Data-Index and Data-Audit¶
Aletyx Enterprise Build of Kogito and Drools 10.0.0 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.
Key Components¶
Two distinct yet complementary components handle queryable data:
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Data-Index – Provides a snapshot of the current process state, accessible via a GraphQL interface that allows filtering and cross-referencing of process instances.
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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.
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.
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.
