Im not sure if i can use Spring Data JDBC also for complex models. My doubts arise especially cause in the Spring Data JDBC (3.0) documentation is written:
"There is a simple model of how to map entities to tables. It probably only works for rather simple cases. If you do not like that, you should code your own strategy. Spring Data JDBC offers only very limited support for customizing the strategy with annotations." https://docs.spring.io/spring-data/jdbc/docs/3.0.0/reference/html/#jdbc.why
I was expecting Spring Data JDBC is also working for more complex cases.
The limitations that rise from this simple model affect mostly legacy projects where you have a database that you maybe can't even change.
Spring Data JDBC is not intended to map an arbitrary database model to an arbitrary Java domain model, but to use a Domain Driven Design approach and construct the database accordingly.
But even in the cases where you hit the limitations of Spring Data JDBC you can always fall back on Springs JdbcTemplate without any conflict with the rest of your model which gets persisted by Spring Data JDBC.
The same is not true for JPA. Of course you can use JdbcTemplate with JPA as well, but you now have to very different approaches to persistence in your application which can and will interact in interesting ways due to JPA caching and dirty checking.
I therefore think Spring Data JDBC is an excellent choice for large application and complex models.
It's limitations will push you in the direction of better defined smaller modules and less complex models.
I'm playing around with Atomikos. Using this as a starting point: https://www.fabiomaffioletti.me/blog/2014/04/15/distributed-transactions-multiple-databases-spring-boot-spring-data-jpa-atomikos/
In my use case, I'm going to have essentially two physically separated databases that are going to be identical.
This requirement is based on GDPR and basically, we will have databases in different regions.
Some of the tables need to be in sync and one of the ideas is to use Atomikos (2-phase commit ) to implement distributed transactions.
What I'm trying to implement is to use one single spring repository that is going to persist data into two physically divided databases.
All examples provided by atomikos are dealing with two databases but with different tables using different repositories.
Is it possible to configure atomikos on the way that I'm using a single repository that is going to persist data in both data sources?
In the ideal case, I'm looking for the possibility to mark service method with a specific annotation and specific transaction manager and only in that case to force that specific method or better to say invoked repository methods in that service methods ( save, saveAll methods and so on ) to persist data in both databases. When there is no specific annotation on a service method to persist only in a single database to not perform distributed transaction.
I am new to Springboot and trying to build a small rest-service. We have a DB deployed on different environments (e.g. DEV, TEST). The rest-service will make a call to the appropriate database based on the received query param (e.g. ?env=TEST). The schemas of the deployed database are the same, the difference is only in connection string. I have some questions related to this task.
I read a few articles how to work with multiple databases using Spring JPA (for example this one: https://www.baeldung.com/spring-data-jpa-multiple-databases). It did work, but in the given example they get different entites from different databases using different queries, in my case the entity and the query is the same, but I still have to duplicate repositories, transactionManagers, entityManagers etc because of different datasources. And this is just two environments and I have more of them.
I have another thought that I might need to recreate the repository each time I process a request (to make the repository non-singleton). I am not sure if it is a good practice.
Maybe it worth to use JDBCTemplate instead of Spring JPA in this case?
Could you please suggest something how to approach such a task?
So, I have my application based on spring and hibernate. The user produces some data (in my case the data is kind of development itself) which is persisted by hibernate.
But for now this won't be accepted by large enterprises. They want to have a development enviroment, a test environment and a production. What I need to implement is a way to deploy data from one environment to another.
To be clear: I am not asking about deploying the application, but its data.
Are there best practices to implement this feature?
To maintain DDL and use same across various environments use liquibase or flyaway which also integrates with seamlessly with spring.
If you want DML to be migrated then vendor specific data migration can be used.
I think you are mostly looking at DDL only so either of above is better solution
I have not used Spring Data before but I've used Hibernate ORM a number of times for MySQL based application. I just don't understand which framework to choose between the two for a MongoDB based application.
I've tried searching for the answer but I can't find the answer which does a comparison between the two in a production environment. Has anyone found problems working with these two frameworks with MongoDB ?
Disclaimer: I am the lead of the Spring Data project, so I'll mostly cover the Spring Data side of things here:
I think the core distinction between the two projects is that the Hibernate OGM team chose to center their efforts around the JPA while the Spring Data team explicitly did not. The reasons are as follows:
JPA is an inherently relational API. The first two sentences of the spec state, that it's an API for object-relational mapping. This is also embodied in core themes of the API: it talks about tables, columns, joins, transactions. Concepts that are not necessarily transferable into the NoSQL world.
You usually choose a NoSQL store because of its special traits (e.g. geospatial queries on MongoDB, being able to execute graph traversals for Neo4j). None of them are (and will be) available in JPA, hence you'll need to provide proprietary extensions anyway.
Even worse, JPA features concepts that will simply guide users into wrong directions if they assume them to work on a NoSQL store like they were defined in JPA: how should a transaction rollback be implemented reasonably on top of a MongoDB?
So with Spring Data, we chose to rather provide a consistent programming model for the supported stores but not try to force everything into a single over-abstracting API: you get the well-known template implementations, you get the repository abstraction, which works identical for all stores but lets you leverage store-specific features and concepts.
Disclaimer: I'm one of the Hibernate OGM developers so I'll try to provide some of the reasons behind it.
Hibernate OGM provides Java Persistence (JPA) support for NoSQL solutions. It reuses Hibernate ORM’s engine but persists entities into a NoSQL datastore instead of a relational database. It also aims to provide access to specific datastore features when JPA does not have a good fit.
This approach is interesting for several reasons:
Known semantic and APIs. Java developers are already familiar with JPA, this means that one won't have to learn lower level API. It also supports both HQL and native backend-queries.
Late backend choice. Choosing the right NoSQL datastore is not trivial. With Hibernate OGM you won't have to commit to a specific NoSQL solution and you will be able to switch and tests different backends easily.
Existing tools and libraries. JPA and Hibernate ORM have been around for a while and you will be able to reuse libraries and tools that uses them underneath.
Most of JPA logical model fits. An example of a good fit is #Embedded, #EmbeddedCollection and #Entity (that can be a node, document or cache based on the datastore of choice). Admittedly, annotation names might be strange because you will also have to deal with #Table and #Column.
JPA abstracts persistence at the object level, leaving room for a lot of tricks and optimizations. We have several ideas planned, like polyglot persistence: storing data in several data stores and use the best one for a specific read job.
The main drawback is that some of the concepts of JPA are not easily mapped to the NoSQL world: transactions for example. While you will have access to transaction demarcation methods, you won't be able to rollback on data stores that don't support transactions natively (transactions, in this case, will be used to group operations and try to optimize the number of calls to the db).
Also, if your dataset is by nature non domain model centric, then Hibernate OGM is not for you.
One can Just go with SpringData. If you recall Spring ORM also uses some JPA things such as Entity, Transaction and provided best commination of things from JPA and Hibernate APIs a. Spring community will take care in future versions if JPA is getting more matured for NoSQL.
Though it is not the main reason. Most of reasons are described by #Oliver Drotbohm.
Read more documentation of SprinData and further analyse your data model, scalability on continuity/growth of data store, find best fit for your solution and consider suggestion given by #Davide.
Many cases SpringData has got more success rate than JPA while integrating with MongoDB.