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<p>在过去的一年多，笔者工作中心逐渐从 Flink 转移到 Iceberg 上。Iceberg 近年发展迅猛，在与 Hudi、Delta 并称的数据湖御三家竞争中脱颖而出，目前基本已是事实标准。这点在不久前的 Databricks 和 Snowflake 这对老对手在 Iceberg 话语权的针锋相对上就足以体现[1]。</p>
<p>虽然自数据湖（准确来说应该是数据湖表格式）兴起已经过去四五个年头，但直至今日数据湖仍未称得上成熟，加上 Hive 迁移数据湖涉及到的业务改造工作量巨大，不少核心业务和老业务仍未有动力推进，可以说数据湖仍有很多未竟之事。文本谈下笔者在 Iceberg 实践中的随想，笔之所至即思之所至。</p>
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<p>Apache Kyuubi [1] 是一个分布式多租户的 SQL 网关，主要功能为接受用户的通过 JDBC/REST 等协议提交的 SQL 并根据多租户隔离策略路由给其管理的 SQL 引擎执行。在最新的 Kyuubi 1.8 版本，Kyuubi Flink Engine 迁移到 Flink SQL Gateway（下简称 FSG） API 之上并支持 Flink Application 模式，这让我们能借助 Kyuubi 快速部署生产可用的分布式 Flink SQL 网关。</p>
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<p>在分布式计算中，Shuffle 是非常关键但常常容易被忽视的一环。比如著名的 MapReduce 的命名跳过 Shuffle ，只包含其前后的 Map 跟 Reduce。背后原因一方面是 Shuffle 是底层框架在做的事情，用户基本不会感知到其存在，另一方面是 Shuffle 听起来似乎是比较边缘的基础服务。然而，笔者认为大数据计算与在线服务最基础的区别之一正在于 Shuffle。</p>
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<p>前不久笔者在 Flink 社区讨论 Flink SQL 中 Savepoint 的 SQL 语法时（见 FLIP-222 [3]），曾提议过参考数据库的 Savepoint 语法。虽然最终未能获得 Flink 社区的大多数赞成，但也引发了笔者的好奇心: Flink Savepoint 和传统的数据库 Savepoint 究竟有何异同？于是经过笔者一番调研与思考，便有了这篇文章。</p>
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