18 dépôts
Storage mechanisms that organize data into structured rows and columns using primary keys.
Distinct from Row Record Access: Focuses on the storage model of rows rather than the runtime access for UI templates.
Explore 18 awesome GitHub repositories matching data & databases · Tabular Row Storage. Refine with filters or upvote what's useful.
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
Organizes information into rows and columns using primary keys and a structured query language.
TechEmpower FrameworkBenchmarks is an open-source project that provides a standardized, reproducible suite for measuring and comparing the performance of web frameworks across many languages. At its core, it defines a set of common server-side tasks—such as JSON serialization, database queries, and server-side template rendering—and executes them uniformly against hundreds of framework implementations to produce comparable throughput and latency metrics. The project is built around a multi-language benchmark harness that automates the full test lifecycle, from dependency installation and serve
Retrieves configurable numbers of random rows from a database table as JSON arrays.
go-sqlmock is a Go library that simulates SQL driver behavior for unit testing by intercepting database calls through the standard database/sql/driver interface, eliminating the need for a real database connection. It provides a comprehensive mocking framework that allows developers to define expectations for SQL operations, including queries, executions, prepared statements, and transaction lifecycles, with precise control over the results and errors returned. The library distinguishes itself through its flexible matching and verification capabilities. It supports regex-based or exact SQL
Expects Query, QueryRow, or their context variants, returning configurable rows or errors, with optional argument matching.
Tablecn is a React-based data table component that combines server-side data processing with real-time collaboration features. It provides a structured approach to building interactive tables using reusable components and a declarative column configuration system that automatically generates filter interfaces. The project distinguishes itself through its contextual action bar pattern, which surfaces batch operations only when rows are selected, keeping the main interface uncluttered. It supports virtualized row rendering for performance with large datasets, and uses WebSocket connections to s
Selects multiple rows in a data table and performs batch actions through a contextual toolbar.
CodeIgniter is a PHP web framework built on the Model-View-Controller pattern, designed for building full-stack web applications. It provides a lightweight toolkit with minimal configuration, organizing application logic into controllers, models, and views for clean separation of concerns. The framework includes a fluent query builder for constructing SQL statements programmatically, PSR-4 autoloading with namespace mapping, and a service-based dependency injection container for managing shared class instances. The framework distinguishes itself through its comprehensive set of built-in tools
Verifies database row counts match expected values in test assertions.
pg is a PostgreSQL object-relational mapper (ORM) for Go that maps Go structs to database tables and provides a fluent query builder for constructing SQL statements programmatically. At its core, it automatically generates CREATE TABLE statements from Go struct definitions using struct tags and naming conventions, and builds queries through method chaining with placeholder-based parameter binding to prevent SQL injection. The library distinguishes itself through relation-aware join generation that automatically constructs JOIN clauses for has-one, has-many, many-to-many, and polymorphic assoc
Combines multiple row insertions, updates, or deletions into single SQL statements to minimize database network calls.
InterviewGuide is a comprehensive technical interview preparation platform that covers the full spectrum of software engineering recruitment, from foundational computer science concepts through to offer negotiation. It provides structured learning paths across algorithms, operating systems, databases, networking, and programming languages, with a particular emphasis on C++ and Go. The platform aggregates real interview experiences and company-specific questions from major tech employers, offering candidates a searchable database of past written exam problems and detailed accounts of actual int
Provides virtual tables for simplified data access and row-by-row processing of query results.
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
Provides cursor-based row-by-row retrieval of query results for iterative processing.
TinyBase est un magasin de données réactif et une base de données relationnelle en mémoire conçue pour la persistance de l'état côté client. Il sert de moteur de synchronisation local-first qui fusionne l'état distribué en utilisant des types de données répliqués sans conflit (CRDT) et des horloges logiques pour assurer une convergence déterministe des données. Le projet présente une bibliothèque de validation de schéma qui convertit les définitions externes provenant d'outils comme Zod, Yup et TypeBox en définitions de magasin typées. Il fournit une infrastructure pour l'édition collaborative en temps réel, utilisant la synchronisation avec Automerge, Yjs et PartyKit pour maintenir un état cohérent entre plusieurs clients et serveurs. La surface de capacité inclut la modélisation de données relationnelles avec des tables et des clés étrangères, des requêtes et indexations de type SQL, et des transactions atomiques pour les mutations groupées. Il prend en charge un large éventail d'adaptateurs de persistance, incluant le stockage navigateur, SQLite et Cloudflare Durable Objects. Le système fournit également une liaison d'état bidirectionnelle et des composants déclaratifs pour l'intégration avec React, SolidJS et Svelte.
Organizes data into structured rows and columns using primary keys for tabular storage.
Tablib est une bibliothèque Python conçue pour importer, exporter et manipuler des jeux de données tabulaires. Elle fonctionne comme un convertisseur et gestionnaire de données multi-formats, permettant aux utilisateurs de déplacer des informations entre différents standards de fichiers. La bibliothèque prend en charge la transformation de données à travers les formats CSV, JSON, YAML et Excel. Elle fournit une interface programmatique pour gérer ces jeux de données en ajoutant des lignes, en filtrant des colonnes et en séparant des enregistrements. Le système utilise une représentation interne commune et un mapping basé sur des adaptateurs pour normaliser diverses sources d'entrée. Cela permet des routines de lecture et d'écriture cohérentes à travers les formats de fichiers pris en charge.
Organizes datasets as a sequence of records to facilitate efficient row-level operations like adding and filtering.
YDB est une base de données SQL distribuée et un moteur analytique conçu pour la scalabilité horizontale et une forte cohérence. Il fonctionne comme un système multi-modèle qui prend en charge les charges de travail transactionnelles et analytiques via une architecture distribuée fournissant des transactions ACID sérialisables. Le système se distingue par sa large compatibilité de protocole, implémentant le protocole wire PostgreSQL pour les pilotes SQL standard et le protocole Kafka pour la messagerie et le streaming. Il sert en outre de base de données vectorielle, prenant en charge les index vectoriels et les recherches de voisins les plus proches approximatifs pour la recherche sémantique et les embeddings. La plateforme gère les données en utilisant un modèle de stockage hybride avec des formats orientés lignes et orientés colonnes, utilisant l'exécution de requêtes vectorisées pour des analyses à l'échelle du pétaoctet. Sa surface opérationnelle inclut le streaming de capture de données modifiées (CDC), des files d'attente persistantes avec garantie d'exécution unique (exactly-once) et une haute disponibilité multi-zone. Le déploiement et la gestion du cycle de vie sont pris en charge via un opérateur Kubernetes et le provisionnement d'infrastructure as code.
Stores data sequentially by row to optimize for transactional queries and fast single-record access.
Fury est un framework de sérialisation binaire multi-langage conçu pour encoder des objets de domaine et des graphes complexes afin de faciliter l'échange de données entre langages. Il inclut un compilateur de langage de définition d'interface (IDL) qui traduit les définitions de schéma en types natifs idiomatiques et en code de sérialisation répétitif à travers plusieurs langages. Le projet se distingue par un lecteur binaire zero-copy qui permet d'accéder à des champs spécifiques sans désérialiser l'objet entier, ainsi qu'un sérialiseur de graphe d'objets qui préserve les références circulaires et l'intégrité référentielle. Il dispose également d'un convertisseur de données qui transforme les données binaires basées sur des lignes en formats Apache Arrow colonnaires pour les charges de travail analytiques. Le framework couvre de vastes domaines de capacités, incluant l'évolution de schéma pilotée par métadonnées pour la compatibilité ascendante et descendante, un processus de compilation AOT au moment du build pour éliminer la réflexion à l'exécution, et une désérialisation sécurisée via une validation de type basée sur liste blanche. Il fournit en outre une intégration pour des appels de procédure à distance haute performance via gRPC.
Uses a row-oriented binary encoding optimized for high-throughput processing and zero-copy data access.
ExcelDataReader is a C# library used to extract data and metadata from Microsoft Excel spreadsheets and CSV files. It functions as a workbook parser that converts spreadsheet content into structured data sets for programmatic access and iteration. The project includes a specialized metadata extractor for retrieving cell-level details, such as number formats, styles, row heights, column widths, and merged cell ranges. It also provides a stream processor for parsing plain text CSV files with customizable encoding and separator detection. The library supports the OpenXML standard for modern spr
Iterates through sheets and rows using a cursor to retrieve field counts, row counts, and sheet names.
Fory is a cross-language serialization framework and binary data serializer designed to convert complex object graphs into a compact binary format for high-performance data exchange. It includes an IDL-based schema compiler to transform interface definition language files into type-safe native data models and a schema evolution manager to maintain forward and backward compatibility. The project features a zero-copy data access layer that allows reading specific fields from binary rows without deserializing the entire object. It supports dual-mode serialization, enabling a toggle between a por
Encodes data in a row format to enable high-throughput processing and zero-copy access for analytics.
Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di
Transforms a single collection of items into individual result rows to allow for granular processing and analysis.
TablePlus is a SQL database management GUI and professional administration tool designed for managing relational and NoSQL databases. It functions as a native SQL query client and relational data browser, providing a graphical interface for database administration, schema management, and data exploration. The project distinguishes itself through a staged-change commit system that buffers GUI-driven modifications locally for review before they are applied to the server. It also features a native-protocol connection layer for low-latency communication and secure connectivity via SSH tunneling.
Retrieves approximate row counts using EXPLAIN to improve performance when loading large datasets.
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
Calculates the total number of records present in a tabular file.
Provides row-level insert, select, update, and delete operations within migration tasks.