After many sleepless nights it's a pleasure to announce the newer, better, moderner ObjectScript compiler which implemented pretty much everything you ever wanted to have in modern ObjectScript:

• Design objective of this new compiler is to parse reasonable subset of current ObjectScript syntax which will look readable for stranger, and not scare them with 1 letter syntax. The good start for compiler was the old-good COS Guidelines from here https://github.com/intersystems-ru/cos-guidelines
• For reasons we mentioned above we do not parse 1 letter syntax. It's declared evil;
• We do not handle dotted syntax for the same reason - modern syntax with {} is proper replacement for dotted syntax blocks;

But we not only parse the modern ObjectScript syntax, we have implemented finally the long-standing request which we always dreamed about. Closures!

This is the second part of my long post about package managers in operating systems and language distributions. Now, hopefully, we have managed to convince you that convenient package manager and rich 3^rd party code repository is one key factor in establishing of a vibrant and fast growing ecosystem. (Another possible reason for ecosystem success is the consistent language design, but it will be topic for another day.)

In this second part we plan to discuss the practical aspects of creating a package manager in general and their projection to the Caché database environment.

Have you ever thought what could be a reason why some development environment (database, language) would eventually become popular? What part of this popularity could be explain as language quality? What by new and idioms approaches introduced by early language adopters? What is due to healthy ecosystem collaboration? What is due to some marketing genius?

In part I of this series we have introduced MapReduce as a generic concept, and in part II we started to approach Caché ObjectScript implementation via introducing abstract interfaces. Now we will try to provide more concrete examples of applications using MapReduce.

In the prior part of this series we have provided introduction to Google MapReduce approach, but still not covered their possible ObjectScript implementation. Which we will start to explain today.

Several years ago everyone got mad about BigData – nobody knew when smallish data will become BIGDATA, but all knows that it’s trendy and the way to go. Time passed, BigData is not a buzz anymore (most of us missed the moment when Gartner has removed BigData term from their 2016 buzzword 2016 curve http://www.kdnuggets.com/2015/08/gartner-2015-hype-cycle-big-data-is-out-machine-learning-is-in.html), so it’s probably a good time to look back and realize what it is (what it was)…

When it becomes “BigData”?

This article created as side effect of preparations to the longer set of articles about simple, but still handy MapReduce implementation in Caché. I was looking for relatively easy way to pass arguments to (potentially) multiple targets via remote calling facilities. And after several attempts I have realized that we do have very powerful mechanism in the Caché ObjectScript which might be of particular help here – dynamic dispatch for methods and properties.

Let assume you have a infinitely scaling algorithm implemented in your application, using replication, ECP, or any other means of horizontal scaling, and let assume you know how to run your system under any volume of requests, the trick is to deploy required number of computing nodes in the cluster. If we are talking about cluster of 2-4 nodes your administrator (or as they call it today "devops engineer") will install anything manually. Probably he will easily handle 5 nodes configuration in the cluster. But what if you need to deploy 10, 100, or 200 properly configured, interconnected nodes?