8 مستودعات
Interfaces for inspecting query performance metrics, execution plans, and stage-level data distribution.
Distinct from Query Performance Monitors: Distinct from general query performance monitors: focuses on deep-dive execution plan visualization.
Explore 8 awesome GitHub repositories matching data & databases · Execution Detail Visualizers. Refine with filters or upvote what's useful.
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
Visualizes query performance metrics and execution plans to help identify bottlenecks.
Dask هو إطار عمل للحوسبة المتوازية وجدول مهام موزع مصمم لتوسيع نطاق سير عمل علوم البيانات في Python من أجهزة فردية إلى مجموعات (clusters) كبيرة. يعمل كمدير موارد للمجموعة يقوم بتنسيق المنطق الحسابي من خلال تمثيل المهام وتبعياتها كرسوم بيانية موجهة غير دورية. تسمح هذه البنية للنظام بأتمتة توزيع أعباء العمل عبر الأجهزة المتاحة مع إدارة متطلبات التنفيذ المعقدة. يتميز المشروع بمحرك تقييم كسول يؤجل عمليات البيانات حتى يتم طلبها صراحة، مما يتيح تحسين الرسم البياني العالمي وتخصيص الموارد بكفاءة. يتضمن خاصية تسريب البيانات الواعية بالذاكرة لمنع تعطل النظام عند معالجة مجموعات البيانات التي تتجاوز الذاكرة المتاحة، ويستخدم دمج الرسم البياني للمهام لدمج تسلسلات العمليات في خطوات تنفيذ واحدة، مما يقلل من عبء الجدولة والاتصال بين العقد. توفر المنصة سطح قدرات شاملاً لتحليلات البيانات واسعة النطاق، بما في ذلك دعم التعلم الآلي الموزع، وتكامل الحوسبة عالية الأداء، ومعالجة البيانات المتوازية. توفر أدوات واسعة النطاق لإدارة دورة حياة المجموعة، وتوصيف الأداء، والمراقبة في الوقت الفعلي لتنفيذ المهام. يمكن للمستخدمين نشر هذه البيئات عبر بنية تحتية متنوعة، بما في ذلك الأجهزة المحلية، ومزودي السحابة، والأنظمة الحاوية، ومجموعات الحوسبة عالية الأداء.
Displays planned task execution sequences to identify bottlenecks or memory-intensive patterns in scheduling.
Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and
Visualizes detailed execution steps of a system to understand the internal flow of requests.
Open Multi-Agent is a TypeScript framework for multi-agent orchestration that decomposes natural language goals into a runtime-generated directed acyclic graph of tasks. It functions as a task orchestrator and workflow state manager, coordinating multiple AI models to execute parallel and sequential operations. The framework is distinguished by a proposer-judge consensus protocol used to validate agent outputs through a quorum of agreement. It employs provider-agnostic model routing to assign specific models to tasks based on roles or execution phases and utilizes state-based workflow checkpo
Generates a visual representation of the post-execution task DAG and outcomes.
Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with large language models. By acting as a reverse-proxy, it provides a centralized layer for routing requests across multiple AI providers, allowing developers to maintain consistent application logic while gaining deep visibility into model performance, usage, and costs. The platform distinguishes itself through a robust suite of traffic management and prompt engineering tools. It enables policy-driven control, including automatic failover between providers, rate limiting, and edge-b
Displays a hierarchical timeline of agent operations, including reasoning steps and tool executions.
PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo
Renders a live web dashboard showing agent status, message flow, and state changes.
Open vSwitch is a software-defined network switch and high-performance data plane engine. It functions as a virtual layer 2 switch and network orchestrator, implementing the OpenFlow protocol to decouple the control plane from the data plane for programmable network forwarding. The project distinguishes itself through a userspace-kernel datapath split and the use of poll-mode drivers for kernel-bypass acceleration. It provides a network tunneling gateway to extend virtual networks across physical hosts and utilizes a transactional, schema-based configuration database with state replication fo
Provides visual representations of internal packet processing steps via datapath trees and graphs.
Lmnr is an LLM observability platform and evaluation framework designed for tracing, logging, and monitoring language model executions. It provides the tools necessary to debug agent behavior, analyze performance, and identify failure patterns in AI agents. The platform differentiates itself through a trace-to-dataset pipeline that converts production logs into labeled test sets for regression testing. It includes a prompt-variant replay engine to compare different prompts or models side-by-side and a state-cached debugging system to replay agent loops without restarting the process. The sys
Displays execution flow through transcripts and timelines that surface reasoning and sub-agent activity.