SQL LATERAL JOIN – A Beginner’s Guide
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
In this article, we are going to see how the SQL LATERAL JOIN works, and how we can use it to cross-reference rows from a subquery with rows in the outer table and build compound result sets.
A LATERAL join can be used either explicitly, as we will see in this article, or implicitly as it’s the case for the MySQL JSON_TABLE function.
Percona PMM – A beginner’s guide
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
This article is a beginner’s guide for the Percona Monitoring and Management (PMM), a tool that’s very handy when it comes to analyzing a given database system.
PMM was released in 2016, and initially, it was available for MySQL. However, since 2018, it supports monitoring PostgreSQL as well, so once you learn it you can use it with the two most popular open-source relational database systems.
Mapping Java Records to JSON columns using Hibernate
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
In this article, I’m going to explain how we can map Java Records to JSON columns when using Hibernate.
Because Hibernate ORM does not offer built-in support for JSON, we are going to use the Hiberate Types library, which allows you to map entity attributes to JSON column types, no matter if you are using Oracle, SQL Server, PostgreSQL, or MySQL.
SQL Server audit logging using triggers
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
In this article, we are going to see how we can implement an audit logging mechanism using SQL Server database triggers to store both the previous and the current state of a given target table record in JSON column types.
The JPA and Hibernate first-level cache
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
In this article, I’m going to explain how the JPA and Hibernate first-level mechanism works and how it can improve the performance of your data access layer.
In JPA terminology, the first-level cache is called Persistence Context, and it’s represented by the EntityManager interface. In Hibernate, the first-level cache is represented by the Session interface, which extends the JPA EntityManager one.
MySQL JSON_TABLE – Map a JSON object to a relational database table
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
In this article, I’m going to explain how the MySQL JSON_TABLE function works, and how you can use it to transform a JSON object into a relational database table.
When using a relational database system, it’s best to design the database schema according to the relational model. However, there are certain use cases when the relational model is too strict, and we are better off storing data in a JSON column type.
For instance, as I explained in this article, when designing an audit log table, it’s much more convenient to store the before and after row snapshots in JSON columns, as this strategy will accommodate future target table modifications while also avoiding to duplicate every target table column in the audit log table. Thanks to the JSON column approach, we can even use a single audit log table for all target tables that we are auditing.
60 years of COBOL – past, present, and future
Imagine having a tool that can automatically detect JPA and Hibernate performance issues. Hypersistence Optimizer is that tool!
Introduction
60 years of COBOL, and, most likely, it’s here for the future. In this article, we have the pleasure of interviewing Adrian Tot about the past, present, and future of this technology that still has a very significant impact on our day-to-day business operations.
As software developers, we dream of using the latest technologies to solve various business use cases. However, lots of software systems still run on COBOL, a 60-year old English-like data processing language inspired by Grace Hopper’s idea of having a programming language that’s machine-independent.
Every now and then, I kept on stumbling on articles about COBOL that made me more curious about it. For instance, it was in 2014 that I read that there are 200 times more COBOL transactions executed on a per-daily basis than Google searches.
Or, I remember reading an article in 2017 about U.S. Digital Service attempts to modernize some legacy systems, which required interconnecting a Java-based application with a COBOL mainframe application. In the article, Marianne Bellotti explained that it was Java that was the bottleneck since the COBOL application response time was just 1 millisecond.
Or, recently, at the beginning of the COVID-19 epidemic, the U.S. Department of Labor was switching to the paper applications because the 40-year old COBOL system responsible for unemployment claims was overwhelmed with the afflux of requests.
This is a very interesting topic, and since I’ve never worked with COBOL, I decided to interview one of my friends, Adrian Tot, who has been using COBOL for over 15 years.
