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B+-Trees Reading: C&B Ch 23 & 29. Dept. of Computing Science, University of Aberdeen2 Recap of Data Storage in Files Data is stored in files using primary.

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Presentation on theme: "B+-Trees Reading: C&B Ch 23 & 29. Dept. of Computing Science, University of Aberdeen2 Recap of Data Storage in Files Data is stored in files using primary."— Presentation transcript:

1 B+-Trees Reading: C&B Ch 23 & 29

2 Dept. of Computing Science, University of Aberdeen2 Recap of Data Storage in Files Data is stored in files using primary organization –Unordered (heap) –Ordered (sequential) –Hashed To speed up data retrieval, indexes are defined on the data files based on –Ordering Key field – unique key values for all the records - primary index OR –Ordering Non-key field – clustering index AND –Non-ordering non-key fields - Secondary indexes To search for a required record (whose key is given in the WHERE part of the query) in the data file, DBMS first searches the index –Once index is located the pointer field of the index leads the DBMS to the disk page where the required record is located –binary search can be performed on the ordered index

3 Dept. of Computing Science, University of Aberdeen3 Primary Indexes (Copied from lecture on file organization) The data file is sequentially ordered on the key field Index file stores all (dense) or some (sparse) values of the key field and the page number of the data file in which the corresponding record is stored B0021 B0031 B0042 B0052 B0073 Branch BranchNoStreetCityPostcode B00256 Clover DrLondonNW10 6EU B003163 Main StGlasgowG11 9QX B00432 Manse RdBristolBS99 1NZ B00522 Deer RdLondonSW1 4EH B00716 Argyll StAberdeenAB2 3SU Branch B002 record Branch B003 record Branch B004 record Branch B005 record Branch B007 record 1 2 3 4 Table Pages on Disk Index

4 Dept. of Computing Science, University of Aberdeen4 Multi-level Index If the index information is large it needs to be stored on the hard disk This means efficient techniques are required for searching indexes as well –Faster than a binary search on the ordered index The key idea used to improve search efficiency is to add another level of index to the initial level of index This idea can be repeated several times to define several levels of index –The top level index is made to fit into a single disk page –This top level search gives the pointer to the required lower level index page or the pointer to the required data page This is the central idea behind Multi-level indexes ISAM uses a Multi-level index

5 Dept. of Computing Science, University of Aberdeen5 Dynamic Multi-level Index Although multi-level indexes (as described earlier) can speed up search they perform poorly with insertions and deletions Dynamic multi-level index addresses this problem by leaving out some space in each of its pages for new entries Dynamic multi-level index is implemented using data structures called B-Trees and B+- Trees –B+-Trees are a variation on B-Trees –B+-Trees are more commonly used for indexing than B-Trees

6 Dept. of Computing Science, University of Aberdeen6 B-Tree B-Tree stands for a Balanced tree –All the paths through a B- Tree from root to different leaf nodes are of the same length (balanced path lengths) All leaf nodes are at the same depth (level) –This ensures that number of disk accesses required for all the searches are same The lesser the depth (level) of an index tree the faster the search 5 *8 *9 *12 *6 *7 *1 *3 * * Is the pointer to the data page B-Tree of order 3

7 Dept. of Computing Science, University of Aberdeen7 B+-Tree B-Tree stores data pointers in non-leaf nodes and also leaf nodes (refer to the figure on Slide 5) B+-Tree stores data pointers in leaf nodes only –This means leaf nodes and non-leaf nodes are structured differently in B+-Tree –The saved space in the non-leaf (internal) nodes is used to store more keys and more tree pointers Reduction in the depth of a B+-Tree Faster search

8 Dept. of Computing Science, University of Aberdeen8 B+-Tree (2) Is a Balanced Tree with the following properties The structure of a B+-Tree is defined based on a parameter called Order denoted by p –Order of a B+-Tree depends upon the page size and the sizes of different fields in the tree nodes The internal and leaf nodes in a B+-Tree are structured differently Therefore the order of leaf node is different from the order of the internal nodes and we use –p – order of internal node –p leaf – order of leaf node

9 Dept. of Computing Science, University of Aberdeen9 Internal Node For a B+-Tree of order p internal nodes are structured as follows –Each internal node is of the form where q<=p and each P i is a tree pointer and K i is an index –Within each internal node, K 1 { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/3/779017/slides/slide_9.jpg", "name": "Dept.", "description": "of Computing Science, University of Aberdeen9 Internal Node For a B+-Tree of order p internal nodes are structured as follows –Each internal node is of the form where q<=p and each P i is a tree pointer and K i is an index –Within each internal node, K 1

10 Dept. of Computing Science, University of Aberdeen10 Leaf Node Leaf nodes are structured as follows –Each leaf node is of the form,,…,,P next > where q<=p,each Pr i is a data pointer, and P next points to the next leaf node in the B+-tree –Within each leaf node, K 1 <=K 2 …,K q- 1,q<=p –each leaf node has at least ceiling(p/2) values –All leaf nodes are the same level - balanced In B+-tree all the leaf nodes are linked together –First level of index as linked list (could be doubly linked as well)

11 Dept. of Computing Science, University of Aberdeen11 Insertion We illustrate index insertion with an example We want to insert the following indexes into an empty B+-Tree of p=3 and p leaf =2 –8, 5, 1, 7, 3, 12 Initially you start with the root node which is of type leaf node (no children yet) 58* *

12 Dept. of Computing Science, University of Aberdeen12 58* * Insert 1: overflow (new level) 8* 15* * 5 Insert 7 Overflow in leaf node Split the leaf node the first j = ceiling((p leaf +1)/2) entries are kept in the original node and the remaining moved to the new leaf node create a new internal node and the j th index value is replicated in the parent internal node a pointer is added to the newly formed leaf node 8*

13 Dept. of Computing Science, University of Aberdeen13 15* * 78* * 5 15* * 5 Insert 7 8* Space available in nodes to store new entries without creating new nodes

14 Dept. of Computing Science, University of Aberdeen14 15* * 78* * 5 Insert 3: overflow (split) 13* * 78* * 35 5* Overflow in leaf node;Split the leaf node the first j = ceiling((p leaf +1)/2) entries are kept in the original node and the remaining moved to the new leaf node the j th index value is replicated in the parent internal node a pointer is added to the newly formed leaf node

15 Dept. of Computing Science, University of Aberdeen15 13* * 78* * 35 Insert 12: overflow (split, propagates, New level) 5* 13* * 78* * 3 5* 12* 5 8 Overflow in internal node;Split the internal node the entries upto P j where j = floor((p+1)/2) are kept in the original node and the remaining moved to the new internal node Create a new internal node and the j th index value is moved to the parent internal node (without replication) pointers are added to the newly formed nodes

16 Dept. of Computing Science, University of Aberdeen16 Insertion (2) You can see that not all insertions required creation of new nodes. B+-Trees ensure that some space is always left in nodes for new entries Also B-Trees also make sure all nodes are at least half full

17 Dept. of Computing Science, University of Aberdeen17 Search Given an index, K to be searched –start at the root node –Search for the pointer to follow to the lower level of the tree until a leaf node is found –Search for the key in the leaf node 13* * 78* * 3 5* 12* 5 8

18 Dept. of Computing Science, University of Aberdeen18 Deletion We illustrate index deletion with an example We want to delete the following indexes from a B+-Tree of p=3 and p leaf =2 –5, 12, 9

19 Dept. of Computing Science, University of Aberdeen19 7 1* 169 56* * 7*89* * 12* Delete 5 7 1* 169 6 * 7*89* * 12*

20 Dept. of Computing Science, University of Aberdeen20 7 1* 169 6 * 7*89* * 12* Delete 12: Underflow (redistribute) 7 1* 168 6 * 7*8*9* Underflow in leaf node if a sibling node (right or left) exists redistribute entries among the node and its siblings so that both are at least half full else merge the node with its siblings to reduce the number of leaf nodes modify the parent internal node to reflect the redistribution

21 Dept. of Computing Science, University of Aberdeen21 7 1* 168 6 * 7*8*9* 6 1* 17 6*7* 8* Delete 9: underflow (merge with left; Redistribute)

22 Dept. of Computing Science, University of Aberdeen22 Summary B+-Trees provide efficient operations of –Search, insert and delete Real databases have nodes of size equal to one disk page (say of 1KB size) –Thus each node stores lot more indexes than the examples shown here –Therefore achieve short search trees (small depth values) leading to faster search B+ trees offer dynamic multilevel index –Dynamic Allow simple insertion and deletion operations in majority of cases –Multilevel First level index in the form of the linked list of all its leaf nodes Each subsequent internal level in a B+-Tree offers another level of index


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