Awesome GitHub RepositoriesData Partitioning

xxl-job is a distributed task scheduling platform and job orchestrator designed to manage and trigger timed jobs across a cluster of remote executor nodes. It provides a centralized system for scheduling tasks, linking dependent jobs, and managing complex execution lifecycles through a relational database that persists configurations and logs. The platform distinguishes itself through a web-based interface for cron job management, allowing users to create and update scheduled tasks without modifying source code. It supports cross-language task execution by triggering logic on third-party exec

Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations

Cassandra is a distributed NoSQL database and wide-column store designed for high availability and linear scalability. It functions as a fault-tolerant distributed system that utilizes an LSM-tree storage engine to optimize write throughput and manage massive datasets. The system is a CQL-compliant database, using a structured query language to manage and retrieve tabular data stored across multiple nodes. It organizes information into rows and columns based on a flexible schema and primary keys. The project provides capabilities for horizontal database scaling, distributed data partitioning

This project provides educational materials and courseware focused on the theoretical and practical foundations of distributed systems design. It serves as a comprehensive curriculum covering the disciplines of consensus, data consistency, reliability engineering, and scalability. The instructional content focuses on achieving cluster agreement through consensus algorithms and managing system-wide state via coordination frameworks. It includes a dedicated guide to data theory, exploring replication strategies, consistency models, and data convergence. The courseware covers a broad capability

This project is a comprehensive knowledge base and study resource designed for mastering technical interviews. It provides structured guides, roadmaps, and curricula focused on data structures, algorithms, system design, and frontend engineering to help candidates prepare for software engineering screenings. The repository distinguishes itself by offering a holistic approach to professional advancement. Beyond technical drills, it includes a career development handbook covering resume optimization, salary benchmarking, and strategic negotiation coaching. It also provides detailed methodologie

This project is a comprehensive educational resource and curriculum focused on site reliability engineering, distributed systems, and infrastructure operations. It provides technical guides, a systems engineering course, and instructional manuals designed to teach the principles of managing large-scale computing environments. The curriculum covers high-level architectural design for scalability and resilience, including fault-tolerant infrastructure, high-availability patterns, and microservices decomposition. It emphasizes the practical application of site reliability engineering through the

oneAPI Threading Building Blocks (oneTBB)

Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

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

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

Fluvio est une plateforme de streaming d'événements distribuée et un moteur de streaming cloud-native conçu pour collecter, persister et répliquer des flux de données en temps réel à travers un cluster distribué. Il fonctionne comme un pipeline de données temps réel pour construire des workflows avec état qui ingèrent, enrichissent et exportent des données entre des sources et des destinations externes. La plateforme se distingue par son utilisation de WebAssembly pour exécuter des modules compilés pour des transformations et filtrages de données en ligne. Cela permet l'exécution d'une logique métier personnalisée pour remodeler l'information en mouvement sans nécessiter de redémarrage du cluster. Le système couvre un large éventail de capacités, incluant l'ingestion de données basée sur des connecteurs depuis des protocoles externes, un stockage immuable structuré en logs avec E/S zéro-copie, et une mise à l'échelle horizontale du cluster. Il prend en charge la création de pipelines complexes pilotés par les événements qui utilisent le traitement avec état, les agrégations par fenêtrage et la distribution de données basée sur les partitions. Le moteur peut être déployé comme un binaire léger sur diverses architectures système, y compris des appareils IoT ARM64 pour le traitement de données en périphérie (edge).

Titan est une base de données de graphes distribuée et un moteur de calcul conçu pour stocker et interroger des jeux de données massifs de nœuds et d'arêtes interconnectés à travers des clusters multi-machines. Il fonctionne comme une couche de stockage de graphes évolutive et un magasin transactionnel, fournissant un framework pour exécuter des tâches de traitement de graphes à grande échelle et des traversées profondes. Le système se distingue par son backend de stockage enfichable, qui découple le moteur de graphe de la couche de persistance physique. Il utilise un partitionnement de données par coupe de sommets (vertex-cut) pour équilibrer les charges de traitement et un modèle de propriété à cardinalité d'ensemble qui permet à des propriétés uniques de stocker plusieurs valeurs. La plateforme couvre un large éventail de capacités, incluant l'indexation de graphes multi-modèles pour les recherches géographiques et en texte intégral, la gestion de schéma globale pour la réindexation des jeux de données, et des opérations transactionnelles assurées par journalisation write-ahead. Elle incorpore également l'expiration d'éléments via des paramètres de durée de vie (TTL) et une surveillance de la performance système pour suivre l'activité des requêtes et la latence des transactions.

WeNet is an end-to-end automatic speech recognition (ASR) toolkit designed for both Chinese and English, built around transformer-based models. It supports streaming and non-streaming inference out of the box, and is structured to be production-ready, with model export and deployment paths for servers and mobile devices. The toolkit distinguishes itself through a chunk-based streaming transformer architecture that processes audio in fixed-size segments for low latency while preserving context across chunks. It jointly trains models with both CTC and attention loss to combine alignment accurac

RavenDB is a multi-model NoSQL document database designed for high-performance, ACID-compliant data storage. It persists structured information as schema-flexible JSON documents and utilizes a unit-of-work session pattern to track entity changes and batch modifications into atomic transactions. The platform is built on a distributed architecture that supports horizontal scaling through sharding and ensures high availability via multi-node, master-to-master cluster replication. The database distinguishes itself through a self-optimizing query engine that automatically creates and maintains ind

IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis

qsv is a high-performance command line toolkit for querying, transforming, and analyzing comma-separated value files. It functions as a data wrangling interface and a tabular data profiler, featuring a query engine capable of executing SQL statements and joins directly on flat files without requiring a database. The project is distinguished by its ability to process massive datasets that exceed available system memory. This is achieved through disk-based external memory processing, including multithreaded merge sorting, on-disk hash tables for deduplication, and lightweight file indexing for

Ce projet est une plateforme orientée recherche conçue pour simuler des environnements de machine learning décentralisés. Il fournit un framework pour entraîner des modèles à travers plusieurs nœuds clients tout en gardant les données brutes localisées, permettant l'évaluation de la convergence et des performances du modèle dans diverses conditions de réseau distribué. Le système utilise une architecture de serveur de paramètres pour coordonner l'entraînement, où un coordinateur central gère l'état global du modèle et agrège les mises à jour de poids des participants distribués. En découplant la logique d'orchestration de l'entraînement des définitions de réseaux de neurones sous-jacentes, le framework permet le test de diverses architectures de modèles et distributions de données. La plateforme inclut des outils pour configurer et exécuter des expériences, tels que la définition de stratégies de participation des clients, la gestion du partitionnement des ensembles de données et la définition des paramètres d'entraînement. Elle prend en charge la simulation d'environnements de données hétérogènes et fournit des utilitaires pour gérer les ensembles de données standard, le tout construit pour garantir la compatibilité avec l'écosystème PyTorch.