OpenRules + Apache Spark: 2.5M Decisions per Second!

Our customers frequently select OpenRules for their decision management needs because of two important factors: 1) Ease of Use; and 2) Performance and Scalability. We have large customers who use OpenRules to create very complex decision models capable to handle large payloads – see an example with 17M records.

Recently we received a request to create a decision service capable to handle 1B records. Luckily this large corporation already uses Apache Spark for scalable computing as thousands of other companies, including 80% of the Fortune 500.

Within a few days, our team built a POC that put an OpenRules-based decision service inside an Apache Spark application. The performance results were really impressive: the total execution time for 1 billion records was under 7 minutes averaging 2.5 million decisions per second! Read more in the new manual “OpenRules-Spark Integration“.

When we converted a POC into a real decision service that handles more than 30,000 complex rules, we received the following execution results:

We Know More Than We Can Tell

Living through the ChatGPT boom, it is interesting to read this article “Is ChatGPT Aware?“:

Polanyi’s paradox, named in honor of the philosopher and polymath Michael Polanyi, states that “we know more than we can tell.” He means that most of our knowledge is tacit and cannot be easily formalized with words. In The Tacit Dimension, Polanyi gives the example of recognizing a face without being able to tell what facial features humans use to make such a distinction.

It brings back some of my related thoughts on “Business Rules and Tacit Knowledge” from 7 years ago. It described how “Human Learning” and “Decision Modeling” were moving in opposite directions. Will we see a change?