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Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx employs a de-normalized persistence model, separating runtime data from queryable data. This ensures high throughput operations without impacting query performance, making the system efficient and scalable.

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Key Components

Two distinct yet complementary components handle queryable data:
  1. Data-Index – Provides a snapshot of the current process state, accessible via a GraphQL interface that allows filtering and cross-referencing of process instances.
  2. Data-Audit – Focuses on long-term storage of audited process events, critical for compliance and regulatory needs. It also offers a GraphQL interface with support for custom queries.
Runtime feeding Data-Index

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Deployment Considerations

  • Data-Index can operate independently from the runtime in a separate container. However, this requires Kafka and a Change Data Capture (CDC) mechanism (e.g., Debezium) to maintain synchronization without relying on transactions.
  • Compact Architecture simplifies deployment by consolidating Data-Index within the application, ensuring data consistency via the database’s transaction manager.
  • Data-Audit is only available in Compact Architecture, as it requires participation in transactional consistency.

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Data Retention

  • Data-Index can be periodically cleaned, keeping only active process data as per business requirements.
  • Data-Audit retains information for an extended period, depending on compliance needs.
By structuring data management this way, Apache KIE achieves a balance between real-time process monitoring and long-term auditability, ensuring both efficiency and compliance.