Carol McDonald
  • Female
  • Jacksonville, Florida
  • United States
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Some Java Concurrency Tips

Started Sep 18, 2009

Java Enterprise Tech Tips: Locking and Concurrency in Java Persistence 2.0

Started Sep 14, 2009

Comparing Tomcat and Glassfish

Started Mar 6, 2009

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Profile Information

What are your main interests in software development?
Java, Web, Ajax, Java EE, Spring, Seam, JPA, Web Services
Do you have a website?
http://weblogs.java.net/blog/caroljmcdonald/
Anything else you'd like to add? Where do you live? (optional!)
Je parle Francais. Ich kann Deutsch.

Carol McDonald's Blog

movie recommendations with Spark machine learning

Posted on August 4, 2015 at 11:15am 0 Comments

Tutorial on Apache Spark, movie recommendations with machine learning 

This post discusses building a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.

https://dzone.com/links/parallel-and-iterative-processing-for-machine-lear.html

An Inside Look at the Components of a Recommendation Engine

Posted on April 13, 2015 at 9:14am 1 Comment

Recommendation engines help narrow your choices to those that best meet your particular needs.  In this post, we’re going to take a closer look at how all the different components of a recommendation engine work together. We’re going to use collaborative filtering on movie ratings data to recommend movies. The key components are a collaborative filtering algorithm in Apache Mahout to build and train a machine learning model,…

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screencast about MySQL for Developers

Posted on March 30, 2009 at 10:30am 0 Comments

Here is a screencast about MySQL for Developers



If you are a developer using MySQL, you should learn enough to take advantage of its strengths, because having an understanding of the database can help you develop better-performing applications. This session will talk about MySQL database design and SQL tuning for developers. Some topics include:



* MySQL Storage Engine Architecture

* Schema, the basic foundation of performance

* Think about performance when… Continue

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Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.

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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

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InfoQ Reading List

Things Software Developers Think They Don’t Need to Care about, But Can Impact Their Job

Holly Cummins gave a keynote at Goto Copenhagen where she urged developers to care about overlooked issues that shape their work. She warned of unintended consequences of design decisions, promoted systems thinking and statistical literacy, stressed mastering concurrency as hardware evolves beyond Moore’s Law, and mentioned the impact of AI on the job market.

By Ben Linders

Bun Introduces Built-in Database Clients and Zero-Config Frontend Development

Bun 1.3 revolutionizes full-stack JavaScript development with unified database APIs and zero-config frontend setup. Experience enhanced performance with built-in Redis support and optimized bundling. With a focus on community feedback, Bun is poised to outpace Node.js and Deno, delivering faster, efficient, all-in-one capabilities for modern developers.

By Daniel Curtis

API Platform Unkey Ditches Serverless After Performance Struggles

Developer Platform Unkey has written about rebuilding its entire API authentication service from the ground up, moving from serverless Cloudflare Workers to stateful Go servers after re-evaluating the constraints of their serverless architecture. The move resulted in a sixfold performance improvement and eliminated the workarounds that had become a dominant part of its engineering efforts.

By Matt Saunders

Presentation: Lessons Learned From Building LinkedIn’s First Agent: Hiring Assistant

Karthik Ramgopal and Daniel Hewlett discuss the evolution of AI at LinkedIn, from simple prompt chains to a sophisticated distributed agent platform. They explain the transition to a supervisor-sub-agent model that enables parallel development and modular quality evaluation. and share a blueprint for building resilient, high-scale agentic systems.

By Karthik Ramgopal, Daniel Hewlett

Kubernetes 1.35 Released with In-Place Pod Resize and AI-Optimized Scheduling

The Cloud Native Computing Foundation (CNCF) announced the release of Kubernetes 1.35, named "Timbernetes", emphasizing its focus on mutability and the optimization of high-performance AI/ML workloads.

By Mostafa Radwan

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