Hibernate reactive and persistence introduction

3 minute read

This note is an introduction to the application’s data stack.


intro to BMC_Anvil’s persistence stack

BMC_Anvil’s data access stack is built around hibernate reactive + panache + reactive postgreSQL client + postgeSQL 15.3 db

as stated on other places everything that can be reactive was chosen over the classic flavour.


panache could be seen as the Quarkus equivalent for SpringData. It simplifies boilerplate code and offers 2 flavours for data access

I chose for the data access layer the repository approach, it extracts all logic to repositories making very clear and clean the data modeling by the entity and the data access by the repository itself.

showcase note:
I will not have a specific showcase for the active record approach on the app itself as it will be very disruptive, and services and resources will all have to be coded differently for the active record Entity.

the example below from quarkus guides illustrates how the active record approach works with panache library:

import io.quarkus.hibernate.reactive.panache.PanacheEntity;

public class Person extends PanacheEntity {
    public String    name;
    public LocalDate birth;
    public Status    status;

    public static Uni<Person> findByName(String name) {
        return find("name", name).firstResult();

    public static Uni<List<Person>> findAlive() {
        return list("status", Status.Alive);

    public static Uni<Long> deleteStefs() {
        return delete("name", "Stef");

a given entity extends a PanacheEntity which is the one encapsulating all basic CRUD data access.

hibernate 6.x reactive

for the moment, Quarkus claims hibernate reactive to be the only Jakarta (JPA) implementation

it is basically the same as the classic implementation with a few minor differences regarding access to the EntityManager and how loading lazy collections is achieved.

we are going to see both peculiarities in other notes.

if you are coming from hibernate 5.x series you will see some core changes:

  • JPA3 - Jakarta: moving to eclipse’s foundation specification means everything under javax.persistence now lives under jakarta.persistence
  • no more hibernate own criteria API
  • ResultTransformer split into 2

there are a few more that are non-breaking changes like the above.

if you are thinking about updating from hibernate 5.x to 6.x try reading thorben jansen’s note on migrating to hibernate 6 first


in the free relational databases, I used MySQL for years with fantastic results, support and great features. I think in the benchmark world it also ranks among the fastest ones if not the fastest.

so, why postgreSQL?

at early stages I used both databases, and at some point I started trying Quarkus’s live reload (dev mode) and found it awesome to iterate and experiment and debug. It turns out to be that when reloading the slowest part of the bootstrap was dropping and recreating all the DB with the seeded data. While MySQL took several seconds, PostgreSQL takes 2 seconds at most. That leaned me to PostgreSQL, you can hook up any DB of your choice.

and then using a DB specific feature…

I read a while ago that using everything in an agnostic ultra compatible fashion is paramount to be able to switch implementations, databases in this case.
if I were to use a MySQL specific functionality, I won’t be able to just swap databases…

I read a while ago that using everything in an agnostic ultra compatible fashion is at some point throwing away the benefits a specific implementation offers.
like having a tow truck and not loading more than xxx bags because they would not fit in a ferrari.

in this case, I did just that… I used a postgreSQL specific DataType the UUID data type

I wanted to use UUIDs as entities PKs instead of auto generated sequences of numbers.

the advantage is that UUIDs are native to PostgreSQL, and they are fast to operate with… blazing fast compared to uuids stored as characters. Not only they are fast, but they are also footprint storage small. A char version of a UUID will be considerably larger (2x +1 byte) than the 128-bit(16 bytes) size of PostgreSQL UUID type!

it is disputed that generating a UUID is slower than increasing an int in a sequence on large sets, but I am not fan of using autoincrement sequences, nor having hibernate generate the sequence for me, which will be commented on a later note.

with MySQL, UUID support is achieved by means of supporting UUID functions

if you like MySQL there is the UUID data Type offered by MariaDB