Database Performance With Proxy Architectures

May 11, 20090 likes729 views

This document discusses database performance issues that can arise with proxy architectures and introduces Tungsten SQL Router as an alternative. Tungsten SQL Router is an embeddable library that provides intelligent failover, load balancing and partitioning for databases in a way that avoids the overhead of traditional proxies. It uses a connection-level routing approach based on the CAP theorem to balance consistency, availability and partition tolerance.

Database Performance With Proxy Architectures

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ProudNet is a server and network technology that supports online game development. It has three main components: 1) a game server and network client that can support over 10,000 concurrent users for MMORPG games with message encryption, compression, and quality of service controls, 2) a client-to-server and peer-to-peer hybrid network system that allows remote method invocation, and 3) a database cache system that simplifies database access and allows for high workloads. ProudNet allows for easy development of distributed server programs and network systems with features like server-to-server communication, peer-to-peer groups, automatic message handling, and a data-tree based database.

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This document compares REST and SOAP web service architectures. REST has advantages over SOAP in being simpler for clients through use of standard HTTP, having shorter requests and responses, and clearly defining operations through HTTP methods. However, SOAP better defines data types upfront and more easily supports attachments. Overall, REST is lighter weight and simpler for clients but more complex for servers, so REST may be better for attracting users to a web service.

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The document summarizes a Portland Performance Practice Project that teaches people about optimizing PostgreSQL performance. The project holds monthly meetings to discuss bottlenecks, baselining techniques like starting from defaults and changing one thing at a time, and constraints like hardware limitations. It also discusses how PostgreSQL thinks about performance internally with techniques like synchronized scans to piggyback on sequential scans, HOT updates to avoid updating indexes unnecessarily, and visibility maps to reduce VACUUM costs. The project organizers are seeking help analyzing their performance data and asking additional questions.

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Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges---and resultant bugs---involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation---the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.

Zilliz Cloud Monthly Technical Review: May 2025Zilliz

About this webinar Join our monthly demo for a technical overview of Zilliz Cloud, a highly scalable and performant vector database service for AI applications Topics covered - Zilliz Cloud's scalable architecture - Key features of the developer-friendly UI - Security best practices and data privacy - Highlights from recent product releases This webinar is an excellent opportunity for developers to learn about Zilliz Cloud's capabilities and how it can support their AI projects. Register now to join our community and stay up-to-date with the latest vector database technology.

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Enterprise Integration Is Dead! Long Live AI-Driven Integration with Apache C...Markus Eisele

We keep hearing that “integration” is old news, with modern architectures and platforms promising frictionless connectivity. So, is enterprise integration really dead? Not exactly! In this session, we’ll talk about how AI-infused applications and tool-calling agents are redefining the concept of integration, especially when combined with the power of Apache Camel. We will discuss the the role of enterprise integration in an era where Large Language Models (LLMs) and agent-driven automation can interpret business needs, handle routing, and invoke Camel endpoints with minimal developer intervention. You will see how these AI-enabled systems help weave business data, applications, and services together giving us flexibility and freeing us from hardcoding boilerplate of integration flows. You’ll walk away with: An updated perspective on the future of “integration” in a world driven by AI, LLMs, and intelligent agents. Real-world examples of how tool-calling functionality can transform Camel routes into dynamic, adaptive workflows. Code examples how to merge AI capabilities with Apache Camel to deliver flexible, event-driven architectures at scale. Roadmap strategies for integrating LLM-powered agents into your enterprise, orchestrating services that previously demanded complex, rigid solutions. Join us to see why rumours of integration’s relevancy have been greatly exaggerated—and see first hand how Camel, powered by AI, is quietly reinventing how we connect the enterprise.

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Presented at All Things Open RTP Meetup Presented by Brent Laster - President & Lead Trainer, Tech Skills Transformations LLC Talk Title: AI 3-in-1: Agents, RAG, and Local Models Abstract: Learning and understanding AI concepts is satisfying and rewarding, but the fun part is learning how to work with AI yourself. In this presentation, author, trainer, and experienced technologist Brent Laster will help you do both! We’ll explain why and how to run AI models locally, the basic ideas of agents and RAG, and show how to assemble a simple AI agent in Python that leverages RAG and uses a local model through Ollama. No experience is needed on these technologies, although we do assume you do have a basic understanding of LLMs. This will be a fast-paced, engaging mixture of presentations interspersed with code explanations and demos building up to the finished product – something you’ll be able to replicate yourself after the session!

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The Future of Cisco Cloud Security: Innovations and AI IntegrationRe-solution Data Ltd

#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada

The Microsoft Excel Parts Presentation.pdfYvonneRoseEranista

UiPath Automation Suite – Cas d'usage d'une NGO internationale basée à GenèveUiPathCommunity

UiPath Agentic Automation: Community Developer OpportunitiesDianaGray10

Cybersecurity Identity and Access Solutions using Azure ADVICTOR MAESTRE RAMIREZ

Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...Raffi Khatchadourian

Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...Raffi Khatchadourian

Zilliz Cloud Monthly Technical Review: May 2025Zilliz

Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...Raffi Khatchadourian

Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma

AsyncAPI v3 : Streamlining Event-Driven API Designleonid54

HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda

Enterprise Integration Is Dead! Long Live AI-Driven Integration with Apache C...Markus Eisele

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