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#ECP

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Enterprise Cache Protocol (ECP): a distributed data caching architecture that manages the distribution of data and locks among a heterogeneous network of server systems.

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· Apr 28, 2025 2m read
Minimum IRIS container footprint

Sometimes customers need a small IRIS instance to do something in the cloud and shut it down, or they need hundreds of containers (i.e. one per end user or one per interface) with small workloads. This exercise came about to see how small an IRIS instance could be. For this exercise we focused on what is the smallest amount of memory we can configure for an IRIS instance. Do you know all the parameters that affect the memory allocated by IRIS ?

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Are you familiar with SQL databases, but not familiar with IRIS? Then read on...

About a year ago I joined InterSystems, and that is how IRIS got on my radar. I've been using databases for over 40 years—much of that time for database vendors—and assumed IRIS would be largely the same as the other databases I knew. However I was surprised to find that IRIS is in several ways quite unlike other databases, often much better. With this, my first article in the Dev Community, I'll give a high-level overview of IRIS for people that are already familiar with the other databases such as Oracle, SQL Server, Snowflake, PostgeSQL, etc. I hope I can make things clearer and simpler for you and save you some time getting started.

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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.

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Mirror Your Database Across the Galaxy with Seeding

Hello cpf fans! This distraction I used the "seed" capability in IRIS to provision an entire IrisCluster mirror, 4 maps wide with compute starting from an IRIS.DAT in a galaxy far far away. This is pretty powerful if you have had a great deal of success with a solution running on a monolithic implementation and want it to scale to the outer rim with Kubernetes and the InterSystems Kubernetes Operator. Even though my midichlorian count is admittely low, I have seen some hardcore CACHE hackers shovel around DATS, compact and shrink and update their ZROUTINES, so this same approach could also be helpful shrinking and securing your containerized workload too. If you squint and feel all living things around you, you can see a glimpse of in place (logical) mirroring in the future as a function of the operator and a migration path to a fully operational mirrored Death Star as the workload matures.


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