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Awesome GitHub RepositoriesHigh-Performance Data Infrastructures

Storage and retrieval layers optimized for high-throughput access to large-scale datasets.

Distinguishing note: Focuses on the infrastructure layer for data performance rather than general data management.

Explore 28 awesome GitHub repositories matching data & databases · High-Performance Data Infrastructures. Refine with filters or upvote what's useful.

Awesome High-Performance Data Infrastructures GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • microsoft/qlibmicrosoft 的头像

    microsoft/qlib

    44,490在 GitHub 上查看↗

    This project is a comprehensive platform for quantitative investment research, machine learning, and algorithmic trading. It provides an end-to-end environment for developing, testing, and executing financial strategies, supporting the entire lifecycle from data ingestion and feature engineering to model training and backtesting. The system is distinguished by its configuration-driven workflow orchestration, which allows researchers to automate complex pipelines and manage experiments through declarative files. It features a high-performance data infrastructure that utilizes custom binary for

    Maximizes throughput for large-scale financial datasets while ensuring point-in-time data integrity.

    Pythonalgorithmic-tradingauto-quantdeep-learning
    在 GitHub 上查看↗44,490
  • alibaba/easyexcelalibaba 的头像

    alibaba/easyexcel

    33,703在 GitHub 上查看↗

    EasyExcel is a Java processing library designed for reading and writing XLS, XLSX, and CSV files. It functions as a memory-efficient spreadsheet parser, an object-relational mapper that binds spreadsheet columns to Java class fields, and a stream-based exporter for handling high-volume data. The library distinguishes itself through a streaming model that processes large files row-by-row via listeners to prevent heap memory overflow. It also operates as a template engine, allowing the population of predefined spreadsheet files with dynamic data while preserving original layouts and styles. Br

    Reduces memory consumption during high-speed parsing through optimized data manipulation.

    Javaexceljavajxl
    在 GitHub 上查看↗33,703
  • stackexchange/dapperStackExchange 的头像

    StackExchange/Dapper

    18,320在 GitHub 上查看↗

    Dapper is a high-performance micro-ORM and SQL object mapper for .NET. It functions as an ADO.NET extension library that adds data mapping capabilities directly to database connections, allowing SQL query results to be transformed into typed objects. The project prioritizes execution speed and low memory overhead by using intermediate language generation to map database columns to object properties. It further optimizes performance through the use of concurrent caching for mapping functions and literal value injection to improve database execution plans. The library covers a broad range of d

    Prioritizes execution speed and low memory overhead using runtime IL generation for result parsing.

    C#
    在 GitHub 上查看↗18,320
  • facebook/frescofacebook 的头像

    facebook/fresco

    17,149在 GitHub 上查看↗

    Fresco is an Android image loading library and cache manager designed to fetch, decode, and display images from network or local sources. It functions as a rendering engine for animated image formats and a streaming system for progressive image loading. The library distinguishes itself through specialized memory management that utilizes off-heap allocation to reduce garbage collection overhead and prevent out-of-memory errors. It includes a dedicated rendering pipeline for animated GIFs and WebP files and supports progressive JPEG decoding to render low-resolution versions of images while the

    Places image data in specialized memory regions to increase processing speed and prevent out-of-memory errors.

    Kotlin
    在 GitHub 上查看↗17,149
  • oxnr/awesome-bigdataoxnr 的头像

    oxnr/awesome-bigdata

    14,454在 GitHub 上查看↗

    This project is a curated directory of software, frameworks, and educational resources designed for building, scaling, and maintaining distributed data processing and storage architectures. It serves as a comprehensive index for the distributed computing ecosystem, helping users identify the appropriate tools for managing large-scale information systems. The repository functions as a central hub for data engineering, offering categorized access to technologies that support batch and stream processing, machine learning, and interactive querying. By organizing these resources, it assists in the

    Maintains scalable, high-performance storage systems for structured and unstructured data across cloud environments.

    awesomeawesome-listbigdata
    在 GitHub 上查看↗14,454
  • golang/groupcachegolang 的头像

    golang/groupcache

    13,326在 GitHub 上查看↗

    Groupcache is a distributed caching library designed to coordinate data retrieval and storage across a cluster of nodes. It functions as a peer-to-peer data store that uses consistent hashing to assign specific keys to canonical owners, ensuring that cached items remain predictable and accessible throughout the network. The system distinguishes itself through a request coalescing engine that merges concurrent requests for the same missing key into a single upstream fetch. This mechanism prevents redundant backend load by ensuring that only one process retrieves the required data while sharing

    Caches frequently accessed items across multiple nodes to eliminate system bottlenecks and maintain fast response times.

    Go
    在 GitHub 上查看↗13,326
  • skypjack/enttskypjack 的头像

    skypjack/entt

    12,294在 GitHub 上查看↗

    EnTT is a C++ library designed for data-oriented design and entity component system architecture. It provides a framework for managing game objects and simulation states by separating entity data from logic, allowing for the efficient organization and manipulation of large collections of related data objects. The library utilizes sparse sets to store entities and components in contiguous memory, which facilitates cache-friendly iteration and constant-time lookups. It employs template metaprogramming for compile-time type reflection and type-erasure techniques to provide a unified interface fo

    Optimizes CPU cache usage and system throughput for large sets of related data objects.

    C++architectural-patternscppcpp17
    在 GitHub 上查看↗12,294
  • coil-kt/coilcoil-kt 的头像

    coil-kt/coil

    11,819在 GitHub 上查看↗

    Coil is an image loading and caching pipeline designed for Android and Compose Multiplatform applications. It functions as a comprehensive loader, caching engine, and rendering utility that asynchronously fetches and displays images from network URLs, local storage, and multiplatform resource systems. The library distinguishes itself through a flexible fetcher-decoder pipeline and an interface-driven component registry, allowing for the integration of custom networking clients and decoders. It provides specialized support for rendering scalable vector graphics, animated formats such as GIF an

    Reduces resource consumption through a combination of memory caching, disk storage, and downsampling.

    Kotlinandroidandroidxcompose
    在 GitHub 上查看↗11,819
  • microsoft/sql-server-samplesmicrosoft 的头像

    microsoft/sql-server-samples

    11,122在 GitHub 上查看↗

    This is a reference implementation library providing a collection of code samples, Transact-SQL scripts, and schemas for SQL Server, Azure SQL, and Azure Synapse. It focuses on providing standardized implementation patterns and reference code for building relational databases and cloud data warehouses. The library distinguishes itself by offering specialized guides and examples for deploying database instances within containerized environments and Azure cloud services. It includes specific reference databases and language extensions for integrating machine learning services and advanced analy

    Implements memory-optimized processing techniques to increase transaction speed and reduce system response time.

    在 GitHub 上查看↗11,122
  • dtm-labs/dtmdtm-labs 的头像

    dtm-labs/dtm

    10,881在 GitHub 上查看↗

    dtm is a distributed transaction framework and polyglot transaction coordinator designed to maintain data consistency across microservices. It functions as a Saga orchestration engine and a two-phase message coordinator, ensuring that multi-service operations either succeed completely or roll back to a consistent state. The project distinguishes itself by supporting multiple consistency patterns, including Saga, TCC, XA, and outbox patterns, allowing users to select the appropriate model for their specific application requirements. It provides a polyglot integration layer via HTTP and gRPC, e

    Processes thousands of requests per second by executing inventory checks and global transactions in memory.

    Gocadencecsharpdatabase
    在 GitHub 上查看↗10,881
  • magicalpanda/magicalrecordmagicalpanda 的头像

    magicalpanda/MagicalRecord

    10,713在 GitHub 上查看↗

    MagicalRecord is a data persistence library and wrapper for Core Data that implements the Active Record pattern. It maps database rows directly to object instances, allowing for the creation, update, and retrieval of records without writing manual query logic. The project functions as a mapping layer that synchronizes object properties with a managed object context. It utilizes generic-based type resolution and model-class querying to enable data fetching directly on model classes, which removes the need for a separate external manager and reduces repetitive fetch request boilerplate. The li

    Enables high-speed data retrieval using simplified single-line requests.

    Objective-C
    在 GitHub 上查看↗10,713
  • nathanmarz/stormnathanmarz 的头像

    nathanmarz/storm

    8,772在 GitHub 上查看↗

    Storm is a distributed stream processing framework and fault-tolerant compute engine designed for executing real-time continuous computations across a cluster of machines. It functions as a stateful stream processor and cluster topology manager, enabling the deployment and monitoring of distributed data flow configurations. The system ensures exactly-once semantics by utilizing transactional state management to guarantee that every message in a data stream is processed exactly one time. It further operates as a distributed RPC system, allowing for the integration of non-native languages throu

    Implements memory-optimized processing by bypassing serialization for intra-process communication.

    Java
    在 GitHub 上查看↗8,772
  • path/fastimagecachepath 的头像

    path/FastImageCache

    8,068在 GitHub 上查看↗

    FastImageCache is an iOS image caching library that provides a persistent disk-based image store. It utilizes a persistent bitmap cache to store images in uncompressed formats and incorporates an image pre-processing pipeline to optimize assets before they are committed to storage. The library optimizes rendering performance by using memory-mapped image tables for constant-time retrieval and byte-aligned data layouts to prevent memory copies. It organizes images of identical dimensions into shared tables and manages disk space through a least-recently-used cache eviction system. The project

    Implements byte-aligned data layouts to prevent expensive memory copies and optimize rendering performance.

    Objective-C
    在 GitHub 上查看↗8,068
  • magicstack/asyncpgMagicStack 的头像

    MagicStack/asyncpg

    7,953在 GitHub 上查看↗

    asyncpg is an asynchronous database driver and binary protocol client for PostgreSQL. It provides a non-blocking interface for executing SQL statements, streaming result sets, and managing data transfer between an application and a PostgreSQL database. The driver implements the PostgreSQL binary protocol directly to facilitate efficient data transfer and type conversion. It includes a connection pool to maintain and reuse open database connections, reducing the latency associated with repeated handshakes. The project covers a broad range of database integration capabilities, including atomic

    Implements cursor-based result streaming to retrieve large datasets while minimizing application memory overhead.

    Pythonasync-programmingasync-pythonasyncio
    在 GitHub 上查看↗7,953
  • zetbaitsu/compressorzetbaitsu 的头像

    zetbaitsu/Compressor

    7,222在 GitHub 上查看↗

    Compressor is an Android image compression library designed to reduce the file size and dimensions of images within mobile applications. It functions as a bitmap optimizer that adjusts image quality and formats to minimize storage footprints and improve network upload speeds. The library operates as an asynchronous image processor, utilizing background threads and reactive streams to compress high-resolution photos. This execution model prevents user interface freezes and maintains application responsiveness during heavy image manipulation tasks. The project covers a broad range of image opt

    Uses optimized memory-mapped regions to decode high-resolution photos without triggering out-of-memory errors.

    Kotlin
    在 GitHub 上查看↗7,222
  • apache/pinotapache 的头像

    apache/pinot

    6,098在 GitHub 上查看↗

    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

    Extracts data from structured response objects using helper methods to access rows, columns, and specific data types.

    Java
    在 GitHub 上查看↗6,098
  • xtaci/algorithmsxtaci 的头像

    xtaci/algorithms

    5,454在 GitHub 上查看↗

    This is a collection of classical algorithms and data structures implemented as a header-only C++ library. It provides a suite of tools for general algorithm implementation, including data structure management, graph theory analysis, and string processing. The library is distinguished by its specialized toolkits for cryptographic hashing and encoding, featuring implementations of MD5, SHA-1, and Base64. It also includes advanced capabilities for high-performance string processing via suffix trees and arrays, as well as computational number theory for primality testing and arbitrary-precision

    Uses specialized filters like Bloom filters to optimize data membership lookups.

    C++
    在 GitHub 上查看↗5,454
  • zendframework/zendframeworkzendframework 的头像

    zendframework/zendframework

    5,441在 GitHub 上查看↗

    Zend Framework is a comprehensive set of decoupled components for building modular, event-driven web applications. It implements an MVC architecture to separate business logic from the user interface and provides a structured request-handling system through a sequential middleware pipeline. The project features a factory-driven dependency injection container to automate object instantiation and manage class lifecycles. It also includes a comprehensive security suite for verifying user identities and restricting resource access using access control lists and role-based access control adapters.

    Employs techniques for fetching large datasets with minimal memory overhead to prevent crashes in constrained environments.

    在 GitHub 上查看↗5,441
  • tensorflow/tputensorflow 的头像

    tensorflow/tpu

    5,281在 GitHub 上查看↗

    This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and deploying large-scale AI models on Cloud TPU hardware. It serves as a framework for managing the lifecycle of accelerator clusters, including hardware orchestration and the provisioning of high-performance compute infrastructure for machine learning workloads. The project specifically enables the pre-training of foundation models, large language models, and complex reasoning architectures through distributed training toolkits and multi-host scaling recipes. It further provide

    Connects optimized high-throughput disks to virtual machines for efficient training data access.

    Jupyter Notebook
    在 GitHub 上查看↗5,281
  • rkyv/rkyvrkyv 的头像

    rkyv/rkyv

    4,267在 GitHub 上查看↗

    rkyv 是一个用于 Rust 的零拷贝反序列化框架,提供了一种用于内存映射数据归档的二进制序列化格式。它允许将复杂的数据结构映射到字节,并直接从缓冲区访问,而无需分配新内存或复制数据。 该项目支持多态类型和特征对象的序列化,在二进制形式中保持其动态行为和结构。它利用相对指针寻址和字节对齐的结构打包,确保数据无论加载到内存中的何处都保持有效。 该框架通过直接内存映射涵盖了高性能数据持久化和低延迟状态管理。它为应用程序提供了零拷贝数据访问和在需要时进行完整数据类型重建的机制。

    Enables high-throughput storage and retrieval of large datasets by eliminating traditional deserialization costs.

    Rustrustserializationzero-copy
    在 GitHub 上查看↗4,267
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探索子标签

  • High-Performance Data GenerationEnables high-performance generation of realistic datasets using statistical models on standard hardware. **Distinct from High-Performance Data Infrastructures:** Distinct from High-Performance Data Infrastructures: focuses on the generation process rather than the storage or retrieval layer.
  • Memory-Optimized Data Retrieval1 个子标签Techniques for fetching large datasets with minimal memory overhead through streaming and optimized mapping. **Distinct from High-Performance Data Infrastructures:** Distinct from High-Performance Data Infrastructures: focuses on client-side memory efficiency during retrieval rather than server-side infrastructure
  • Memory-Optimized Processing1 个子标签Techniques for manipulating data in memory to maximize CPU cache efficiency. **Distinct from High-Performance Data Infrastructures:** Distinct from High-Performance Data Infrastructures: focuses on CPU cache-friendly memory manipulation rather than general data infrastructure.
  • One-Line FetchingOptimized execution paths for retrieving data via single-line request syntax. **Distinct from High-Performance Data Infrastructures:** Focuses on the syntactic conciseness and execution speed of individual fetches rather than general infrastructure.
  • Result Parsing OptimizationsTechniques for minimizing memory overhead and maximizing speed during the transformation of database rows to objects. **Distinct from High-Performance Data Infrastructures:** Distinct from High-Performance Data Infrastructures by focusing on the application-level parsing of results rather than the storage layer.