Vlad Mihalcea

Optimistic locking retry with MongoDB

Imagine having a tool that can automatically detect if you are using JPA and Hibernate properly. Hypersistence Optimizer is that tool!

In my previous post I talked about the benefit of employing optimistic locking for MongoDB batch processors. As I wrote before, the optimistic locking exception is a recoverable one, as long as we fetch the latest Entity, we update and save it.

Because we are using MongoDB we don’t have to worry about local or XA transactions. In a future post, I’ll demonstrate how you can build the same mechanism when using JPA.

The Spring framework offers a very good AOP support and, therefore, it makes easy implementing an automatic retry mechanism, and this is how I did it.

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MongoDB optimistic locking

Imagine having a tool that can automatically detect if you are using JPA and Hibernate properly. Hypersistence Optimizer is that tool!

Introduction

When moving from JPA to MongoDB you start to realize how many JPA features you’ve previously taken for granted. JPA prevents “lost updates” through both pessimistic and optimistic locking. Optimistic locking doesn’t end up locking anything, and it would have been better named optimistic locking-free or optimistic concurrency control because that’s what it does anyway.

Lost updates

So, what does it mean to “lose updates”?

A real-life example would be when multiple background tasks update different attributes of some common Entity.

optimisticlocking

In our example, we have a Product Entity with a quantity and a discount which are resolved by two separate batch processors.

  1. the Stock batch loads the Product with {quantity:1, discount: 0}
  2. the Stock changes the quantity, so we have {quantity:5, discount: 0}
  3. the Discount batch loads the Product with {quantity:1, discount: 0}
  4. the Discount changes the discount, so we have {quantity:1, discount: 15}
  5. Stock saves the Product {quantity:5, discount: 0}
  6. Discount saves the Product {quantity:1, discount: 15}
  7. the saved quantity is 1, and the Stock update is lost

In JPA you may provide the @Version field (usually an auto-incremented number) and Hibernate takes care of the rest. Behind the scenes there is a safety mechanism that checks the updated rows number when given a specific version. If no row was updated, then the version has changed and an optimistic locking exception is thrown.

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Open-minded architect

Imagine having a tool that can automatically detect if you are using JPA and Hibernate properly. Hypersistence Optimizer is that tool!

While chit-chatting with one of my colleagues, I was surprised to hear they use a PHP team for developing their front-end application, while the back-end services are implemented using Java. Since their project is doing great, this really got my thinking why I haven’t ever considered such an architecture.

Most large Java web application I’ve been involved with have shone on the server-side part, while the client-side has been the Achilles heel.

While you can find great Java web developers, not every Java developer has web-based skills. But PHP developers are great when it comes to web programming, and they don’t have a zillion of frameworks to specialize in. PHP developing is pretty much standard, as opposed to Java web programming. I have always been anxious when joining a project using a new web framework I didn’t know anything about (e.g. Wicket), but that’s not the case for a PHP developer. They can always join a new project, and the learning curve is not that steep.

I remember reading many comparisons tests for Java vs PHP or Python, and I don’t remember seeing a single test  not aiming to pick-up a winner. Such test targets only the language, but disregards the community and especially its developers.

Sometimes the winning solution is not a single technology but a clever mix of those that are best suited within a given context. A similar concept is the polyglot persistence.

So as an architect you always have to stay open-minded and be objective of any technology you happen to love. After all, I love Java, but I also know it’s not always the best solution to all my clients’ problems.

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Batch processing best practices

Imagine having a tool that can automatically detect if you are using JPA and Hibernate properly. Hypersistence Optimizer is that tool!

Introduction

Most applications have at least one batch processing task, executing a particular logic in the background. Writing a batch job is not complicated but there are some basic rules you need to be aware of, and I am going to enumerate the ones I found to be most important.

From an input type point of view, the processing items may come through polling a processing item repository or by being pushed them into the system through a queue. The following diagram shows the three main components of a typical batch processing system:

  • the input component (loading items by polling or from an input queue)
  • the processor: the main processing logic component
  • the output component: the output channel or store where results will sent

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21st century logging

Imagine having a tool that can automatically detect if you are using JPA and Hibernate properly. Hypersistence Optimizer is that tool!

I think logging should get more attention than we are currently giving it. When designing an application a great deal of effort goes into modeling the customer business logic, making sure all use cases are covered and handled properly. The business model is mapped to a persistence storage (be at a RDBMS or a NoSQL solution), frameworks are being chosen: web, middle-ware, batch jobs, and probably SLF4J with log4j or logback.

This has been the case of almost all applications I’ve been involved with, and logging was always a second-class citizen, relying on good old string logging frameworks for that.

But recently I come to realize there is much more to logging than the current string based logging systems. Especially if my system gets deployed in the cloud and takes advantage of auto-scaling, then gathering text files and aggregating them to a common place smells like hacking.

In my latest application, we implemented a notification mechanism that holds more complex information since the String based log wasn’t sufficient. I have to thank one of my colleagues I work with who opened my eyes when saying “Notifications are at the heart of our application”. I haven’t ever thought of logging as the heart of any application. Business Logic is the heart of the application, not logging. But there is a lot of truth in his words since you can’t deploy something without a good mechanism of knowing if your system is actually doing what it was meant for.

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