#InterSystems IRIS

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?

We are using IRIS with a sharded architecture. Complex SQL queries (with joins, aggregates, and subqueries) are performing slowly. How can I design queries or indexes to optimize distributed execution across shards?

Deploying new IRIS instances can be a time-consuming task, especially when setting up multiple environments with mirrored configurations.

I’ve encountered this issue many times and want to share my experience and recommendations for using Ansible to streamline the IRIS installation process. My approach also includes handling additional tasks typically performed before and after installing IRIS.

This code provide the configured production items with enabled or disabled status.

Include (Ensemble, EnsUI, EnsUtil)

Class Test.ProductionConfig
{