As applications grow, every database eventually hits scaling limits. Whether it's storage capacity, concurrent users, query throughput, or I/O bandwidth, single-server architectures have inherent constraints. This guide explains fundamental approaches to database scalability and shows how InterSystems IRIS implements these patterns to support enterprise-scale workloads.

We'll explore two complementary scaling strategies: horizontal scaling for user volume (distributing computational load) and sharding for data volume (partitioning datasets). Understanding the general principles behind these approaches will help you make informed decisions about when and how to scale your IRIS applications.

The examples in this guide use InterSystems IRIS in Docker containers.

Hi, now I would like to continuous on the topic that we talk about previously

Using Postman for testing the OAuth2.0 of the InterSystems FHIR repository - Part1

Question 1: Where is my client_id and client_secret come form?

Short answer: Authentication Server.

Over the last year, the CCR development team has prioritized changes to the CCR application to better expose and encourage best practices for using CCR. This article will highlight some of the areas that we've focused on:

• Organization Dashboard
• System Alerts
• Branch Hygiene

In each of these areas, we have increased the visibility of potential CCR usage issues and given users tools to proactively address them.

Organization Dashboard

We have continued to build out the available information on the Organization details page

If you want to generate JWT from x509 cert/key, any operation (including reading) on %SYS.X509Credentials requires U on %Admin_Secure resource.%Admin_Secure is required because %SYS.X509Credentials is persistent, and it's implemented th

InterSystems IRIS is built on an architecture that separates the logical organization of data (namespaces) from its physical storage location (databases). Understanding this separation and the distinction between Namespaces and Databases is crucial for effective data management, security, and especially, high-performance data sharing.

In this article, I will discuss these foundational components and provide a practical guide on leveraging global mappings to share native data structures (globals) across different logical environments.

Databases: Physical Reality

A database represents the physical reality of where the data is stored on the disk. First and foremost, it’s a file in a file system called IRIS.dat (e.g., <Install folder>\mgr\user\IRIS.DAT). The maximum size of this file is 32TB. It is the container for all the actual data and the code. Databases are managed by the IRIS kernel, which handles caching, journaling, and transaction logging at the physical file level.

When you install InterSystems IRIS DBMS, the following databases are installed automatically:

If one of your packages on OEX receives a review, you get notified by OEX only of YOUR own package.
The rating reflects the experience of the reviewer with the status found at the time of review.
It is kind of a snapshot and might have changed meanwhile.
Reviews by other members of the community are marked by * in the last column.

You probably know this situation:
Some time ago, you found a very special $ZU function for a very specific type of problem. Some kind of mystical formula. It became popular and was used by many developers throughout your code and across all your installations.

Several versions and updates later, you are informed by ISC that your mystical $ZU is deprecated and no longer supported. You are advised to replace it with a new $something().

Embeddedpy-bridge: A Toolkit for Embedded Python

Overview

Embedded Python is a game-changer for InterSystems IRIS, offering access to the vast Python ecosystem directly within the database. However, bridging the gap between ObjectScript and Python can sometimes feel like translating between two different worlds.