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parallel-execution

52 articles · Page 1 of 1
AI Digital Ideal
AI Digital Ideal
Jul 29, 2026 · Artificial Intelligence

Lesson 8: Building a Multi‑Agent Orchestration Toolchain with cmux, superpowers, and OpenClaw

This lesson walks through configuring the cmux session manager, the superpowers skill‑sharing layer, and the OpenClaw orchestration engine to enable multiple AI agents to work in parallel, avoid serial bottlenecks, and coordinate their outputs through isolated worktrees and unified reporting.

AI orchestrationGit worktreeOpenClaw
0 likes · 13 min read
Lesson 8: Building a Multi‑Agent Orchestration Toolchain with cmux, superpowers, and OpenClaw
AI Engineering
AI Engineering
Jul 23, 2026 · Artificial Intelligence

Is Graph Engineering Really New? Why LangChain Says It’s Not

The article explains that Graph Engineering isn’t a brand‑new concept but an evolution of Prompt, Loop, and Harness engineering, detailing how LangGraph has been used for three years, the core components of graph‑based agents, practical patterns, pitfalls, and when to choose graphs over other approaches.

AI workflowAgent GraphsDynamic Routing
0 likes · 13 min read
Is Graph Engineering Really New? Why LangChain Says It’s Not
PaperAgent
PaperAgent
Jul 21, 2026 · Artificial Intelligence

Why Loop Engineering Is Dead and Graph Engineering Is the Future

The article explains how traditional Loop Engineering for AI agents is being replaced by Graph Engineering, detailing nodes as tasks, edges as data contracts, parallel execution, barriers, validation, isolation, dynamic workflows, and cost‑effective topology design for scalable agentic systems.

AI agentsAgent ContractsClaude
0 likes · 19 min read
Why Loop Engineering Is Dead and Graph Engineering Is the Future
Linyb Geek Road
Linyb Geek Road
Jul 11, 2026 · Artificial Intelligence

How to Slash Token Costs When Using AI Agents

The article analyzes why AI agents quickly consume token quotas and presents seven practical strategies—shortening sessions, avoiding parallel sub‑agents, giving concise prompts, providing precise context, pre‑defining rules, automating mechanical tasks, and investing in clear prompts—to dramatically reduce token usage and lower operational costs.

AI agentsAutomationPrompt Engineering
0 likes · 10 min read
How to Slash Token Costs When Using AI Agents
James' Growth Diary
James' Growth Diary
Jun 27, 2026 · Artificial Intelligence

Sub‑Agent Delegation: Turning Complex Tasks into Parallel Sub‑Tasks

The article explains how Hermes' sub‑agent delegation transforms a serial, context‑heavy workflow—such as researching multiple vector databases—into parallel, isolated sub‑tasks, detailing three‑layer isolation, orchestrator role, heartbeat monitoring, approval safety, credential handling, and compares industry approaches.

AI agentsContext IsolationHermes
0 likes · 18 min read
Sub‑Agent Delegation: Turning Complex Tasks into Parallel Sub‑Tasks
Su San Talks Tech
Su San Talks Tech
Jun 26, 2026 · Artificial Intelligence

Codex vs Claude Code: Which AI Coding Assistant Is Better for Your Workflow?

The article compares OpenAI's Codex and Anthropic's Claude Code across architecture, token efficiency, benchmark scores, feature sets, installation steps, and real‑world use cases, helping developers decide which tool aligns with their workflow, security preferences, and budget.

AI coding assistantClaude CodeCodex
0 likes · 16 min read
Codex vs Claude Code: Which AI Coding Assistant Is Better for Your Workflow?
James' Growth Diary
James' Growth Diary
Jun 20, 2026 · Artificial Intelligence

Task Atomization: Isolating AI Tasks into Independent, Clean-Context Units

The article explains how LLM context windows are a scarce resource plagued by breadth‑vs‑depth, long‑task attention decay, and serial‑parallel trade‑offs, and proposes task atomization—splitting work into independently loadable, executable, and verifiable units with isolated contexts and parallel sub‑agents—to achieve clean context, local rollback, and scalable performance.

AI workflowLLM contextSoftware Engineering
0 likes · 16 min read
Task Atomization: Isolating AI Tasks into Independent, Clean-Context Units
Coder Trainee
Coder Trainee
Jun 11, 2026 · Artificial Intelligence

Deep Dive into Function Calling for AI Agents: Enabling External Tool Integration

This article explains the concept of Function Calling in large language models, walks through defining function schemas, shows step‑by‑step API call flows, demonstrates multi‑tool orchestration, parallel execution, tool‑chain composition, and integrates Function Calling with LangChain, while providing best‑practice guidelines and code examples.

AI agentsFunction CallingLangChain
0 likes · 16 min read
Deep Dive into Function Calling for AI Agents: Enabling External Tool Integration
AI Engineering
AI Engineering
Jun 8, 2026 · Artificial Intelligence

Six Core Patterns of Claude Code Dynamic Workflows Explained by an Engineer

The article analyzes the limitations of Claude Code's monolithic execution, introduces a JavaScript‑based dynamic workflow system with two core APIs, and details six reusable patterns—Classify‑and‑Act, Fan‑out‑and‑Synthesize, Adversarial Verification, Generate‑and‑Filter, Tournament, and Loop‑until‑Done—along with concrete use cases, trade‑offs, and practical tips.

AI agentsClaude CodeDynamic workflow
0 likes · 11 min read
Six Core Patterns of Claude Code Dynamic Workflows Explained by an Engineer
AI Code to Success
AI Code to Success
Jun 2, 2026 · Artificial Intelligence

Claude Code’s Dynamic Workflows Eliminate Manual Task Splitting – A Hands‑On Test

Claude Code introduced dynamic workflows on May 28, 2026, enabling the AI to automatically decompose tasks, run dozens to hundreds of sub‑agents in parallel, and cross‑validate results, which acts like a project manager and can cut multi‑hour jobs down to minutes while offering guidance on when to use or avoid the feature.

AI automationClaude Codedynamic workflows
0 likes · 9 min read
Claude Code’s Dynamic Workflows Eliminate Manual Task Splitting – A Hands‑On Test
Ubiquitous Tech
Ubiquitous Tech
May 30, 2026 · Artificial Intelligence

How Claude Dynamic Workflows Redefine AI‑Powered Software Engineering

Claude’s new Dynamic Workflows move planning and coordination out of the chat context into executable JavaScript, enabling hundreds of parallel sub‑agents, adversarial verification, and checkpoint recovery, which the article demonstrates with a Bun migration case study, a novel‑generation workflow, and detailed architectural analysis.

AI agentsAutomationClaude
0 likes · 32 min read
How Claude Dynamic Workflows Redefine AI‑Powered Software Engineering
Golang Shines
Golang Shines
Apr 24, 2026 · Artificial Intelligence

Can One Developer Do the Work of Five? Exploring GitHub Copilot CLI’s /fleet Parallel Agent Feature

GitHub Copilot CLI’s /fleet command turns the AI assistant into an orchestrator that splits a large task into parallel subtasks, runs multiple agents with separate context windows, and aggregates the results, letting a single developer modify several files, run tests, and update documentation simultaneously.

AI orchestrationCLIGitHub Copilot
0 likes · 6 min read
Can One Developer Do the Work of Five? Exploring GitHub Copilot CLI’s /fleet Parallel Agent Feature
James' Growth Diary
James' Growth Diary
Apr 24, 2026 · Artificial Intelligence

How LangGraph Turns LLMs into a State Machine

This article dissects LangGraph's core execution engine, showing how it transforms LLM calls into a state‑machine workflow with mutable State, Nodes, Edges, Reducers, a scheduler loop, conditional branching, and parallel fan‑out/fan‑in execution.

JavaScriptLLMLangGraph
0 likes · 12 min read
How LangGraph Turns LLMs into a State Machine
Machine Heart
Machine Heart
Apr 18, 2026 · Artificial Intelligence

Eliminating ‘Think‑Then‑Act’ Stalls: StreamingVLA Boosts VLA Speed by 2.4×

StreamingVLA introduces action‑flow matching and adaptive early observation to parallelize generation, execution, and perception in vision‑language‑action models, cutting per‑action latency from 49.9 ms to 31.6 ms, reducing stall time 6.5‑fold, and achieving up to 2.4× end‑to‑end speedup in LIBERO benchmarks and real‑world robot tests.

LIBEROStreamingVLAVision-Language-Action
0 likes · 13 min read
Eliminating ‘Think‑Then‑Act’ Stalls: StreamingVLA Boosts VLA Speed by 2.4×
Open Source Tech Hub
Open Source Tech Hub
Apr 16, 2026 · Backend Development

Run Parallel PHP Code with Spatie Fork: A Practical Guide

This guide explains how to install the Spatie Fork package, meet its requirements, and use its run, before, after, and concurrent methods to execute multiple PHP closures in parallel within CLI environments, including handling return values and database connections.

CLIPHPSpatie Fork
0 likes · 7 min read
Run Parallel PHP Code with Spatie Fork: A Practical Guide
AI Architecture Hub
AI Architecture Hub
Apr 14, 2026 · Artificial Intelligence

When Do Multi‑Agent LLM Systems Beat Single Agents? A Practical Guide

This article analyzes the trade‑offs between single‑agent and multi‑agent large language model architectures, identifies three scenarios where multi‑agent setups excel, explains context protection, parallelism and tool specialization, and provides concrete design patterns, code examples, and verification strategies to avoid common pitfalls.

Context Managementagent orchestrationmulti-agent systems
0 likes · 17 min read
When Do Multi‑Agent LLM Systems Beat Single Agents? A Practical Guide
Baidu Geek Talk
Baidu Geek Talk
Apr 8, 2026 · Artificial Intelligence

How to Engineer Reliable Long‑Running AI Coding Tasks: Harnessing Agents for Scale

This article analyzes the challenges of using AI coding agents for large‑scale, long‑running tasks such as bulk file migration or code review, and presents a systematic engineering approach—including task decomposition, parallel execution, persistent progress files, resumable workflows, and multi‑level retry strategies—backed by concrete script examples and real‑world case studies.

AI agentsMeta SkillRetry Strategy
0 likes · 31 min read
How to Engineer Reliable Long‑Running AI Coding Tasks: Harnessing Agents for Scale
AgentGuide
AgentGuide
Mar 30, 2026 · Artificial Intelligence

What Is a Multi-Agent System? Three Core Working Modes Interviewers Expect

The article explains that multi-agent systems typically operate in three patterns—sequential execution, parallel execution, and an evaluator-optimizer loop—covers when each pattern is appropriate, and offers interview tips on how to discuss these designs effectively.

AI InterviewAgent ArchitectureSequential Execution
0 likes · 3 min read
What Is a Multi-Agent System? Three Core Working Modes Interviewers Expect
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Mar 28, 2026 · Artificial Intelligence

Mastering Multi‑Agent Systems: Design, Parallel Execution, and Interview Strategies

This article dissects the shortcomings of single‑agent LLM pipelines, introduces the Supervisor‑based Multi‑Agent architecture with LangGraph, demonstrates parallel task execution, robust error handling, and result merging, and provides concrete interview guidance backed by real performance data.

AI architectureLLMLangGraph
0 likes · 19 min read
Mastering Multi‑Agent Systems: Design, Parallel Execution, and Interview Strategies
Frontend AI Walk
Frontend AI Walk
Mar 21, 2026 · Artificial Intelligence

How to Orchestrate Multiple AI Agents for Collaborative Development

This guide explains how to decompose a software project, schedule specialist AI agents, run them in parallel, and integrate their outputs, using OpenClaw and Sisyphus to build a full‑stack blog system and a user‑authentication service while covering best‑practice patterns, monitoring, and troubleshooting.

AI orchestrationOpenClawmulti-agent collaboration
0 likes · 18 min read
How to Orchestrate Multiple AI Agents for Collaborative Development
Tech Ocean
Tech Ocean
Feb 17, 2026 · Artificial Intelligence

How Oh-My-OpenCode’s Six Specialized Agents Can Double Your AI Coding Efficiency

Oh‑My‑OpenCode replaces a single AI coder with six specialized agents—Sisyphus, Oracle, Explore, Librarian, Visual Engineering, and Writing—automating task decomposition, parallel execution, and expert analysis, delivering 3‑8× speedups across code understanding, bug fixing, refactoring, documentation, and full‑stack development, as shown in real‑world case studies.

AIAgentsAutomation
0 likes · 20 min read
How Oh-My-OpenCode’s Six Specialized Agents Can Double Your AI Coding Efficiency
Code Wrench
Code Wrench
Jan 27, 2026 · Artificial Intelligence

Building a Multi‑Agent AI System: Easy‑Agent’s Foreman, Coder, and Researcher

This article explains how the easy‑agent project evolved from a single monolithic AI into a multi‑agent architecture with specialized Foreman, Coder, and Researcher agents, covering design principles, communication mechanisms, task decomposition, fault tolerance, parallel execution, observability, and future extensions, complete with code examples and open‑source links.

AIAgent ArchitectureGo
0 likes · 13 min read
Building a Multi‑Agent AI System: Easy‑Agent’s Foreman, Coder, and Researcher
AI Architecture Hub
AI Architecture Hub
Dec 31, 2025 · Artificial Intelligence

Why LangGraph Is the Next‑Generation Framework for LLM Agent Orchestration

This article explains the motivation behind LangGraph, walks through a quick start, details its core syntax and state management, demonstrates conditional branching, parallel execution, tool integration, multi‑agent orchestration, and real‑time monitoring, and finally discusses future directions for the framework.

LLMLangGraphPython
0 likes · 32 min read
Why LangGraph Is the Next‑Generation Framework for LLM Agent Orchestration
Data STUDIO
Data STUDIO
Aug 29, 2025 · Backend Development

Why Selenium Is Losing Ground and Playwright Is Gaining Momentum

The article compares Selenium and Playwright for web automation, showing how Playwright’s multi‑browser support, smart waiting, session persistence, headless stability, PDF export, and parallel testing make it a more reliable and efficient choice for robust automation tasks.

Headless TestingPlaywrightWeb Automation
0 likes · 8 min read
Why Selenium Is Losing Ground and Playwright Is Gaining Momentum
Huolala Tech
Huolala Tech
Oct 29, 2024 · Mobile Development

Boost Mobile App Testing Efficiency with Parallel Multi‑Device Synchronization

This article explores how Hu Jia‑chun and the Huolala testing team tackled the exploding complexity of mobile app testing across Android, iOS, and HarmonyOS by applying task decomposition, parallel execution, cloud‑based device farms, OCR‑driven precise control, and custom multi‑device synchronization tools to dramatically improve coverage and speed.

Mobile TestingOCR automationcloud testing
0 likes · 22 min read
Boost Mobile App Testing Efficiency with Parallel Multi‑Device Synchronization
Programmer1970
Programmer1970
Sep 25, 2024 · Backend Development

How to Use XXL‑JOB in SpringBoot for Flexible Sharding of Massive Data

This article explains how to configure XXL‑JOB with a sharding‑broadcast strategy in a SpringBoot application, use ID modulo hashing to partition large tables, implement the task with XxlJobHelper, write MyBatis sharding SQL, and verify correct parallel processing through detailed logs.

Batch ProcessingMyBatisSharding
0 likes · 7 min read
How to Use XXL‑JOB in SpringBoot for Flexible Sharding of Massive Data
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Feb 4, 2024 · Backend Development

How to Orchestrate Parallel and Dependent Tasks with AsyncTool

This article introduces AsyncTool, a Java concurrency framework that enables flexible parallel, serial, dependent, and callback‑driven task orchestration, explains its core components (worker, callback, wrapper), and provides multiple code‑first examples illustrating complex workflow compositions.

Task orchestrationasynccallback
0 likes · 9 min read
How to Orchestrate Parallel and Dependent Tasks with AsyncTool
Shepherd Advanced Notes
Shepherd Advanced Notes
Sep 29, 2023 · Backend Development

How to Cut API Latency from 5 s to 0.5 s with CompletableFuture Async Optimizations

The article explains how to dramatically reduce the response time of an API that must call multiple downstream services by converting serial calls into parallel ones using Java 8's CompletableFuture, covering its relationship to Future, core APIs, task composition, exception handling, and practical best‑practice recommendations.

Asynchronous ProgrammingCompletableFutureException Handling
0 likes · 20 min read
How to Cut API Latency from 5 s to 0.5 s with CompletableFuture Async Optimizations
macrozheng
macrozheng
Sep 27, 2023 · Backend Development

Master Java CompletableFuture: From Basics to Advanced Async Patterns

This comprehensive guide explains Java's CompletableFuture API, covering its fundamentals, creation methods, chaining operations, exception handling, and best practices for parallel execution, while providing clear code examples and performance tips for building efficient asynchronous workflows in backend development.

Asynchronous ProgrammingCompletableFutureFuture
0 likes · 20 min read
Master Java CompletableFuture: From Basics to Advanced Async Patterns
DataFunSummit
DataFunSummit
Aug 31, 2022 · Databases

Alibaba Cloud Graph Database (GDB): Product Overview, Capabilities, Execution Engine, and Applications

The article introduces Alibaba Cloud's Graph Database (GDB), detailing its product features, supported query languages, high‑performance and high‑availability architecture, parallel execution engine based on the Volcano model and Morsel‑driven parallelism, and showcases real‑world use cases such as DingTalk friend recommendation and Hema Fresh recommendation.

Alibaba CloudDatabase ArchitectureGraph Database
0 likes · 10 min read
Alibaba Cloud Graph Database (GDB): Product Overview, Capabilities, Execution Engine, and Applications
ByteDance Terminal Technology
ByteDance Terminal Technology
Feb 22, 2022 · Fundamentals

Optimizing CPython for True Parallel Execution: Implementing a Multi-Interpreter Architecture

This article details a novel approach to overcoming CPython's Global Interpreter Lock by implementing a multi-interpreter architecture that isolates execution states, manages shared variables through thread-specific data, and introduces a subinterpreter pool to significantly enhance multi-core CPU utilization and algorithm execution performance.

CPythonGIL OptimizationMulti-Interpreter Architecture
0 likes · 15 min read
Optimizing CPython for True Parallel Execution: Implementing a Multi-Interpreter Architecture
Code DAO
Code DAO
Dec 11, 2021 · Artificial Intelligence

Nimble: A Lightweight Parallel GPU Scheduler Boosting Deep Learning Performance

The article analyzes how Nimble reduces GPU scheduling overhead and enables parallel execution through ahead‑of‑time scheduling and automatic multi‑stream assignment, achieving up to 22.3× inference speedup over PyTorch and significantly improving GPU utilization for deep learning workloads.

GPU schedulingPerformance Accelerationahead-of-time
0 likes · 9 min read
Nimble: A Lightweight Parallel GPU Scheduler Boosting Deep Learning Performance
FunTester
FunTester
Feb 3, 2021 · Fundamentals

Why Atomic Test Cases Boost Automation Speed and Reliability

The article explains how designing atomic automation test cases—each focusing on a single function with minimal UI interaction—provides precise feedback, shortens test chains, improves coverage, enables parallel execution, and offers practical strategies for data injection and handling non‑testable applications.

UI testingatomic test casesautomated testing
0 likes · 8 min read
Why Atomic Test Cases Boost Automation Speed and Reliability
FunTester
FunTester
Dec 3, 2019 · Operations

How to Calculate Selenium Test Automation ROI: Metrics & Best Practices

This guide explains how to evaluate the return on investment of Selenium‑based cross‑browser test automation by defining key metrics, outlining calculation formulas, discussing common pitfalls, and offering practical steps to maximize efficiency, coverage, and risk reduction.

ROITest AutomationTesting Metrics
0 likes · 18 min read
How to Calculate Selenium Test Automation ROI: Metrics & Best Practices
360 Quality & Efficiency
360 Quality & Efficiency
Aug 30, 2019 · Mobile Development

Parallel Multi‑Device Testing with Appium: Dynamic Desired Caps, Multi‑Process Services, and Port Management

This article explains how to set up a Windows environment with Node.js, Python, and Appium, dynamically generate desired capabilities for multiple phones, launch parallel Appium services and tests using multi‑process techniques, and handle port detection and release to achieve efficient multi‑device automation.

AppiumAutomationMobile Testing
0 likes · 5 min read
Parallel Multi‑Device Testing with Appium: Dynamic Desired Caps, Multi‑Process Services, and Port Management
Qunar Tech Salon
Qunar Tech Salon
Aug 13, 2019 · Databases

Efficient Deduplication of Large MySQL Tables Using Indexes, Variables, and Window Functions

This article demonstrates how to efficiently remove duplicate rows from a million‑record MySQL table by comparing created_time and item_name, exploring various approaches such as correlated subqueries, joins, user‑defined variables, index optimization, window functions, and parallel execution with shell scripts and MySQL events to achieve significant performance gains.

MySQLSQL PerformanceWindow Functions
0 likes · 21 min read
Efficient Deduplication of Large MySQL Tables Using Indexes, Variables, and Window Functions
ITPUB
ITPUB
Nov 24, 2018 · Databases

Why Did My Oracle SQL Run in Parallel? Uncovering Hidden Parallelism and Fixing It

This article walks through diagnosing unexpected Oracle parallel execution, resolving DBLINK‑induced plan anomalies after a database upgrade, and fixing ORA‑00604 errors caused by missing domain indexes, providing step‑by‑step queries, parameter checks, and corrective ALTER statements to restore normal performance.

DBLINKDomain IndexOracle
0 likes · 12 min read
Why Did My Oracle SQL Run in Parallel? Uncovering Hidden Parallelism and Fixing It
Senior Brother's Insights
Senior Brother's Insights
Apr 8, 2018 · Blockchain

How EOS.IO Redefines Scalable Blockchain Architecture with DPOS and Parallel Execution

EOS.IO introduces a novel blockchain architecture that combines delegated proof‑of‑stake consensus, OS‑like account and permission models, deterministic parallel transaction execution, and flexible resource and governance mechanisms, enabling million‑user scale, low‑latency, fee‑free decentralized applications while addressing security, upgradeability, and cross‑chain communication.

DPOSEOSIOgovernance
0 likes · 36 min read
How EOS.IO Redefines Scalable Blockchain Architecture with DPOS and Parallel Execution
ITPUB
ITPUB
Feb 10, 2018 · Operations

How to Parallelize Shell Loops on Linux Without External Tools

This guide explains why serial shell loops become a bottleneck for large‑scale tasks, then demonstrates three pure‑bash techniques—background execution, a simulated queue using arrays, and a FIFO‑based token system—to run multiple loop iterations concurrently while controlling the number of active processes.

FIFObashparallel-execution
0 likes · 12 min read
How to Parallelize Shell Loops on Linux Without External Tools
dbaplus Community
dbaplus Community
May 25, 2016 · Databases

How Parallel Execution Supercharges SQL Server Queries—and the Pitfalls to Avoid

This article explains the theory behind SQL Server's parallel execution, illustrates its performance gains with Amdahl's Law, lists operators that block parallelism, discusses configuration settings, warns of deadlocks and thread starvation, and presents practical MapReduce‑style optimizations for real‑world workloads.

Amdahl's lawDeadlockMapReduce
0 likes · 16 min read
How Parallel Execution Supercharges SQL Server Queries—and the Pitfalls to Avoid
Java High-Performance Architecture
Java High-Performance Architecture
May 8, 2016 · Operations

Mastering Fabric: Automate Server Management and Deployments with Python

This article introduces Fabric, a Python-based automation tool for server management and application deployment, explains its core features, showcases real-world use cases like Instagram, and provides step‑by‑step code examples covering basic commands, parameter passing, local and remote execution, role‑based tasks, parallel execution, and installation instructions.

FabricPythonparallel-execution
0 likes · 7 min read
Mastering Fabric: Automate Server Management and Deployments with Python
ITPUB
ITPUB
Apr 22, 2016 · Databases

Boost Oracle Performance: Master Parallel Execution Techniques

This article explains Oracle's parallel execution features—including parallel query, DML, and DDL—covers object, session, and statement level settings, provides practical SQL examples, discusses common pitfalls, and presents performance test results that demonstrate significant speed improvements for large data migration tasks.

OracleSQLparallel-execution
0 likes · 10 min read
Boost Oracle Performance: Master Parallel Execution Techniques
dbaplus Community
dbaplus Community
Apr 22, 2016 · Databases

Mastering Oracle Parallel Query: How It Works and When to Use It

This article explains Oracle's parallel query feature, covering its benefits, resource costs, required conditions, various data‑distribution methods such as broadcast, replicate and hash, how to read parallel execution plans, and practical monitoring techniques to avoid performance pitfalls.

Database PerformanceHash JoinOracle
0 likes · 31 min read
Mastering Oracle Parallel Query: How It Works and When to Use It
dbaplus Community
dbaplus Community
Dec 28, 2015 · Databases

Mastering Oracle Real‑Time SQL Monitoring for Faster Performance Diagnosis

This guide explains Oracle Real‑Time SQL Monitoring, covering terminology, activation conditions, how to access it via Enterprise Manager Cloud Control, and detailed walkthroughs of its various panels, metrics, and practical examples for diagnosing and optimizing long‑running queries and index builds.

DBAOracleSQL Monitoring
0 likes · 29 min read
Mastering Oracle Real‑Time SQL Monitoring for Faster Performance Diagnosis
dbaplus Community
dbaplus Community
Nov 6, 2015 · Databases

Misconfigured Oracle Parallel Parameters Caused a System Outage – DBA Case Study

In this detailed DBA case study, the speaker explains how improper settings of Oracle's parallel execution parameters on an AIX‑based 10gR2 database led to process saturation, undo‑related wait events, and temporary connection issues, and describes the step‑by‑step diagnosis, parameter adjustments, and lessons learned for future tuning.

AIXDatabase AdministrationOracle
0 likes · 12 min read
Misconfigured Oracle Parallel Parameters Caused a System Outage – DBA Case Study