Awesome GitHub RepositoriesQuery Routing

This project is a comprehensive reference collection of practical implementation examples and patterns for building applications with Spring Boot. It serves as a Java web application template and a showcase for developing functional web services featuring REST endpoints, template engines, and global exception handling. The repository distinguishes itself by providing detailed demonstrations of enterprise-grade features, including distributed locking, task scheduling, and asynchronous message exchange using brokers like RabbitMQ. It also includes reference implementations for automated API doc

graphql-engine is an automated GraphQL API engine that transforms database tables and relationships into a queryable GraphQL schema. It functions as a federation gateway and mapper, instantly generating APIs with built-in filtering, pagination, and mutations from existing databases and remote schemas. The project distinguishes itself through a fine-grained access control layer that enforces row-level and field-level permissions. It further provides a real-time data subscription server that converts standard queries into live streams and a system for triggering event-driven webhooks and notifi

OceanBase is a distributed SQL database designed for high availability and strong consistency across multiple nodes and regions. It functions as a hybrid transactional and analytical processing engine, allowing real-time analytics and transactions to execute on a single data copy. The system also serves as a vector database engine for indexing and querying vector data to power semantic search and recommendation systems. The platform features native compatibility layers for MySQL and Oracle, enabling the migration of legacy workloads without rewriting SQL code. It utilizes a Paxos-based distri

Kingshard is a MySQL database proxy and sharding middleware that routes SQL traffic between clients and multiple database nodes. It functions as a load balancer, read-write splitter, and SQL query firewall to manage how data is accessed and distributed across a database infrastructure. The system implements data sharding using hash, range, or date strategies to split tables across multiple nodes. It enables read-write splitting by directing data modification requests to a master node while distributing read-only queries across a pool of slave replicas. The proxy provides traffic management t

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

AliSQL is a fork of MySQL by Alibaba that extends the relational database management system with enhancements for high performance, scalability, and enterprise-grade availability. It retains the core MySQL identity as a SQL-based database for storing, organizing, and retrieving structured data, while adding optimizations for large-scale transactional and analytical workloads. The project differentiates itself through a set of Alibaba-specific improvements, including a columnar engine for accelerating analytical queries directly on MySQL tables, and a distributed, shared-nothing NDB Cluster en

Pgcat ist eine PostgreSQL-Middleware-Lösung für Connection Pooling, Sharding, Load Balancing und Failover-Management. Sie fungiert als Proxy, der die Ressourcennutzung und Skalierbarkeit optimiert, indem er Datenbanksitzungen verwaltet und Abfragen auf mehrere Instanzen verteilt. Das System zeichnet sich durch Traffic-Mirroring aus, bei dem Live-Produktionsabfragen zur Validierung an Testdatenbanken dupliziert werden. Zudem implementiert es einen Sharding-Proxy, um Daten und Abfragen basierend auf Sharding-Keys auf mehrere Datenbankinstanzen zu verteilen. Das Projekt deckt Hochverfügbarkeit durch automatisiertes Failover und Server-Health-Monitoring ab. Es umfasst Traffic-Routing zur Trennung von Lese- und Schreiboperationen, Transport Layer Security für verschlüsselte Kommunikation sowie eine virtuelle administrative Datenbank für das Systemmanagement und den Export von Leistungsmetriken. Der Dienst wird als Docker-Container bereitgestellt, um eine konsistente Bereitstellung in verschiedenen Umgebungen zu gewährleisten.

pgdog is a PostgreSQL sharding proxy, distributed SQL router, and connection pooler. It is designed to enable horizontal data distribution by splitting tables and indices across multiple independent servers to scale storage and processing capacity. The project distinguishes itself through online resharding capabilities, using logical replication to move data between shards without application downtime. It supports multiple routing strategies, including hash, list, and range-based query routing, and manages distributed atomic transactions using a two-phase commit process to ensure consistency

Dieses Projekt ist eine Datenbankerweiterung, die horizontale Skalierung und verteilte Datenpartitionierung über mehrere PostgreSQL-Knoten hinweg ermöglicht. Sie fungiert als Middleware-Lösung, die große Datenbanktabellen über einen Cluster verteilt, um die Gesamtspeicherkapazität zu erhöhen und die Schreib-Performance für wachsende Datensätze zu verbessern. Das System verwendet hash-basierte Partitionierung, um Tabellendatensätze über physische Knoten zu verteilen, und nutzt einen zentralen Metadaten-Katalog, um Shard-Standorte zu verfolgen. Es bietet transparentes Query-Routing, das Standard-SQL-Befehle von einem Koordinator-Knoten an die entsprechenden Worker-Knoten weiterleitet, ohne dass Änderungen an der bestehenden Anwendungslogik erforderlich sind. Das Framework enthält Tools zur Aufrechterhaltung der Datenredundanz und Cluster-Integrität durch Shard-Replikation und automatisierte Konsistenz-Reparaturprozesse. Es unterstützt zudem Dateningestion mit hohem Durchsatz und nutzt gleichzeitige Verarbeitung, um massive Datensätze in verteilte Tabellen zu importieren.