All the tutorials and books for node.js seem to use Mongo as the database.  I am not sold on 'document' databases and would like to know how difficult it is to use any version for plain old tried-and-true SQL with Node.js.

Does anybody have any experience in this area?

Views: 259

Reply to This

Replies to This Discussion

One of the traditional knocks on JS is the volatility of doing sql queries from a interpreted script. Not to mention security. In other words, how do you regulate resource for results in a varying client environment. Node.js is supposed to provide a server side capability. However I would be skeptical of it its implementation of a transnational capability. A memento pattern, or ability to rollback transactions, at least until thoroughly tested. Given the fact that most discussions are coupled with no-sql db's is a clue as to what its intended usage should be. Perhaps caches for local search tools like solr. Easy to update, and rebuild, but less likely to be an efficient engine for individualized rdbms queries.

Reply to Discussion

RSS

Happy 10th year, JCertif!

Notes

Welcome to Codetown!

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.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

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

Looking for Jobs or Staff?

Check out the Codetown Jobs group.

 

Enjoy the site? Support Codetown with your donation.



InfoQ Reading List

Uforwarder: Uber’s Scalable Kafka Consumer Proxy for Efficient Event-Driven Microservices

Uber has open-sourced uForwarder, a push-based Kafka consumer proxy built to handle trillions of messages and multiple petabytes of data daily. The system introduces context-aware routing, head-of-line blocking mitigation, adaptive auto-rebalancing, and partition-level delay processing to improve scalability, workload isolation, and hardware efficiency in large-scale event-driven microservices.

By Leela Kumili

TSSLint 3.0: Final Major Release with Reduced Dependencies

TSSLint 3, the lightweight TypeScript linting tool by Johnson Chu, enhances performance with a reduced dependencies and improved migration paths from legacy linters. As a spiritual successor to TSLint, it offers near-instant diagnostics and fixes, leveraging native Node support for .ts imports. Enhanced developer tooling and a new TSL compatibility layer simplify linting in large-scale projects.

By Daniel Curtis

Article: Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners

This article presents a least-privilege AI Agent Gateway that places clear controls between AI agents and infrastructure. Agents do not access infrastructure APIs directly. Instead, every request is validated, authorized using policy as code with Open Policy Agent (OPA), and executed in short-lived, isolated environments, with built-in observability using OpenTelemetry.

By Nabin Debnath

Podcast: Software Evolution with Microservices and LLMs: A Conversation with Chris Richardson

In this podcast, Michael Stiefel spoke with Chris Richardson about using microservices to modernize software applications and the use of artificial intelligence in software architecture. We first discussed the problems of monolithic enterprise software and how to use microservices to evolve them to enable fast flow - the ability to achieve rapid software delivery.

By Chris Richardson

Anthropic Study: AI Coding Assistance Reduces Developer Skill Mastery by 17%

Anthropic research shows developers using AI assistance scored 17% lower on comprehension tests when learning new coding libraries, though productivity gains were not statistically significant. Those who used AI for conceptual inquiry scored 65% or higher, while those delegating code generation to AI scored below 40%.

By Steef-Jan Wiggers

© 2026   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service