It seems easy to tune k8s pods for your Java Spring Boot application, right? Well it is not. Lets dive into it.

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“I gemiti di OpenAI” we whould say in Italian. It seems Google Gemini is catching up ChatGPT in a strong way. Let’s undertand what does it imply.

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We already discussed about Queue management solutions in the past, and I am always happy to write about it. Queue managers are not easy to implement, and there is a reson if IBM MQ Series is still a successful product. Some month ago, a big bank customer asked me to provide a small queue implementation to increase asynchronous internal processing of our payment solution.

The project had very strong contraints: I could not use existing queue system because they were not yet available, and I need to be able to provide microservice parallelism in a cloud-environent.

I have very little time to provide a solid solution, and re-inventing the wheel was not an option. Performance was important, but we plan to have a managable numbers of transactions per seconds, far behind modern cloud database capacity.

Challenge accepted.

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Test driven development is a beast to tame. I like TDD but in the past it was quite difficult to achive, at least in a pure-consultant approach with a "turn key" framework behind.

There are two major concept often not grasped correctly.

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Some co-workers started using Apache Kafka con a bunch of our Customers.

Apache Kafka is a community distributed event streaming platform capable of handling trillions of events a day. Initially conceived as a messaging queue, Kafka is based on an abstraction of a distributed commit log[*].

To get this goal, Apache Kafka needs a complex servers setup, even more complex if you want the certification for the producing company (Confluent).  Now, if you are planning to use Kafka like a simple JavaMessaeSystem (JMS) implementation, think twice before going on this route.

PostgreSQL 12 offers a fair (and open source) partition implementation, whereas if money are not a problem, Oracle 12c can happy scale on billions of record before running into troubles (and ExaData can scale even more).

PostgreSQL and Oracle offer optimizations for partitioned data, called “Partition Pruning” in PostreSQL teminology:

With partition pruning enabled, the planner will examine the definition of each partition and prove that the partition need not be scanned because it could not contain any rows meeting the query's WHERE clause. When the planner can prove this, it excludes (prunes) the partition from the query plan.

This feature is quite brand new (popped in PostreSQL 11) but it is essential to a successful partition strategy. Before these feature, partitioning was a black magic art. Now it is simpler to manage.
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I think constants are evil (just joking but….I will explain why).

I am tired of seeing a lot of Java classes filled with the same constants repeated over and over again, instead of collecting them in a common file, which in C Language was called include files (ooooah!).

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In the 1980 comments were used to reduce the “gap” between unstructured code and “rationale” behind the code.

1. C/64 Peek and poke where oscure.
2. Gosub on function pointed by numbers was bad
3. 8-bit systems was tight on command names and so on (think to write a python interpreter in Arduino/bbc:Micro, or look at the memory consumed by the COMMAND STRINGS on C/64 Basic v2)
4. C code was not expressive, but good code required little comment, as you can see here

Today comments are used to:

1. Comment obscure code -> instead of writing it better
2. Comment caching code you will end up to commit breaking production ->instead of having a cache config to disable cache on dev machines
3. Comment not working code will end up in production, producing functional breaks -> because you will forget it and  commit the bad version

When I am sent on a Red Project, the code is often obscure and not commented at all. So I dare to suggest to deprecate comments at all, replacing it with a set of meta annotation like:

@Comment("Documentation of this class/method/function/closure")
@Fixme("Reason")
@Todo("Why",  deadline=20230423)

 

 

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Vjdbc is a remote access for JDBC-Datasources developed more then ten years ago (last news are from 2007, with last comit from 2013.

The project was migrated to Maven 3, but the code base is very old.

I decided to give it a try, spin a github repository and started working on it, because you know, is nice to have a database driver proxy-logger :)

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