Memory consumption grows differently based on index type due to their underlying data structures. Hash indexes (e.g., in-memory hash tables) typically scale linearly with dataset size. Each entry requires storage for a key, value, and potential collision resolution (e.g., linked list nodes). For example, a hash table with 1 million entries might use 1MB for keys and 1MB for values, but collisions or resizing (e.g., doubling the bucket array) can add 20-30% overhead. B-tree indexes also grow linearly but with lower constants: each node stores multiple sorted keys and child pointers, reducing overhead. A B-tree with 1 million entries might use 5MB (assuming 100 keys per node), but the exact size depends on node fill factor. Bitmap indexes, however, scale with both dataset size and data cardinality. For a column with 1,000 distinct values and 1 million rows, each bitmap requires 125KB (1 million bits), totaling ~125MB. High cardinality (e.g., 1 million unique values) makes bitmaps impractical.
To estimate memory usage, use mathematical models tailored to each index type. For hash indexes, calculate bucket count (including resizing), entry size (key + value + pointers), and collision overhead. For B-trees, estimate node count as (total entries / keys per node) multiplied by node size (keys + pointers). For bitmaps, compute (distinct values × dataset size / 8) bytes. Sampling a subset (e.g., 10% of data) can help extrapolate growth rates. Tools like sys.dm_db_index_physical_stats in SQL Server or pg_relation_size in PostgreSQL provide empirical measurements for existing datasets. Profiling memory during load tests (e.g., doubling dataset size iteratively) reveals real-world scaling behavior.
Controlling memory involves trade-offs. Choose index types strategically: avoid bitmaps for high-cardinality columns and prefer B-trees for range queries. Apply compression: B-trees benefit from prefix/suffix compression, while bitmaps use run-length encoding. Partition datasets (e.g., sharding) to distribute indexes across nodes. Tune parameters: reduce hash table load factor (e.g., 0.7) to minimize collisions, or adjust B-tree fill factor to balance insert speed and node density. Use approximate structures (e.g., Bloom filters) for existence checks with lower memory. Finally, offload rarely accessed data to disk or use tiered storage (e.g., in-memory for hot data, disk for cold).