To completely deny a user access to the Management Portal while allowing them to access dashboards, you can utilize InterSystems IRIS web application settings and roles effectively. Here is how:

1. Restrict Access to the Management Portal Web Applications:

   • Navigate to the Management Portal: System Administration > Security > Applications > Web Applications.
   • Find the relevant web applications that make up the Management Portal (e.g., /csp/sys, /csp/sys/mgr, /csp/sys/op) and edit them.
   • Set the "Resource Required" to a custom resource that the user’s role does not have permission to access [1][2].

2. Grant Specific Permissions for Dashboard Viewing:

   • Create a role for the user that includes access to the resource %DeepSee_Portal with USE permission.
   • Ensure the web application hosting dashboards (e.g., /csp/app or similar) allows this resource [3][4][5].

3. Auditing and Public Permissions:

   • Review public permissions for the resources associated with the web applications to prevent unintended access.
   • Enable auditing to help monitor user access and ensure security configurations are correctly implemented [6].

By designing custom resources and roles, the user can be restricted from accessing the Management Portal while retaining access to the required dashboards [2][6].

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The issue of failure to compile the macro containing a simple "if" statement in a .inc file can arise due to several factors related to how macros are defined and used. Here are key considerations:

1. Macro Definition Syntax:
   Ensure the macro syntax is correct. Macros are defined using #define, and any spaces within the expression in the macro definition can cause syntax errors. For example:

   #define TestIf(%arr) if (%arr>0) {QUIT 5}
   

   The entire expression, if (%arr>0) {QUIT 5}, should not contain spaces that disrupt the syntax [1][2].

2. Including Macros Properly:
   The targeted .inc file must be included correctly at the top of the class definition or method where the macro is used. The inclusion syntax is:

   include MacroFileName
   

   Make sure the .inc file name is referenced without the .inc suffix [1][3].

   Additionally, when using macros in methods directly, you may need a specific inclusion inside the body of that method if required at compile time [1].

3. Preprocessor Directive Spacing:
   It’s critical that spaces do not appear unnecessarily in expressions in macro definitions. For example:

   #define TestIf(%arr) if %arr>0 QUIT 5
   

   Spaces must follow conventions depending on where they appear in definitions and expressions [1].

4. Macro Symbol and Order:
   When invoking macros ($$$macroname), ensure they are defined earlier in the code or included properly in the .inc file. Undefined macros or incorrectly referenced ones will lead to compilation errors [1].

5. Use of System Macros:
   If the macro references system macros (e.g., $SYSTEM or $GET), verify that the appropriate system .inc files are included in the class. For example:

   include %occStatus
   

   Missing system includes can cause recognized macros (such as $$$OK) to fail [4].

Addressing these aspects should resolve the compilation issues for a macro with an "if" statement. Let me know if you need further clarification or examples.

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To define a For loop in an *.inc file, you can use ObjectScript syntax in the following ways:

1. Define the loop with numeric increments:
   You can use a counter variable, specifying the start, increment, and stop values:

   FOR var=start:increment:stop {
      // Code block content
   }
   

   In this syntax:

   • start is the initial value assigned to the loop counter.
   • increment specifies how much the counter increases on every iteration.
   • stop is the end condition of the loop. The loop will terminate when the counter exceeds this value [1][2].

2. Iterate through a comma-separated list:
   You can iterate over predefined values directly:

   FOR var="val1","val2","val3" {
      // Code block content
   }
   

   This executes the code block for each value in the list [2].

3. Dynamic iteration with delimiters:
   If you have a string of comma-separated values, you can split and iterate:

   SET customString = "1,3,9"
   FOR index=1:1:$LENGTH(customString, ",") {
      SET val = $PIECE(customString, ",", index)
      // Do something with val
   }
   

   The $LENGTH function obtains the count of items in the list, and $PIECE extracts each element [3].

4. Infinite loops with FOR and break conditions:
   A loop can also work without a definite stop value:

   SET x=8
   FOR { 
      WRITE "Running loop x=",x,!
      SET x = x-1
      QUIT:x=3
   }
   

   This continues until a QUIT, RETURN, or GOTO command exits the loop [1][2].

5. Custom Macros in an *.inc file:
   An INC file can also define new For-like macros to simplify global or variable iteration. For example:

   #define ForAll(%in, %gn) SET gn%in=$NAME(%gn) SET %in="" FOR { SET %in=$ORDER(@gn%in@(%in)) QUIT:%in="" }
   

   This allows iteration over a global or list defined in the INC file [4].

References: [5][1][2][3].

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To make a message editable in Ensemble's Message Viewer and modify XML messages before resending, you should follow these steps:

1. Make a Message Editable in Message Viewer:

   • Use the "Edit and Resend" option available in the Message Viewer. This allows you to modify the contents of the message.
   • If you see that "Edit and Resend" is unavailable, there could be specific restrictions in place based on the class or namespace configuration. For example, messages that are custom classes using %XML.Adaptor or certain complex structures may not allow direct editing and resending [1][2].

2. Modify an XML Message Before Resending:

   • When you click “Edit and Resend” for an XML message, you'll be able to amend its content in a format similar to what is displayed. However, there may be cases where a lack of proper setup for messages or XML projection can cause issues with editing.
   • If editing is consistently unavailable, ensure that the properties within your Ensemble classes supporting the messages are properly configured as extendable and compatible with Ens.Message or %XML.Adaptor [1][2].

Additionally, note that merging lists, ensuring seamless resending and analyzing, involves configuring development settings accordingly and could potentially require code-level edits or additional debugging about why classes aren't exposing editable features.

If further customization or debugging is required, reviewing the configuration and class definitions might be necessary.

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To manage disk usage related to error logging in %SQL_Diag.Result and %SQL_Diag.Message, here are some possible approaches:

1. Limiting Redundant Logging:
   There are no specific mentions of built-in mechanisms in the documents retrieved to limit repeated logs or filter out redundant error messages in %SQL_Diag tables directly. However, you could achieve this via custom application logic or by introducing conditions around your LOAD DATA or SQL processes.

2. Data Cleanup:
   There isn’t a specific retention or automated cleanup mechanism for %SQL_Diag.Result and %SQL_Diag.Message. Such tables would likely require periodic manual or automated maintenance tasks, such as DELETE statements or archiving historical records.

3. Moving Tables to a Separate Database:
   Moving diagnostic tables to a separate database with storage limits is a viable strategy. This allows you to isolate and control the potential disk usage without impacting the primary database. Implementing this change might involve:

   • Redirecting the error logging functionality to use tables stored in the designated database.

4. Transaction Commands:
   While managing transactions should include commands like TStart and TCommit to ensure database integrity, these don't inherently affect logging configurations but do guarantee atomicity. Locking mechanisms could help manage concurrent operations during troubleshooting large batches [1].

5. SQL and ObjectScript Integration:
   For advanced operations based on requirements, ObjectScript commands and SQL functionality can be further customized and integrated.

Unfortunately, no direct system option or detailed guideline about controlling disk usage specifically for the %SQL_Diag tables and limiting repetitive entries is provided. Custom scripting and database management strategies will likely be required to achieve this goal.

Let me know if you need assistance with a specific implementation!

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To mark an HL7 NACK as "completed," ignoring an error without generating an alert, you can utilize the Reply Code Actions settings of the HL7 operation in the InterSystems platform. This involves customizing the Reply Code Actions string based on the specific NACK codes or conditions you want to handle differently.

Here are some relevant details for handling NACKs:

1. Default Reply Code Actions:
   The default Reply Code Actions is:

   ':?R=RF,:?E=S,:~=S,:?A=C,:*=S,:I?=W,:T?=C'
   

   It specifies actions for the different response codes received (e.g., AR, AE, CR, etc.) [1].

2. Customizing Reply Code Actions:
   To suspend messages with the specified error or mark them "Completed," you should update the Reply Code Actions string. For instance:

   AR=S,:?R=S,:?E=S,:~=S,:?A=C,:*=S,:I?=W,:T?=C
   

   In this example, NACKs with the "AR" in the MSA:1 segment would be matched and the message would be suspended, and other error conditions would follow the default behavior [1].

3. Use Conditional Matching:
   If you want to mark a message completed based on specific criteria in an error within the NACK, you can use text matching conditions. For example:

   E*Date of birth in PID.7 must not be empty=S,:?R=S,:?E=S,:I?=W,:T?=C
   

   This indicates that when a NACK contains the specified text in the error description, the message will be marked as "Suspended" or "Completed" with the action code S [2].

Make sure no extra spaces or unnecessary quotation marks are added around the action code string, as it could lead to errors during execution.

These updates to the Reply Code Actions string should achieve the desired functionality. If further customization is needed, extending the operation and overriding specific methods like OnReplyDocument() or OnGetReplyAction() may be required [3][4][2].

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You need to edit the %ShowContents method in your class to properly display custom objects like the Metadata property in the Message Viewer. The issue is related to how the data is being exported.

You can achieve your goal by ensuring your class and its referenced objects (such as bd.webapp.OutboundMessageRequestMetadata) extend %JSON.Adaptor. This way, you can easily display their contents as JSON in the Message Viewer. Here's an example of how this can be done:

1. Make the classes JSON-adaptable:
   Update your class definitions so that they extend %JSON.Adaptor. For instance:

   Class bd.webapp.OutboundMessageRequest Extends (%Persistent, Ens.Request, %JSON.Adaptor)
   {
      Property Metadata As bd.webapp.OutboundMessageRequestMetadata;
      Property MessageContent As %DynamicObject;
   }
   
   Class bd.webapp.OutboundMessageRequestMetadata Extends (%RegisteredObject, %JSON.Adaptor)
   {
      Property RegionSlug As %String;
      Property TenantSlug As %String;
      Property SystemSlug As %String;
      Property MessageId As %String;
      Property MessageType As %String [ InitialExpression = "JSON" ];
      Property OccurredAt As %String;
      Property CorrelationId As %String;
   }
   

2. Customize the %ShowContents Method:
   Override %ShowContents to format and display the data in JSON format. For example:

   Method %ShowContents(pZenOutput As %Boolean = 0)
   {
      set jsonExport = ""
      do ..%JSONExportToString(.jsonExport)
      set formatter = ##class(%JSON.Formatter).%New()
      do formatter.FormatToString(jsonExport, .formattedJson)
      if pZenOutput {
          &html<<pre>#(formattedJson)#</pre>>
      } else {
          write formattedJson
      }
   }
   

This setup will allow the Message Viewer to display properly formatted JSON containing the Metadata property, along with any other nested or dynamic properties defined within your class structure [1][2].

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Your concerns about implementing FHIR within InterSystems IRIS for Health can be addressed by focusing on the following areas:

FHIR Implementation Patterns in InterSystems IRIS for Health

• InterSystems IRIS for Health provides comprehensive support for different modes of FHIR implementations, including FHIR repositories, facades, and customized FHIR servers.
• A FHIR façade abstracts complexity and bridges with non-FHIR systems by translating operations into native formats. This is suitable when an existing backend system needs to support FHIR without migrating to a repository [1][2][3].

What is a FHIR Resource?

• A resource in FHIR is a core building block that encapsulates specific healthcare-related data, such as details about a patient, a diagnosis, or a lab test result.
• For example:
  • "Patient" as a resource represents demographic and contact details.
  • A resource instance is the actual data for a specific entity (e.g., information for a specific patient) [4][5].

Creation of a FHIR Server with Custom Methods and MPI Integration

1. FHIR Server Customization for Use Cases:

   • The InterSystems IRIS for Health FHIR repository is highly extensible. You can enforce additional constraints, cleanse or enrich resources before storage, and dynamically modify responses to comply with specific requirements [1].
   • This customization can be performed using ObjectScript, Python, or templates [1].

2. Handling Normal Methods Alongside Custom Logic:

   • When creating a FHIR server, you can implement business logic to process requests and integrate with other systems like the HealthShare MPI [6][7].
   • The HS.FHIRServer.Interop.Service and HS.FHIRServer.Interop.Operation classes enable you to route FHIR requests to retrieve or update data, and optionally send information to HealthShare MPI [6][8][9].

3. Interacting with the HealthShare MPI:

   • Use an interoperability approach to convert and route HL7 messages or other formats to HealthShare MPI for master patient indexing.
   • For storing and sharing data, you can use business processes and transformations to convert data between FHIR and the required MPI format [6][8].

4. Error Handling and Reliability:

   • Implement robust error-checking mechanisms when forwarding processed data to MPI, and validate the integrity of FHIR responses returned to requesting systems.

Challenges Addressed and Getting Expert Help

• A segmented implementation can cause inconsistencies. By utilizing the FHIR capabilities provided in IRIS for Health, you can streamline processes.
• For complex cases requiring custom development or when troubleshooting issues with FHIR interoperability, it might be valuable to consult an expert well-versed in the FHIR and IRIS environment, potentially through InterSystems' support channels.

Additional Learning Resources

• Review documentation on customizing the FHIR repository, creating custom servers, and integrating with other InterSystems products for a better understanding of the underlying capabilities [3][6][4].

If assistance with code samples or detailed steps is required, these can also be explored further.

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Capturing Data Quality issues using the Workflow Engine to populate a worklist can be managed effectively in InterSystems IRIS. Here’s a detailed explanation and the steps involved:

1. Workflow Process Creation:
   To automate tasks with ensemble workflow, create a Workflow process using the Business Process Designer. Define tasks to be handled manually (which correspond to data quality issues) as asynchronous calls within your BPL logic. Each call should involve the creation of a task object using the EnsLib.Workflow.TaskRequest class. This class can hold details like the message subject, additional contextual messages, and even form fields for user input or review. The task responses are carried using the EnsLib.Workflow.TaskResponse class [1][2].

2. Integration with Workflow Operations:
   Define roles and users associated with these tasks within the Management Portal under Workflow Roles and Workflow Users. Ensure that Workflow Operations are correctly configured to correspond to these roles in your production. Each operation handles tasks assigned under its scope, making them visible in the connected Workflow portal [3][2].

3. DTL Usage:
   Data Transformation Language (DTL) in Ensemble primarily handles format conversions and transformations. For capturing data issues and linking it to workflows, however, it is necessary to go beyond DTL work. The Workflow subsystem requires specific orchestration of tasks based on specified roles and users, which is typically managed through business processes, not directly through DTL [4][2].

4. Interaction with Workflow Portal:
   All created tasks can then populate the Workflow portal for specific users to review, accept, or address. This ensures that Data Quality issues populate efficiently into a reviewable worklist and tasks don't overwhelm the Managed Alerts system directly [5][1].

5. Workflow Sample and Testing:
   InterSystems provides a sample workflow setup in its HelpDesk application and other examples where automated workflows interact with manual tasks via class definitions like EnsLib.Workflow.TaskRequest. Reviewing these examples can help clarify how to connect DTL transformations with the Workflow responses effectively [6][2].

Tasks from the workflow system are sent to users with defined roles, and responses can update back to the originating process once the data quality issue is resolved.

For any further configurations or testing specifics, please refer to Ensemble Workflow testing techniques and sample configurations [7][2].

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To create an index in DTL based on a variable value, you can utilize looping capabilities in DTL to dynamically handle variable counts and indexes. Here are some potential approaches to achieve this:

1. Using a foreach Loop: This DTL element is specifically designed for iterating over repeating source segments. Inside a foreach, you can dynamically set the index based on the current iteration. Take advantage of the foreach loop to iterate through the AIS segments and assign dynamic indexes to the RGS elements accordingly. This technique would replace the hardcoded indexes with variable-driven ones [1].

2. Using Code Blocks: Code blocks in DTL allow embedded ObjectScript to execute custom transformations. You can add a code block inside your DTL and use a counter to keep track of the current index. This counter can drive the dynamic allocation of index values for the RGS segments [2].

3. Utilizing XML Structure: The DTL XML allows you to include actions like assignment and iteration. For example, you can define DTL elements to loop through the source AIS segments and assign values using target.{RGS} with an incremented index [1][2].

By combining variable-driven iterations like foreach or custom code blocks for the dynamic logic, you can dynamically set the RGS index based on AIS segments received in the message. References for specific DTL functionality are found in [1][2].

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The issue with the code arises due to the presence of invalid characters in the target name when there is no space between quotes and the underscore. According to routing rule constraints, the target name should not contain certain characters like underscores or double quotes directly next to each other. These characters are disallowed to maintain proper syntax and rule compilation behavior. It is not just a compiler error but suggests that syntax conventions prevent the format that lacks a space to ensure clarity and compliance [1][2].

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To process files captured using EnsLib.RecordMap.Service.FTPService one by one, you can use the following approach:

The EnsLib.RecordMap.Service.FTPService class monitors a defined FTP directory and automatically processes files as they arrive. Ensuring one file processes completely before the next requires modifying how the service interacts with the Business Process.

1. Synchronous Processing: Set the SynchronousSend flag to 1 to ensure messages are sent and processed one at a time synchronously. However, from your description, this may not currently behave as expected [1].

2. Semaphore Spec Implementation: A potential option is to implement a semaphore mechanism. This involves placing a semaphore file in the FTP directory for every file uploaded. This configuration ensures the Business Service checks for the semaphore file before processing each new file, effectively creating a queue-like mechanism. Details for implementing semaphore can be found here: [2].

3. Custom Service Extension: Extend the EnsLib.RecordMap.Service.FTPService class and modify its behavior. In the OnProcessInput method, you might introduce a custom logic to ensure the processing waits until you confirm that the previous file has been completely processed, possibly by interacting with the Business Process regarding completion before proceeding to the next file [3].

4. On-Demand Execution: Another solution is setting up an API to trigger the processing of the FTP directory. Instead of automatically processing files upon upload, the directory can be read manually or triggered via an API request. This way, you can ensure the files are processed one by one [2].

For detailed guidance and support, consider exploring semaphore as outlined or consult the Developer Community for further clarification.

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The difference in behavior of $C() between the two systems can be explained by the differences in the encoding configurations of the systems. $C() (also known as $CHAR) converts an integer to its corresponding ASCII or Unicode character. However:

1. $CHAR() behavior and the characters it outputs depend on the context of the character encoding (e.g., whether the device supports UTF-8, ASCII, or another encoding standard). If one system is using a different default encoding (e.g., an environment set for UTF-8 versus a different encoding on another system), the results will vary [1].

2. The unexpected behavior on the second machine (e.g., ú for 250 instead of ú) can be attributed to interpreting the numeric 250 in one encoding and displaying it as encoded bytes in another. For example, ú in UTF-8 is represented by two bytes, which may be displayed as ú if the output device incorrectly assumes an encoding like ISO-8859-1 instead [2].

To fix this:
- Ensure the character set encoding (e.g., UTF-8) is consistent across both machines.
- Check the National Language Support (NLS) configuration of your IRIS environments or terminal configurations.

This issue is tied to encoding discrepancies rather than a $C() function bug or version difference.

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To set a default value in a dataCombo component, you can use the zenSetProp JavaScript function. Here’s a working example:

1. In your Zen ClientMethod, you can use the following code:

   zenSetProp('mycombo', 'value', 'yourDefaultValue');
   

   Replace 'yourDefaultValue' with the actual value you want to set as the default.

2. You can also set the value directly during the page creation:

   zenPage.getComponentById('mycombo').setProperty('value', 'yourDefaultValue');
   

Make sure:
- The value you set matches one of the dataCombo options.
- If the dataCombo fetches data from a query, ensure the component is refreshed or the options are loaded before applying the default value [1].

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Implementing event-sourced architecture with journal-based replay using IRIS globals includes the following approaches:

1. Using IRIS Journaling:

   • IRIS journaling records all updates made to a database. This ensures recovery and replay capabilities in cases of failure. Journaling can be set up to track all database modifications and replay them using utilities like ^JRNRESTO. It provides mechanisms to recover databases to a point captured in the journal files [1].
   • You can configure journaling functions, customize journal properties, and manage them using utilities like ^JOURNAL. Journaling archives are ideal for ensuring reliable storage and replay scenarios [2].

2. Custom Event Store in Globals:

   • You can design your event store using IRIS globals, ensuring efficient storage and retrieval. Mapping event data to specific nodes in globals helps maintain the event sequence and guarantees quick access to individual events [3].
   • InterSystems IRIS supports multi-dimensional global structures, which are suitable for modeling events efficiently [4].

3. Replay Mechanism:

   • Replay can happen by iterating over the global structure or journal files to reconstruct the application state based on the event history. Utilities like ^JRNRESTO facilitate restoring from journal files [2].
   • For configurations focused purely on custom global-based solutions, you must develop a replay engine capable of processing event data stored in the respective global.

4. Event Handlers:

   • If dynamic event handling is required, server-side constructs such as system events could be utilized. Packages and tools like ToolBox-4-Iris provide enhancements, adding capabilities for synchronous or asynchronous event handling using queues [3].

To leverage IRIS journaling effectively:
- Activate journaling on databases where events are stored.
- Use IRIS utilities for maintaining the organization of journal files such as purging and switching journals periodically.
- Alternatively, set up a custom logging and replay mechanism by persisting raw event data into globals and developing a parsing engine for replay functionality [2][1].

If deeper customization or use-case-specific configurations are required, integration with tools like event queues or creating bespoke event handlers might be needed [1][2].

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Building a decentralized ledger (DLT) inside InterSystems IRIS by leveraging deterministic globals and implementing external consensus modules (like PBFT or Raft) is a feasible concept:

1. Modeling the Blockchain Structure:
   You can use deterministic globals in IRIS to create the data structures required for DLT. Globals allow you to define blocks that include attributes such as block index, timestamp, transactions, hash, previous block hash, and other metadata. An example project demonstrates the creation of a financial blockchain using globals and Embedded Python on IRIS [1].

2. Consensus Mechanism:
   While IRIS doesn't natively provide consensus mechanisms such as PBFT or Raft, you can implement these externally. An external system can handle the consensus and once agreement is reached, update the ledger in IRIS to ensure consistency across nodes [1].

3. Integration Capabilities:
   IRIS offers flexibility for integrating with other systems and scripting languages, including Python, which supports blockchain implementation. The use of tools like Docker for deployment facilitates distributed operations [1].

4. Durability and ACID Compliance:
   Globals provide ACID-compliant transactions, ensuring data durability and consistency, which are critical for blockchain integrity. For example, durability tests using hard container cut-offs showed IRIS's ability to maintain data integrity [2].

Overall, IRIS's globals combined with an external consensus layer make it viable as a backend for lightweight blockchain or DLT applications. For a step-by-step example and practical repository, you may want to explore projects like "blockchain - IRIS Python" [1].

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To create a custom High Throughput WebSocket inbound adapter for InterSystems IRIS-based productions, follow these considerations and steps:

1. Choose Asynchronous Operation for WebSocket Processing:
   Use asynchronous operations for your WebSocket server to handle many concurrent socket connections efficiently. This setup shares a pool of gateway connections between multiple clients, avoiding the overhead of dedicated connections [1][2].

2. Extend %CSP.WebSocket for a Server-Side Implementation:
   Create a server class that extends %CSP.WebSocket. Implement required server methods such as Server(), OnPreServer(), and OnPostServer() for managing WebSocket lifecycle events and processing messages [3][2].

3. Configure Shared Connections:
   In the OnPreServer() method, set the SharedConnection property to 1 to enable pooling of Web Gateway connections. This approach helps in efficiently managing resource allocation for many WebSocket clients [3][2].

4. Implement Message Processing Workflow:

   • Use the Read() and Write() methods to manage incoming and outgoing messages.
   • Implement a robust queuing mechanism to handle messages from multiple clients efficiently.
   • Use IRIS globals or %DynamicObject for storing and manipulating incoming JSON data [2].

5. Design for Scalability:

   • Consider distributing the load across multiple application servers in a load-balanced environment.
   • Use a shared notification table available to all server instances [2].

6. Integration with IRIS Interoperability Productions:

   • Treat each WebSocket inbound message as an interoperability message.
   • Map messages received via the WebSocket server to business processes or operations within your IRIS production.

7. Test Across Load Scenarios:

   • Simulate high WebSocket traffic in your testing environment.
   • Profile the system performance using tools or scripts that connect multiple clients to your WebSocket adapter and generate traffic.

8. Asynchronous Callback Management (Optional):
   Implement asynchronous callbacks to gracefully handle scenarios where you need to defer WebSocket responses to a later server operation [3].

By following these steps, you can create a high-throughput WebSocket inbound adapter that works effectively within your InterSystems IRIS platform.

Further reference:
- [3][2]: Guide to implementing WebSocket-based servers and managing a pool of Web Gateway connections.

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To design a scalable HL7-to-FHIR ETL pipeline in InterSystems IRIS Interoperability, follow these steps:

1. Data Ingestion and Processing:

   • Use the InterSystems IRIS Interoperability Production framework, which provides "Business Services" to connect to external data sources and ingest HL7 data via various protocols (e.g., HTTP, SOAP, or File Adapters).
   • Configure "Business Processes" to process messages and define the workflow for message handling [1].
   • Transform custom data into standard formats (HL7 to FHIR) using the native Data Transformation Language (DTL) and IRIS Interoperability Toolkit. DTL simplifies mappings between HL7 v2 segments and FHIR resources.

2. FHIR Transformation:

   • Enable HL7-to-FHIR transformations using the built-in FHIR interoperability components in IRIS. Configure the FHIR Interoperability Adapter to transform HL7 v2 messages into FHIR resources in real-time or batch [2].
   • Use the Summary Document Architecture (SDA) as an intermediary structure for easier transformation between HL7 V2 and FHIR [2].

3. FHIR Repository Configuration:

   • Deploy the native FHIR Repository provided by InterSystems IRIS, which serves as a centralized platform for storing and managing FHIR resources at scale. This repository supports bulk operations and complex FHIR queries efficiently [3][2].

4. Scalability with Horizontal Scaling:

   • Configure your production architecture for horizontal scaling by deploying multiple instances of IRIS Interoperability components (e.g., services, processes, and operations) to handle high message volumes concurrently.
   • IRIS supports distributed deployments and clustering to ensure that workloads are balanced and failover resilience is achieved [4].

5. Tools for Efficiency and Maintenance:

   • Use the FHIR SQL Builder to query the FHIR resources stored in the repository using ANSI SQL, providing real-time analytics capabilities [3].
   • Integrate logging, monitoring, and data persistence features to ensure traceability and debugging capability in your ETL solution [1][5].

For comprehensive data pipeline management:
- Visualize and trace message flows efficiently using the IRIS Management Portal.
- Leverage interoperability features like transaction resilience (FIFO queue preservation), message persistence, and automatic recovery [4][1].

This pipeline design ensures that large-scale terabyte data streams from HL7 are reliably converted to FHIR, stored, and scaled horizontally across computing nodes for performance stability.

Sources: [3][2][4][1]

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You can customize and implement your own domain-specific compression/decompression algorithms for %Stream.GlobalBinary in InterSystems IRIS. The %Stream.GlobalBinary class is designed to store binary data in global nodes, and these classes inherit from %Stream.Object, which provides a common interface for stream objects.

By default, stream compression in %Stream.GlobalBinary is controlled by the COMPRESS class parameter, which automatically compresses new stream data unless the data is deemed unsuitable for compression, such as already compressed formats (e.g., JPEG), or data in small chunks. For finer or customized control, you would need to extend or override methods in these stream classes. For example:

1. Create a custom class extending %Stream.GlobalBinary.
2. Implement or override methods for writing and reading the stream, embedding your compression logic during write operations and decompression logic during read operations.
3. Utilize IRIS utility functions like $System.Util.Compress or external compression libraries via ObjectScript to handle the actual compression tasks [1][2].

Keep in mind that stream compression is enabled by default in IRIS since 2021.2 and uses efficient algorithms like ZLIB or ZSTD for compression, meaning the default implementation already provides substantial storage savings. However, implementing a custom algorithm allows optimization for specific data types or domain requirements [2][1].

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To integrate InterSystems IRIS with Apache Flink for real-time event stream processing, consider the following approaches based on IRIS's connectivity and streaming capabilities:

1. JDBC for Relational Data Access: IRIS offers a high-performance Type 4 JDBC driver compliant with the JDBC 4.2 specification. Apache Flink supports integration with JDBC databases; you can use the JDBC connector to define either IRIS as a source (to fetch data) or a sink (to store processed results). The connection URL for IRIS would typically look like this:

   jdbc:IRIS://<ip_address>:<port>/<namespace>
   

   In Flink, you can configure this JDBC URL, along with the necessary credentials, to interact with IRIS in a streaming job [1][2].

2. IRIS Native API for Java: If your Flink application is developed in Java, you can use the InterSystems IRIS Native SDK for direct, low-latency access to IRIS's data structures, including globals. This enables you to bypass relational constraints and work with data in its native form for high-speed processing [3][1].

3. Kafka Integration: While Flink commonly uses Kafka for message ingestion and distribution, you can use InterSystems IRIS's built-in support for Kafka to act as a producer or consumer. This enables IRIS to interact with Flink indirectly via Kafka topics for seamless data exchange in real time. Instructions for configuring Kafka with IRIS for this purpose are available [4][5].

4. Business Processes in IRIS Interoperability: For advanced use cases, IRIS's interoperability productions can orchestrate data flows between external systems like Flink and other data sources or sinks. This involves creating business services and operations tailored to Flink's data stream API for bi-directional communication [6].

Each of these options provides unique advantages, and their suitability depends on your processing requirements, data architecture, and existing pipeline design. For code examples and further customization, exploring the JDBC integration and the robust APIs provided by IRIS will likely meet most streaming needs.

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