How to run integration tests at warp speed using Docker and tmpfs
Introduction In this article, I’m going to show you how to run integration tests on PostgreSQL, MySQL, MariaDB 20 times faster using Docker and mapping the data folder on tmpfs. As previously explained, you can run database integration tests 20 times faster! The trick is to map the data directory in memory, and my previous article showed you what changes you need to do when you have a PostgreSQL or MySQL instance on your machine. In this post, I’m going to expand the original idea, and show you how you can achieve… Read More
How to map JSON objects using generic Hibernate Types
Introduction In this article, we are going to see how you can map a JSON column to a JPA entity attribute using the Hypersistence Utils open-source project. While you can create your own custom Hibernate Types, for mapping JSON column types on Oracle, SQL Server, PostgreSQL, or MySQL, you don’t need to implement your own Hibernate Type since the Hypersistence Utils project already offers this functionality.
How to call PostgreSQL functions (stored procedures) with JPA and Hibernate
Introduction This article is part of a series of posts related to calling various relational database systems stored procedures and database functions from Hibernate. The reason for writing this down is because there are many peculiarities related to the underlying JDBC driver support and not every JPA or Hibernate feature is supported on every relational database.
How to run database integration tests 20 times faster
Introduction In-memory databases such as H2, HSQLDB, and Derby are great to speed up integration tests. Although most database queries can be run against these in-memory databases, many enterprise systems make use of complex native queries which can only be tested against an actual production-like relational database. In this post, I’m going to show you how you can run PostgreSQL and MySQL integration tests almost as fast as any in-memory database.
How do LockModeType.PESSIMISTIC_READ and LockModeType.PESSIMISTIC_WRITE work in JPA and Hibernate
Introduction Java Persistence API comes with a thorough concurrency control mechanism, supporting both implicit and explicit locking. The implicit locking mechanism is straightforward and it relies on: Optimistic locking: Entity state changes can trigger a version incrementation Row-level locking: Based on the current running transaction isolation level, the INSERT/UPDATE/DELETE statements may acquire exclusive row locks While implicit locking is suitable for many scenarios, an explicit locking mechanism can leverage a finer-grained concurrency control. In my previous posts, I covered the explicit optimistic lock modes: OPTIMISTIC OPTIMISTIC_FORCE_INCREMENT PESSIMISTIC_FORCE_INCREMENT In this post, I am… Read More
A beginner’s guide to database locking and the lost update phenomena
Introduction A database is highly concurrent system. There’s always a chance of update conflicts, like when two concurring transactions try to update the same record. If there would be only one database transaction at any time then all operations would be executed sequentially. The challenge comes when multiple transactions try to update the same database rows as we still have to ensure consistent data state transitions. The SQL standard defines three consistency anomalies (phenomena): Dirty reads, prevented by Read Committed, Repeatable Read and Serializable isolation levels Non-repeatable read, prevented by Repeatable Read… Read More
How to import CSV data into PostgreSQL
Introduction Many database servers support CSV data transfers and this post will show one way you can import CSV files to PostgreSQL. SQL aggregation rocks! My previous post demonstrated FlexyPool metrics capabilities and all connection related statistics were exported in CSV format. When it comes to aggregation tabular data SQL is at its best. If your database engine supports SQL:2003 windows functions you should definitely make use of this great feature.
