4 个仓库
Execution of differential computations like aggregations and joins to maintain up-to-date streaming views.
Distinct from Incremental Data Streaming: Focuses on the execution of differential logic (joins/aggs) rather than just memory-efficient streaming of data.
Explore 4 awesome GitHub repositories matching data & databases · Incremental Computation. Refine with filters or upvote what's useful.
RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process continuous data streams. It functions as a streaming data lakehouse, combining the capabilities of a streaming SQL database with a platform that integrates streaming ingestion with open table formats. The system is distinguished by its use of the PostgreSQL wire protocol, allowing it to integrate with existing SQL tools and drivers. It employs a decoupled compute and storage architecture, persisting streaming state and materialized views in cloud object storage to enable independen
Executes incremental aggregations and joins to maintain real-time views of streaming data.
Fast n-dimensional filtering and grouping of records.
Computes histograms and top-K lists incrementally as filter conditions change, avoiding full recomputation.
Cocoindex is an incremental data processing engine that builds and maintains live indexes for AI agents, with a core focus on codebase indexing and knowledge graph extraction. The engine uses a function-graph execution model where user-defined Python functions are composed into a directed acyclic graph, and it processes data incrementally so only changed source records or code paths are re-computed, avoiding full recomputation at any scale. It supports automatic schema inference from transformation pipeline type annotations and provides full data lineage tracing, tagging every output record wi
Processes data changes incrementally so only modified content is re-computed, keeping large corpora fresh without full recomputation.
Stumpy 是一个 Python 库,专注于矩阵轮廓(matrix profile)算法的实现,用于可扩展的时间序列分析。它提供了一个框架,用于计算距离轮廓以识别时间序列数据中的重复模式和异常。 该项目通过其使用 Dask 将繁重计算扩展到 GPU 硬件和分布式集群的能力而脱颖而出。它支持多维分析以发现并发数据流中的基序(motif),并提供用于实时流分析的增量计算。 该库涵盖了广泛的时间序列挖掘技术,包括基序发现、异常检测和序列模式匹配。它还提供用于语义分割以检测状态变化,以及提取相似子序列的时间排序链的工具。
Calculates matrix profiles incrementally as new data arrives to monitor time series in real time.