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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To trace internal locking behavior in InterSystems IRIS for debugging deadlocks in object transactions and identify cyclic dependencies, the following tools and strategies can be applied:

1. Management Portal Monitoring:

   • Use the Management Portal to examine and manage locks. Navigate to "System Operation" -> "Locks", and then select "View Locks" to inspect the current locks within the system. This provides details about lock owners, mode counts, and waiting requests [1][2].

2. ^LOCKTAB Utility:

   • Run the ^LOCKTAB routine in the %SYS namespace to view detailed lock information, including exclusive and shared locks, delocks, and waiting requests. This utility can help identify processes that may be involved in a deadlock and remove problematic locks if necessary. Note that it's better to identify and terminate the blocking process rather than just remove a lock [1][3].

3. Structured System Variables:

   • Use the ^$LOCK system variable to programmatically retrieve lock information, which can be helpful for real-time analysis or logging information for debugging purposes [3].

4. Concurrency Control Practices:

   • Follow strict locking protocols, such as applying locks in a consistent order (like collating sequence order) across all processes. Avoid using incremental locks (LOCK +name) unnecessarily, as they can increase the likelihood of deadlocks. Instead, consider simple locks where previous locks are released before new ones are applied [4][2].

5. Timeout Management:

   • Add a timeout argument to lock requests, such as LOCK +^a(1):10, to avoid indefinite waiting and handle potential deadlocks more gracefully. This approach provides an opportunity to log and react to lengthy wait conditions [4][2].

6. Locking Debugging Mechanisms:

   • Investigate the "Deadlock Avoidance" techniques and details of lock requests in the documentation, which describe scenarios, resolution strategies, and lock queue behaviors [4][2].

By combining these approaches, you can effectively analyze and trace locking behavior, helping to detect and address deadlocks in real-time. Utilize locking tools judiciously and follow defined best practices for long-term stability. [1][2][3]

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To create a hybrid REST and GraphQL API layer over InterSystems IRIS data using ObjectScript:

1. REST API:

   • IRIS provides native support for REST APIs using ObjectScript and the %CSP.REST framework. You can define routes with methods like GET, POST, etc., using the UrlMap XData block. Business logic can be linked to each route via methods in your ObjectScript class [1][2].

2. GraphQL Integration:

   • InterSystems IRIS supports GraphQL via the open-source package GraphQL. This allows you to define a GraphQL schema and resolver functions to interact with IRIS data. You can create a GraphQL schema for your object models and expose them as APIs.
   • Two common methods include:
     • Using the GraphQL package on the Open Exchange, with modules for defining and exposing your schemas.
     • Running a GraphQL server connected to IRIS using Python frameworks like Graphene and SQLAlchemy. The setup involves creating database models and defining the GraphQL schema [3][4][5].

3. Best Practices for Hybrid Approach:

   • Both endpoints (REST and GraphQL) can share the same data models by mapping ObjectScript methods and persistent classes into the GraphQL schema and REST handlers.
   • Use GraphQL to expose flexible query endpoints where clients can specify their desired fields and operations.
   • Use REST for simpler, predefined resources or operations such as CRUD services [1][3].

4. Documentation and Tools:

   • You can find step-by-step guides for implementing GraphQL over IRIS on open GitHub repositories or Open Exchange projects, some including Flask-based setups as entry points.
   • Review and configure permissions for these APIs on the IRIS instance for security [3][4][5].

For full implementation details, consider looking into available open-source GraphQL projects like iris-graphql-demo or using InterSystems GraphQL resources. Though REST and GraphQL serve distinct needs, setting up both in IRIS provides a comprehensive API system.

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To implement secure identity federation (OAuth2, OIDC, SAML) for InterSystems IRIS web apps, especially using Azure AD or Okta as identity providers, here are the best practices and steps to follow:

1. Understanding Key Concepts:

   • OAuth2 enables token-based authentication, where users can authorize apps to access their data without revealing credentials. OpenID Connect (OIDC) extends OAuth2, providing user authentication and detailed user information via ID tokens.
   • SAML is another protocol for exchanging authentication and authorization data that supports Single Sign-On (SSO) across web-based applications [1][2].

2. Using OAuth2 and Azure AD/Okta:

   • InterSystems IRIS supports using OAuth2 for federated authentication. Azure AD supports OAuth2 flows, while Okta provides tools such as an identity management system, policy engine, and integration capabilities [3][1].
   • For SSO, Okta offers out-of-the-box support for creating custom Authorization Servers. You can configure your applications to interact with Okta-provided endpoints, such as the Authorization and Token endpoints, for authentication [4][1].
   • To use Okta with OAuth2, you must:
     • Register the application with Okta.
     • Configure IRIS to recognize the Okta Authorization Server by providing the Issuer Discovery URL, redirect URLs, and client credentials [4][1].

3. Using SAML with Azure AD or Okta:

   • In SAML integration, IRIS acts as the Service Provider (SP), and Azure AD or Okta as the Identity Provider (IdP). Essential configurations include:
     • Setting up metadata XML, SP Entity ID, and IdP certificates [2].
     • Configuring Assertion Consumer Service (ACS) URL on IRIS for processing SAMLResponses from IdP [2].

4. Implementation Workflow:

   • For OAuth2/OIDC:
     • Use client libraries or HTTP calls for token handling via IRIS's %OAuth2.AccessToken and related classes [1][5].
     • Specify grant types, redirect URIs, scopes, and endpoints in IRIS applications.
     • Optional: Utilize delegated authentication via a ZAUTHENTICATE routine to handle session management [5][1].
   • For SAML:
     • Follow best practices like metadata sharing, secure use of certificates, and SSO enforcement. Utilize examples provided in open repositories like interis-saml-example [2].

5. Enhancing Security:

   • Implement HTTPS for all communications between applications and authorization servers.
   • Use short-lived tokens in OAuth2 to minimize security risks.
   • Use appropriate session timeout and logout configurations to reduce unauthorized access risks [6][1].

By following the documentation and best practices mentioned, InterSystems IRIS web apps can securely employ identity federation and integrate with identity providers like Azure AD and Okta effectively.

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