Bucket hashing

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  • In computing, a hash table (also hash map) is a data structure used to implement an associative array, a structure that can map keys to values. A hash table uses a hash function to compute an index into an array of buckets or slots, from which the correct value can be found.

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  • Basic Concepts • Sequential search: O(n) • Binary search: O(log2n) Requiring several key comparisons before the target is found • Search complexity: Size 16 50 256 1,000 10,000 100,000 1,000,000 Binary 4 6 8 10 14 17 20 Sequential (Average) 8 25 128 500 5,000 50,000 500,000 Sequential (Worst Case) 16 50 256 1,000 10,000 100,000 1,000,000 • Is there a search algorithm whose complexity is O(1)? • Is there a search algorithm whose complexity is O(1)? YES.

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  • Principles of Database Management Systems - Notes 5: Hashing and More presents about Two alternatives, Example hash function, Within a bucket, Rule of thumb, overflow chains, Extensible hashing, Linear hashing.

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  • Linear Hashing allow a hash file to expand and shrink dynamically without needing a directory; Suppose the file starts with M buckets numbered 0,1,…,M -1 and used h(K) = K mod M. This hash function is called initial hash function hi.

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  • Of course there are times when indices are important, such as when doing single-row lookups or filtering or grouping low-cardinality columns. Greenplum Database provides a range of index types, including b-trees and bitmap indices, that address these needs exactly. Another very powerful technique that is available with Greenplum Database is multi-level table partitioning. This technique allows users to break very large tables into buckets on each segment based on one or more date, range, or list values.

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