NoSQL is not just about BigData

Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Wouldn’t that be just awesome?

Well, Hypersistence Optimizer is that tool! And it works with Spring Boot, Spring Framework, Jakarta EE, Java EE, Quarkus, or Play Framework.

So, enjoy spending your time on the things you love rather than fixing performance issues in your production system on a Saturday night!


There is so much debate on the SQL vs NoSQL subject, and probably this is our natural way of understanding and learning what’s the best way of storing data. After publishing the small experiment on MongoDB aggregating framework, I was challenged by the JOOQ team to match my results against Oracle. Matching MongoDB and Oracle is simply honoring Mongo, as Oracle is probably the best SQL DB engine. Being a simple experiment, it’s dangerous to draw any conclusion, since I was only testing the out-of-the-box Mongo performance, without taking advantage of any optimization. I will leave the optimization part as a subject of a future post, and dedicate this post to why NoSQL is a great tool in the Architect’s toolbox.

I really like how JOOQ defends SQL, and I’m constantly learning a lot from their blog and JOOQ documentation. SQL is one of the best ways of modeling data, and most of my projects requirements call for a relational data model. I would always recommend Oracle vs any other free RDBMS, since it’s safer to go with the best available tool, but not all of our clients want to spend their money on Oracle licenses, and therefore we have to build their products on top of MySQL or PostgreSQL (not a big issue so far).

But we’ve been successfully using NoSQL even for small data. I believe in polyglot persistence since it’s practical and cost-effective. So here are some use-cases where MongoDB was the right tool for the right job:

Web resource monitoring

One of our projects requires processing media resources from various providers, and since we have to download them, we want to know which of those resource providers are slowing down our workflows. Therefore, we thought of recording timing events in a capped collection of only 100000 documents, and since it has a fixed size we don’t have to worry about running out of disk-space or implementing a deletion mechanism. The timing events are processed asynchronously and thanks to the MongoDB aggregation framework we can calculate the latest average host time response and export the results via JMX to our centralized management application. It was so easy to design and implement it, and it works like a charm.

Persistent cache

Some media resources go through a complex processing pipeline and we can always benefit from caching results for previously computed data. From out Java objects to the MongoDB documents there is no ORM involved, and this simplifies the design/implementation of this caching solution.

Time series graphs

This is one of my favorite examples. In this rather simple project, we had to display some time series in a very user-friendly UI graph, and we rapidly implemented everything in JavaScript. The MongoDB stored the time events and some asynchronous batch processing tasks were pre-calculating the time series. The middle-ware was implemented on top of Node.js and the client-server communication was using WebSockets on top of From the DB to the UI graph there was no transformation required since everything was JSON based.

Now, quoting the aforementioned JOOQ post:

50M is not Big Data

CERN has Big Data. Google does. Facebook does. You don’t. 50M is not “Big Data”. It’s just your average database table.

CERN has indeed Big Data and big funds as well, being backed by the European Community, so they can afford any solution they want to choose from.

Google has Big Data and it inspired Hadoop which makes possible products like Hunk from which may benefit even small companies with huge logs (to be analyzed). Facebook does have Big Data and it still uses MySQL, which is a solid proof that SQL can scale.

It’s up to you to decide if your product data fits into a relational data model or a document/graph/wide-column store paradigm.

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