Hi all,

It's me again 😁. In the pervious article Writing a REST api service for exporting the generated FHIR bundle in JSON, we actually generated a resource DocumentReference, with the content data encoded in Base64

Question!! Is it possible to write a REST service for decoding it? Because I am very curious what is the message data talking about🤔🤔🤔

OK, Let's start!

1. Create a new utility class datagen.utli.decodefhirjson.cls for decoding the data inside the DocumentReference
 

Class datagen.utli.decodefhirjson Extends%RegisteredObject
{
}

2. Write a Python function decodebase

I am trying to add Plotly Bar graph in a div to a CSPpage. I am working in IRIS 2022.1. I created persistent class.

I copied relevant code into github repo:

oliverwilms/iris-python-plotly
 

iris-python-plotly/csp/otwPlotly.csp at master · oliverwilms/iris-python-plotly
 

Set tPlotlyDiv = ##class(dc.python.test).PlotlyDiv(trnYear,trnMonth)

ClassMethod PlotlyDiv(pTrnYear = 2025, pTrnMonth = 8) As %String
{
    Set importlib = ##class(%SYS.Python).Import("importlib")
    Set plotdiv = ##class(%SYS.Python).Import("plotdiv")
    Do importlib.reload(plotdiv)
    Set div = plotdiv.gendiv(pTrnYear,p

Technical Documentation — Quarkus IRIS Monitor System

1. Purpose and Scope

This module enables integration between Quarkus-based Java applications and InterSystems IRIS’s native performance monitoring capabilities.
It allows a developer to annotate methods with @PerfmonReport, which triggers IRIS’s ^PERFMON routines automatically around method execution, generating performance reports without manual intervention.

2. System Components

2.1 Annotation: @PerfmonReport

• Defined as a CDI InterceptorBinding.
• Can be applied to methods or classes.
• Directs the framework to wrap method execution with IR

In my previous article, Using LIKE with Variables and Patterns in SQL, we explored how the LIKE predicate behaves in different scenarios, from Embedded SQL to Dynamic SQL, and what happens to performance when wildcards and variables come into play. That piece was about getting comfortable writing a working LIKE query. But writing SQL that works is only the starting point. To build applications that are reliable, scalable, and secure, you need to understand the best practices that underpin all SQL, including queries that use LIKE.

This article takes the next step. We’ll look at a few key points to help strengthen your SQL code, avoid common pitfalls, and make sure your SELECT statements run not just correctly, but also efficiently and safely. I'll use SELECT statements with LIKE predicate as an example along the way, showing how these broader principles directly affect your queries and their results.

*This is what Gemini came up with for this article, kinda cute.

The built-in %JSONExport() fails with circular references in deeply nested objects. How can I write a custom serializer that supports circular detection and reference tracking?

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. While XML is

I want to build an event store using raw globals and replay events for rebuilding state. Can I hook into IRIS journaling or write my own event log and replay engine?

We want to use IRIS as a backend for a lightweight blockchain or DLT. Can we model blocks using deterministic globals and implement consensus (PBFT, Raft) externally while using IRIS as the ledger?

I need to ingest streaming data over WebSockets. How can I create a custom inbound adapter for IRIS productions that supports many concurrent socket connections?

We require user-specific row access (row-level security). How can we enforce this in SQL and ObjectScript using custom class parameters and dynamic WHERE clause injections?

We’re ingesting high-volume HL7 messages and converting them to FHIR in near-real-time. How do we design a streaming ETL pipeline using interoperability (productions) that scales horizontally?

For space optimization, we want to apply a domain-specific compression algorithm to binary stream data before writing to %Stream.GlobalBinary. Is it possible to override or extend stream classes to include compression/decompression?

We use Apache Flink for complex event processing. Is there a way to integrate IRIS (as a source/sink) with Flink’s streaming API, possibly using the IRIS Native API or JDBC?

What are the best practices for creating a multi-tenant app in IRIS? How can I isolate data per tenant using namespaces, control resource usage, and delegate access via roles securely?

We’re encountering occasional deadlocks when accessing persistent objects. How can I trace lock acquisition and identify cyclic dependencies in real time?

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?

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?

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?

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 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?

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

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?

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?

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?

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 guide assumes you have a basic understanding of how Ansible works, so I won’t go into much detail on its fundamentals. However, if you have questions about anything mentioned here, feel free to a

To manage the accumulation of production data, InterSystems IRIS enables users to manage the database size by periodically purging the data. This purge can apply to messages, logs, business processes, and managed alerts.

Please check the documentation for more details on the settings of the purge task:
https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=EGMG_purge#EGMG_purge_settings

An issue that many users ran into is still finding old messages after running the purge task for messages. For example, a user has a purge task for messages that has NumberOfDaysToKeep=45

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

Include (Ensemble, EnsUI, EnsUtil)

Class Test.ProductionConfig
{

ClassMethod GetConfigItems() As %Status
{
Set tStatement = ##class(%SQL.Statement).%New()
Set tSC = tStatement.%PrepareClassQuery("Ens.Config.Production","EnumerateConfigItems")
Write tSC,!
Write "ConfigItemName"," ","Enabled",!
#Dim exp As %Exception.SystemException
#Dim rset As %SQL.StatementResult
Set rset = tStatement.%Execute()
Write rset,!
Write rset.%SQLCODE,!
While rset.%Next() {
Try {
Set tConfigName = rset.%GetData(1)
Set tEnabled = rset.%GetData

 I am creating a class to validate JSON body of requests. When I use the method %ValidateObject to check errors in the object, if I define some properties with %DynamicObject or %DynamicArray and use the Required parameter, this method does not work, it ignores validation, only works with properties %String, %Integer etc.

Class test.Example Extends%RegisteredObject
{

Property id As%Integer [ Required ];Property name As%String [ Required ];Property fieldOptions As%DynamicArray [ Required ];

Method %OnNew(id As%Integer, name As%String, fieldOptions As%DynamicArray) As%Sta