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Hash-Based Indexes Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY Introduction As for any index, 3 alternatives for data entries k*: Data record with key value k k, rid of data record with search key valuek k, list of rids of data records with search key k Choice orthogonal to the indexing technique Hash-based indexes are best for equality selections. Cannot support range searches. Static and dynamic hashing techniques exist; trade-offs similar to ISAM vs. B+ trees. Static Hashing The number of primary pages is fixed. Primary pages are allocated sequentially, never de-allocated; overflow pages if needed. h(k) mod N = bucket to which data entry with key k belongs. (N = number of buckets) Static Hashing (Contd.) Buckets contain data entries. Hash function works on search key field of record r. Must distribute values over range 0 ... N-1. h(key) = (a * key+ b) usually works well. a and b are constants; lots known about how to tune h. Long overflow chains can develop and degrade performance. Extendible and Linear Hashing: Dynamic techniques to fix this problem. Extendible Hashing Situation: Bucket (primary page) becomes full. Why not re-organize file by doubling the number of buckets? Reading and writing all pages is expensive! Idea of Extendible Hashing: Use directory of pointers to buckets, double the number of buckets by doubling the directory, splitting just the bucket that overflowed! Extendible Hashing (Contd.) Directory is much smaller than file, so doubling it is much cheaper. Only one page of data entries is split. No overflow page! Trick lies in how hash function is adjusted! Points to Note 20 = binary 10100. Last 2bits (00) tell us r belongs in A or A2. Last 3bits needed to tell which. Global depth of directory: Max # of bits needed to tell which bucket an entry belongs to. Local depth of a bucket: # of bits used to determine if an entry belongs to this bucket. When does bucket split cause directory doubling? Before insert, local de
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