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structured streaming

Q: structured streaming 最近有什么新动态？

traeai 已收录 2 篇与 structured streaming 相关的内容。最新一篇是「Apache Spark Real-Time Mode for Gaming: A Better Way to Do Real-Time Sessionization」，由 Databricks 发布。

Spark 的结构化流处理 API，提供易用的 DDL/SQL 风格接口与微批处理能力，用于构建实时数据管道。

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Apache Spark Real-Time Mode for Gaming: A Better Way to Do Real-Time Sessionization

Databricks · 8.7 分

以 Apache Spark Real-Time Mode 和 transformWithState 为核心，为游戏实时会话化提供统一、低延迟（亚秒级）架构，替代 Flink 或自研方案，支持百万级玩家的个性化、推荐与内容调度。

How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase

Databricks · 8.2 分

Databricks发布基于Spark Real-Time Mode (RTM)和Lakebase的实时欺诈检测解决方案加速器，可实现亚300ms流处理，比Apache Flink快92%，帮助金融机构在交易结算前阻止欺诈，每年挽回约330亿美元的欺诈损失。

Apache Spark Real-Time Mode for Gaming: A Better Way to Do Real-Time Sessionization

Databricks6月4日1290 字 (约 6 分钟)

Leverages Apache Spark Real-Time Mode and transformWithState to deliver a unified, sub-second real-time sessionization architecture, replacing Flink or in-house solutions to power personalization, recommendation engines, and dynamic content scheduling for millions of players.

入选理由：使用 transformWithState + Real-Time Mode 实现单引擎统一架构，输入处理与定时触发均可达亚秒级精度。

FeaturedArticle#Apache Spark#Real-Time Mode#transformWithState#Structured Streaming#Gaming英文

How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase

Databricks5月20日2097 字 (约 9 分钟)

Databricks releases a real-time fraud detection solution based on Spark RTM and Lakebase, achieving sub-300ms stream processing and 92% faster performance than Apache Flink, helping financial institutions block fraud before transaction settlement to prevent $33B annual losses.

入选理由：Databricks推出开源实时欺诈检测参考实现，核心采用Spark RTM实现亚100ms P99延迟处理，结合Lakebase无服务器Postgres数据库存储欺诈记录。

FeaturedArticle#Apache Spark#Real-Time Mode#Lakebase#Fraud Detection#Databricks英文

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