Jens Schauder

Spring Data team member

Brunswick, Germany

Blog Posts by Jens Schauder

Spring Data Neumann M4 released

March 11, 2020

On behalf of the community, I’d like to announce the availability of the Spring Data Neumann M4 milestone release. It is planned to be the last milestone before the first release candidate.

Neumann M4 ships with 69 tickets fixed. It is built on top of Spring Framework 5.2.4 and will be picked up by Spring Boot 2.3.0 M3 for your convenience.

Finally, here are links to the reference documentation, changelogs, and artifacts of the individual project releases:

Spring Data Commons 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data JDBC 2.0 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data JPA 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Geode 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data KeyValue 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Neo4j 5.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Solr 4.2 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data MongoDB 3.0 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Cassandra 3.0 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data R2DBC 1.1 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data LDAP 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Envers 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data REST 3.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Redis 2.3 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Elasticsearch 4.0 M4 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Couchbase 4.0 M4 - Artifacts - Javadoc - Documentation - Changelog

Spring Data Moore SR3 and Lovelace SR14 released

Releases

December 4, 2019

On behalf of the community, we are pleased to announce that Spring Data Moore SR3 and Lovelace SR14 are now available from Maven Central. Both releases ship with almost 70 tickets in total, mostly bugfixes and dependency upgrades.

Moore SR3 is built on top of the recently released Spring Framework 5.2.2 and will be picked up by Spring Boot 2.2.2 for easier consumption and Lovelace SR14 is built on top of the recently released Spring Framework 5.1.12 and will be picked up by Spring Boot 2.1.11 for easier consumption.

Spring Data Moore SR1 and Lovelace SR12 released

Releases

November 5, 2019

On behalf of the team, I’m pleased to announce Spring Data releases Moore SR1, and Lovelace SR12. All releases pick up the most recent Spring Framework releases and will be picked up by Spring Boot 2.2.1, and 2.1.10 respectively.

Moore SR1

Spring Data Commons 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data JDBC 1.1.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data JPA 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data KeyValue 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Gemfire 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Neo4j 5.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data MongoDB 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Cassandra 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Solr 4.1.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Geode 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data LDAP 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Envers 2.2.1 - Artifacts - Javadoc - Documentation
Spring Data REST 3.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Redis 2.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Elasticsearch 3.2.1 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Couchbase 3.2.1 - Artifacts - Javadoc - Documentation - Changelog

Spring Data Lovelace SR6, Kay SR14, Ingalls SR20 Released

Engineering

April 10, 2019

On behalf of the entire team I’d like to announce the availability of three service releases for Spring Data release trains: Lovelace SR6, Kay SR14, and Ingalls SR20.

The releases are recommended upgrades as they contain fixes for a CVE in Spring Data JPA.

Spring Boot 2.1.4, 2.0.9, and 1.5.20 already pull in the above Spring Data versions, including the fixes that were released last week, and are now also available for use.

Here’s the content of the releases:

Spring Data Lovelace SR6

Spring Data Commons 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data JDBC 1.0.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data JPA 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Solr 4.0.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data MongoDB 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data KeyValue 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Gemfire 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Neo4j 5.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Cassandra 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data for Apache Geode 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data LDAP 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Envers 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data REST 3.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Redis 2.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Elasticsearch 3.1.6 - Artifacts - Javadoc - Documentation - Changelog
Spring Data Couchbase 3.1.6 - Artifacts - Javadoc - Documentation - Changelog

Spring Data JDBC, References, and Aggregates

Engineering

September 24, 2018

In my previous blog article, I described how to set up and use Spring Data JDBC. I also described the premise of making Spring Data JDBC easier to understand than JPA. This becomes interesting once you consider references. As a first example, consider the following domain model:

class PurchaseOrder {

  private @Id Long id;
  private String shippingAddress;
  private Set<OrderItem> items = new HashSet<>();

  void addItem(int quantity, String product) {
    items.add(createOrderItem(quantity, product));
  }

  private OrderItem createOrderItem(int quantity, String product) {

    OrderItem item = new OrderItem();
    item.product = product;
    item.quantity = quantity;
    return item;
  }
}

Introducing Spring Data JDBC

Engineering

September 17, 2018

With the upcoming Lovelace GA release, we’re going to ship a new Spring Data Module: Spring Data JDBC.

The idea behind Spring Data JDBC is to provide access to relational databases without submitting to the complexities of JPA. JPA offers such features as lazy loading, caching, and dirty tracking. While these are great if you need them, they can actually make thinking about JPA and its behavior harder than it has to be.

Lazy loading might trigger expensive statements when you don’t expect it or it might fail with an exception. Caching can get in your way when you actually want to compare two versions of an entity and being dirty makes it hard to find a single point where all persistence operations pass through.