <a href="https://github.com/apache/flink/blob/release-1.17/flink-core/src/main/java/org/apache/flink/api/connector/sink2/Sink.java">new |
| sinks</a> |
| and <a href="https://github.com/apache/flink/blob/release-1.17/flink-core/src/main/java/org/apache/flink/api/common/io/OutputFormat.java">OutputFormat |
| sinks</a>, |
| are aware of the number of attempts. With the number of attempts, sinks are able |
| to isolate the produced data of different attempts of the same subtask, even if |
| the attempts are running at the same time. The <em>FinalizeOnMaster</em> interface is |
| also improved so that OutputFormat sinks can see which attempts are finished and |
| then properly commit the written data. Once a sink can work well with concurrent |
| attempts, it can implement the decorative interface |
| <a href="https://github.com/apache/flink/blob/release-1.17/flink-core/src/main/java/org/apache/flink/api/common/SupportsConcurrentExecutionAttempts.java">SupportsConcurrentExecutionAttempts</a> |
| so that Speculative Execution is allowed to be performed on it. Some built in |
| sinks are enabled to do Speculative Execution, including DiscardingSink, |
| PrintSinkFunction, PrintSink, FileSink, FileSystemOutputFormat and |
| HiveTableSink.</p> |
| <p>The slow task detection is improved for Speculative Execution. Previously, it |
| only considered the execution time of tasks when deciding which tasks are slow. |
| It now takes the input data volume of tasks into account. Tasks which have a |
| longer execution time but consume more data may not be considered as slow. This |
| improvement helps to eliminate the negative impacts from data skew on slow task |
| detection.</p> |
| <h2 id="adaptive-batch-scheduler"> |
| Adaptive Batch Scheduler |
| <a class="anchor" href="#adaptive-batch-scheduler">#</a> |
| </h2> |
| <p><a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/deployment/elastic_scaling/#adaptive-batch-scheduler">Adaptive Batch Scheduler</a> |
| is now used for batch jobs by default. This scheduler can automatically decide |
| a proper parallelism of each job vertex, based on how much data the vertex processes. |
| It is also the only scheduler which supports speculative execution.</p> |
| <p>The configuration of Adaptive Batch Scheduler is improved for ease of use. Users |
| no longer need to explicitly set the global default parallelism to -1 to enable |
| automatically deciding parallelism. Instead, the global default parallelism, if |
| set, will be used as the upper bound when deciding the parallelism. The keys of |
| Adaptive Batch Scheduler configuration options are also renamed to be easier to |
| understand.</p> |
| <p>The capabilities of Adaptive Batch Scheduler are also improved. It now supports |
| evenly distributing data to downstream tasks, based on fine-grained data |
| distribution information. The limitation that the decided parallelism of |
| vertices can only be a power of 2 is no longer needed and therefore removed.</p> |
| <h2 id="hybrid-shuffle-mode"> |
| Hybrid Shuffle Mode |
| <a class="anchor" href="#hybrid-shuffle-mode">#</a> |
| </h2> |
| <p>Various important improvements to the Hybrid Shuffle Mode are available in this |
| release.</p> |
| <ul> |
| <li>Hybrid Shuffle Mode now supports Adaptive Batch Scheduler and Speculative |
| Execution.</li> |
| <li>Hybrid Shuffle Mode now supports reusing intermediate data when possible, |
| which brings significant performance improvements.</li> |
| <li>The stability is improved to avoid stability issues in large scale production.</li> |
| </ul> |
| <p>More details can be found at the |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/ops/batch/batch_shuffle/#hybrid-shuffle">Hybrid-Shuffle</a> |
| section of the documentation.</p> |
| <h2 id="tpc-ds-benchmark"> |
| TPC-DS Benchmark |
| <a class="anchor" href="#tpc-ds-benchmark">#</a> |
| </h2> |
| <p>Starting with Flink 1.16, the performance of the Batch engine has continuously |
| been optimized. In Flink 1.16, dynamic partition pruning was introduced, but not |
| all TPC-DS queries could be optimized. In Flink 1.17, the algorithm has been |
| improved, and most of the TPC-DS results are now optimized. In Flink 1.17, a |
| dynamic programming join-reorder algorithm is introduced, which has a better |
| working, larger search space compared to the previous algorithm.. The planner |
| can automatically select the appropriate join-reorder algorithm based on the |
| number of joins in a query, so that users no longer need to care about the join-reorder |
| algorithms. (Note: the join-reorder is disabled by default, and you need |
| to enable it when running TPC-DS.) In the operator layer, dynamic hash local |
| aggregation strategy is introduced, which can dynamically determine according to |
| the data distribution whether the local hash aggregation operation is needed to |
| improve performance. In the runtime layer, some unnecessary virtual function |
| calls are removed to speed up the execution. To summarize, Flink 1.17 has a 26% |
| performance improvement compared to Flink 1.16 on a 10T dataset for partitioned |
| tables.</p> |
| <center> |
| <img src="/img/blog/2023-03-23-release-1.17.0/tpc-ds-benchmark.png" style="width:90%;margin:15px"> |
| </center> |
| <h2 id="sql-client--gateway"> |
| SQL Client / Gateway |
| <a class="anchor" href="#sql-client--gateway">#</a> |
| </h2> |
| <p>Apache Flink 1.17 introduces a new feature called “gateway mode” for the SQL |
| Client, which enhances its functionality by allowing it to connect to a remote |
| gateway and submit SQL queries like it does in embedded mode. This new mode |
| offers users much more convenience when working with the SQL Gateway.</p> |
| <p>In addition, the SQL Client/SQL Gateway now provides new support for managing |
| job lifecycles through SQL statements. Users can use SQL statements to display |
| all job information stored in the JobManager and stop running jobs using their |
| unique job IDs. With this new feature, SQL Client/Gateway now has almost the |
| same functionality as Flink CLI, making it another powerful tool for managing |
| Flink jobs.</p> |
| <h2 id="sql-api"> |
| SQL API |
| <a class="anchor" href="#sql-api">#</a> |
| </h2> |
| <p>Row-Level SQL Delete & Update are becoming more and more important in modern big |
| data workflows. The use cases include deleting a set of rows for regulatory |
| compliance, updating a set of rows for data correction, etc. Many popular |
| engines such as Trino or Hive have supported them. In Flink 1.17, the new Delete & |
| Update API is introduced in Flink, which works in batch mode and is exposed to |
| connectors. Now external storage systems can implement row-level modification via |
| this new API. Furthermore, the ALTER TABLE syntax is extended to include the |
| ability to ADD/MODIFY/DROP columns, primary keys, and watermarks. These enhanced |
| capabilities provide users with the flexibility to maintain their table schema |
| metadata according to their needs.</p> |
| <h2 id="hive-compatibility"> |
| Hive Compatibility |
| <a class="anchor" href="#hive-compatibility">#</a> |
| </h2> |
| <p>Apache Flink 1.17 brings new improvements to the Hive table sink, making it more |
| efficient than ever before. In previous versions, the Hive table sink only |
| supported automatic file compaction in streaming mode, but not in batch mode. In |
| Flink 1.17, the Hive table sink can now automatically compact newly written files |
| in batch mode as well. This feature can greatly reduce the number of small files. |
| Also, for using Hive built-in functions via |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/connectors/table/hive/hive_functions/">HiveModule</a>, |
| Flink introduces several native Hive aggregation functions including |
| SUM/COUNT/AVG/MIN/MAX to HiveModule. These functions can be executed using the |
| hash-based aggregation operator and then bring significant performance |
| improvements.</p> |
| <h1 id="streaming-processing"> |
| Streaming Processing |
| <a class="anchor" href="#streaming-processing">#</a> |
| </h1> |
| <p>In Flink 1.17, difficult Streaming SQL semantics and correctness issues are |
| addressed, checkpoint performance is optimized, watermark alignment is enhanced, |
| Streaming FileSink expands ABFS(Azure Blob Filesystem) support, Calcite and |
| FRocksDB have been upgraded to newer versions. These improvements further |
| enhance the capabilities of Flink in the field of stream processing.</p> |
| <h2 id="streaming-sql-semantics"> |
| Streaming SQL Semantics |
| <a class="anchor" href="#streaming-sql-semantics">#</a> |
| </h2> |
| <p>In terms of correctness and semantic enhancement, Flink 1.17 introduces the |
| experimental feature |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/dev/table/sql/explain/#explaindetails">PLAN_ADVICE</a> |
| that detects potential correctness risks and provides optimization suggestions. |
| For example, if a <a href="https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/concepts/determinism/#3-determinism-in-streaming-processing">NDU (Non-deterministic |
| Updates)</a> |
| issue is detected by EXPLAIN PLAN_ADVICE, the optimizer will append the advice |
| at the end of the physical plan, the optimizer will then tag the advice id to the |
| relational node of related operations, and recommend that users update their |
| configurations accordingly. By providing users with this specific advice, the |
| optimizer can help them improve the accuracy and reliability of their query |
| results.</p> |
| <pre tabindex="0"><code>== Optimized Physical Plan With Advice == |
| ... |
| |
| advice[1]: [WARNING] The column(s): day(generated by non-deterministic function: CURRENT_TIMESTAMP ) can not satisfy the determinism requirement for correctly processing update message('UB'/'UA'/'D' in changelogMode, not 'I' only), this usually happens when input node has no upsertKey(upsertKeys=[{}]) or current node outputs non-deterministic update messages. Please consider removing these non-deterministic columns or making them deterministic by using deterministic functions. |
| </code></pre><p>The PLAN_ADVICE also helps users improve the performance and efficiency of their |
| queries. For example, when a GroupAggregate operation is detected and can be |
| optimized to a more efficient local-global aggregation. By providing users with |
| this specific advice for optimization, the optimizer enables users to easily |
| improve the performance and efficiency of their queries.</p> |
| <pre tabindex="0"><code>== Optimized Physical Plan With Advice == |
| ... |
| advice[1]: [ADVICE] You might want to enable local-global two-phase optimization by configuring ('table.optimizer.agg-phase-strategy' to 'AUTO'). |
| </code></pre><p>In addition, Flink 1.17 resolved several incorrect plan optimizations which led |
| to incorrect results reported in |
| <a href="https://issues.apache.org/jira/browse/FLINK-29849">FLINK-29849</a>, |
| <a href="https://issues.apache.org/jira/browse/FLINK-30006">FLINK-30006</a>, and |
| <a href="https://issues.apache.org/jira/browse/FLINK-30841">FLINK-30841</a>.</p> |
| <h2 id="checkpoint-improvements"> |
| Checkpoint Improvements |
| <a class="anchor" href="#checkpoint-improvements">#</a> |
| </h2> |
| <p>Generic Incremental Checkpoint (GIC) aims to improve the speed and stability of |
| the checkpoint procedure. Some experimental results in the WordCount case are |
| shown as below. More details can be found in this <a href="https://www.ververica.com/blog/generic-log-based-incremental-checkpoint">blog |
| post</a>.</p> |
| <p>Table1: Benefits after enabling GIC in WordCount case</p> |
| <center> |
| <img src="/img/blog/2023-03-23-release-1.17.0/beneifts-after-enabling-gic-in-wordcount-case.png" style="width:90%;margin:15px"> |
| </center> |
| <p>Table2: Costs after enabling GIC in WordCount case</p> |
| <center> |
| <img src="/img/blog/2023-03-23-release-1.17.0/costs-after-enabling-gic-in-wordcount-case.png" style="width:90%;margin:15px"> |
| </center> |
| <p>Unaligned Checkpoints (UC) greatly increase the completed ratio of checkpoints |
| under backpressure. The previous UC implementation would write many small files |
| which may cause high load for the namenode of HDFS. In this release, this problem |
| is resolved to make UC more usable in the production environment.</p> |
| <p>In 1.17, a <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/ops/rest_api/#jobs-jobid-checkpoints-1">REST |
| API</a> |
| is provided so that users can manually trigger checkpoints with a self-defined |
| checkpoint type while a job is running. For example, for a job running with |
| incremental checkpoint, users can trigger a full checkpoint periodically or |
| manually to break the incremental checkpoint chain to avoid referring to files |
| from a long time ago.</p> |
| <h2 id="watermark-alignment-support"> |
| Watermark Alignment Support |
| <a class="anchor" href="#watermark-alignment-support">#</a> |
| </h2> |
| <p>In earlier versions, <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-182%3A+Support+watermark+alignment+of+FLIP-27+Sources">FLIP-182</a> |
| proposed a solution called watermark alignment to tackle the issue of data skew |
| in event time applications caused by imbalanced sources. However, it had a |
| limitation that the source parallelism had to match the number of splits. This |
| was because a source operator with multiple splits might need to buffer a |
| considerable amount of data if one split emitted data faster than another. To |
| address this limitation, Flink 1.17 introduced <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-217%3A+Support+watermark+alignment+of+source+splits">FLIP-217</a>, |
| which enhances watermark alignment to align data emission across splits within |
| a source operator while considering watermark boundaries. This enhancement |
| ensures more coordinated watermark progress in the source, preventing downstream |
| operators from buffering excessive data and improving the execution efficiency of |
| streaming jobs.</p> |
| <h2 id="streaming-filesink-expansion"> |
| Streaming FileSink Expansion |
| <a class="anchor" href="#streaming-filesink-expansion">#</a> |
| </h2> |
| <p>Following the addition of ABFS support, the |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/connectors/datastream/filesystem/#file-sink">FileSink</a> |
| is now able to function in streaming mode with a total of five different |
| filesystems: HDFS, S3, OSS, ABFS, and Local. This expansion effectively covers |
| the majority of main filesystems, providing a comprehensive range of options and |
| increased versatility for users.</p> |
| <h2 id="rocksdbstatebackend-upgrade"> |
| RocksDBStateBackend Upgrade |
| <a class="anchor" href="#rocksdbstatebackend-upgrade">#</a> |
| </h2> |
| <p>This release has updated the version of |
| <a href="https://github.com/ververica/frocksdb">FRocksDB</a> to 6.20.3-ververica-2.0 which |
| brings improvements for RocksDBStateBackend:</p> |
| <ol> |
| <li>Support build FRocksDB Java on Apple Silicon chipsets, such as Mac M1 and M2.</li> |
| <li>Improve the performance of compaction filter by avoiding expensive ToString()</li> |
| <li>Upgrade ZLIB version of FRocksDB to avoid memory corruption</li> |
| <li>Add periodic_compaction_seconds option to RocksJava</li> |
| </ol> |
| <p>Please see <a href="https://issues.apache.org/jira/browse/FLINK-30836">FLINK-30836</a> for |
| more details.</p> |
| <p>This release also widens the scope of sharing memory between slots to |
| TaskManager, which can help to increase the memory efficiency if the memory |
| usage of slots in a TaskManager is uneven. Furthermore, it can reduce the |
| overall memory consumption at the expense of resource isolation after tuning. |
| Read more about |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/docs/deployment/config/#state-backend-rocksdb-memory-fixed-per-tm">state.backend.rocksdb.memory.fixed-per-tm</a> |
| configuration.</p> |
| <h2 id="calcite-upgrade"> |
| Calcite Upgrade |
| <a class="anchor" href="#calcite-upgrade">#</a> |
| </h2> |
| <p>Flink 1.17 is upgraded to <a href="https://calcite.apache.org/">Calcite</a> version 1.29.0 |
| to improve the performance and efficiency of the Flink SQL system. Flink 1.16 |
| uses Calcite 1.26.0 which has severe issues with RexNode simplification caused |
| by the SEARCH operator. This leads to wrong data from query optimization as |
| reported in <a href="https://issues.apache.org/jira/browse/CALCITE-4325">CALCITE-4325</a> and <a href="https://issues.apache.org/jira/browse/CALCITE-4352"> |
| CALCITE-4352</a>. By upgrading |
| the version of Calcite, Flink can take advantage of its improved |
| performance and new features in Flink SQL processing. This resolves multiple |
| bugs and leads to faster query processing times.</p> |
| <h1 id="others"> |
| Others |
| <a class="anchor" href="#others">#</a> |
| </h1> |
| <h2 id="pyflink"> |
| PyFlink |
| <a class="anchor" href="#pyflink">#</a> |
| </h2> |
| <p>The Flink 1.17 release includes updates to PyFlink, the Python interface for |
| Apache Flink. Notable improvements include support for Python 3.10 and execution |
| capabilities on Apple Silicon chipsets, such as the Mac M1 and M2 computers. |
| Additionally, the release includes minor optimizations that enhance |
| cross-process communication stability between Java and Python processes, enable |
| the specification of data types of Python UDFs via strings to improve usability, |
| and support access to job parameters in Python UDFs. This release focuses on |
| improving PyFlink’s functionality and usability, rather than introducing new |
| major features. However, these enhancements are expected to improve the user |
| experience and facilitate efficient data processing.</p> |
| <h2 id="daily-performance-benchmark"> |
| Daily Performance Benchmark |
| <a class="anchor" href="#daily-performance-benchmark">#</a> |
| </h2> |
| <p>In Flink 1.17, daily performance monitoring has been integrated into the |
| <a href="https://apache-flink.slack.com/archives/C0471S0DFJ9">#flink-dev-benchmarks Slack channel</a>. This feature is crucial in quickly |
| identifying regressions and ensuring the quality of the code. Once a regression |
| is identified through the Slack channel or the <a href="http://codespeed.dak8s.net:8000">speed |
| center</a>, developers can refer to the guidance |
| provided in the <a href="https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=115511847">Benchmark’s |
| wiki</a> |
| to address the issue effectively. This feature helps the community take a |
| proactive approach in ensuring system performance, resulting in a better product |
| and increased user satisfaction.</p> |
| <h2 id="subtask-level-flame-graph"> |
| Subtask Level Flame Graph |
| <a class="anchor" href="#subtask-level-flame-graph">#</a> |
| </h2> |
| <p>Starting with Flink 1.17, Flame Graph provides “drill down” visualizations to |
| the task level, which allows users to gain a more detailed understanding of the |
| performance of their tasks. This feature is a significant improvement over |
| previous versions of Flame Graph, as it empowers users to select a subtask of |
| interest and see the corresponding flame graph. By doing so, users can identify |
| specific areas where their tasks may be experiencing performance issues and take |
| steps to address them. This can lead to significant improvements in the overall |
| efficiency and effectiveness of their data processing pipelines.</p> |
| <center> |
| <img src="/img/blog/2023-03-23-release-1.17.0/subtask-level-flame-graph.png" style="width:90%;margin:15px"> |
| </center> |
| <h2 id="generalized-delegation-token-support"> |
| Generalized Delegation Token Support |
| <a class="anchor" href="#generalized-delegation-token-support">#</a> |
| </h2> |
| <p>Previously, Flink supported Kerberos authentication and Hadoop based tokens. |
| With |
| <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-272%3A+Generalized+delegation+token+support">FLIP-272</a> |
| being finalized, Flink’s delegation token framework is generalized to make it |
| authentication protocol agnostic. This will allow contributors in the future |
| to add support for non-Hadoop compliant frameworks where the authentication |
| protocol is not based on Kerberos. Additionally, |
| <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-211%3A+Kerberos+delegation+token+framework">FLIP-211</a> |
| was implemented which improves Flink’s interactions with Kerberos: It reduces |
| the number of requests that are necessary to exchange delegation tokens in Flink.</p> |
| <h1 id="upgrade-notes"> |
| Upgrade Notes |
| <a class="anchor" href="#upgrade-notes">#</a> |
| </h1> |
| <p>The Flink community tries to ensure that upgrades are as seamless as possible. |
| However, certain changes may require users to make adjustments to certain parts |
| of the program when upgrading to version 1.17. Please refer to the |
| <a href="https://nightlies.apache.org/flink/flink-docs-release-1.17/release-notes/flink-1.17/">release notes</a> |
| for a comprehensive list of adjustments to make and issues to check during the |
| upgrading process.</p> |
| <h1 id="list-of-contributors"> |
| List of Contributors |
| <a class="anchor" href="#list-of-contributors">#</a> |
| </h1> |
| <p>The Apache Flink community would like to express gratitude to all the |
| contributors who made this release possible:</p> |
| <p>Ahmed Hamdy, Aitozi, Aleksandr Pilipenko, Alexander Fedulov, Alexander Preuß, |
| Anton Kalashnikov, Arvid Heise, Bo Cui, Brayno, Carlos Castro, ChangZhuo Chen |
| (陳昌倬), Chen Qin, Chesnay Schepler, Clemens, ConradJam, Danny Cranmer, Dawid |
| Wysakowicz, Dian Fu, Dong Lin, Dongjoon Hyun, Elphas Toringepi, Eric Xiao, |
| Fabian Paul, Ferenc Csaky, Gabor Somogyi, Gen Luo, Gunnar Morling, Gyula Fora, |
| Hangxiang Yu, Hong Liang Teoh, HuangXingBo, Jacky Lau, Jane Chan, Jark Wu, |
| Jiale, Jin, Jing Ge, Jinzhong Li, Joao Boto, John Roesler, Jun He, JunRuiLee, |
| Junrui Lee, Juntao Hu, Krzysztof Chmielewski, Leonard Xu, Licho, Lijie Wang, |
| Mark Canlas, Martijn Visser, MartijnVisser, Martin Liu, Marton Balassi, Mason |
| Chen, Matt, Matthias Pohl, Maximilian Michels, Mingliang Liu, Mulavar, Nico |
| Kruber, Noah, Paul Lin, Peter Huang, Piotr Nowojski, Qing Lim, QingWei, |
| Qingsheng Ren, Rakesh, Ran Tao, Robert Metzger, Roc Marshal, Roman Khachatryan, |
| Ron, Rui Fan, Ryan Skraba, Salva Alcántara, Samrat, Samrat Deb, Samrat002, |
| Sebastian Mattheis, Sergey Nuyanzin, Seth Saperstein, Shengkai, Shuiqiang Chen, |
| Smirnov Alexander, Sriram Ganesh, Steven van Rossum, Tartarus0zm, Timo Walther, |
| Venkata krishnan Sowrirajan, Wei Zhong, Weihua Hu, Weijie Guo, Xianxun Ye, |
| Xintong Song, Yash Mayya, YasuoStudyJava, Yu Chen, Yubin Li, Yufan Sheng, Yun |
| Gao, Yun Tang, Yuxin Tan, Zakelly, Zhanghao Chen, Zhenqiu Huang, Zhu Zhu, |
| ZmmBigdata, bzhaoopenstack, chengshuo.cs, chenxujun, chenyuzhi, chenyuzhi459, |
| chenzihao, dependabot[bot], fanrui, fengli, frankeshi, fredia, godfreyhe, |
| gongzhongqiang, harker2015, hehuiyuan, hiscat, huangxingbo, hunter-cloud09, |
| ifndef-SleePy, jeremyber-aws, jiangjiguang, jingge, kevin.cyj, kristoffSC, kurt, |
| laughingman7743, libowen, lincoln lee, lincoln.lil, liujiangang, liujingmao, |
| liuyongvs, liuzhuang2017, luoyuxia, mas-chen, moqimoqidea, muggleChen, noelo, |
| ouyangwulin, ramkrish86, saikikun, sammieliu, shihong90, shuiqiangchen, |
| snuyanzin, sunxia, sxnan, tison, todd5167, tonyzhu918, wangfeifan, wenbingshen, |
| xuyang, yiksanchan, yunfengzhou-hub, yunhong, yuxia Luo, yuzelin, zhangjingcun, |
| zhangmang, zhengyunhong.zyh, zhouli, zoucao, 沈嘉琦</p> |
| </p> |
| </article> |
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| <ul> |
| <li><a href="#towards-streaming-warehouses">Towards Streaming Warehouses</a></li> |
| <li><a href="#batch-processing">Batch processing</a> |
| <ul> |
| <li><a href="#speculative-execution">Speculative Execution</a></li> |
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| <li><a href="#streaming-filesink-expansion">Streaming FileSink Expansion</a></li> |
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| </ul> |
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| <li><a href="#upgrade-notes">Upgrade Notes</a></li> |
| <li><a href="#list-of-contributors">List of Contributors</a></li> |
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