GINQ for the win

Using Groovy 4: GINQ for the Win

Rik Scarborough Development Technologies, Groovy, Java, Programming 4 Comments

In my last blog post Back in the Groovy 4, I briefly mentioned Groovy-Integrated Query (GINQ). I’ve been wanting to write about how I would use this new feature, and I decided to take this opportunity to do so.

In this post, I will be describing two examples in which I used GINQ. The first requirement I faced on a recent project of mine and demonstrating how I used GINQ to fulfill it. A quick disclaimer: this is not a tutorial on GINQ. This blog is merely a discussion of how I’ve used GINQ and how I plan on making it part of my toolkit.

Native MongoDB to Sequelize with PostgreSQL

Native MongoDB to Sequelize with PostgreSQL

John Boardman Databases, Heroku, MongoDB, PostrgreSQL Leave a Comment

Every long-term project will outlive at least some of the technologies it was originally built with. For example, a project I have been involved with recently ran into this situation. The app is hosted on Heroku, and over the years, the available MongoDB add-ons have changed and dwindled until now, there is only one.

Several migrations between MongoDB add-ons have already happened because of shutdowns. So, it was decided that rather than migrating to the last one still in existence, the project would switch to using PostgreSQL, which is supported directly by the Heroku team.

Large Datasets with Spring Batch

Utilizing Spring Batch for Large Dataset Summarization

Clayton Neff Databases, Java, Spring, Spring Batch Leave a Comment

I was recently tasked with summarizing the data of a several-million-row table, and the task proved to be a bit grueling at first. Eventually, I found a way to summarize the large dataset with Spring Batch, but not without a wrong turn or two at first. In this post, I’ll walk you through my process and how I overcame this …

Integrating Azure Functions with Cosmos DB SQL API in .NET Core 2.2

Zach Gardner .NET Core, Azure, Cloud, Development Technologies, Tutorial Leave a Comment

I am working on a project that leverages both Azure Functions as well as Cosmos DB. In trying to get both of these components wired together, I found that there are very few examples that work with the most recent versions of these components. I also saw examples that could work at a small scale, but don’t show industry-standard best practices, and would lead to performance issues if deployed in an environment with any meaningful traffic.

To that end, I put together this blog post showing how to set up an Azure Functions project in .NET Core 2.2 to integrate with Cosmos DB’s SQL API using its native tooling.