Index Manager

The Index Manager is a core subsystem of the Metadata Database Engine that handles all aspects of database indexing. It creates indexes based on metadata schemas, updates them as data changes, reorganizes them for performance, and provides query optimization services by selecting the most efficient indexes for data retrieval operations. The component supports eight index types, including B-tree, hash, GiST, SP-GiST, GIN, BRIN, bitmap, and full-text, allowing flexibility for various data access patterns. It can handle indexes up to 32 TB in size, limited by file system and storage capacity. On standard hardware with SSD storage, it achieves an index creation throughput of 100,000 rows per second and an index update rate of 50,000 updates per second for single-row updates with indexed columns. Index lookup latency for point queries on indexed columns ranges from 1 to 10 milliseconds. The component supports up to 64 concurrent index operations, such as builds or scans. Index storage overhead is typically 20–50% relative to table size, depending on the index type. The software has a memory footprint of 100–500 MB, scaling with index size. It operates within an ambient temperature range of 0–40°C and relative humidity of 20–80% (non-condensing), with power consumption of 5–15 W typical for a software component. The component is developed under an ISO 9001 quality management system, but this is a reference standard for procurement verification, not a guarantee of certification for any specific instance. The Index Manager is designed to minimize I/O operations and improve query response times by analyzing access patterns and recommending optimal index usage. It is a part-level component intended for integration into larger database systems, and its performance characteristics must be verified for the specific model and application.

The Index Manager operates by monitoring database operations and metadata changes. When new data is added or modified, it updates relevant indexes using B-tree, hash, or other data structures. During query processing, it analyzes access patterns and recommends optimal index usage to minimize I/O operations and improve query response times. It also reorganizes indexes to maintain performance and handles concurrent operations efficiently.