Carol McDonald
  • Female
  • Jacksonville, Florida
  • United States
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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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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

Presentation: Maximizing Success with Limited Time, Resources, and Energy: Lessons from Startup Engineering

David Gudeman discusses battle-tested architectural patterns for resource-constrained engineering teams. Drawing from over a decade of startup experience, he explains how pairing GCP, Firebase, and Cloud Run accelerates product-market fit. Learn how to eliminate redundant frontend state, structure event-driven backends, and maintain lean DevOps while building for long-term scalability.

By David Gudeman

Changesets v3: Bumps Peer Dependents by Patch and Ships ESM Only with an 88% Smaller Install

Changesets v3.0, released on August 11, 2026, introduces significant updates for JavaScript monorepos. It offers leaner installations, a rebuilt CLI, and changes to peer dependency management that resolve long-standing complaints. Upgrading involves configuration updates. The tool focuses on versioning and changelogs while supporting contributors in declaring release intents.

By Daniel Curtis

Out-of-Order HTML Streaming Moves from JS Frameworks into the Browser

Out-of-order HTML streaming, a user experience pattern popularized by front-end frameworks and that allows users to view and interact faster with a page before it is entirely loaded, is making its way to web browsers. The proposal has the browser patching placeholders as data arrives. Developers can use either declarative HTML or matching JavaScript APIs. Available in Chrome 151.

By Bruno Couriol

Java News Roundup: JDK 27, Open J Proxy, A2A Jakarta, Azul Payara, BoxLang, Netflix ja

This week's Java roundup for September 14th, 2026, features news highlighting: the GA release of JDK 27, LibericaJDK 27, Open J Proxy 1.0 and A2A Jakarta 1.0; point releases of Azul Payara, JHipster, JetBrains Ktor and BoxLang; and introducing Netflix ja, a modern JMS command line development experience for Java developers.

By Michael Redlich

Uber Redesigns M3DB Sharding with Subclusters to Limit Failure Impact

Uber has redesigned shard placement in M3DB with fixed size subclusters to limit the impact of node failures, maintenance, and cluster scaling. The approach bounds shard dependencies, preserves replica isolation, and uses a greedy algorithm to select shard migrations while avoiding a separate rebalancing pass and unnecessary data movement.

By Leela Kumili

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