Various database indexing techniques: B-tree Hash Bitmap R-tree Full-Text Inverted Graph based B-tree: facilitates effective insertion, deletion and searching Hash: works best in scenarios when the likelihood of querying every entry is equal Bitmap: represents data as a bitmap vector and is primarily used in decision support systems and data warehouses R-tree: is specialized for spatial data indexing and querying Full-Text: is designed for efficient text search operations Inverted: is commonly used in search engines for fast keyword-based searches Graph based indexing methods like HNSW( Hierarchical Navigable Small World) are used for Approximate Nearest Neighbour(ANN) searches Indexing improves query and database performance

"Good performance starts with efficient indexing." To address performance issues caused by inefficient indexing: Review Existing Indexes: Remove unused or redundant indexes to reduce overhead. Optimize Indexing Strategy: Use compound indexes on high-query columns. Use Partitioning: Partition large tables to speed up data retrieval and minimize index scanning. Rebuild Indexes: Regularly rebuild or reorganize indexes to reduce fragmentation. Monitor Performance: Continuously track query performance and adjust indexes as needed.

To fix performance issues from inefficient indexing, first analyze slow queries using logs or EXPLAIN to spot columns used in filters, joins, and sorting. Review current indexes—remove redundant ones and add composite or covering indexes based on query patterns. For low-cardinality columns, use bitmap indexes. Implement partitioning to reduce data scanned. Rebuild fragmented indexes and update statistics regularly. Test performance after changes and use tools like Redshift or Azure Index Advisor for ongoing optimization. Proper indexing ensures faster queries and better resource use in your data warehouse.

The cost in terms of speed of indexing can slow the ingestion process. So, I would first figure out which queries need performance improvement and perform the trade-off analysis. I would then look at how to move forward -- indexing using the DB, creating a smaller datamart on a different compute with the specialized indexing, or potentially building pre-aggregated tables.

Try this, 1) Focus on high-selectivity columns for indexing 2) Consider composite indexes for common query patterns 3) Schedule regular index maintenance (rebuilds/reorganizations) to reduce fragmentation 4) Consider partitioning large tables to work with smaller index segments 5) Finally, try Consider materialized views for complex, frequently-run queries ( Alternative approach)