2 个仓库
Awesome GitHub RepositoriesCount-Based Windowing
Grouping stream elements into windows based on a specific number of events rather than time.
Distinguishing note: Existing candidates focus on UI counts, relationship counts, or predicate counting, not stream windowing by volume.
Explore 2 awesome GitHub repositories matching data & databases · Count-Based Windowing. Refine with filters or upvote what's useful.
Awesome Count-Based Windowing GitHub Repositories
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- zhisheng17/flink-learning
zhisheng17/flink-learning
15,071在 GitHub 上查看This project is a collection of educational resources and reference implementations for the Apache Flink stream processing framework. It provides a learning resource focused on mastering distributed stream processing through implementation guides, performance tuning tutorials, and practical examples. The repository features detailed walkthroughs for building real-time data pipelines using the DataStream and Table APIs. It includes specific integration examples for connecting Apache Flink with Kafka brokers and Elasticsearch indices, as well as reference implementations for real-time deduplica
Provides implementations for performing computations over count-based intervals in data streams.
Javaclickhouseelasticsearchflink - cube2222/octosql
cube2222/octosql
5,258在 GitHub 上查看Octosql 是一个联邦 SQL 查询引擎、数据转换器和流式 SQL 处理器。它允许用户跨多个异构数据源（包括不同类型的数据库和文件格式）执行单一 SQL 语句，从而合并并转换结果集。 该系统的独特之处在于将 CSV、JSONLines 和 Parquet 文件视为虚拟表，并利用基于插件的架构扩展对外部存储引擎的连接。它作为无限数据流的流式处理器，使用水印（watermarks）、撤回（retractions）和翻滚窗口（tumbling windows）来维持乱序事件的一致性。此外，它还可用作 SQL 数据生成器，通过表值函数生成合成数据集和记录流。 该引擎具备跨源数据连接和多源分析能力，并通过源端谓词下推（predicate push-down）进行优化，以减少数据传输。它通过包含联合类型的静态类型系统管理复杂数据，并提供查询执行计划可视化功能以增强可观测性。
Organizes streaming records into tumbling windows and triggers output updates based on record counts or watermarks.
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