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External Sorting

External Sorting. Chapter 13. Why Sort?. A classic problem in computer science! Data requested in sorted order e.g., find students in increasing gpa order Sorting is first step in bulk loading B+ tree index.

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External Sorting

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  1. External Sorting Chapter 13

  2. Why Sort? • A classic problem in computer science! • Data requested in sorted order • e.g., find students in increasing gpa order • Sorting is first step in bulk loadingB+ tree index. • Sorting useful for eliminating duplicate copies in a collection of records • Sort-merge join algorithm involves sorting. • Problem: sort 1Gb of data with 1Mb of RAM. • why not virtual memory?

  3. Using secondary storage effectively • General Wisdom : • I/O costs dominate • Design algorithms to reduce I/O

  4. 2-Way Sort: Requires 3 Buffers • Phase 1: PREPARE. • Read a page, sort it, write it. • only one buffer page is used • Phase 2, 3, …, etc.: MERGE: • three buffer pages used. INPUT 1 OUTPUT INPUT 2 Main memory buffers Disk Disk

  5. Two-Way External Merge Sort 6,2 2 Input file 3,4 9,4 8,7 5,6 3,1 PASS 0 1,3 2 1-page runs 3,4 2,6 4,9 7,8 5,6 PASS 1 • Idea:Divide and conquer: sort subfiles and merge into larger sorts 4,7 1,3 2,3 2-page runs 8,9 5,6 2 4,6 PASS 2 2,3 4,4 1,2 4-page runs 6,7 3,5 6 8,9 PASS 3 1,2 2,3 3,4 8-page runs 4,5 6,6 7,8 9

  6. Two-Way External Merge Sort 6,2 2 Input file 3,4 9,4 8,7 5,6 3,1 PASS 0 1,3 2 1-page runs 3,4 2,6 4,9 7,8 5,6 PASS 1 • Costs for pass : all pages • # of passes : height of tree • Total cost : product of above 4,7 1,3 2,3 2-page runs 8,9 5,6 2 4,6 PASS 2 2,3 4,4 1,2 4-page runs 6,7 3,5 6 8,9 PASS 3 1,2 2,3 3,4 8-page runs 4,5 6,6 7,8 9

  7. Two-Way External Merge Sort 6,2 2 Input file 3,4 9,4 8,7 5,6 3,1 PASS 0 1,3 2 1-page runs 3,4 2,6 4,9 7,8 5,6 • Each pass we read + write each page in file. • N pages in file => 2N • Number of passes • So total cost is: PASS 1 4,7 1,3 2,3 2-page runs 8,9 5,6 2 4,6 PASS 2 2,3 4,4 1,2 4-page runs 6,7 3,5 6 8,9 PASS 3 1,2 2,3 3,4 8-page runs 4,5 6,6 7,8 9

  8. External Merge Sort • What if we had more buffer pages? • How do we utilize them wisely ? - Two main ideas !

  9. Phase 1 : Prepare INPUT 1 . . . INPUT 2 . . . INPUT B Disk Disk B Main memory buffers • Construct as large as possible starter lists.

  10. Phase 2 : Merge INPUT 1 Compose as many sorted sublists into one long sorted list. . . . INPUT 2 . . . OUTPUT INPUT B-1 Disk Disk B Main memory buffers

  11. General External Merge Sort • How can we utilize more than 3 buffer pages? • To sort a file with N pages using B buffer pages: • Pass 0: use B buffer pages. Produce sorted runs of B pages each. • Pass 1, 2, …, etc.: merge B-1 runs. INPUT 1 . . . . . . INPUT 2 . . . OUTPUT INPUT B-1 Disk Disk B Main memory buffers

  12. Cost of External Merge Sort • Number of passes: • Cost = 2N * (# of passes)

  13. Example • Buffer : with 5 buffer pages • File to sort : 108 pages • Pass 0: • Size of each run? • Number of runs? • Pass 1: • Size of each run? • Number of runs? • Pass 2: ???

  14. Example • Buffer : with 5 buffer pages • File to sort : 108 pages • Pass 0: = 22 sorted runs of 5 pages each (last run is only 3 pages) • Pass 1: = 6 sorted runs of 20 pages each (last run is only 8 pages) • Pass 2: 2 sorted runs, 80 pages and 28 pages • Pass 3: Sorted file of 108 pages • Total I/O costs: ?

  15. Example • Buffer : with 5 buffer pages • File to sort : 108 pages • Pass 0: = 22 sorted runs of 5 pages each (last run is only 3 pages) • Pass 1: = 6 sorted runs of 20 pages each (last run is only 8 pages) • Pass 2: 2 sorted runs, 80 pages and 28 pages • Pass 3: Sorted file of 108 pages • Total I/O costs: 2*N * (4 passes)

  16. Number of Passes of External Sort • gain of utilizing all available buffers • importance of a high fan-in during merging

  17. Optimizing External Sorting • Cost metric ? • I/O only (till now) • CPU is nontrivial, worth reducing

  18. Internal Algorithm : Heap Sort • Quicksort is a fast way to sort in memory. • An alternative is “tournament sort” (a.k.a. “heapsort”) • Top:Read in B blocks • Output:move smallest record to output buffer • Read in a new record r • insert r into “heap” • if r not smallest, then GOTO Output • else remove r from “heap” • output “heap” in order; GOTO Top

  19. Internal Sort Algorithm 2 8 10 12 3 . . . 4 5 INPUT CURRENT SET OUTPUT • 1 input, 1 output, B-2 current set • Main idea: repeatedly pick tuple in current set with smallest k value that is still greater than largest k value in output buffer and append it to output buffer

  20. Internal Sort Algorithm 2 8 10 12 3 . . . 4 5 INPUT CURRENT SET OUTPUT • Input & Output? new input page is read in if it is consumed, output is written out when it is full • When terminate current run? When all tuples in current set are smaller than largest tuple in output buffer.

  21. B More on Heapsort • Fact: average length of a run in heapsort is 2B • The “snowplow” analogy • Worst-Case: • What is min length of a run? • How does this arise? • Best-Case: • What is max length of a run? • How does this arise? • Quicksort is faster, but ...

  22. Optimizing External Sorting • Further optimization for external sorting. • Blocked I/O • Double buffering

  23. I/O for External Merge Sort • Thus far : do 1 I/O a page at a time • But cost also includes real page read/write time. • Reading a blockof pages sequentially is cheaper! • Suggests we should make each buffer (input/output) be a blockof pages. • But this will reduce fan-out during merge passes! • In practice, most files still sorted in 2-3 passes.

  24. I/O for External Merge sort • Example buffer blocks = b pagesset one buffer block for input, one buffer block for outputmerge |B-b/b| runs in each pass e.g., 10 buffer pages 9 runs at a time with one-page input and output buffer blocks 4 runs at a time with two-page input and output buffer block

  25. Double Buffering – Overlap CPU and I/O • To reduce wait time for I/O request to complete, can prefetch into `shadow block’. • Potentially, more passes; in practice, most files still sorted in 2-3 passes. INPUT 1 INPUT 1' INPUT 2 OUTPUT INPUT 2' OUTPUT' b block size Disk INPUT k Disk INPUT k' B main memory buffers, k-way merge

  26. Sorting Records! • Sorting has become a blood sport! • Parallel sorting is the name of the game ... • Datamation: Sort 1M records of size 100 bytes • Typical DBMS: 15 minutes • World record: 3.5 seconds • 12-CPU SGI machine, 96 disks, 2GB of RAM • New benchmarks proposed: • Minute Sort: How many can you sort in 1 minute? • Dollar Sort: How many can you sort for $1.00?

  27. Using B+ Trees for Sorting • Scenario: Table to be sorted has B+ tree index on sorting column(s). • Idea: Can retrieve records in order by traversing leaf pages. • Is this a good idea? • Cases to consider: • B+ tree is clusteredGood idea! • B+ tree is not clusteredCould be a very bad idea!

  28. Clustered B+ Tree Used for Sorting • Cost: root to left-most leaf, then retrieve all leaf pages (Alternative 1) • For Alternative 2, additional cost of retrieving data records: each page fetched just once. Index (Directs search) Data Entries ("Sequence set") Data Records • Always better than external sorting!

  29. Unclustered B+ Tree Used for Sorting • Alternative (2) for data entries; each data entry contains rid of a data record. • In general, one I/O per data record! Index (Directs search) Data Entries ("Sequence set") Data Records

  30. External Sorting vs. Unclustered Index • p: # of records per page • B=1,000 and block size=32 for sorting • p=100 is the more realistic value.

  31. Summary • External sorting is important; DBMS may dedicate part of buffer pool for sorting! • External merge sort minimizes disk I/O costs: • Ideas: Pass 0 produces sorted runs of size B; while later passes merge runs (depends on buffer/block size) • In practice, # of runs rarely more than 3. • Choice of internal sort algorithm may matter. • The best sorts are wildly fast: • Despite 40+ years of research, we’re still improving! • Clustered B+ tree is good for sorting; unclustered tree is usually very bad.

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