Introduction

The InterSystems IRIS Data Platform has long been known for its performance, interoperability, and flexibility across programming languages. For years, developers could use IRIS with Python, Java, JavaScript, and .NET — but Go (or Golang) developers were left waiting.

That wait is finally over.

The new go-irisnative driver brings GoLang support to InterSystems IRIS, implementing the standard database/sql API. This means Go developers can now use familiar database tooling, connection pooling, and query interfaces to build applications powered by IRIS.

Why GoLang Support Matters

GoLang is a language designed for simplicity, concurrency, and performance — ideal for cloud-native and microservices-based architectures. It powers some of the world’s most scalable systems, including Kubernetes, Docker, and Terraform.

Bringing IRIS into the Go ecosystem enables:

• Lightweight, high-performance services using IRIS as the backend.
• Native concurrency for parallel query execution or background processing.
• Seamless integration with containerized and distributed systems.
• Idiomatic database access through Go’s database/sql interface.

This integration makes IRIS a perfect fit for modern, cloud-ready Go applications.

We are happy to present the bonuses page for the applications submitted to the InterSystems .Net, Java, Python, and JavaScript Contest!

See the results below.

Hi Community,

It's voting time! Cast your votes for the best applications in our InterSystems .Net, Java, Python, and JavaScript Contest:

🔥 VOTE FOR THE BEST APPS 🔥

How to vote? Details below.

Given a JSON schema at runtime, I want to programmatically create persistent classes with corresponding properties and indices. Is there a way to dynamically compile and load such classes?

The built-in task manager is limited. How can I implement a robust, distributed job scheduler in IRIS with support for dependencies, CRON syntax, and failover recovery?

We're encountering occasional deadlocks in a mirrored IRIS deployment. How can we track down which global or object write caused the lock cycle, and how does IRIS mirror lock propagation internally?

We need to authenticate users via Azure AD or Okta. What are the best practices to implement federated authentication using OAuth2/OIDC or SAML in IRIS Management Portal or custom web apps?

Can the IRIS SQL engine be extended with custom optimization rules (e.g., prioritizing certain indices or join orders)? If not, is there a supported way to influence cost models?

We want to expose both REST and GraphQL endpoints over the same data models. Is there a way to implement or integrate GraphQL with ObjectScript and map to class methods?

I have large joins involving millions of rows. How can I profile and tune the SQL engine’s parallel execution? Are there EXPLAIN plan features to inspect threading and task distribution?

For geographically distributed nodes using async mirroring or ECP, how can I detect and resolve data conflicts manually (custom logic) while maintaining eventual consistency?

When writing dynamic SQL queries using embedded SQL, how can I force or ensure that filter conditions are pushed down to the data access layer rather than evaluated in memory?

I’m using recursive CTEs for hierarchical data, but the planner seems to produce inefficient plans. Can I influence or extend the query optimizer behavior in IRIS?

Instead of default storage classes, I want to implement my own SQL storage mapping for a persistent class (e.g., denormalized or sparse matrix structures). How do I define and manage custom storage definitions?