Full-text search is one of Elasticsearch's strengths.
However, what if we wanted to find similar documents based on something more abstract - like the meaning of a word or the style of writing?
This is where Elasticsearch's dense vector field datatype and script-score queries for vector fields come into play.
This is the first part of a two-part blog post that is concerned with extracting data from Jira and using it for further applications such as visualization, evaluation, and using the power of machine learning to gain valuable insights in the data.
Integrating external services into an application is often challenging.
Instead of doing the testing manually, the setup could be tested also automated.
In case you are using Spring Boot, for a couple of services there exist an integration.
This blog post will show how you can setup your Kafka tests to use an embedded Kafka server.
Storing dates in databases was traditionally done with java.sql.Date and java.sql.Timestamp.
Since we would like to write modern applications, with Java 8 or 11, it is preferable to use an OffsetDateTime or a LocalDateTime.
Spring Data JPA, for example, is mapping those fields automatically to the correct database field.
There are still some challenges in combination with PostgreSQL.
Recently we achieved great maintainable API documentation with Spring REST Docs. Let's learn how Spring Auto REST Docs can make it even easier.