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BTrees - Great alternative to Red Black, AVL and other BSTs

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BTrees - designed by Rudolf Bayer and Ed McCreight - fundamental data structure in computer science. Great alternative to BSTs. Very appropriate for disk based access.

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BTrees - Great alternative to Red Black, AVL and other BSTs

  1. 1. CS 6213 – Advanced Data Structures Lecture 4 BTREES AN EXCELLENT DATA STRUCTURE FOR DISK ACCESS
  2. 2.  Instructor Prof. Amrinder Arora amrinder@gwu.edu Please copy TA on emails Please feel free to call as well  TA Iswarya Parupudi iswarya2291@gwmail.gwu.edu L4 - BTrees CS 6213 - Advanced Data Structures - Arora 2 LOGISTICS
  3. 3. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 3 CS 6213 Basics Record / Struct Arrays / Linked Lists / Stacks / Queues Graphs / Trees / BSTs Advanced Trie, B-Tree Splay Trees R-Trees Heaps and PQs Union Find
  4. 4.  T.K.Prasad @ Purdue University  Prof. Sin-Min Lee @ San Jose State University  Rada Mihalcea @ University of North Texas L4 - BTrees CS 6213 - Advanced Data Structures - Arora 4 CREDITS
  5. 5.  Eventually you run out of RAM  Plus, you need persistent storage  Storing information on disk requires different approach to efficiency  Access time includes seek time and rotational delay  Assuming that a disk spins at 3600 RPM, one revolution occurs in 1/60 of a second, or 16.7ms.  In other words, one disk access takes about the same time as 200,000 instructions L4 - BTrees CS 6213 - Advanced Data Structures - Arora 5 MOTIVATION FOR B-TREES
  6. 6.  Assume that we use an AVL tree to store about 20 million records  log2 20,000,000 is about 24  24 operations in terms of time is very small (4 GHz CPU, etc).  Normal data operation should take a few nanoseconds.  However, a large binary tree in a file will cause lots of different disk accesses  24 * 16.7ms = 0.4 seconds  Suddenly database query response time in seconds starts making sense. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 6 MOTIVATION FOR B-TREES (CONT.)
  7. 7.  We can’t improve the theoretical log n lower bound on search for a binary tree  But, the solution is to use more branches and thus reduce the height of the tree!  As branching increases, depth decreases L4 - BTrees CS 6213 - Advanced Data Structures - Arora 7 MOTIVATION FOR B-TREES (CONT.)
  8. 8.  Invented by Bayer and McCreight in 1972  (Bayer also invented Red Black Trees)  Definition is in terms of “order”, which is not always clear, and different researchers mean different things, but concepts remain the same.  We will use Knuth’s terminology, where order represents the maximum number of children. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 8 B-TREES
  9. 9.  B-tree of order m (where m is an odd number) is an m-way search tree, where keys partition the keys in the children in the fashion of a search tree, with following additional constraints: 1. [max] a node contains up to m – 1 keys and up to m children (Actual number of keys is one less than the number of children) 2. [min] all non-root nodes contain at least (m-1)/2 keys 3. [leaf level] all leaves are on the same level 4. [root] the root is either a leaf node, or it has at least two children L4 - BTrees CS 6213 - Advanced Data Structures - Arora 9 B-TREE: DEFINITION
  10. 10.  While as per Knuth’s definition B-Tree of order 5 is a tree where a node has a maximum of 5 children nodes, the same tree may be defined as a [2,4] tree in the sense that for any node, the number of keys is between 2 and 4, both inclusive. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 10 B-TREE: ALTERNATE DEFINITION
  11. 11. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 11 AN EXAMPLE B-TREE 51 6242 6 12 35 55 60 7564 9245 1 2 4 7 8 13 15 18 32 38 40 46 48 53 A B-tree of order 5: • Root has at least 2 children • Every other non-leaf node has at least 2 keys and 3 children • Each leaf has at least two keys • All leaves are at same level. 61
  12. 12.  Different approach compared AVL Trees  Don’t insert a new leaf, rather split the root and add a new level above the root. This automatically increases the height of ALL the leaves by one. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 12 KEEPING THE HEIGHT SAME
  13. 13.  We want to construct a B-tree of order 5  Suppose we start with an empty B-tree and keys arrive in the following order: 1 12 8 2 25 5 14 28 17 7 52 16 48 68 3 26 29 53 55 45  The first four items go into the root: L4 - BTrees CS 6213 - Advanced Data Structures - Arora 13 CONSTRUCTING A B-TREE 1 2 8 12
  14. 14.  To put the fifth item in the root would violate constraint 1 (max)  Therefore, when 25 arrives, we pick the middle key to make a new root L4 - BTrees CS 6213 - Advanced Data Structures - Arora 14 CONSTRUCTING A B-TREE (CONT.) 1 2 8 12 25
  15. 15.  6, 14, 28 get added to the leaf nodes L4 - BTrees CS 6213 - Advanced Data Structures - Arora 15 CONSTRUCTING A B-TREE (CONT.) 1 2 8 12 146 25 28
  16. 16.  Adding 17 to the right leaf node would violate constraint 1 (max), so we promote the middle key (17) to the root and split the leaf L4 - BTrees CS 6213 - Advanced Data Structures - Arora 16 CONSTRUCTING A B-TREE (CONT.) 8 17 12 14 25 281 2 6
  17. 17.  7, 52, 16, 48 get added to the leaf nodes L4 - BTrees CS 6213 - Advanced Data Structures - Arora 17 CONSTRUCTING A B-TREE (CONT.) 8 17 12 14 25 281 2 6 16 48 527
  18. 18.  Adding 68 causes us to split the right most leaf, promoting 48 to the root, and adding 3 causes us to split the left most leaf, promoting 3 to the root; 26, 29, 53, 55 then go into the leaves L4 - BTrees CS 6213 - Advanced Data Structures - Arora 18 CONSTRUCTING A B-TREE (CONT.) 3 8 17 48 52 53 55 6825 26 28 291 2 6 7 12 14 16
  19. 19.  Adding 45 causes a split of  But we observe that this does not cause the problem of leaves at different heights.  Rather, we promote 28 to go to the root.  However, root is already full:  So, this causes the root to split: 17 then becomes the new root. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 19 CONSTRUCTING A B-TREE (CONT.) 25 26 28 29 3 8 17 48
  20. 20. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 20 CONSTRUCTING A B-TREE (CONT.) 17 3 8 28 48 1 2 6 7 12 14 16 52 53 55 6825 26 29 45
  21. 21.  Attempt to insert the new key into a leaf  If this would result in that leaf becoming too big, split the leaf into two, promoting the middle key to the leaf’s parent  If this would result in the parent becoming too big, split the parent into two, promoting the middle key  This strategy might have to be repeated all the way to the top  If necessary, the root is split in two and the middle key is promoted to a new root, making the tree one level higher L4 - BTrees CS 6213 - Advanced Data Structures - Arora 21 SUMMARY: INSERTING INTO A B-TREE
  22. 22.  Insert the following keys to a 5-way B-tree:  13, 27, 51, 3, 2, 14, 28, 1, 7, 71, 89, 37, 41, 44 L4 - BTrees CS 6213 - Advanced Data Structures - Arora 22 EXERCISE IN INSERTING A B-TREE
  23. 23. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 23 REMOVAL FROM A B-TREE – 4 SCENARIOS Scenario 1: • Key to delete is a leaf node, and removing it doesn’t cause that leaf node to have too few keys, then simply remove the key to be deleted. Scenario 2: • Key to delete is not in a leaf and moving its successor or predecessor does not cause the leaf node to have too few keys. (We are guaranteed by the nature of a B-tree that its predecessor or successor will be in a leaf.) Scenario 3: • Key to delete is a leaf node, but deleting it will have the leaf to have too few keys, and we can borrow from an adjacent leaf node. Scenario 4: • Key to delete is a leaf node, but deleting it will have the leaf to have too few keys, and we cannot borrow from an adjacent leaf node. Then the lacking leaf and one of its neighbours can be combined with their shared parent (the opposite of promoting a key) and the new leaf will have the correct number of keys; if this step leave the parent with too few keys then we repeat the process up to the root itself, if required
  24. 24. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 24 SIMPLE LEAF DELETION 12 29 52 2 7 9 15 22 56 69 7231 43 We want to delete 2: Since there are enough keys in the node, we can just delete it Scenario1
  25. 25. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 25 SIMPLE LEAF DELETION (CONT.) 12 29 52 7 9 15 22 56 69 7231 43 That’s it, we deleted 2 and we are done. Scenario1
  26. 26. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 26 SIMPLE NON-LEAF DELETION 12 29 52 7 9 15 22 56 69 7231 43 Borrow the predecessor or (in this case) successor We want to delete 52. So, we delete it, and see that the successor can be moved up. Scenario2
  27. 27. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 27 SIMPLE NON-LEAF DELETION (CONT.) 12 29 56 7 9 15 22 69 7231 43 Done. 52 is gone. 56 promoted to the non-leaf node. Leaf nodes are still meeting the min constraint. Scenario2
  28. 28. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 28 TOO FEW KEYS IN NODE, BUT WE CAN BORROW FROM SIBLINGS 12 29 7 9 15 22 695631 43 We want to delete 22 – that will lead to too few keys in the node (constraint 2). But we can borrow from the adjacent node (via the root). Demote root key and promote leaf key Scenario3
  29. 29. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 29 TOO FEW KEYS IN NODE, BUT WE CAN BORROW FROM SIBLINGS (CONT.) 12 297 9 15 31 695643 Done – 22 is gone. 29 came down from the parent node, and 31 has gone up from the right adjacent node. Scenario3
  30. 30. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 30 TOO FEW KEYS IN NODE AND ITS SIBLINGS 12 29 56 7 9 15 22 69 7231 43 We want to delete 72. This will lead to too few keys in this node (constraint 2). We cannot borrow from the adjacent sibling as it only has two. So, we need to combine 31, 43, 56 and 69 into one node. Scenario4
  31. 31. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 31 TOO FEW KEYS IN NODE AND ITS SIBLINGS (CONT.) 12 29 7 9 15 22 695631 43 Done. 72 is gone. 31, 43, 56 and 69 combined into one node. Scenario4
  32. 32.  The maximum number of items in a B-tree of order m and height h: root m – 1 level 1 m(m – 1) level 2 m2(m – 1) . . . level h mh(m – 1)  So, the total number of items is (1 + m + m2 + m3 + … + mh)(m – 1) = [(mh+1 – 1)/ (m – 1)] (m – 1) = mh+1 – 1  When m = 5 and h = 2 this gives 53 – 1 = 124 L4 - BTrees CS 6213 - Advanced Data Structures - Arora 32 ANALYSIS OF B-TREES
  33. 33.  Since there is a lower bound on the number of child nodes of non-root nodes, a B-Tree is at least 50% “full”.  On average it is 75% full.  The advantage of not being 100% full is that there are empty spaces for insertions to happen without going all the way to the root. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 33 ANALYSIS OF B-TREES (CONT.)
  34. 34.  If m = 3, the specific case of B-Tree is called a 2-3 tree.  For in memory access, 2-3 Tree may be a good alternative to Red Black or AVL Tree. L4 - BTrees CS 6213 - Advanced Data Structures - Arora 34 2-3 TREES
  35. 35.  For small in-memory data structures, BSTs, Arrays, Hashmaps, etc. work well.  When we exceed the size of the RAM, or for persistence reasons, the problem becomes quite different.  The cost of each disc transfer is high but doesn't depend much on the amount of data transferred, especially if adjacent items are transferred  B-Trees are a great alternative (and very highly used) data structure for disk accesses  A B-Tree of order m allows each node to have from m/2 up to m children.  There is flexibility that allows for gaps. This flexibility allows: (i) some new elements to be stored in leaves with no other changes, and (ii) some elements to be deleted easily without changes propagating to root  If we use a B-tree of order 101, a B-tree of order 101 and height 3 can hold 1014 – 1 items (approximately 100 million) and any item can be accessed with 3 disc reads (assuming we hold the root in memory) L4 - BTrees CS 6213 - Advanced Data Structures - Arora 35 CONCLUSIONS & RECAP OF CENTRAL IDEA
  36. 36.  If we take m = 3, we get a 2-3 tree, in which non-leaf nodes have two or three children (i.e., one or two keys)  B-Trees are always balanced (since the leaves are all at the same level), so 2-3 trees make a good type of balanced tree L4 - BTrees CS 6213 - Advanced Data Structures - Arora 36 CONCLUSIONS AND RECAP (CONT.)

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