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.

This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?

https://www.anthropic.com/research/small-samples-poison

The ObjectScript language has incredible JSON support through classes like %DynamicObject and %JSON.Adaptor. This support is due to the JSON format's immense popularity over the previous dominance of XML. JSON brought less verbosity to data representation and increased readability for humans who needed to interpret JSON content. To further reduce verbosity and increase readability, the YAML format was created. The very easy-to-read YAML format quickly became the most popular format for representing configurations and parameterizations, due to its readability and minimal verbosity.

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?

We run mixed workloads in IRIS. For analytical queries, are bitmap indexes effective? What are the caveats for concurrent OLTP updates, and how should I maintain bitmap indexes efficiently?