Motivation

I didn't know about ObjectScript until I started my new job. Objectscript isn't actually a young programming language. Compared to C++, Java and Python, the community isn't as active, but we're keen to make this place more vibrant, aren't we?

I've noticed that some of my colleagues are finding it tricky to get their heads around the class relationships in these huge projects. There aren't any easy-to-use modern class diagram tool for ObjectScript.

Related Work

I have tried relavant works:

Since the introduction of Embedded Python there has always been doubt about its performance compared to ObjectScript and on more than one occasion I have discussed this with

I attended Cloud Native Security Con in Seattle with full intention of crushing OTEL day, then perusing the subject of security applied to Cloud Native workloads the following days leading up to CTF as a professional excercise. This was happily upended by a new understanding of eBPF, which got my screens, career, workloads, and atitude a much needed upgrade with new approaches to solving workload problems.

So I made it to the eBPF party and have been attending clinic after clinic on the subject ever since, here I would like to "unbox" eBPF as a technical solution, mapped directly to what we do in practice (even if its a bit off), and step through eBPF through my experimentation on supporting InterSystems IRIS Workloads, particularly on Kubernetes, but not necessarily void on standalone workloads.

eBee Steps with eBPF and InterSystems IRIS Workloads

Introduction

Database performance has become a critical success factor in a modern application environment. Therefore identifying and optimizing the most resource-intensive SQL queries is essential for guaranteeing a smooth user experience and maintaining application stability.

This article will explore a quick approach to analyzing SQL query execution statistics on an InterSystems IRIS instance to identify areas for optimization within a macro-application.

Rather than focusing on real-time monitoring, we will set up a system that collects and analyzes statistics pre-calculated by IRIS once an hour. This approach, while not enabling instantaneous monitoring, offers an excellent compromise between the wealth of data available and the simplicity of implementation.

We will use Grafana for data visualization and analysis, InfluxDB for time series storage, and Telegraf for metrics collection. These tools, recognized for their power and flexibility, will allow us to obtain a clear and exploitable view.

More specifically, we will detail the configuration of Telegraf to retrieve statistics. We will also set up the integration with InfluxDB for data storage and analysis, and create customized dashboards in Grafana. This will help us quickly identify queries requiring special attention.

To facilitate the orchestration and deployment of these various components, we will employ Docker.

Here at InterSystems, we often deal with massive datasets of structured data. It’s not uncommon to see customers with tables spanning >100 fields and >1 billion rows, each table totaling hundred of GB of data. Now imagine joining two or three of these tables together, with a schema that wasn’t optimized for this specific use case. Just for fun, let’s say you have 10 years worth of EMR data from 20 different hospitals across your state, and you’ve been tasked with finding….

High-Performance Message Searching in Health Connect

The Problem

Have you ever tried to do a search in Message Viewer on a busy interface and had the query time out? This can become quite a problem as the amount of data increases. For context, the instance of Health Connect I am working with does roughly 155 million Message Headers per day with 21 day message retention. To try and help with search performance, we extended the built-in SearchTable with commonly used fields in hopes that indexing these fields would result in faster query times. Despite this, we still couldn't get some of these queries to finish at all.