Download Flink

Author: g | 2025-04-24

Download Flink and Start Flink cluster Hudi works with Flink 1.13 (up to Hudi 0.14.x release), Flink 1.14, Flink 1.15, Flink 1.16, Flink 1.17, Flink 1.18, Flink 1.19 and Flink 1.20. You can follow the instructions here for setting up Flink. Then, start a standalone

Flink Flink On Yarn - Flink - SegmentFault

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Application Execution in Flink - Apache Flink

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apache/flink: Apache Flink - GitHub

Pay-as-you-go monthly plan and commitment plan.With the pay-as-you-go monthly plan, you receive the Confluent Cloud consumption charges on your Azure monthly bill.With a commitment plan, you sign up for a minimum spend amount and get a discount on your committed usage of Confluent Cloud.You decide which billing option to use when you create the service.Subscribe to Confluent CloudYou can subscribe to this service through the Azure Marketplace's online store or through the Azure portal by searching for the service by name, Apache Kafka and Apache Flink on Confluent Cloud.Subscribe from the Azure portalBegin by signing into the Azure portal.From the Azure portal menu's global search bar, search for marketplace. Select Marketplace from the Services results.The Marketplace's Get Started page displays in the working pane.From the command bar, type the name of the service into the Search the Marketplace search bar.Choose the provider from the search results displayed in the working pane.Choose your preferred plan, then select Subscribe.The Create resource pane displays in the working pane.Next stepsQuickStart: Get started with Apache Kafka & Apache Flink on Confluent Cloud --> Feedback Additional resources In this article. Download Flink and Start Flink cluster Hudi works with Flink 1.13 (up to Hudi 0.14.x release), Flink 1.14, Flink 1.15, Flink 1.16, Flink 1.17, Flink 1.18, Flink 1.19 and Flink 1.20. You can follow the instructions here for setting up Flink. Then, start a standalone Download Flink and Start Flink cluster Hudi works with Flink 1.13 (up to Hudi 0.14.x release), Flink 1.14, Flink 1.15, Flink 1.16, Flink 1.17, Flink 1.18, Flink 1.19 and Flink 1.20. You can follow the instructions here for setting up Flink. Then, start a standalone Flink cluster within hadoop environment.

The State of Flink on Docker - Apache Flink

Deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn... GearpumpLightweight real-time big data streaming engine over Akka SaaSHubwww.saashub.comfeaturedSaaSHub - Software Alternatives and Reviews.SaaSHub helps you find the best software and product alternativesNOTE:The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.Hence, a higher number means a better Apache Flink alternative or higher similarity.Apache Flink discussionApache Flink reviews and mentions Posts with mentions or reviews of Apache Flink. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2025-03-06.Exploring the Power and Community Behind Apache Flink2 projects|dev.to|6 Mar 2025In conclusion, Apache Flink is more than a big data processing tool—it is a thriving ecosystem that exemplifies the power of open source collaboration. From its impressive technical capabilities to its innovative funding model, Apache Flink shows that sustainable software development is possible when community, corporate support, and transparency converge. As industries continue to demand efficient real-time data processing solutions, the future looks bright for Apache Flink. Whether you’re a developer, business analyst, or technology enthusiast, understanding the dynamics behind Apache Flink provides valuable insights into the evolving landscape of open source software. For further exploration of this subject, visit the official Apache Flink website or explore the comprehensive details hosted by the Apache Software Foundation.Stay tuned for more articles that delve into how open source models are shaping the future of technology. Happy coding!Automating Enhanced Due Diligence in Regulated Applications9 projects|dev.to|13 Feb 2025For real-time data streaming and analysis, tools like Apache Kafka and Apache Flink

Flink Backward - The Apache Flink Retrospective

前面几篇文章带大家了解了 Flink 是什么、能做什么，本篇将带大家了解 Flink 究竟是如何完成这些的，Flink 本身架构是什么样的，让大家先对 Flink 有整体认知，便于后期理解。一、Flink 组件栈Flink是一个分层架构的系统，每一层所包含的组件都提供了特定的抽象，用来服务于上层组件。Flink分层的组件栈如下图所示：Deployment 层该层主要涉及了Flink的部署模式，Flink支持多种部署模式：本地、集群（Standalone/YARN）云（GCE/EC2）Standalone部署模式与Spark类似。我们看一下Flink on YARN的部署模式，如下图所示：通过上图可以看到，YARN AM 与 Flink JobManager 在同一个 Container 中，这样 AM 可以知道 Flink JobManager 的地址，从而 AM 可以申请 Container 去启动 Flink TaskManager。待 Flink 成功运行在 YARN 集群上，Flink YARN Client 就可以提交 Flink Job 到 Flink JobManager，并进行后续的映射、调度和计算处理。Runtime层Runtime 层提供了支持 Flink 计算的全部核心实现，比如：支持分布式 Stream 处理JobGraph 到 ExecutionGraph 的映射、调度等等，为上层API层提供基础服务。API层API 层主要实现了面向无界 Stream 的流处理和面向 Batch 的批处理 API。其中面向流处理对应 DataStream API，面向批处理对应 DataSet API。Libraries 层该层也可以称为 Flink 应用框架层，根据 API 层的划分，在 API 层之上构建的满足特定应用的实现计算框架，也分别对应于面向流处理和面向批处理两类。面向流处理支持：CEP（复杂事件处理）、基于 SQL-like 的操作（基于 Table 的关系操作）；面向批处理支持：FlinkML（机器学习库）、Gelly（图处理）。二、Flink 集群架构主要为 Runtime 层细分。Flink 的通用系统架构如下图所示。用户在客户端提交作业（Job）到服务端。服务端为分布式的主从架构。Dispatcher 服务负责提供 REST 接口来接收 Client 提交的 Job，运行 Web UI，并负责启动和派发 Job 给 JobManager。Resource Manager 负责计算资源（TaskManager）的管理，其调度单位是 slots。JobManager 负责整个集群的任务管理、资源管理、协调应用程序的分布执行，将任务调度到 TaskManager 执行、检查点（checkpoint）的创建等工作。TaskManager（worker）负责 SubTask 的实际执行，提供一定数量的 Slots，Slots 数就是 TM 可以并发执行的task数。当服务端的 JobManager 接收到一个 Job 后，会按照各个算子的并发度将 Job 拆分成多个 SubTask，并分配到 TaskManager 的 Slot 上执行。任务的提交流程如下所示：三、编程模型（API层次结构）主要为 API & Library 层细分。Flink提供了不同层次的接口，方便开发者灵活的开发流处理、批处理应用，根据接口使用的便捷性、表达能力的强弱分为四层：最底层提供了有状态流：可以自定义状态信息和处理逻辑，但是也需要你自己管理状态的生命周期，容错，一致性等问题。核心开发层：包括 DataStream API 和 DataSet API，它们提供了常见的数据转换，分组，聚合，窗口，状态等操作。这个层级的 api 适合大多数的流式和批式处理的场景。声明式 DSL 层：是以表为中心的声明式 DSL，其中表可能会动态变化（在表达流数据时）。Table API 提供了例如 select、project、join、group-by、aggregate 等操作结构化层：SQL API，它是最高层的 api，可以直接使用 SQL 语句进行数据处理，无需编写 Java 或 Scala 代码。这个层级的 api 适合需要快速响应业务需求，缩短上线周期，和自动调优的场景，但也最不灵活和最不具有表现力。四、Flink 数据流图前一篇《WordCount 实现》文章中，我们写了一个入门程序，那么代码中的输入、输出、计算等算子是如何与上面的概念对应起来的呢？程序由多个 DataStream API 组成，这些 API，又被称为算子 （Operator），共同组成了逻辑视角。在实际执行过程中，逻辑视角会被计算引擎翻译成可并行的物理视角。在实际执行过程中，这些 API 或者说这些算子是并行地执行的。分区：在大数据领域，当数据量大到超过单台机器处理能力时，就将一份数据切分到多个分区（pattition）上，每个分区分布在一个虚拟机或物理机。并行：从物理视角上看，每个算子是并行的，一个算子有一个或多个算子子任务（Subtask），每个算子子任务只处理一小部分数据，所有算子子任务共同组成了一个算子。根据算子所做的任务不同，算子子任务的个数可能也不同。独立：算子子任务是相互独立的，一个算子子任务有自己的线程，不同算子子任务可能分布在不同的物理机或虚拟机上。数据交换：直传：source -> map，数据完全传递重分配：map -> keyBy，数据按照一定方式重新分配到多个算子中聚合：keyBy -> sink，多个算子的输出数据合并到一个算子中五、小结本篇文章从 Flink 组件栈开始，介绍 Flink 的分层架构，然后对每一层（Deploment、Runtime、API）进行了细致的讲解，说明每一层的作用和架构。最后对 Flink 数据流图进行了讲解，说明 Flink 代码是如何对应到具体执行的 task 的。通过本篇讲解带大家了解了 Flink 整体架构，对 Flink 工作结构有了一个基础的认知，后面将会对每个 Flink 核心概念和组件进行细致的讲解。参考文章：Flink CookBook—Apach Flink核心知识介绍Flink架构及工作原理介绍 - Workspace of LionHeartFlink 架构 - 官方文档God-Of-BigData/大数据框架学习/Flink核心概念综述.md at master · wangzhiwubigdata/God-Of-BigData

Learning Apache Flink: What is Flink?

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