Deep dive into Hibernate ORM 6.4 features

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Hibernate ORM constantly progresses and it's hard to keep track of all the new features. Interested to hear about the new innovations that help developers meet future business needs? Then join my presentation which provides an overview and deep dives into some selected features. Learn about the new soft delete support, vector search extension for AI and much more.

Tecnologia

What’s new in Hibernate 6.4?
by Christian Beikov

Who am I?
Christian Beikov
Long time Hibernate community contributor
Full time Hibernate developer at Red Hat since 2020
Founder of Blazebit and creator of Blaze-Persistence
Living in Vienna/AT and Bonn/DE
Like to play tennis and go running

Noteworthy in 6.2
@Embeddableas @Structor JSON/XML
Java Record support
Uniﬁed value generation @ValueGenerationType
Table partitioning
SQL generation learned the MERGEstatement
https://in.relation.to/2023/03/30/orm-62-ﬁnal/

Noteworthy in 6.3
DAO/Repository support in annotation processor
Simple querying with CriteriaDefinition
CriteriaQueryfrom HQL
Upsert (a.k.a. insert-or-update) with StatelessSession
Revamped documentation
https://in.relation.to/2023/08/31/orm-630/

Noteworthy in 6.4
Soft delete
Array functions
Count query derivation
JFR events
Vector similarity search
https://in.relation.to/2023/11/23/orm-640-ﬁnal/

AI creates embedding (vector) out of unstructured data
Related data have vectors “near” each other
Nearest neighbour search allows to ﬁnd similar/related data
What is vector similarity search?
AI
Data Vector Vector store
Query Vector Similarity search

Vector is a numeric array e.g. [1.0, 5.6, -0.38, 2.59, …]
Precision can be e.g. float64, float32, float16etc.
Speed up nearest neighbour search with index
● Flat (deterministic) e.g. IVFFlat (Inverted File Flat)
● Graph (approximate) e.g. HNSW (Hierarchical Navigable Small World)
What is vector similarity search?

Vector similarity search use cases?
Content ﬁltering
Recommendation system
Fraud detection
Semantic search
Image search
etc.

Vector search supported databases
PostgreSQL pgvector extension
Oracle to provide implementation for Oracle DB
Ideas for supporting other databases or extensions?
Could support any database with array support, but slow without indexes

Vector search Pros and Cons
+ Improve search relevance “magically“
+ Efﬁciency
+ Easy to implement
- AI and embedding vectors are black boxes
- Trial and error for data preparation
- Additional infrastructure/cost

Demo source
https://github.com/beikov/quarkus-insights

ORM 6.5 outlook
Insert on conflictclause a.k.a. Upsert
Joins in DML (update, delete)
@FractionalSecondsfor temporal columns
Generated values retrieval through returning clause
Conﬁgure query cache layout

What’s next?
ORM 6.6
● Nested array support
● Ensure orm.xml can fully replace hbm.xml
ORM 7.0
● Boot abstraction for type discovery and reﬂection
● Jakarta Persistence 3.2
● Java 17 requirement
● Remove deprecated APIs
● Remove hbm.xml?

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Deep dive into Hibernate ORM 6.4 features

1. What’s new in Hibernate 6.4? by Christian Beikov

2. Who am I? Christian Beikov Long time Hibernate community contributor Full time Hibernate developer at Red Hat since 2020 Founder of Blazebit and creator of Blaze-Persistence Living in Vienna/AT and Bonn/DE Like to play tennis and go running

3. Noteworthy in 6.2 @Embeddableas @Structor JSON/XML Java Record support Uniﬁed value generation @ValueGenerationType Table partitioning SQL generation learned the MERGEstatement https://in.relation.to/2023/03/30/orm-62-ﬁnal/

4. Noteworthy in 6.3 DAO/Repository support in annotation processor Simple querying with CriteriaDefinition CriteriaQueryfrom HQL Upsert (a.k.a. insert-or-update) with StatelessSession Revamped documentation https://in.relation.to/2023/08/31/orm-630/

5. Noteworthy in 6.4 Soft delete Array functions Count query derivation JFR events Vector similarity search https://in.relation.to/2023/11/23/orm-640-ﬁnal/

6. AI creates embedding (vector) out of unstructured data Related data have vectors “near” each other Nearest neighbour search allows to ﬁnd similar/related data What is vector similarity search? AI Data Vector Vector store Query Vector Similarity search

7. Vector is a numeric array e.g. [1.0, 5.6, -0.38, 2.59, …] Precision can be e.g. float64, float32, float16etc. Speed up nearest neighbour search with index ● Flat (deterministic) e.g. IVFFlat (Inverted File Flat) ● Graph (approximate) e.g. HNSW (Hierarchical Navigable Small World) What is vector similarity search?

8. Vector similarity search use cases? Content ﬁltering Recommendation system Fraud detection Semantic search Image search etc.

9. Vector search supported databases PostgreSQL pgvector extension Oracle to provide implementation for Oracle DB Ideas for supporting other databases or extensions? Could support any database with array support, but slow without indexes

10. Vector search Pros and Cons + Improve search relevance “magically“ + Efﬁciency + Easy to implement - AI and embedding vectors are black boxes - Trial and error for data preparation - Additional infrastructure/cost

11. Demo source https://github.com/beikov/quarkus-insights

12. ORM 6.5 outlook Insert on conflictclause a.k.a. Upsert Joins in DML (update, delete) @FractionalSecondsfor temporal columns Generated values retrieval through returning clause Conﬁgure query cache layout

13. What’s next? ORM 6.6 ● Nested array support ● Ensure orm.xml can fully replace hbm.xml ORM 7.0 ● Boot abstraction for type discovery and reﬂection ● Jakarta Persistence 3.2 ● Java 17 requirement ● Remove deprecated APIs ● Remove hbm.xml?

14. Q & A

Deep dive into Hibernate ORM 6.4 features

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Deep dive into Hibernate ORM 6.4 features