Hello!!!

Data migration often sounds like a simple "move data from A to B task" until you actually do it. In reality, it is a complex process that blends planning, validation, testing, and technical precision.

Over several projects where I handled data migration into a HIS which runs on IRIS (TrakCare), I realized that success comes from a mix of discipline and automation.

Here are a few points which I want to highlight.

1. Start with a Defined Data Format.

Before you even open your first file, make sure everyone, especially data providers, clearly understands the exact data format you expect. Defining templates early avoids unnecessary bank-and-forth and rework later.

While Excel or CSV formats are common, I personally feel using a tab-delimited text file (.txt) for data upload is best. It's lightweight, consistent, and avoids issues with commas inside text fields.

PatID   DOB Gender  AdmDate
10001   2000-01-02  M   2025-10-01
10002   1998-01-05  F   2025-10-05
10005   1980-08-23  M   2025-10-15

Make sure that the date formats given in the file is correct and constant throughout the file because all these files are usually converted from an Excel file and an Basic excel user might make mistakes while giving you the date formats wrong. Wrong date formats can irritate you while converting into horolog.

I need to build an integration solution that reads messages from a Kafka topic. The topic has 3 partitions and contains several million messages.

For certain reasons, I can only use the standard EnsLib.Kafka.Service class and cannot use either KafkaClient or Python.

This article outlines the process of utilizing the renowned Jaeger solution for tracing InterSystems IRIS applications. Jaeger is an open-source product for tracking and identifying issues, especially in distributed and microservices environments. This tracing backend that emerged at Uber in 2015 was inspired by Google's Dapper and Twitter's OpenZipkin. It later joined the Cloud Native Computing Foundation (CNCF) as an incubating project in 2017, achieving graduated status in 2019. This guide will demonstrate how to operate the containerized Jaeger solution integrated with IRIS.

In IRIS, every time a request need to be processed, a specific IRIS process (IRISDB.EXE) need to be assigned to handle that request.

If there is no spare IRIS process at that time, process will need to be created (and later destroyed). On some Windows systems (especially with security/antimalware solutions being active) creating new processes can be slow, which can results in delays during peak times (due to inrush of requests).

Having been inspired with Shared code execution speed question/discussion, I dare to ask another one which is annoying me and my colleagues for several weeks.

We have a routine called Lib that comprises 200 $$-functions of 1500 code lines total. It was noticed that after calling _any_ function of another rather big routine (1900 functions, 32000 lines) the next call of $$someFunction^Lib(x) is getting 10-20% slower than previous call of the same function. This effect doesn't depend on:

Hello,

I have created this script that does lot of writes to a single global. DB write performance is much slower than expected (compared to another similar systems).

Let's suppose two different routines use one and the same chunk of code. From the object-oriented POV, a good decision is to have this chunk of code in a separate class and have both routines call it. However, whenever you call code outside of the routine as opposed to calling code in the same routine, some execution speed is lost. For reports churning through millions of transactions this lost speed might be noticeable. Any advice how to optimize specifically speed?

Hello Community,

The subroutine ^routine is not executed while the queue is being processed in WorkMgr. However, it works when defined as a function. Is it mandatory to define subroutine^routine as a function for it to execute properly?

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.

InterSystems has been at the forefront of database technology since its inception, pioneering innovations that consistently outperform competitors like Oracle, IBM, and Microsoft.

Hello everyone,

I’m facing issues with replicating data from my Caché 2016 database to a PostgreSQL database. I need to handle around 300 data updates per minute, and whenever certain tables are modified, those changes must be reflected in other databases.

So far, I’ve tried various approaches, including:

The rise of Big Data projects, real-time self-service analytics, online query services, and social networks, among others, have enabled scenarios for massive and high-performance data queries. In response to this challenge, MPP (massively parallel processing database) technology was created, and it quickly established itself. Among the open-source MPP options, Presto (https://prestodb.io/) is the best-known option. It originated in Facebook and was utilized for data analytics, but later became open-sourced.