Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke1 External Sorting Chapter 13.

Slides:



Advertisements
Similar presentations
External sorting R & G – Chapter 13 Brian Cooper Yahoo! Research.
Advertisements

External Sorting The slides for this text are organized into chapters. This lecture covers Chapter 11. Chapter 1: Introduction to Database Systems Chapter.
CMU SCS Carnegie Mellon Univ. Dept. of Computer Science /615 - DB Applications C. Faloutsos – A. Pavlo Lecture#12: External Sorting.
Equality Join R X R.A=S.B S : : Relation R M PagesN Pages Relation S Pr records per page Ps records per page.
Database Management Systems 3ed, R. Ramakrishnan and Johannes Gehrke1 Evaluation of Relational Operations: Other Techniques Chapter 14, Part B.
Database Management Systems, R. Ramakrishnan and Johannes Gehrke1 Evaluation of Relational Operations: Other Techniques Chapter 12, Part B.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 External Sorting Chapter 11.
1 External Sorting Chapter Why Sort?  A classic problem in computer science!  Data requested in sorted order  e.g., find students in increasing.
External Sorting “There it was, hidden in alphabetical order.” Rita Holt R&G Chapter 13.
External Sorting CS634 Lecture 10, Mar 5, 2014 Slides based on “Database Management Systems” 3 rd ed, Ramakrishnan and Gehrke.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 Query Evaluation Chapter 11 External Sorting.
FALL 2004CENG 351 Data Management and File Structures1 External Sorting Reference: Chapter 8.
External Sorting R & G Chapter 13 One of the advantages of being
FALL 2006CENG 351 Data Management and File Structures1 External Sorting.
1 Overview of Storage and Indexing Yanlei Diao UMass Amherst Feb 13, 2007 Slides Courtesy of R. Ramakrishnan and J. Gehrke.
External Sorting R & G Chapter 11 One of the advantages of being disorderly is that one is constantly making exciting discoveries. A. A. Milne.
CS 4432lecture #31 CS4432: Database Systems II Lecture #3 Professor Elke A. Rundensteiner.
Query Processing 1: Joins and Sorting R&G, Chapters 12, 13, 14 Lecture 8 One of the advantages of being disorderly is that one is constantly making exciting.
Query Optimization 3 Cost Estimation R&G, Chapters 12, 13, 14 Lecture 15.
6/27/20151 PSU’s CS External Sorting  Motivation  2-way External Sort: Memory, passes,cost  General External Sort: Memory, passes, cost  Optimizations.
1 External Sorting Chapter Why Sort?  A classic problem in computer science!  Data requested in sorted order  e.g., find students in increasing.
External Sorting 198:541. Why Sort?  A classic problem in computer science!  Data requested in sorted order e.g., find students in increasing gpa order.
1 External Sorting for Query Processing Yanlei Diao UMass Amherst Feb 27, 2007 Slides Courtesy of R. Ramakrishnan and J. Gehrke.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 Tree-Structured Indexes Chapter 9.
Tree-Structured Indexes. Range Searches ``Find all students with gpa > 3.0’’ –If data is in sorted file, do binary search to find first such student,
External Sorting Chapter 13.. Why Sort? A classic problem in computer science! Data requested in sorted order  e.g., find students in increasing gpa.
External Sorting Problem: Sorting data sets too large to fit into main memory. –Assume data are stored on disk drive. To sort, portions of the data must.
Query Processing 2: Sorting & Joins
BTrees & Sorting 11/3. Announcements I hope you had a great Halloween. Regrade requests were due a few minutes ago…
Sorting.
Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke1 External Sorting Chapter 13.
Database Management Systems, R. Ramakrishnan and J. Gehrke 1 External Sorting Chapter 13.
CPSC 404, Laks V.S. Lakshmanan1 External Sorting Chapter 13 (Sec ): Ramakrishnan & Gehrke and Chapter 11 (Sec ): G-M et al. (R2) OR.
CPSC 404, Laks V.S. Lakshmanan1 External Sorting Chapter 13: Ramakrishnan & Gherke and Chapter 2.3: Garcia-Molina et al.
1 External Sorting. 2 Why Sort?  A classic problem in computer science!  Data requested in sorted order  e.g., find students in increasing gpa order.
1 Database Systems ( 資料庫系統 ) December 7, 2011 Lecture #11.
ZA classic problem in computer science! zData requested in sorted order ye.g., find students in increasing cap order zSorting is used in many applications.
B+ Trees: An IO-Aware Index Structure Lecture 13.
Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke1 B+-Tree Index Chapter 10 Modified by Donghui Zhang Nov 9, 2005.
Internal and External Sorting External Searching
FALL 2005CENG 351 Data Management and File Structures1 External Sorting Reference: Chapter 8.
Introduction to Database Systems1 External Sorting Query Processing: Topic 0.
Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke1 External Sorting Chapters 13: 13.1—13.5.
CPSC Why do we need Sorting? 2.Complexities of few sorting algorithms ? 3.2-Way Sort 1.2-way external merge sort 2.Cost associated with external.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 External Sorting Chapter 11.
External Sorting. Why Sort? A classic problem in computer science! Data requested in sorted order –e.g., find students in increasing gpa order Sorting.
External Sorting Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY courtesy of Joe Hellerstein for some slides.
CS 405G: Introduction to Database Systems Instructor: Jinze Liu Fall 2007.
Database Management Systems, R. Ramakrishnan and J. Gehrke1 File Organizations and Indexing Chapter 8 Jianping Fan Dept of Computer Science UNC-Charlotte.
What Should a DBMS Do? Store large amounts of data Process queries efficiently Allow multiple users to access the database concurrently and safely. Provide.
1 Lecture 16: Data Storage Wednesday, November 6, 2006.
Database Systems (資料庫系統)
External Sorting Chapter 13
Database Management Systems (CS 564)
Lecture#12: External Sorting (R&G, Ch13)
External Sorting The slides for this text are organized into chapters. This lecture covers Chapter 11. Chapter 1: Introduction to Database Systems Chapter.
External Sorting Chapter 13
Selected Topics: External Sorting, Join Algorithms, …
CS222P: Principles of Data Management UCI, Fall 2018 Notes #09 External Sorting Instructor: Chen Li.
CS222: Principles of Data Management Lecture #10 External Sorting
General External Merge Sort
External Sorting.
CS222P: Principles of Data Management Lecture #10 External Sorting
CENG 351 Data Management and File Structures
Database Systems (資料庫系統)
External Sorting Chapter 13
CS222/CS122C: Principles of Data Management UCI, Fall 2018 Notes #09 External Sorting Instructor: Chen Li.
External Sorting Dina Said
Presentation transcript:

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke1 External Sorting Chapter 13

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke2 Why Sort?  A classic problem in computer science!  Data requested in sorted order  e.g., find students in increasing gpa order  Where did we encountered sorting?  Sorting is first step in bulk loading B+ tree index.  Sorting useful for eliminating duplicate copies in a collection of records (Why?)  Sort-merge join algorithm involves sorting.

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke3 Recall (from OS Class) Memory Hierarchy Put equal emphasis on all CS classes! Minimize the number of disk accesses!

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke4 Cost Model  Big O notation versus I/O cost  General Wisdom  I/O costs dominate  Design algorithms to reduce I/O Internal Sort versus External Sort

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke5 External Sorting  Problem: Sort 1000Gb of data with 1Gb of RAM.  why not virtual memory?  When a file doesn’t fit in memory, there are two stages in sorting: 1.File is divided into several segments, each of which sorted separately 2.Sorted segments are merged Each stage involves reading and writing the file at least once!

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke6 2-Way Sort: Requires 3 Buffers  Pass 1: Read a page, sort it, write it.  only one buffer page is used  Pass 2, 3, …, etc.:  three buffer pages used. Main memory buffers INPUT 1 INPUT 2 OUTPUT Disk

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke7 Main-Memory Merge Sort Merge-Sort(A) 01 if length(A) > 1 then 02 Copy the first half of A into array A1 03 Copy the second half of A into array A2 04 Merge-Sort(A1) 05 Merge-Sort(A2) 06 Merge(A, A1, A2) Merge-Sort(A) 01 if length(A) > 1 then 02 Copy the first half of A into array A1 03 Copy the second half of A into array A2 04 Merge-Sort(A1) 05 Merge-Sort(A2) 06 Merge(A, A1, A2) Divide Conquer Combine

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke8... Main Memory 2-Way Merge Sort  Settings:  A frame holds 4 keys Input page 1Input page 2 Output page Disk

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke9 Two-Way External Merge Sort  Each pass we read + write each page in file.  N pages in the file => the number of passes  So toal cost is:  Idea: Divide and conquer: sort subfiles and merge Input file 1-page runs 2-page runs 4-page runs 8-page runs PASS 0 PASS 1 PASS 2 PASS 3 9 3,4 6,2 9,48,75,63,1 2 3,45,62,64,97,8 1,32 2,3 4,6 4,7 8,9 1,3 5,62 2,3 4,4 6,7 8,9 1,2 3,5 6 1,2 2,3 3,4 4,5 6,6 7,8

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke10 General External Merge Sort  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 2, …, etc.: merge B-1 runs. B Main memory buffers INPUT 1 INPUT B-1 OUTPUT Disk INPUT 2... * More than 3 buffer pages. How can we utilize them?

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke11 Cost of External Merge Sort  Number of passes:  Cost = 2N * (# of passes)  E.g., with 5 buffer pages, to sort 108 page file:  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

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke12 Number of Passes of External Sort

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke13 Finding the minimum item  When the number of lists is small (K  8) sequential search among items works nicely. (O(K))  When the number of lists is large, we could place the items in a priority queue (an array heap).  The min value will be at the root (1 st position in array)  Replace the root with the next value from the associated list.  This insert operation is O(log K)

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke14 Internal Sort Algorithm  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

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke15 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... B

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke16 I/O for External Merge Sort  … longer runs often means fewer passes!  Actually, do I/O a page at a time  In fact, read a block of pages sequentially!  Suggests we should make each buffer (input/output) be a block of pages.  But this will reduce fan-out during merge passes!  In practice, most files still sorted in 2-3 passes.

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke17 Number of Passes of Optimized Sort * Block size = 32, initial pass produces runs of size 2B.

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke18 Double Buffering  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. OUTPUT OUTPUT' Disk INPUT 1 INPUT k INPUT 2 INPUT 1' INPUT 2' INPUT k' block size b B main memory buffers, k-way merge

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke19 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 clustered  B+ tree is not clustered Could be a very bad idea!

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke20 Clustered B+ Tree Used for Sorting  Cost: root to the left- most leaf, then retrieve all leaf pages (Alternative 1)  If Alternative 2 is used?  Additional cost of retrieving data records: each page fetched just once. * Always better than external sorting! (Directs search) Data Records Index Data Entries ("Sequence set")

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke21 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! (Directs search) Data Records Index Data Entries ("Sequence set")

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke22 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.

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke23 Summary  External sorting is important; DBMS may dedicate part of buffer pool for sorting!  External merge sort minimizes disk I/O cost:  Pass 0: Produces sorted runs of size B (# buffer pages). Later passes: merge runs.  # of runs merged at a time depends on B, and block size.  Larger block size means less I/O cost per page.  Larger block size means smaller # runs merged.  In practice, # of runs rarely more than 2 or 3.

Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke24 Summary, cont.  Choice of internal sort algorithm may matter:  Quicksort: Quick!  Heap/tournament sort: slower (2x), longer runs  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.