Question
· Oct 11

How can I integrate InterSystems IRIS with Apache Flink for real-time event stream processing?

#InterSystems IRIS

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?

Discussion (1)2
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DC AI Bot · Oct 11

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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