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87 Repos

Awesome GitHub RepositoriesSQL Query Execution

Execution, formatting, and management of database queries.

Explore 87 awesome GitHub repositories matching data & databases · SQL Query Execution. Refine with filters or upvote what's useful.

Awesome SQL Query Execution GitHub Repositories

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  • apache/supersetAvatar von apache

    apache/superset

    73,451Auf GitHub ansehen↗

    Superset is a web-based business intelligence platform designed for data exploration, visualization, and interactive dashboarding. It functions as a query-driven analytics engine that connects to various SQL databases, allowing users to perform ad-hoc analysis, define virtual metrics, and build complex data visualizations through a centralized interface. The platform distinguishes itself through a robust semantic layer that transforms raw database schemas into calculated columns and virtual metrics, enabling consistent business logic across an organization. It features a plugin-based visualiz

    Executes database queries, manages session history, formats SQL code, and exports results while tracking performance metrics.

    TypeScriptanalyticsapacheapache-superset
    Auf GitHub ansehen↗73,451
  • apache/flinkAvatar von apache

    apache/flink

    26,086Auf GitHub ansehen↗

    Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve

    Parses SQL and applies optimization rules to generate efficient execution code for faster data retrieval.

    Java
    Auf GitHub ansehen↗26,086
  • dolthub/doltAvatar von dolthub

    dolthub/dolt

    23,592Auf GitHub ansehen↗

    Dolt is a relational database engine that integrates version control directly into the database management layer. It functions as a version-controlled SQL database that tracks every row and schema change using a commit-based history, allowing users to branch, merge, and audit data modifications. By implementing a wire-protocol-compatible server, the system enables standard SQL clients and tools to interact with versioned data as if they were connecting to a traditional relational database. The platform distinguishes itself by applying repository-style workflows to data management, including s

    Provides a standard SQL interface for managing tables, indexes, and stored procedures using common database drivers.

    Gocommand-linedata-version-controldata-versioning
    Auf GitHub ansehen↗23,592
  • vonng/ddiaAvatar von Vonng

    Vonng/ddia

    22,648Auf GitHub ansehen↗

    This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

    Covers the execution and management of SQL queries for analytical data processing.

    Pythonbookdatabaseddia
    Auf GitHub ansehen↗22,648
  • beekeeper-studio/beekeeper-studioAvatar von beekeeper-studio

    beekeeper-studio/beekeeper-studio

    22,030Auf GitHub ansehen↗

    Beekeeper Studio is a cross-platform desktop application designed for database management and SQL development. It provides a unified graphical interface to connect to, query, and modify data across a wide range of relational and NoSQL database systems. The application functions as a comprehensive workspace, integrating tools for schema design, record editing, and data visualization. The project distinguishes itself through a focus on secure, flexible connectivity and AI-assisted workflows. It supports advanced authentication methods, including enterprise single sign-on, multi-factor authentic

    Runs custom SQL statements against connected databases with support for code completion, parameterization, and multiple execution contexts.

    TypeScriptbigquerycassandracockroachdb
    Auf GitHub ansehen↗22,030
  • voltagent/awesome-claude-code-subagentsAvatar von VoltAgent

    VoltAgent/awesome-claude-code-subagents

    21,906Auf GitHub ansehen↗

    This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven

    Refines database operations to maximize execution speed and resource efficiency.

    Shellai-agent-frameworkai-agent-toolsai-agents
    Auf GitHub ansehen↗21,906
  • timescale/timescaledbAvatar von timescale

    timescale/timescaledb

    21,876Auf GitHub ansehen↗

    TimescaleDB is an open-source PostgreSQL extension that adds native time-series capabilities to the database. At its core, it transforms standard PostgreSQL tables into hypertables—automatically partitioned by time intervals—so data is stored in fixed-size chunks without manual sharding. The extension includes a library of over 200 built-in SQL functions purpose-built for time-series workloads, such as time bucketing, gap filling, percentile estimation, and time-weighted averages. What distinguishes TimescaleDB from generic PostgreSQL is its set of integrated time-series features that work th

    Eliminates irrelevant storage chunks and columnar batches through partition pruning, metadata, indexes, and vectorized parallel processing.

    Canalyticsdatabasefinancial-analysis
    Auf GitHub ansehen↗21,876
  • prefecthq/prefectAvatar von PrefectHQ

    PrefectHQ/prefect

    21,640Auf GitHub ansehen↗

    Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep

    Executes SQL queries against data warehouses and returns results as structured dataframes.

    Pythonautomationdatadata-engineering
    Auf GitHub ansehen↗21,640
  • automaapp/automaAvatar von AutomaApp

    AutomaApp/automa

    21,425Auf GitHub ansehen↗

    Automa is a browser-based automation platform that enables users to build, schedule, and execute repetitive web tasks through a visual, no-code interface. By operating as a browser extension, it provides a canvas-based environment where users construct workflows by connecting functional blocks to interact with web elements, manage browser state, and process data. The platform distinguishes itself through its deep integration with the browser environment, allowing for complex orchestration such as event-driven triggers, cross-origin request handling, and the ability to package workflows as sta

    The product records the history of completed automation processes in logs to allow review of past activity.

    Vueautomationbrowser-automationbrowser-extension
    Auf GitHub ansehen↗21,425
  • mybatis/mybatis-3Avatar von mybatis

    mybatis/mybatis-3

    20,385Auf GitHub ansehen↗

    MyBatis is a Java persistence framework that functions as a database query mapper and object-relational mapping tool. It decouples SQL statements from application code, allowing developers to manage database interactions by mapping Java objects to relational database records. The framework provides a centralized approach to SQL query management, enabling the use of either XML configuration files or annotations to define persistence logic. It automates the transformation of database result sets into structured objects, which eliminates the need for manual data conversion and reduces repetitive

    Centralizes and organizes complex database queries to maintain separation between business logic and data access.

    Javajavamybatissql
    Auf GitHub ansehen↗20,385
  • mysqljs/mysqlAvatar von mysqljs

    mysqljs/mysql

    18,623Auf GitHub ansehen↗

    This project is a MySQL database driver and client for Node.js. It provides a JavaScript implementation of the MySQL protocol to facilitate connecting to, querying, and managing data within MySQL databases. The driver includes a connection pool manager to maintain a cache of reusable database connections, reducing the overhead of frequent network handshakes. It also supports row-by-row result streaming to process large datasets without loading entire result sets into memory. Core capabilities cover SQL query execution, the management of database transactions, and the coordination of multiple

    Sends SQL statements to the database and processes results through callbacks or options.

    JavaScriptjavascriptmysqlnodejs
    Auf GitHub ansehen↗18,623
  • stackexchange/dapperAvatar von StackExchange

    StackExchange/Dapper

    18,320Auf GitHub ansehen↗

    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

    Executes synchronous and asynchronous SQL statements, including single operations and bulk updates with parameter collections.

    C#
    Auf GitHub ansehen↗18,320
  • dapperlib/dapperAvatar von DapperLib

    DapperLib/Dapper

    18,331Auf GitHub ansehen↗

    Dapper is a lightweight object-relational mapper for .NET that functions as a high-performance data access library. It operates by extending standard database connection interfaces, allowing developers to execute raw SQL queries while automating the mapping of database results to strongly-typed objects. The library distinguishes itself through its use of runtime code generation, which creates high-performance instructions to map database rows to object properties with minimal overhead. It provides flexible data retrieval options, supporting both memory-buffered loading for speed and row-by-ro

    Performs database operations without blocking the main execution thread to improve system performance.

    C#ado-netdappersql
    Auf GitHub ansehen↗18,331
  • haoel/leetcodeAvatar von haoel

    haoel/leetcode

    18,058Auf GitHub ansehen↗

    This project is a library of source code implementations designed to solve algorithmic challenges and mathematical problems. It serves as a collection of solved LeetCode problems, providing a reference for data structure usage and efficient logic. The repository is a polyglot code collection, implementing the same algorithmic logic across various programming environments, including general-purpose languages, SQL for database queries, and Bash for shell scripting. The content covers a broad range of computational tasks, including data querying, text processing, and the implementation of compl

    Implements refined database scripts to retrieve and manipulate data efficiently.

    C++
    Auf GitHub ansehen↗18,058
  • sqlc-dev/sqlcAvatar von sqlc-dev

    sqlc-dev/sqlc

    17,882Auf GitHub ansehen↗

    sqlc is a code generation tool that compiles raw SQL queries into type-safe application code. By analyzing SQL statements against database schema definitions during the build process, it eliminates the need for manual data mapping and prevents runtime type errors. The project functions as a schema-aware generator that translates database column types into native language primitives. It distinguishes itself through a modular, plugin-based architecture that allows for the extension of the generation pipeline to support diverse programming languages and custom frameworks beyond its default capab

    Validates SQL statements against the database schema during compilation to confirm syntax and type compatibility.

    Gocode-generatorgokotlin
    Auf GitHub ansehen↗17,882
  • prestodb/prestoAvatar von prestodb

    prestodb/presto

    16,711Auf GitHub ansehen↗

    Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

    Embeds custom query language strings directly into SQL statements to leverage specialized search and retrieval capabilities.

    Javabig-datadatahadoop
    Auf GitHub ansehen↗16,711
  • kilo-org/kilocodeAvatar von Kilo-Org

    Kilo-Org/kilocode

    15,616Auf GitHub ansehen↗

    Kilocode is an autonomous engineering platform designed to orchestrate AI agents for complex software development tasks. It functions as a comprehensive system for automating coding, testing, and repository management by integrating directly with your codebase and terminal. The platform provides a unified gateway for model orchestration, allowing for the management of agentic workflows, event-driven automation, and persistent session state across distributed development environments. The platform distinguishes itself through its federated task management and policy-based access control, which

    Maintains a centralized log of automated triage actions to allow users to review AI reasoning and reprocess classifications.

    TypeScriptaiai-ageai-coding
    Auf GitHub ansehen↗15,616
  • electric-sql/pgliteAvatar von electric-sql

    electric-sql/pglite

    14,707Auf GitHub ansehen↗

    Pglite is a client-side relational database engine that runs a full-featured PostgreSQL instance directly within browser and Node.js environments. By leveraging WebAssembly, it provides a persistent SQL storage solution that enables complex data management and querying without requiring an external database server. The project distinguishes itself through a reactive SQL data layer that automatically synchronizes user interface components with live query results. It manages database operations using worker threads to prevent main-thread blocking and coordinates access across multiple browser t

    Runs standard SQL commands and parameterized queries directly within browser or server environments.

    TypeScriptdatabasepostgreswasm
    Auf GitHub ansehen↗14,707
  • druid-io/druidAvatar von druid-io

    druid-io/druid

    14,020Auf GitHub ansehen↗

    Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume

    Distributes complex queries across multiple data nodes and merges partial results via a central broker.

    Java
    Auf GitHub ansehen↗14,020
  • apache/incubator-druidAvatar von apache

    apache/incubator-druid

    14,020Auf GitHub ansehen↗

    Apache Druid is a real-time OLAP database and distributed analytics engine. It functions as a columnar time-series database designed for high-performance analytical queries and the real-time ingestion of streaming and batch datasets. The system provides a framework for high-concurrency analytics, allowing multiple simultaneous users to execute SQL and native queries across large-scale data. It supports mixed data ingestion, combining real-time streaming and batch loading into a single system for unified analysis. The platform includes capabilities for distributed cluster management, enabling

    Distributes query fragments across multiple data nodes and aggregates results at a central broker.

    Java
    Auf GitHub ansehen↗14,020
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  6. SQL Query Execution

Unter-Tags erkunden

  • Beeline Query ExecutionConnects to HiveServer2 using the Beeline command-line client and runs SQL queries against the warehouse. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on the Beeline CLI client specifically, not general SQL query execution.
  • Document Modification HistoriesChronological logs of changes made to specific documents to attribute modifications to users. **Distinct from History Tracking:** Distinct from SQL Query History: focuses on data mutations and user attribution in a document context rather than query retrieval.
  • Dynamic SQL Query Executions1 Sub-TagExecution of SQL queries whose text changes in response to user input or component state, enabling interactive components. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on queries that change dynamically based on user interaction, not static execution.
  • Execution AuthorizationMechanisms requiring explicit user confirmation before executing database queries to prevent accidental data modification. **Distinct from SQL Query Execution:** Distinct from general SQL execution: focuses specifically on the safety-gating of query execution.
  • Execution LogsHistorical records of automated process runs, including status, timestamps, and output data for auditing and review. **Distinct from History Tracking:** Distinct from database history tracking: focuses on workflow execution state and process history rather than query-level database operations.
  • History Tracking4 Sub-TagsSearchable logs of previously executed database queries for quick retrieval and re-execution. **Distinct from SQL Query Execution:** Distinct from performance-focused slow query tracking: focuses on user-facing query history management.
  • Markdown-Embedded SQL ExecutionsRuns SQL code inside fenced code blocks within markdown files and displays the results as a table on the page. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: specifically executes SQL embedded in markdown code fences, not general query execution.
  • Parallel Query Execution1 Sub-TagDecomposition of complex SQL statements into fragments for concurrent execution across distributed nodes. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on the parallelization of query fragments across a cluster rather than general query management.
  • Query Execution WrappersWraps Django's database backend to intercept every SQL query, recording its execution time, tables, and stack trace. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on wrapping the database backend to intercept and record queries, not just executing them.
  • Query Favorites1 Sub-TagSaving frequently used SQL queries as named favorites for quick replay. **Distinct from SQL Query Execution:** Distinct from general SQL Query Execution: focuses on saving and replaying named query favorites rather than ad-hoc execution.
  • Query Optimizers3 Sub-TagsTools for refining database operations to maximize execution speed. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on performance tuning and resource efficiency rather than just running queries.
  • Tez-Based Query ExecutionsExecutes SQL queries using the Tez framework for lower latency than MapReduce. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on Tez-specific execution for lower latency, not general SQL query execution.
  • UI-Embedded SQL ExecutionRunning SQL queries from a browser-based UI and displaying results in a dedicated results tab. **Distinct from SQL Query Execution:** Distinct from SQL Query Execution: focuses on execution from a web UI with result display, not CLI or API execution.