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Awesome GitHub RepositoriesExecution Plan Exporters

Utilities for saving structural query execution plans in machine-readable formats for performance analysis.

Distinct from Query Planning: Distinct from query planning: focuses on the export of the final plan for debugging rather than the planning process itself.

Explore 7 awesome GitHub repositories matching data & databases · Execution Plan Exporters. Refine with filters or upvote what's useful.

Awesome Execution Plan Exporters GitHub Repositories

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  • prefecthq/prefectPrefectHQ 的头像

    PrefectHQ/prefect

    21,640在 GitHub 上查看↗

    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

    Computes and validates execution graphs and selector resolution without triggering actual model builds.

    Pythonautomationdatadata-engineering
    在 GitHub 上查看↗21,640
  • prestodb/prestoprestodb 的头像

    prestodb/presto

    16,711在 GitHub 上查看↗

    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

    Displays logical or distributed execution plans for SQL statements to validate syntax and data processing paths.

    Javabig-datadatahadoop
    在 GitHub 上查看↗16,711
  • dask/daskdask 的头像

    dask/dask

    13,746在 GitHub 上查看↗

    Dask 是一个并行计算框架和分布式任务调度器,旨在将 Python 数据科学工作流从单机扩展到大型集群。它作为一个集群资源管理器,通过将任务及其依赖项表示为有向无环图来编排计算逻辑。这种架构允许系统在管理复杂执行要求的同时,自动将工作负载分配到可用硬件上。 该项目通过一个延迟评估引擎脱颖而出,该引擎将数据操作推迟到明确请求时才执行,从而实现全局图优化和高效的资源分配。它结合了内存感知数据溢出功能,以防止在处理超过可用内存的数据集时系统崩溃,并利用任务图融合将操作序列组合成单个执行步骤,从而最大限度地减少调度开销和节点间通信。 该平台为大规模数据分析提供了全面的功能面,包括对分布式机器学习、高性能计算集成和并行数据处理的支持。它提供了用于集群生命周期管理、性能分析和任务执行实时监控的广泛工具。用户可以在各种基础设施上部署这些环境,包括本地硬件、云提供商、容器化系统和高性能计算集群。

    Generates human-readable text or graphical representations of optimized computation workflows for debugging data processing logic.

    Pythondasknumpypandas
    在 GitHub 上查看↗13,746
  • graphile/postgraphilegraphile 的头像

    graphile/postgraphile

    12,929在 GitHub 上查看↗

    PostGraphile is an automated tool that converts a PostgreSQL database schema into a fully functional GraphQL API. It serves as a GraphQL execution engine and schema orchestrator, utilizing database schema introspection to retrieve strongly typed metadata directly from PostgreSQL. The project features a modular system for composing and standardizing GraphQL schemas through plugins, which manage naming conventions and connections. It includes a PostgreSQL query builder that constructs dynamic, SQL-injection-proof queries using tagged template literals. The system employs a declarative query pl

    Optimizes GraphQL request processing through a declarative planning engine to reduce server load.

    TypeScript
    在 GitHub 上查看↗12,929
  • apache/datafusionapache 的头像

    apache/datafusion

    8,908在 GitHub 上查看↗

    Apache DataFusion is an extensible, columnar SQL query engine that runs embedded within a host application without requiring a separate server process. It processes data in columnar batches using Apache Arrow for memory-efficient analytics, and can scale analytic workloads across multiple nodes for parallel execution. The engine supports both SQL and DataFrame queries through a modular, streaming architecture that allows custom operators, data sources, functions, and optimizer rules. The engine distinguishes itself through its modular extension framework, which enables building custom query e

    Ships a feature to convert logical plans back into SQL for debugging and serialization.

    Rustarrowbig-datadataframe
    在 GitHub 上查看↗8,908
  • feast-dev/feastfeast-dev 的头像

    feast-dev/feast

    6,727在 GitHub 上查看↗

    Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

    Shows the execution plan of a feature retrieval query before running it.

    Pythonbig-datadata-engineeringdata-quality
    在 GitHub 上查看↗6,727
  • gaozhangmin/boxplayergaozhangmin 的头像

    gaozhangmin/boxplayer

    6,550在 GitHub 上查看↗

    Boxplayer is a cloud media player and storage manager that enables high-definition video streaming and file administration across multiple cloud storage providers through a unified interface. It functions as a cloud media player with subtitle and audio support, a metadata organizer for structuring media libraries, and a high-speed download manager using a multi-threaded engine. The project implements a Model Context Protocol server, which exposes cloud storage and media management functions as programmable tools and context for AI agents. This allows for AI-driven storage automation and the a

    Tracks a history of file changes to create and execute mirror operations for rolling back batch updates.

    TypeScriptelectron-applinuxmacos
    在 GitHub 上查看↗6,550
  1. Home
  2. Data & Databases
  3. Query Planning
  4. Execution Plan Exporters

探索子标签

  • Logical Plan Lowering1 个子标签Transformation of abstract operations into concrete execution strategies based on data structure and partitioning. **Distinct from Execution Plan Exporters:** Distinct from Execution Plan Exporters: focuses on the transformation process itself rather than exporting the final plan.
  • Transformation Plan PreviewersUtilities for computing and validating execution graphs without triggering actual data transformations. **Distinct from Execution Plan Exporters:** Distinct from Execution Plan Exporters: focuses on the validation and preview of transformation graphs rather than exporting structural plans.