Organizing Files for Performance Chapter 6 Jim Skon
Organizing Files for Performance • Data Compression • Reclaiming space in files • Fast Searching • Keysorting
Data Compression • Making files smaller • Use less storage, save space • Faster Transmission • Processed faster • Data Compression • encoding information more efficiently • Many techniques exist
Data Compression • Consider fields with fixed length or fixed set of values • A binary representation can save space • States - 50 states - 6 bits (one byte) • Zip - 0 to 99999. 17 bits (three bytes) • Called Compact Notation • Redundancy reduction
Data Compression • Cost of binary representations • file not readable as test • Processing time for conversion • All software must including appropriate/compatable encoding and decoding routines. • Potential lost of flexibility
Data Compression • Suppressing repreating sequences • Consider a picture • Series of pixels - each a color • Colors represented by 8 bit value • usually come in bunches, e.g. • 24 23 22 22 22 22 22 25 25 25 25 25 25 65 65 66 66 66 66 • Run length encoding • Represent long runs with a prefix (FF) follwed by count, followed by color • 24 23 FF 05 22 FF 06 25 65 65 FF 04 66 • Simple images would be small, busy images would be no bigger.
Data Compression • Assigning variable length codes • Some codes are more likely then others • Use shorter codes for often used values, longer ones for less used values. • Each code must have the property of a unique prefix • No code is the prefix of any other code • Thus we always know if we are at the end of a given code
Variable length codes • Example: Letter: a b c d e f g Prob: 0.4 0.1 0.1 0.1 0.1 0.1 0.1 Code: 1 010 011 0000 0001 0010 0011 • Can be decoded with a binary tree! • Called Huffman code • Algorithm exists to easily create optimal code • Requires that a table of codes be mainted with file • Most often used for fixed codes • Example - Type 3 FAX
Data Compression • Irreversible Compression • Compression which losses some information • Example - compress a 400x400 image into a 100x100 image by averaging groups of 16 adjacent pixels • Saves space, but resolution of picture reduced • Used most often for visual or audio information (which has inherient redundancy)
Data Compression • Compression in UNIX • pack and unpack programs • Uses Huffman coding • 25% to 40% savings on text files • much less on binary files • Uses “.z” file prefix • compress and uncompress programs • Uses Lempel-Ziv compression • No coding table needed - self coding • Uses “.Z” file prefix
Reclaiming space in files • Suppose a variable length record in the middle of a file is modified so it is: • Longer? • Shorter? • Suppose a record is • Added to to the middle? • Deleted from middle?
Reclaiming space in files • Record deletion and storage compaction • storage compaction • recovering unused space in a file • from deletion or from record size changing • Consider deleted records • Must be able to recognize deleted records • Have a special mark for record • e,g, asterisk in first charater in key field • May be undeleted if not overwritten!
Dealing with Deleted records • Occasional compaction • Dynamic maintanance
Occasional compaction • A process periodically run which reads file, and rewrites with no empty space. • Could happen every night automactically every night/week/month • File unavailable while operation underway.
Dynamic maintanance • Delete records by marking • Reuse deleted records a new records added, updated • Need: • Way of knowing if deleted records exist • Where deleted records are so we can jump right to them
Dynamic maintanance • Solution: linked list of deleted records • Each deleted record contains a mark, and a pointer to the next deleted record • The file header contains a pointer to the first deleted record.
Linked list of deleted records • Fixed-length records • Variable-length records
Linked list of deleted records • Fixed-length records • Simply maintain a stack of deleted records rooted in header record • Deletion - add to front of list • Addition - use record at front of list • Minimal list maintanance cost
Linked list of deleted records • Variable-length records • Store for each deleted record • Deletion Marker • link to nect deleted record • record size indicator
Variable-length records • Insertion • Which deleted record? • Deletion • Add records to list (stack?) • Where
Variable-length records - Insertion • Select and use a deleted record • Break up records • pick a record • If size of deleted record bigger, break into two - a record to use and a new, smaller, deleted record. • Put smaller deleted record back in list • Leave empty space at end • pick a record • If size of deleted record bigger, just leave empty space at end.
Variable-length records - Fragmentation • Recall fragmentation in Fixed-length records • At the end of fields if fixed length fields • At the end of records in variable length fields • Called internal fragmentation • Leaving space and the end of a variable length records also leads to internal fragmentation. • Breaking up variable length records get rid of fragmentation, right? Wrong!
Variable-length records - Fragmentation • As records get broken up, smaller and smaller pieces get left over. • These pieces are external fragmentation
Variable-length records - Insertion strategy • How to pick record to use? • First Fit • Use first deleted record found in list • Best Fit • Use deleted record closest in size • Worst Fit • Use deleted record that is largest • No good when not breaking up records!
Variable-length records - Insertion • How do we find the record with the desired size? • Search them ALL! • Keep the records in sorted order by record size • Increasing size facilitates Best fit • Decreasing size facilitates worst fit (just pick first in list) • This increases deletion time!
Variable-length records - Reducing fragmentation • Merge adjacent free records • How do we know if a newly deleted record is adjacent to a free record? • Search the deleted list • Keep deleted records sorted by position in file • This makes finding of adjacent free space trivial • Costs more at deletion time
Fast Searching • Binary Searching • O(log n), where n is number of records • requires file be sorted • Question - how do we sort file?
File Sorting • Sort in Ram • read in entire file - sort • Called internal sorting • Limited by size of memory
Binary Search - Problems • Binary searching requires more then one or two accesses • Accesses are VERY expensive • Access are very random (much seek time) • 100,000 requires average of 16.5 accesses • We would like to approach the speed of a direct lookup!
Binary Search - Problems • Keeping a file sorted is expensive • Every record added must be entered in sorted order • Reordering is costly • Internal sorted is limited to small files • We will see there are sort methods to sort a file that will not fit in memory. But it is still expensive!
Keysorting • Rather then sorting file, we could sort an array of primary keys, where each key is accompanied by the address of the associated record. • Pointer could be a byte offset from start, or (if records fixed length) a RRN. • After sort keys, the file can be rewritten in order.
Keysorting • Advantages • Keys can be sorted in smaller space then whole file • Faster to sort (swap!) keys then entire records
Keysorting • Disadvantages • Still limited in size to key lists which fit in memory • Sequential processing cannot not take advantage of buffering!
Keysorting • Alternative - keeping sorted keylist,pointer structure around. • Is a type of index file! • Can be read in and searched in memory!
Key Sorted Index • Advantages • Keys and pointers can be searched in memery. Only one I/O per lookup! • File can be maintained in ANY order. Searching and key order sequential processing still possible.
Key Sorted Index • Disadvantages • Sequential processing cannot not take advantage of buffering! • Pinned records • Records in main file cannot change location without invalidating index file! • Must either maintain index in parallel, or rebuild!
