/by apr/ I recently came across a java based commercial ISO-8583 encoder/decoder kind of application that claimed to perform as much as 2000 encode/decode operations per second on an average machine. Wow! that's fast! They even described their benchmark, they were talking about average messages with a 16 field set and a secondary bitmap present. It was not clear to me if they were doing both operations (encode/decode) in each cycle, or just one. I decided to give jPOS a try on an average machine [], I created a message with said number of fields, including a secondary bitmap, and a little loop including a pack and an unpack operation. I was pleased with the result: in excess 20000 pack/unpack operations per second. Then I decided to compare this same test against another commercial product, our own High performance, ISO-8583 C lightweight library. It was fast, but not as fast as I would expect, we get 40000+ pack/unpack operations per second, 2x compared to jPOS, but hey, that's C, I was expecting a 5x or 10x ratio there... The conclusions are pretty obvious to me... if you are up to, you can make Java perform slow like a turtle, and you can still charge people for it. [] Pentium 4, 1.6 Ghz, aprox 3k bogomips

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Many jPOS based applications have to deal with accounts and have to accurately and efficiently keep track of its transactions and balances.

I found myself writing ad-hoc transaction logging software using custom database schemas and balance calculation strategies over and over, and I think this may have happened to many of you as well.

While writting custom logic is certainly a feasible option and may seem like the simplest approach, code that looks okay in a test environment may degrade once you load it with hundreds of millions of transactions.

Code that passes all tests in a development environment may encounter nasty problems under heavy load, caused by race conditions, poor performance, etc. And then you need a myriad of supporting applications for extracts, analysis, reporting, data-entry, UI, etc. that have to be customized to your unique data model.

miniGL attempts to address this problem by providing a minimalistic solution to the recurring task of safely and efficiently keeping track of accounts.

While doing so, miniGL explores the possibility of leveraging double-entry accounting methods in situations were one would tend to use just single-entry.

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