1. Chap6. Organizing Files for Performance

2. Chapter Objectives(1)  Look at several approaches to data compression  Look at storage compaction as a simple way of reusing space in a file  Develop a procedure for deleting fixed-length records that allows vacated file space to be reused dynamically  Illustrate the use of linked lists and stacks to manage an avail list  Consider several approaches to the problem of deleting variable-length records  Introduce the concepts associated with the terms internal fragmentation and external fragmentation

3. Chapter Objectives(2)  Outline some placement strategies associated with the reuse of space in a variable-length record file  Provide an introduction to the idea underlying a binary search  Undertake an examination of the limitations of binary searching  Develop a keysort procedure for sorting larger files; investigate the costs associated with keysort  Introduce the concept of a pinned record

4. Contents 6.1 Data compression 6.2 Reclaiming space in files 6.3 Finding things quickly: An Introduction to internal sorting and binary searching 6.4 Keysorting

5. Data Compression(1)  Reasons for data compression less storage transmitting faster, decreasing access time processing faster sequentially

6. Data Compression(2) : Using a different notation  Fixed-Length fields are good candidates  Decrease the # of bits by finding a more compact notation ex) original state field notation is 16bits, but we can encode with 6bit notation because of the # of all states are 50  Cons. unreadable by human cost in encoding time decoding modules => increase the complexity of s/w => used for particular application

7. Data Compression(3) : Suppressing repeating sequences  Run-length encoding algorithm read through pixels, copying pixel values to file in sequence, except the same pixel value occurs more than once in succession when the same value occurs more than once in succession, substitute the following three bytes Êspecial run-length code indicator((ex) ff) Ëpixel value repeated Ìthe number of times that value is repeated –ex) 22 23 24 24 24 24 24 24 24 25 26 26 26 26 26 26 25 24 Ô22 23 ff 24 07 25 ff 26 06 25 24

8. Data Compression(3) : Suppressing repeating sequences  Run-length encoding (cont’d) example of redundancy reduction cons. –not guarantee any particular amount of space savings –under some circumstances, compressed image is larger than original image –Why? Can you prevent this?

9. Data Compression(4) : Assigning variable-length codes  Morse code: oldest & most common scheme of variable-length code  Some values occur more frequently than others that value should take the least amount of space  Huffman coding base on probability of occurrence –determine probabilities of each value occurring –build binary tree with search path for each value –more frequently occurring values are given shorter search paths in tree

10. Data Compression(5) : Assigning variable-length codes  Huffman coding Letter:abcdefg Prob:0.40.10.10.10.10.10.1 Code:10100110000000100100011 ex) the string “abde” è101000000001

11. d(0000)e(0001) f(0010) g(0011) b(010)c(011) a(1) Huffman Tree 0 00 01 000001

12. Data Compression(6) : Irreversible compression techniques  Some information can be sacrificed  Less common in data files  Shrinking raster image 400-by-400 pixels to 100-by-100 pixels 1 pixel for every 16 pixels  Speech compression voice coding (the lost information is of no little or no value)

13. Compression in UNIX  System V pack & unpack use Huffman codes after compress file, appends “.z” to end of packed file  Berkeley UNIX compress & uncompress use Lempel-Ziv method after compress file, appends “.Z” to end of compressed file

14. Record Deletion and Storage Compaction  Storage compaction record deletion : just marks each deleted record reclamation of all deleted records

15. Deleting Fixed-length Records for Reclaiming Space Dynamically(1)  Reuse the space from deleted records as soon as possible deleted records must be marked in special way we could find the deleted space  To make record reuse quickly, we need a way to know immediately if there are empty slots in the file a way to jump directly to one of those slots if they exist => Linked lists or Stacks for avail list * avail list : a list that is made up of deleted records

16. Deleting Fixed-length Records for Reclaiming Space Dynamically(2) Linked List Stack

17. Deleting Fixed-length Records for Reclaiming Space Dynamically(3)  Linking and stacking deleted records arranging and rearranging links are used to make one available record slot point to the next second field of deleted record points to next record

18. Sample file showing linked list of deleted records Edwards...  레코드 3 삭제  레코드 5 삭제  레코드 1 삭제  세 개의 새로운 레코드 삽입  레코드 3 삭제  레코드 5 삭제  레코드 1 삭제  세 개의 새로운 레코드 삽입 Edwards...Bates...Wills...*-1Maters...Browns...Chavez 0123456 List head (first available record) => 3 Edwards...Bates...Wills...*-1Maters...*3Chavez 0123456 List head (first available record) => 5 Edwards...*5Wills...*-1Maters...*3Chavez 0123456 List head (first available record) => 1 Edwards... 1st new rec.. Wills... 3rd new rec.. Maters... 2nd new rec.. Chavez 0123456 List head (first available record) => -1

19. Deleting Variable-length Records  Avail list of variable-length records it has byte count of record at beginning of each record use byte offset instead of RRN  Adding and removing records in adding records, search through avail list for right size (=>big enough)

20. Size 47 Size 38 Size 72 Size 68 Size 47 Size 68 Size 38 Size 72 New Link Removed record (a)Before removal (b)After removal Removal of a record from an avail list with variable-length records

21. Storage Fragmentation  Internal fragmentation (in fixed-length record) waste space within a record in variable-length records, minimize wasted space by doing away with internal fragmentation  External fragmentation (in variable-length record) unused space outside or between individual records three possible solutions Êstorage compaction ·coalescing the holes: a single, larger record slot ¸minimizing fragmentation by adopting placement strategy

22. Internal Fragmentation in Fixed-length Records Ames | John | 123 Maple | Stillwater | OK | 740751 |................................... Morrison | Sebastian | 9035 South Hillcrest | Forest Village | OK | 74820 | Brown | Martha | 625 Kimbark | Des Moines | IA | 50311 |......................... 64-byte fixed-length records Unused space -> Internal fragmentation

23. External Fragmentation in Variable-length Records 40 Ames | Jone | 123 Maple | Stillwater | OK | 740751 | 64 Morrison | Sebastian | 9035 South Hillcrest | Forest Village | OK | 74820 | 45 Brown | Martha | 625 Kimb bark | Des Moines | IA | 50311 | Record[1]Record[2] Record[3] ex) Delete Record[2] and Insert New Record[i] : 12-byte unused space 52 Adams | Kits | 3301 Washington D.C | Forest Village | IA | 43563 | External fragmentation record length Record[i]

24. Placement Strategies  First-fit select the first available record slot suitable when lost space is due to internal fragmentation  Best-fit select the available record slot closest in size avail list in ascending order suitable when lost space is due to internal fragmentation  Worst-fit select the largest record slot avail list in descending order suitable when lost space is due to external fragmentation

25. Finding Things Quickly(1)  Goal: Minimize the number of disk accesses  Finding things in simple field and record files may have many seeks  Binary search algorithm for fixed-sized record int BinarySearch(FixedRecordFile &file, RecType &obj, KeyType &key) // binary search for key. { int low = 0; int high = file.NumRecs() - 1; while (low <= high){ int guess = (high - low)/2; file.ReadByRRN(obj, guess); if(obj.Key () == key) return 1; // record found if(obj.Key() < key) high = guess - 1; // search before guess else low = guess + 1; // search after guess } return 0; // loop ended without finding key }

26. Classes and Methods for Binary Search Class KeyType {public int operator == (KeyType &); int operator < (KeyType &); }; class RecType {public: KeyType Key();}; class FixedRecordFile{public: int NumRecs(); int ReadByRRN (RecType & Record, int RRN); };

27. Finding Things Quickly(2)  Binary search vs. Sequential search binary search –O(log n) –list is sorted by key sequential search –O(n)

28. Finding Things Quickly(3)  Sorting a disk file in RAM read the entire file from disk to memory use internal sort (=sort in memory) –UNIX sort utility uses internal sort  Limitations of binary search & internal sort binary search requires more than one or two access c.f.) single access by RRN keeping a file sorted is very expensive an internal sort works only on small files

29. Internal Sort unsorted file unsorted file sorted file Read the entire file Sort in memory disk memory

30. Key Sorting & Its Limitations  So called, “tag sort” : sorted thing is “key” only  Sorting procedure ¶Read only the keys into memory ·Sort the keys ¸Rearrange the records in file by the sorted keys  Advantage less RAM than internal sort  Disadvantages(=Limitations) reading records in disk twice is required a lot of seeking for records for constructing a new(sorted) file

31. 1212 3 k HARRISON KELLOG HARRIS BELL........ Harrison|Susan|387 Eastern.... Kellog|Bill|17 Maple.... Harris|Margaret|4343 West.... Bell|Robert|8912 Hill.... KEYRRN Records In RAMOn secondary storage k3k3 1 2 HARRISON KELLOG HARRIS BELL........ Harrison|Susan|387 Eastern.... Kellog|Bill|17 Maple.... Harris|Margaret|4343 West.... Bell|Robert|8912 Hill.... KEYRRN Records Conceptual view after sorting keys in RAM Conceptual view before sorting KEYNODES array

32. Pseudocode for keysort(1)  Program: keysort  open input file as IN_FILE  create output file as OUT_FILE  read header record from IN_FILE and write a copy to OUT_FILE  REC_COUNT := record count from header record  /* read in records; set up KEYNODES array */  for i := 1 to REC_COUNT  read record from IN_FILE into BUFFER  extract canonical key and place it in KEYNODES[i].KEY  KEYNODES[i].KEY = i (continued....)

33. Pseudocode for keysort(2)  /* sort KEYNODES[].KEY, thereby ordering RRNs correspondingly */  sort(KEYNODES, REC_COUNT)  /* read in records according to sorted order, and write them out in this order */  for i := 1 to REC_COUNT  seek in IN_FILE to record with RRN of KEYNODES[I].RRN write BUFFER contents to OUT_FILE  close IN_FILE and OUT_FILE  end PROGRAM

34. Two Solutions :why bother to write the file back?  Write out sorted KEYNODES[] array without writing records back in sorted order  KEYNODES[] array is used as index file

35. k3k3 1 2 HARRISON KELLOG HARRIS BELL........ Harrison|Susan|387 Eastern.... Kellog|Bill|17 Maple.... Harris|Margaret|4343 West.... Bell|Robert|8912 Hill.... KEYRRN Records Index file Original file Relationship between the index file and the data file

36. Pinned records(1)  Records that are referenced to physical location of themselves by other records  Not free to alter physical location of records for avoiding dangling references  Pinned records make sorting more difficult and sometimes impossible solution: use index file, while keeping actual data file in original order

37. Pinned records(2) File with pinned records Record(i) Pinned Record Record (i+1)Pinned Record delete pinned record dangling pointer

38. Let’s Review !!! 6.1 Data compression 6.2 Reclaiming space in files 6.3 Finding things quickly: An Introduction to internal sorting and binary searching 6.4 Keysorting