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Awesome GitHub RepositoriesConcurrent Query Processing

Capabilities for executing parallel analytical queries alongside high-throughput data ingestion without blocking.

Distinct from High-Performance Ingestion: Distinct from High-Performance Ingestion: focuses on the concurrency of analytical query execution rather than just ingestion throughput.

Explore 5 awesome GitHub repositories matching data & databases · Concurrent Query Processing. Refine with filters or upvote what's useful.

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Awesome Concurrent Query Processing GitHub Repositories

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  • dzhng/deep-researchdzhng 的头像

    dzhng/deep-research

    19,136在 GitHub 上查看↗

    This project is an AI research tool designed for autonomous web information gathering and automated topic research. It utilizes agent orchestration to combine search engines and web scraping, enabling the system to discover detailed information and build a comprehensive understanding of complex subjects without manual step-by-step guidance. The tool employs an iterative research execution model that recursively generates targeted search queries and refines directions based on previous results. It includes a feedback loop that compares current findings against initial objectives to identify kn

    Processes multiple search and scraping queries concurrently to accelerate the information gathering phase.

    TypeScriptagentaigpt
    在 GitHub 上查看↗19,136
  • questdb/questdbquestdb 的头像

    questdb/questdb

    17,062在 GitHub 上查看↗

    QuestDB is a high-performance, distributed time-series database designed for the ingestion, storage, and analysis of massive datasets. It functions as a real-time analytics platform that utilizes a columnar storage engine to optimize disk input and output, enabling efficient analytical scans and complex windowing operations on streaming data. The platform distinguishes itself through specialized capabilities for handling asynchronous time-series streams, including advanced join algorithms that align disparate data sets based on precise timestamp lookups. It supports high-volume ingestion thro

    Supports simultaneous high-throughput ingestion and parallel analytical queries to maintain consistent performance under heavy workloads.

    Javacapital-marketscppdatabase
    在 GitHub 上查看↗17,062
  • apache/dorisapache 的头像

    apache/doris

    15,526在 GitHub 上查看↗

    Doris is a distributed SQL data warehouse designed for high-performance analytical workloads and real-time data processing. It functions as a unified platform that integrates traditional relational warehousing with lakehouse query capabilities, allowing users to execute analytical operations directly against external data lakes without requiring data migration. The system distinguishes itself through a shared-nothing, massively parallel processing architecture that utilizes vectorized query execution and columnar storage to maintain sub-second latency. It supports dynamic schema evolution, en

    Supports high-concurrency analytical query processing, handling thousands of requests per second for enterprise-scale operations.

    Javaagentaibigquery
    在 GitHub 上查看↗15,526
  • ardanlabs/gotrainingardanlabs 的头像

    ardanlabs/gotraining

    12,212在 GitHub 上查看↗

    This repository provides curated learning paths, structured courseware, and technical materials for mastering Go programming, container orchestration, and software architecture. It serves as a comprehensive educational resource for systems programming, focusing on language mechanics, memory safety, and high-performance backend design. The project distinguishes itself through a multi-modal instructional design that combines instructor-led workshops, project-based curricula, and competency-based certifications. It offers specialized guidance on building production-grade AI infrastructure, inclu

    Teaches the execution of parallel database lookups using lightweight threads to increase throughput.

    Go
    在 GitHub 上查看↗12,212
  • 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

    Handles high-concurrency analytical queries by scaling horizontally across distributed clusters.

    Java
    在 GitHub 上查看↗6,098