In this previous article I implemented a small, enterprisy benchmark to compare the concept of garbage collection used by Kotlin/JVM with the concept of Rust, which claims to not have any garbage collector at all. My conclusion was, that for a moderate increase in the complexity of the programming language the benchmark in Rust performed roughly 3 times faster than using Kotlin on the JVM. A colleague of mine, who uses Rust for some time now, looked at the code and gave me some hints for further speed improvements.
Continue readingBuilding an ETL Pipeline with Open Source Tools
What Is ETL
ETL stands for Extract, Transform, Load. Extraction is the process by which data from many sources and formats is collected. The data is then processed to allow for ease of storing and future processing. This can include data cleaning, or format normalization into file structures such as JSON. From here the data can then be persisted for storage and access by interested stakeholders.
Continue readingYes, Rust has Garbage Collection, and a Fast One
Rust is getting more and more popular. Thus, a group of colleagues, including myself, evaluated it for half a day to build up our own opinion, if Rust is of strategic interest for us or not. We did some coding following the standard introduction book, looked at some frameworks, and watched the presentation “Considering Rust”. The general conclusion was more or less in the line of: yeah, a nice new programming language, but without a full-grown ecosystem and without any garbage collection, it will be too cumbersome and unproductive for us in our projects. My gut feeling did not agree with the assessment regarding garbage collection. Thus, I did some more digging and testing and came up with my current conclusion: Rust does indeed garbage collection, but in a very clever way.
Continue readingJPA Pitfalls (14): EntityManager.persist and Transactions
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 the result of EntityManager.persist inside and outside of a transaction.
Continue readingJPA Pitfalls (13): EntityManager.merge Result
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 common error when using EntityManager.merge.
Continue readingJPA Pitfalls (12): Lazy Loading of Relationships in a Loop
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 performance problem when loading relationships in a loop.
Continue readingJPA Pitfalls (11): Iteration / Sorting in Memory or in DB
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.
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