Awesome GitHub RepositoriesPredicate Pushdown

cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large tabular datasets. It provides a high-level API for executing filtering, joining, and aggregating operations directly on GPU hardware. The project integrates the Apache Arrow memory format to enable zero-copy data transfers and includes a just-in-time compiler for executing custom user-defined functions on the GPU. The library features specialized acceleration for existing workflows by redirecting standard Pandas dataframe calls and Polars query plans to a GPU backend. It also p

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

ParadeDB is a database extension that integrates full-text search, vector database capabilities, and real-time analytics directly into a relational engine. It functions as a plugin that adds new storage and query execution capabilities to an existing database architecture. The project distinguishes itself by supporting hybrid search workflows that combine lexical keyword matching with dense and sparse vector similarity in a single query. It utilizes reciprocal rank fusion to merge these ranked result sets and employs logical replication to synchronize data from external instances, removing th

Octosql ist eine föderierte SQL-Query-Engine, ein Datentransformer und ein Streaming-SQL-Prozessor. Es ermöglicht die Ausführung einzelner SQL-Statements über mehrere heterogene Datenquellen hinweg – einschließlich verschiedener Datenbanktypen und Dateiformate –, um Ergebnisse zu einem einheitlichen Datensatz zusammenzuführen und zu transformieren. Das System zeichnet sich dadurch aus, dass es CSV-, JSONLines- und Parquet-Dateien als virtuelle Tabellen behandelt und eine Plugin-basierte Architektur nutzt, um die Konnektivität zu externen Speichersystemen zu erweitern. Es fungiert als Streaming-Prozessor für unendliche Datenströme und verwendet Watermarks, Retractions und Tumbling Windows, um die Konsistenz bei ungeordneten Ereignissen zu wahren. Zudem dient es als SQL-Datengenerator, der synthetische Datensätze und Record-Streams über tabellenwertige Funktionen erzeugen kann. Die Engine umfasst Funktionen für Cross-Source-Joins und Multi-Source-Analysen, die durch Source-Side Predicate Push-down optimiert werden, um den Datentransfer zu reduzieren. Sie verwaltet komplexe Daten über ein statisches Typsystem mit Union-Types und bietet Observability durch die Visualisierung von Query-Ausführungsplänen.

MiniOB is an open-source educational relational database kernel designed for learning the internals of database systems. It implements a dual-engine storage architecture combining B+ Tree and LSM-Tree, supports SQL parsing and query execution, and provides transactional processing with multi-version concurrency control. The system communicates with clients using the MySQL wire protocol and includes a vector database extension for storing and querying high-dimensional vectors. The project distinguishes itself through its comprehensive coverage of core database concepts in a single, learnable c