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CommunityOverCode (原 ApacheCon) 是 Apache 软件基金会（ASF）的官方全球系列大会。自 1998 年以来--在 ASF 成立之前 -- ApacheCon 已经吸引了各个层次的参与者，在 300 多个 Apache 项目及其不同的社区中探索 "明天的技术"。CommunityOverCode 通过动手实作、主题演讲、实际案例研究、培训、黑客松活动等方式，展示 Apache 项目的最新发展和新兴创新。
CommunityOverCode 展示了无处不在的 Apache 项目的最新突破和 Apache 孵化器中即将到来的创新，以及开源开发和以 Apache 之道领导社区驱动的项目。与会者可以了解到独立于商业利益、企业偏见或推销话术之外的核心开源技术。
CommunityOverCode 项目是动态的，每次活动的内容都是由精选的 Apache 项目开发者和用户社区直接推动的。CommunityOverCode 提供了最先进的内容，在一个协作、厂商中立的环境中，展示了大数据、云计算、社区发展、金融科技、物联网、机器学习、消息中间件、编程、搜索、安全、服务器、流媒体、网络框架等方面的最新开源进展。
本次会议将以线下的方式于 2023 年 8 月 18 日至 8 月 20 日在北京丽亭华苑酒店举行，并进行线上直播， 欢迎各位嘉宾的光临。
Since 1999 when The Foundation was established, the open source landscape has changed in many ways but the founding principles remain. The ASF continues to operate as a charity to serve the public interest. The projects, under the direction of the Project Management Committees, are the primary governing bodies, subject to oversight by the Board of Directors.
Over the past few years, both internal and external events have required changes to the way the ASF operates:
Governments have recognized that open source software presents new security challenges to the way the internet works;
Privacy concerns require changes to the ASF approach to transparency;
The ASF needs to recognize that new communications products and protocols change the way communities interact, both within and external to them.
如果说在过去 Apache Doris 更多是服务于高性能在线实时分析场景的话， 2022年底发布的 1.2 版本无疑标志着 Apache Doris 能力边界得到进一步拓展，越来越多用户开始基于 Apache Doris 构建高效的实时数据分析服务，而最近发布的 2.0 版本更是全面强化了 Apache Doris 在半结构化数据分析、混合工作负载以及数据湖联邦分析等场景下等场景下的能力。在本次的分享中，我将会为大家揭秘 Apache Doris 2.0 版本的最新重磅特性。同时结合过去几年里社区研发方向的思考，将会分享后续社区的重要发展方向以及版本迭代的详细计划。
阿里巴巴自 2009 年开始采用 Apache Hadoop 技术进行大数据分析，2010 年第一次将 Apache HBase 技术在商品搜索中大规模投产，2016 年将处于萌芽状态的 Apache Flink 在双 11 实时推荐场景落地，并在同年阿里云上发布支持 Apache Hadoop/Hive/Spark/Kafka 等主流开源大数据技术的 E-MapReduce 云产品。在最近几年，阿里云开源大数据 Flink 团队作为 Apache Flink 最主要的贡献者推动 Flink 成为全球流计算事实标准，并向 ASF 捐赠了 Apache Celeborn 和 Apache Paimon 开源大数据项目，本议题将介绍阿里云大数据如何一步步从拥抱、贡献开源走向开源社区的引领者。
在我们的数字世界中，开源软件已经成为了像路桥一样的基础设施的一部分，发挥着越来越大的作用。然而，随着开源生态系统的发展，我们也面临着诸多挑战。开源软件供应链安全，开源的可持续发展，以及如何处理好开源与商业之间的关系，已成为开源世界急需需要解决的问题。在这次圆桌讨论中，我们将与 Apache软件基金会的资深人士一起，探讨开源世界面临的挑战以及可能的解决方案。
随着计算任务的复杂性和数据量的增加，传统的通用计算平台已经无法满足高性能计算的需求。异构计算体系的加速到来，不同的计算平台具有不同的指令集和架构特点。面向异构计算的编译器体系可以提供更高的性能和效率，并且支持不同类型的计算单元和平台之间的无缝集成，从而推动计算技术的发展和创新。
BentoML provides tooling for packaging, deploying, and serving machine learning models at scale. Apache Spark is an open-source cluster computing framework for large-scale data processing. This talk will highlight how BentoML can unify real-time and batch inference workloads by integrating with Apache Spark. BentoML has rapidly gained popularity among its user base owing to its seamless open standards for constructing online AI applications as distributed services through simple Python code. In this regard, we present the novel integration of BentoML with Spark, which allows users to employ the Bento service, originally designed for real-time inference, within a Spark cluster for offline batch inference without altering any code. This functionality is enabled by the run_in_spark API, which automatically propagates the models and inference logic across all Spark worker nodes during batch inference. This integration offers an optimal solution for teams to manage both their real-time and batch inference logic under the same standards, facilitated with version control, and ensuring consistent library dependencies. As a result, this eliminates the concerns regarding divergence in the inference logic over time between real-time and batch inferences. The unified approach ensures consistent model application, fostering efficient AI service development and deployment. Attendees will learn how to:
1. Package models with BentoML;2. Deploy BentoServices to production;3. Invoke BentoServices from Spark for batch inference at scale;4. Leverage the same models for both real-time and batch predictions.
典型的流计算主要针对表模型的处理场景，而针对图模型如何进行流式的处理和分析，目前通用流计算还难以支持。本次分享主要介绍蚂蚁自研的流式图引擎GeaFlow,以及GeaFlow如何围绕Apache Calcite和Apache Gremlin构建流式图查询语言的能力。同时也会分享基于流式图计算在蚂蚁内部的实践和应用。
"Stream processing is rapidly evolving to meet the high-demand, real-time requirements of today's data-driven world. As organizations seek to leverage the real-time insights offered by streaming data, the need for robust, highly concurrent analytics platforms has never been greater. This presentation introduces Apache Druid, a modern, open-source data store designed for such real-time analytical workloads. Apache Druid's key strength lies in its ability to ingest massive quantities of event data and provide sub-second queries, making it a leading choice for high concurrency streaming analytics. Our exploration will cover the architecture, its underlying principles, tuning principals and the unique features that make it optimal for high concurrency use-cases. We'll dive into real-life applications, demonstrate how Druid addresses the challenge of immediate data visibility, and discuss its role in powering interactive, exploratory analytics on streaming data. Participants will gain an in-depth understanding of Apache Druid’s value in the rapidly evolving landscape of streaming analytics and will be equipped with the knowledge to harness its power in their own data-intensive environments. Join us as we delve into the future of real-time analytics, discovering how to 'Shaping the Future: Unveiling High-Concurrency Streaming Analytics with Apache Druid'.
Apache Pulsar 社区最近推出了 Apache Pulsar 3.0，这是 Pulsar 的第一个 LTS 版本。 在本次演讲中，我们将深入探讨Pulsar LTS 版本的重要性。 我们还将介绍 Pulsar 3.0 中引入的主要特性，包括新的负载均衡器、大规模延迟消息的支持以及Direct IO 优化等。
Currently, Kafka relies on ZooKeeper to store its metadata, ex: brokers info, topics, partitions...etc. KRaft is a new generation of Kafka that runs without Zookeeper. This talk will include: 1. Why Kafka needs to develop the new KRaft feature. 2. The architectures of the old (with Zookeeper) Kafka and new (without Zookeeper) Kafka 3. Benefit of adopting KRaft 4. How it works internally. 5. The monitoring metrics 6. Tools to help troubleshoot issues in KRaft 7. A demo to show what we've achieved so far. 8. The roadmap for the Kafka community to move toward KRaft. After this talk, the audience can have better knowledge of what KRaft is, and how it works, and what's the difference with Zookeeper based Kafka, and most importantly, how to monitor it and troubleshoot it.
字节跳动开源办公室首席布道师，前华为开源管理中心技术专家，Apache 软件基金2022,2023 年度董事，Apache软件基金会孵化器导师， 前红帽软件首席软件工程师，Apache 本地北京社群（ALC Beijing）发起人，有十余年企业级开源中间件开发经验，有丰富的Java 开发和使用经验。
Apache Member and Incubator Mentor
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