I would like to read and process a whole table of accounts using akka actors. We have a multi-threaded actors framework that currently does this using simple jdbc queries that read "chunks" of the data. We now want to take advantage of JPA/Hibernate's mapping and object graph. We have a spring application.
How do I use Hibernate and still take advantage of the multi-threading? My experience with Hibernate is creating DAOs with an EntityManager and calling myDao.getById(...) but how do I work on data that we have ALREADY fetched using jdbc and now manage it using Hibernate?
Of course you can use hibernate with akka actors you just should be carefully and follow this answer.
But for using actors with an ORM I recommend you SORM
SORM is a Scala ORM-framework designed to eliminate boilerplate code
and solve the problems of scalability with a high level abstraction
and a functional programming style. Features
Complete abstraction from relational concepts. You work with case classes, collections and other standard Scala data types instead of
tables, rows, foreign keys and relations.
Complete separation of domain model from persistence layer. There are no annotations, special types or any other dependencies on persistence layer present in model declaration. This house is clear!
An intuitive and centralized connection-agnostic API. No tangled implicit constructions polluting your namespace and functionality scattered across multiple components. No manual management of connections.
Concurrency. A single SORM instance can safely be used across multiple threads and seamlessly integrates into actor-based concurrent systems, like Akka.
Integrated connection pooling. Scalable just by setting a “poolSize” parameter.
Automated schema generation.
Related
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 want to implement a Spring Data Repository for a database which is not currentlty supported (hyphothetical question - no need to ask about the database).
How is this possible and where can I have an example of that?
Short answer is "yes, definitely". One of the main Spring-data's intentions is to unify access to different data storage technologies under same API style. So you can implement spring-data adapter for any database as long as it is worth implementing a connector to that database in Java (which is definitely possible for the majority of databases).
Long answer would take several blog posts or even a small book :-) But let me just highlight couple of moments. Each of the existing spring-data modules expose one of (or both) the API flavors:
imperative - in a form of various template classes (e.g. RedisTemplate). It is mostly for the databases that don't have query language, but only a programmatic API. So you're just wrapping your db's API into template class and you're done.
declarative - in a form of so called Declarative Repositories, quite sophisticated mechanism of matching annotations on method signatures or method signatures themselves to a db's native queries. Luckily spring-data-commons module provides a lot of scaffolding and common infrastructure code for this, so you just need to fill the gaps for your specific data storage mechanism. You can look at slide deck from my conference talk, where I explained on a high level the mechanics of how particular spring-data module generates real implementations of repositories based on user declarations. Or you can just go into any of the existing modules and look into source code. The most interesting parts there are usually RepositoryFactory and QueryLookupStrategy implementations.
That is extremely simplified view of the spring-data concepts. In order to get more detailed information and explanations of core principles, I'd suggest reading spring-data-commons reference documentation and having a look at spring-data-keyvalue project, which is a good starting point to implement Spring Data Module for key-value storages.
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.
I'm wondering what is the typical usecase of Spring DAO where we can easily switch between different persistence frameworks.
Apart from abstracting the boiler-plate code (for JDBC, Hibernate like) Why does any application want to change its ORM frameworks so frequently?
By using a DAO pattern with a distinct DAO interface, this enables you to mock the DAO implementation. With this you improve testability of your code, as you are then able to write tests that do not need database access.
It is not only about frequently being able to switch between ORM frameworks, but is also about reducing effort if you are enforced to change the ORM.
Another reason is, that you might have different data sources like a database, a webservice or the file system for example. In this case you don't abstract the ORM but simply the persistence mechanism in general.
I think the real important idea behind DAOs are that you have just one spot where all data access related code for a particular entity ist located. That makes testing and refactoring of your persistence layer easier and your code is better readable.
Furthermore, it makes the code better readable. Think of a new developer in your team that should implement a feature. If she needs to access the databasase she would look into the dao for data access methods.
If you scatter the data access code in different services the risk is pretty high that someone produces code duplicates.
What are the advantages and disadvantages of the Session Façade Core J2EE Pattern?
What are the assumptions behind it?
Are these assumptions valid in a particular environment?
Session Facade is a fantastic pattern - it is really a specific version of the Business Facade pattern. The idea is to tie up business functionality into discrete bundles - such as TransferMoney(), Withdraw(), Deposit()... So that your UI code is accessing things in terms of business operations instead of low level data access or other details that it shouldn't have to be concerned with.
Specifically with the Session Facade - you use a Session EJB to act as the business facade - which is nice cause then you can take advantage of all the J2EE services (authentication/authorization, transactions, etc)...
Hope that helps...
The main advantage of the Session Facade pattern is that you can divide up a J2EE application into logical groups by business functionality. A Session Facade will be called by a POJO from the UI (i.e. a Business Delegate), and have references to appropriate Data Access Objects. E.g. a PersonSessionFacade would be called by the PersonBusinessDelegate and then it could call the PersonDAO. The methods on the PersonSessionFacade will, at the very least, follow the CRUD pattern (Create, Retrieve, Update and Delete).
Typically, most Session Facades are implemented as stateless session EJBs. Or if you're in Spring land using AOP for transactions, you can create a service POJO that which can be all the join points for your transaction manager.
Another advantage of the SessionFacade pattern is that any J2EE developer with a modicum of experience will immediately understand you.
Disadvantages of the SessionFacade pattern: it assumes a specific enterprise architecture that is constrained by the limits of the J2EE 1.4 specification (see Rod Johnson's books for these criticisms). The most damaging disadvantage is that it is more complicated than necessary. In most enterprise web applications, you'll need a servlet container, and most of the stress in a web application will be at the tier that handles HttpRequests or database access. Consequently, it doesn't seem worthwhile to deploy the servlet container in a separate process space from the EJB container. I.e. remote calls to EJBs create more pain than gain.
Rod Johnson claims that the main reason you'd want to use a Session Facade is if you're doing container managed transactions - which aren't necessary with more modern frameworks (like Spring.)
He says that if you have business logic - put it in the POJO. (Which I agree with - I think its a more object-oriented approach - rather than implementing a session EJB.)
http://forum.springframework.org/showthread.php?t=18155
Happy to hear contrasting arguments.
It seems that whenever you talk about anything J2EE related - there are always a whole bunch of assumptions behind the scenes - which people assume one way or the other - which then leads to confusion. (I probably could have made the question clearer too.)
Assuming (a) we want to use container managed transactions in a strict sense through the EJB specification then
Session facades are a good idea - because they abstract away the low-level database transactions to be able to provide higher level application transaction management.
Assuming (b) that you mean the general architectural concept of the session façade - then
Decoupling services and consumers and providing a friendly interface over the top of this is a good idea. Computer science has solved lots of problems by 'adding an additional layer of indirection'.
Rod Johnson writes "SLSBs with remote interfaces provide a very good solution for distributed applications built over RMI. However, this is a minority requirement. Experience has shown that we don't want to use distributed architecture unless forced to by requirements. We can still service remote clients if necessary by implementing a remoting façade on top of a good co-located object model." (Johnson, R "J2EE Development without EJB" p119.)
Assuming (c) that you consider the EJB specification (and in particular the session façade component) to be a blight on the landscape of good design then:
Rod Johnson writes
"In general, there are not many reasons you would use a local SLSB at all in a Spring application, as Spring provides more capable declarative transaction management than EJB, and CMT is normally the main motivation for using local SLSBs. So you might not need th EJB layer at all. " http://forum.springframework.org/showthread.php?t=18155
In an environment where performance and scalability of the web server are the primary concerns - and cost is an issue - then the session facade architecture looks less attractive - it can be simpler to talk directly to the datbase (although this is more about tiering.)