PPT-Hashing Techniques

CST2032 Database Management Systems Lecture 7 Disadvantages on index structure We must access an index structure to locate data or must use binary search and that results in more IO operations. 1 Overview Hashing is a great practical tool with an interesting and su btle theory too In addition to its use as a dictionary data structure hashing also comes up in many di 64256erent areas including cryptography and complexity theory In this lectu Up to this point the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically signicant probability that a failure occurs during the insertion of an item requiring an expensive rehashing of all items in th Uri . Zwick. January 2014. Hashing. 2. Dictionaries. D .  . Dictionary() . – Create an empty dictionary. Insert(. D. ,. x. ) . – Insert item . x. into . D. Find(. D. ,. k. ) . – Find an item with key . Hash Tables. Open Address Hashing. Goals . Open Address Hashing. Hash Tables: . Resolving Collisions. There are two general approaches to resolving collisions:. Open address hashing: . if a spot is full, probe for next empty spot. COL 106. Shweta Agrawal, . Amit. Kumar. Slide Courtesy : Linda Shapiro, . Uwash. Douglas W. Harder, . UWaterloo. 12/26/03. Hashing - Lecture 10. 2. The Need for Speed. Data structures we have looked at so far. Yunchao. Gong. UNC Chapel Hill. yunchao@cs.unc.edu. The problem. Large scale image search:. We have a candidate image. Want to search a . large database . to find similar images. Search the . internet. Approximate Near Neighbors. Ilya Razenshteyn (CSAIL MIT). Alexandr. . Andoni. (Simons Institute). Approximate Near Neighbors (ANN). Dataset:. . n. points in . d. dimensions. Query:. a point within . Shannon Quinn. (with . thanks to William Cohen of Carnegie Mellon University, and J. . Leskovec. , A. . Rajaraman. , and J. Ullman of Stanford . University). First: Upcoming deadlines!. TONIGHT @ 11:59:59pm: Assignment 3!. Martin Åkerblad. William . bruce. What is . Hashing?. Index. Key. 1. 2. 3. 4. 5. 6. 7. Key. 56. 84. 23. 42. 71. 97. 55. Hash. . function. 56. 42. 71. 23. 84. 55. 97. Value. When to use hashing.. Quick searching in large databases. Victor Zakhary, . Divyakant. Agrawal, Amr El . Abbadi. 1. The old problem of Caching. Disk. RAM. L. 2. L. 1. Larger. Slower. Cheaper. Smaller. Faster. Expensive. 2. The old problem of Caching. Smaller. Similarity Search. Alex . Andoni. (Columbia University). Nearest Neighbor Search (NNS). Preprocess: . a set . . of points. Query:. given a . query point . , report a point . with the smallest distance to . Hashing & Partitioning. 1. Peng Sun. Server Load Balancing. Balance load across servers. Normal techniques: . Round-robin? . 2. Limitations of Round Robin. Packets of a single connection spread over several servers. In static hashing, function . h. maps search-key values to a fixed set of . B. . buckets, that contain a number of (K,V) entries.. . . Problem: d. atabases . grow . (or shrink) . with time. . If initial number of buckets is too small, and file grows, performance will degrade due to too much overflows.. Nhan Nguyen. & . Philippas. . Tsigas. ICDCS 2014. Distributed Computing and Systems. Chalmers University of Technology. Gothenburg, Sweden. Our contributions: a concurrent hash table. Nhan D. Nguyen.