This blog article is part of a “series of blog articles” about common pitfalls using JPA and ways to avoid them. In this article, we describe a potential problem when filtering in memory instead of DB.
Continue readingJPA Pitfalls (10): Query Flush Mode
This blog article is part of a “series of blog articles” about common pitfalls using JPA and ways to avoid them. In this article, we describe unexpected results of JPQL queries with respect of the query flush mode.
Continue readingJPA Pitfalls (9): UPDATE/DELETE and Persistence Context
This blog article is part of a “series of blog articles” about common pitfalls using JPA and ways to avoid them. In this article, we describe bulk the effect of JPQL operations on the EntityManager cache.
Continue readingJPA Pitfalls (8): Navigation of Relationships
This blog article is part of a “series of blog articles” about common pitfalls using JPA and ways to avoid them. In this article, we describe unexpected results of navigational JPQL queries.
Continue readingCo- and Contravariance in Kotlin From an Introductionary Domain Level Perspective
Covariance and contravariance are properties of type systems of programming languages. They can be pretty intimidating words for a software developer who just wants to implement user requirements. In this article I approach the meaning of these two properties in Kotlin from a domain level perspective. It was part of an internal small workshop on Kotlin.
Continue readingJPA Pitfalls (7): Null Parameter
This blog article is part of a “series of blog articles” about common pitfalls using JPA and ways to avoid them. In this article, we describe issues with using null parameter values.
Continue readingJPA Pitfalls (6): Query Result with Duplicates
This blog article is part of a “series of blog articles” about common pitfalls using JPA and ways to avoid them. In this article, we describe unexpected duplicates in the result of a JPQL query.
Continue readingPostgres Performance Troubleshooting – An Example
Recently we encountered some severe performance problems in one of our projects. Some SQL queries were very slow.
A certain query took over 6 seconds to finish in Azure’s Postgres service and about 350ms locally on my laptop. Both are unacceptable.
I don’t know much more than the postgres basics and the project was in all parts relevant here more or less new to me. But let me show you how I approach the unknown, what I learned and how I solved it.
By the way: The issue is a prime example of not spending more time on optimisation than what feels necessary at the time. So even though the initial code looks a bit dumb in hindsight I still consider this ok as the application was fast enough for over 2 years in production and as we later learned the issue was caused by some up to that point unprecedented data.
Now, let’s dive in!
Part 2: GraphQL with Spring Boot, JPA and Kotlin
In Part 1 we set up a simple GraphQL backend application using Spring Boot, JPA and Kotlin. In the second part we will discuss best practices for such applications – especially how to test them.
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