Synchronization Methods for Many-To-Many Associations

The many-to-many association is a common thing in data modeling. In JPA entities, it is implemented as collections that store associated entities from the other side of the association. To keep collections consistent on both sides, developers usually implement data synchronization methods. This article will highlight common issues that happen when adding synchronized methods for many-to-many bidirectional associations in JPA entities.

Many-To-Many Bidirectional Associations: Why Synchronize?

Before speaking about sync methods, let's look at bidirectional associations and their implementation in JPA in detail. Imagine a blog application where we can mark every post with several tags. We can make two JPA entities for this application: Post and Tag with appropriate attributes like ID, text, etc. Now we need to establish the association between one post and many tags. To do this, let's define a tags attribute on the Post entity, the type of this attribute is Set<Tag>. Notice that each tag can be reused for more than one post. In this case, we can create the posts attribute of type Set<Post> in the Tag entity. This is the bidirectional many-to-many association: we have references to several entities on both sides of the association. And now, our data model looks like this:

Building a RESTful Service Using ASP.NET Core and dotConnect for PostgreSQL

The term REST is an abbreviation for Representational State Transfer. It is a software architectural style created to assist the design and development of the World Wide Web architecture. REST defines a set of constraints that define how a distributed hypermedia system, such as the Web, should be architected. Restful Web Services are HTTP-based, simple, lightweight, fast, scalable, and maintainable services that adhere to the REST architectural style.

The REST architectural style views data and functionality as resources accessed via Uniform Resource Identifiers (URIs). Restful architecture is a client-server paradigm that utilizes a stateless communication protocol, often HTTP, for data exchange between server and client. In REST, the clients and servers interact through a defined and standardized interface.

Database Lifecycle in Jmix: Migration Challenges

Introduction

In Jmix, the JPA data model, hence database is a cornerstone of the application.

In the case of software development, the principle “if it works, don’t touch it” does not always work. Software changes are inevitable, database migrations too. And, if a database update goes wrong, you can lose data, which is one of the most valuable things for the business.

SQLAlchemy Basics

Today we released “SQLAlchemy Basics,” a new course from Treehouse instructor Megan Amendola. The 190 minute Python course will teach you how to: Create a database model, interact with a database using CRUD (create, read, update, and delete), and use...

The post SQLAlchemy Basics appeared first on Treehouse Blog.

Upgrade Your Data Layer With Dapper (a .NET Micro ORM)

Introduction

Dapper is a .NET-based, light-weight, fast, and simple to use micro ORM created by the brilliant Stack Exchange team. The word ORM means Object Relation Mapper, which means it facilitates mapping between .NET objects and databases.

One great thing about Dapper is that it works with any database. So, its not just for SQL Server. You can use it with PostgreSQL, MySQL, or others.

Scaling Databases With EclipseLink And Redis

Overview

EclipseLink has two types of caches: the shared cache (L2) maintains objects read from database; and the isolated cache (L1) holds objects for various operations during the lifecycle of a transaction. L2 lifecycle is tied to a particular JVM and spans multiple transactions. Cache coordination between different JVMs is off by default. EclipseLink provides a distributed cache coordination feature that you can enable to ensure data in distributed applications remains current. Both L1 and L2 cache store domain objects.

“Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.” — redis.io

Working With ORMs Using Entity Developer

Entity Developer from Devart is a modeling and code generation tool that lets you design your data access layer visually - at the drop of a hat. You can take advantage of Entity Developer to generate data access layer code automatically using one unified interface – as a result, chances of error creeping into your Data Access code are minimal.

In this article we’ll take advantage of Entity Developer to connect to and work with various ORM tools such as, EF Core, NHibernate, Telerik Data Access and LinqConnect. This article starts with a brief discussion on what ORMs are, why Entity Developer is useful, and how you can leverage Entity Developer to connect to and work with all these ORMs.

How to get Type-Safe and Intuitive Hibernate/JPA Queries

A large proportion of Java database applications are using Hibernate/JPA to bridge the gap between Java and SQL. Until recently, we were forced to mix Java and JPQL or to use complex imperative criteria builders to create database queries. Both of these methods are inherently neither type-safe nor very intuitive.  The newly launched  open-source library JPAstreamer addresses these issues by allowing you to express Hibernate/JPA queries using Java Streams. This means we can avoid any impedance mismatches between JPQL/HQL and Java and get full type-safety. In this article, I will show you how to put Java Stream queries to work in your application using JPAstreamer.

JPAstreamer in a Nutshell

As mentioned, JPAstreamer allows JPA queries to be expressed as standard Java Streams using short and concise, type-safe declarative constructs. This makes our code shorter, less complex, and easier to read and maintain. Best of all, we can stick to using only Java code without needing to mix it with SQL/JPQL or other language constructs/DSL.

In short, we can query a database like this:

GORM Association (t+1) to 1 Database Query

GORM is one of the many ORM (Objet-Relationational Mapper) for Go programming language. It comes with some nice intuitive methods to deal with the association. For details, refer to the documentation.

GORM comes with a drawback when we try to fetch associations, and then it queries the database for associated tables, which leads to (t+1) queries where t is the number of related tables in a query. 

Java: How to Create Lightweight Database Microservices

Build database microservices that are as light as a feather!

The number of cloud-based Java database applications grows by the minute. Many organizations deploy hundreds — if not thousands — of microservice instances. However, most applications carry an astounding amount of unnecessary overhead with respect to the runtime environment. This, in turn, makes the application slower and more expensive to run.

In this article, I will demonstrate how to write a database application that is 10 times smaller than normal(*). The storage requirement will be about 32 MB instead of the usual(*) ~300 MB taking both the application, third-party libraries, and the Java runtime into account. As a bonus, the required RAM to run the application will also be reduced by 25 percent.

Mapping Java Entities for Persistence With Hibernate (Part 4)

Learn more about mapping Java entities for persistence with Hibernate.

This post explores additional Java ORM mapping features in Hibernate/JPA, focusing on entity association mapping. Make sure to check the earlier posts: part 1, part 2, and part 3.

You may also like:  Hibernate Mapping Many-to-One Using Annotations

Also as a reminder, Hibernate is a powerful and feature-rich library – these posts serve as an overview of how to use some of its features to map Java domain model classes to relational tables. There are other topics that can be looked up in the official documentation.

Fetching Data With ORMs Is Easy! Is It?

Introduction

Almost any system operates with external data stores in some way. In most cases, it is a relational database and very often data fetching is delegated to some ORM implementations. ORM covers a lot of routine operations and brings along a few new abstractions in return.

Martin Fowler wrote an interesting article about ORM and one of the key thoughts there is “ORMs help us deal with a very real problem for most enterprise applications... They aren't pretty tools, but then the problem they tackle isn't exactly cuddly either. I think they deserve a little more respect and a lot more understanding.”

Introducing Hrorm: A Simple, Declarative, Type-Checked ORM

A Question

A lot of cyber ink has been spilled about the problem of connecting models in Java code with models in relational (SQL) databases. The topic of object-relational mappings (ORMs), is indeed rich, and the full scope of things you might want to do to transfer information between an application and a database is enormous. Many ORM tools attempt to cover as much of that space as they possibly can; their designs and implementations provide for a great deal of flexibility and they are packed with features.

Of course, to achieve that flexibility, they are burdened with complexity. This is not their fault: Java and SQL are quite different worlds and covering all (or even many) of the ways to pull them together is intrinsically complex. What about a different approach? What if instead of designing an ORM tool to handle a wide variety of object and schema models and all the accompanying use-cases, an ORM tool was designed with some assumptions in mind about how objects and schemas look and work and how they are used? How simple could an ORM tool be?