Hi 

How can I set a default value in a dataCombo?

I  tried 
ClientMethod onloadHandler() [ Language = javascript ]
{

 zenPage.getComponentById('mycombo').setProperty('selectedIndex',0);

 //zenSetProp('mycombo','selectedIndex',0);

}

and didn't work, I can not use the value property because I don't know what the values in my combo are going to because they loaded OnCreateResultSet,  I'm only looking to make always the first value selected by default 

Thanks

To better understand Streams in IRIS I'll start with a short

History

In the beginning (before IRIS), there was just basic access to external devices.
The 4 commands OPEN, CLOSE, READ, WRITE still work and are documented 
by  Introduction to I/O in detail.
Especially for files, this is a direct access to your actual file system.
You have to take care of any status or other signal in your code.  
Also, any code conversion or similar is up to you. 

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
{
}

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)

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 IRIS monitoring logic.

2.2 Interceptor: PerfmonReportInterceptor

• Intercepts calls to annotated methods.

• Execution flow:

  1. Log start event (LOG.infof("INIT: …"))
  2. Call monitorSystem.startPerfmon()
  3. Proceed with context.proceed()
  4. In a finally block:
     • Call monitorSystem.generateReportPerfmon(...)
     • Call monitorSystem.stopPerfmon()
     • Log end event with execution time

• Ensures that monitoring is always ended, even if an exception is thrown.

2.3 DAO Bean: MonitorSystem

• CDI bean annotated with @ApplicationScoped.

• Holds a single instance of IRIS initialized at startup.

• Configuration injected via @ConfigProperty (JDBC URL, user, password).

• Uses DriverManager.getConnection(...) to obtain a raw IRISConnection.

• Contains methods:

  • startPerfmon()
  • generateReportPerfmon(String reportName)
  • stopPerfmon()

• Each calls the appropriate ObjectScript methods in iris.src.dc.AdapterPerfmonProc via iris.classMethodVoid(...).

2.4 ObjectScript Adapter: iris.src.dc.AdapterPerfmonProc

• Defines the routines that encapsulate ^PERFMON logic:

    Class iris.src.dc.AdapterPerfmonProc Extends %RegisteredObject
    {
        ClassMethod start() As %Status
        {
            Set namespace = $NAMESPACE
            zn "%SYS"
            set status = $$Stop^PERFMON()
            set status = $$Start^PERFMON()
            zn namespace
            return status
        }
  
        ClassMethod generateReport(nameReport As %String = "report.txt") As %Status
        {
            Set namespace = $NAMESPACE
            zn "%SYS"
            Set tempDirectory = ##class(%SYS.System).TempDirectory()
            set status = $$Report^PERFMON("R","R","P", tempDirectory_"/"_nameReport)
            zn namespace
  
            return status
        }
  
        ClassMethod stop() As %Status
        {
            Set namespace = $NAMESPACE
            zn "%SYS"
            Set status = $$Stop^PERFMON()
            zn namespace
  
            return status
        }
    }
  

• Operates in namespace %SYS to access ^PERFMON routines, then returns to the original namespace.

3. Execution Flow

1. A request enters the Quarkus application.

2. The CDI interceptor detects the @PerfmonReport annotation and intercepts the method call.

3. monitorSystem.startPerfmon() is invoked, triggering IRIS ^PERFMON monitoring.

4. The business method executes normally (data access, transformations, logic, etc.).

5. After the method returns or throws an exception, the interceptor ensures:

   • monitorSystem.generateReportPerfmon(...) is called to create a .txt performance report.
   • monitorSystem.stopPerfmon() is executed to stop the monitoring session.
   • The total Java-side execution time is logged using Logger.infof(...).

6. The generated report file is stored in the IRIS temporary directory, typically: /usr/irissys/mgr/Temp/

   • The file name follows the pattern: <ClassName><MethodName><timestamp>.txt

4. Technical Challenges and Solutions

5. Use Cases and Benefits

• Enables real-time visibility into IRIS runtime activity from Java code.
• Simplifies performance analysis and query optimization for developers.
• Useful for benchmarking, profiling, and system regression testing.
• Can serve as a lightweight performance audit trail for critical operations.

6. Practical Example of Use

The complete source code and deployment setup are available at:

• Open Exchange: https://openexchange.intersystems.com/package/quarkus-iris-monitor-system
• GitHub Repository: https://github.com/Davi-Massaru/quarkus-iris-monitor-system

6.1 Overview

The application runs a Quarkus server connected to an InterSystems IRIS instance configured with the FHIRSERVER namespace.
The ORM layer is implemented using Hibernate ORM with PanacheRepository, allowing direct mapping between Java entities and IRIS database classes.

When the application starts (via docker-compose up), it brings up:

• The IRIS container, hosting the FHIR data model and ObjectScript routines (including AdapterPerfmonProc);
• The Quarkus container, exposing REST endpoints and connecting to IRIS via the native JDBC driver.

6.2 REST Endpoint

A REST resource exposes a simple endpoint to retrieve patient information:

@Path("/patient")
public class PatientResource {

    @Inject
    PatientService patientService;

    @GET
    @Path("/info")
    @Produces(MediaType.APPLICATION_JSON)
    public PatientInfoDTO searchPatientInfo(@QueryParam("key") String key) {
        return patientService.patientGetInfo(key);
    }
}

This endpoint accepts a query parameter (key) that identifies the patient resource within the FHIR data repository.

### 6.3 Service Layer with @PerfmonReport

The PatientService class contains the business logic to retrieve and compose patient information. It is annotated with @PerfmonReport, which means each request to /patient/info triggers IRIS performance monitoring:

@ApplicationScoped
public class PatientService {

    @Inject
    PatientRepository patientRepository;

    @PerfmonReport
    public PatientInfoDTO patientGetInfo(String patientKey) {

        Optional<Patient> patientOpt = patientRepository.find("key", patientKey).firstResultOptional();
        Patient patient = patientOpt.orElseThrow(() -> new IllegalArgumentException("Patient not found"));

        PatientInfoDTO dto = new PatientInfoDTO();
        dto.setKey(patient.key);
        dto.setName(patient.name);
        dto.setAddress(patient.address);
        dto.setBirthDate(patient.birthDate != null ? patient.birthDate.toString() : null);
        dto.setGender(patient.gender);
        dto.setMedications(patientRepository.findMedicationTextByPatient(patientKey));
        dto.setConditions(patientRepository.findConditionsByPatient(patientKey));
        dto.setAllergies(patientRepository.findAllergyByPatient(patientKey));

        return dto;
    }
}

6.4 Execution Flow

A request is made to: GET /patient/info?key=Patient/4

Quarkus routes the request to PatientResource.searchPatientInfo().

The CDI interceptor detects the @PerfmonReport annotation in PatientService.patientGetInfo().

Before executing the service logic:

• The interceptor invokes MonitorSystem.startPerfmon(), which calls the IRIS class iris.src.dc.AdapterPerfmonProc.start().

• The method executes business logic, querying patient data using Hibernate PanacheRepository mappings.

After the method completes:

• MonitorSystem.generateReportPerfmon() is called to create a performance report.

• MonitorSystem.stopPerfmon() stops the IRIS performance monitor.

A .txt report is generated under: usr/irissys/mgr/Temp/

Example filename: PatientService_patientGetInfo_20251005_161906.txt

6.5 Result

The generated report contains detailed IRIS runtime statistics, for example:

                         Routine Activity by Routine

Started: 10/11/2025 05:07:30PM                    Collected: 10/11/2025 05:07:31PM

Routine Name                        RtnLines  % Lines   RtnLoads  RtnFetch  Line/Load Directory
----------------------------------- --------- --------- --------- --------- --------- ---------
Other                                     0.0       0.0       0.0       0.0         0
PERFMON                                  44.0       0.0       0.0       0.0         0 /usr/irissys/mgr/
%occLibrary                         3415047.0      34.1   48278.0       0.0      70.7 /usr/irissys/mgr/irislib/
iris.src.dc.AdapterPerfmonProc.1          7.0       0.0       2.0       0.0       3.5 /usr/irissys/mgr/FHIRSERVER/
%occName                            5079994.0      50.7       0.0       0.0         0 /usr/irissys/mgr/irislib/
%apiDDL2                            1078497.0      10.8   63358.0       0.0      17.0 /usr/irissys/mgr/irislib/
%SQL.FeatureGetter.1                 446710.0       4.5   66939.0       0.0       6.7 /usr/irissys/mgr/irislib/
%SYS.WorkQueueMgr                       365.0       0.0       1.0       0.0     365.0 /usr/irissys/mgr/
%CSP.Daemon.1                            16.0       0.0       1.0       0.0      16.0 /usr/irissys/mgr/irislib/
%SYS.TokenAuth.1                         14.0       0.0       5.0       0.0       2.8 /usr/irissys/mgr/
%Library.PosixTime.1                      2.0       0.0       0.0       0.0         0 /usr/irissys/mgr/irislib/
%SYS.sqlcq.uEXTg3QR7a7I7Osf9e8Bz...      52.0       0.0       1.0       0.0      52.0 /usr/irissys/mgr/
%SYS.SQLSRV                              16.0       0.0       0.0       0.0         0 /usr/irissys/mgr/
%apiOBJ                                 756.0       0.0       0.0       0.0         0 /usr/irissys/mgr/irislib/
FT.Collector.1                            0.0       0.0       0.0       0.0         0 /usr/irissys/mgr/
SYS.Monitor.FeatureTrackerSensor.1        0.0       0.0       0.0       0.0         0 /usr/irissys/mgr/
%SYS.Monitor.Control.1                    0.0       0.0       0.0       0.0         0 /usr/irissys/mgr/
%SYS.DBSRV.1                            252.0       0.0       4.0       0.0      63.0 /usr/irissys/mgr/
%sqlcq.FHIRSERVER.cls12.1                19.0       0.0       0.0       0.0         0 /usr/irissys/mgr/irislocaldata/
%sqlcq.FHIRSERVER.cls13.1                74.0       0.0       0.0       0.0         0 /usr/irissys/mgr/irislocaldata/
%sqlcq.FHIRSERVER.cls14.1                74.0       0.0       0.0       0.0         0 /usr/irissys/mgr/irislocaldata/
%sqlcq.FHIRSERVER.cls15.1                52.0       0.0       0.0       0.0         0 /usr/irissys/mgr/irislocaldata/
%SYS.System.1                             1.0       0.0       0.0       0.0         0 /usr/irissys/mgr/

This data provides precise insight into what routines were executed internally by IRIS during that REST call — including SQL compilation, execution, and FHIR data access.

Insight: The %sqlcq.FHIRSERVER.* routines capture all SQL cache queries executed by Quarkus within the method. Monitoring these routines allows developers to analyze query execution, understand code behavior, and identify potential performance bottlenecks. This makes them a powerful tool for development and debugging of FHIR-related operations.

6.6 Summary

This example demonstrates how a standard Quarkus service can transparently leverage IRIS native monitoring tools using the @PerfmonReport annotation. It combines:

• CDI interceptors (Quarkus)

• Hibernate PanacheRepositories (ORM)

• IRIS native ObjectScript routines (^PERFMON)

The result is a fully automated and reproducible performance profiling mechanism that can be applied to any service method in the application.

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?

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?

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?

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?

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?