Hash/B+ Tree/R Tree Muneeb Mahmood Ashfaq Ahmed Jim Kang

Outline Hash Table B+ Tree R – Tree SQL Parser Demo Performance

Project Functionality – Hash Table Developed by Ashfaq Ahmed Indexing Scheme: Hash Maps Load Indexes For all the attributes Select Queries of both Still and Motion Multiple Conditions using And/Or Insert At the end of the file/ update indexes

Hash Table Contd.. Update Index gets updated Delete Index gets updated

Hash Table contd.. Performance: Used Random Access File to access Stored Line Numbers in the Hash Map Problems: While reading the flat file Adding junk characters at the end of file Pointing to specific location ( random access)

Implementation of B+ and R Trees Presented by Muneeb GiST Generalized Search Tree Developed by University of California, Berkley Can be used to represent all types of search trees Binary Search Trees B+ Trees R Trees hBTrees TV Trees Ch Trees Partial Sum Trees etc.

Implementation of B+ and R Trees Specific implementations of GiST BT package (for B+ Trees) RS package (for R Trees) Developed at CS department of University of Cape Town Written using a version of persistent java called PJama

Implementation of B+ Tree Index Indexing was implemented on all the attributes of both still and motion table Each index was stored in the form a B+ Tree Root Nodes – Min. occupancy = 2 Max. occupancy = 4 Points to sub nodes Leaves – Have no sub nodes - Stores the data ( line numbers) Random access to data file Indexing used for: Select Insert Update Delete

Project Functionality – B+ Tree Developed by Muneeb 1) Select : Supports searching of both still and motion tables Support complicated searching with an AND or OR after Where statement 2) Insert: Supports insertion into both still and motion tables Inserts dynamically into database and same time indexes the new tuple

Project Functionality – B+ Tree Developed by Muneeb 3) Update: Supports updates for still table Support complicated updating with an AND or OR after Where statement Dynamically updates both database file and index 4) Delete: Supports updates for both still and motion tables Support complicated deleting with an AND or OR after Where statement Dynamically updates both database file and index

Project Functionality – B+ Tree Developed by Muneeb BPlusTreeIndexprinter Utility that prints out the B+Tree Index on a specified attribute of still or motion table Displays - The root node - The sub nodes - Leaves - Data stored in the leaves

Implementation of R Tree Index Goal: Create an index on each pair of attributes to make search time optimal Problem: Because queries can be in any order, need to take care of every combination. Still Table: 11 attributes -> 121 Indexes Motion Table: 6 attributes -> 36 Indexes Solution: Create one index for each attribute

Differences between B+ and R Tree Queries done on R Trees are executed as Ranges. Our test show that R Trees perform better than Hash and B+ Tree. (10 Tests)

Project Functionality – R Tree Developed by Jim Kang 1) Select : Supports searching of both still and motion tables Support complicated searching with an AND or OR after Where statement 2) Insert: Supports insertion into both still and motion tables Inserts dynamically into database and same time indexes the new tuple

Project Functionality – R Tree Developed by Jim Kang 3) Update: Supports updates for still table Support complicated updating with an AND or OR after Where statement Dynamically updates both database file and index 4) Delete: Supports updates for both still and motion tables Support complicated deleting with an AND or OR after Where statement Dynamically updates both database file and index

SQL Functionality Developed by Jim Kang using ZQL SQL Parser Parses SQL using ZQL Complete Error Checking Select Queries AND/OR within where statements Including Update and Delete Insert Inserts into File and index, Still and Motion Update Updates into File and index, Still and Motion (Update file done by B+ and R Tree only) Delete Deletes from File and index, Still and Motion (Delete from file done by B+ and R Tree only)

Demo

Performance Load Time

Performance Average Select Query Performance (20 test Queries)

Performance Update Time for 1 tuple

Performance Insert Time for 1 tuple

Performance Delete Time for 1 tuple

Performance Average Query Range Time

References /p.html /p.html Help from Yan Hu on R Trees. Thanks!