Indexing and Hashing
- 1. Indexing and Hashing By SATHISHKUMAR G (sathishsak111@gmail.com)
- 2. Indexing and Hashing • Cost estimation • Basic Concepts • Ordered Indices • B+ - Tree Index Files • B - Tree Index Files • Static Hashing • Dynamic Hashing • Comparison of Ordered Indexing and Hashing • Index Definition in SQL • Multiple-Key Access
- 3. Estimating Costs • For simplicity we estimate the cost of an operation by counting the number of blocks that are read or written to disk. • We ignore the possibility of blocked access which could significantly lower the cost of I/O. • We assume that each relation is stored in a separate file with B blocks and R records per block.
- 4. Basic Concepts • Indexing is used to speed up access to desired data. » E.g. author catalog in library • A search key is an attribute or set of attributes used to look up records in a file. Unrelated to keys in the db schema. • An index file consists of records called index entries. • An index entry for key k may consist of » An actual data record (with search key value k) » A pair (k, rid) where rid is a pointer to the actual data record » A pair (k, bid) where bid is a pointer to a bucket of record pointers • Index files are typically much smaller than the original file if the actual data records are in a separate file. • If the index contains the data records, there is a single file with a special organization.
- 5. Index Evaluation Metrics • Access time for: » Equality searches – records with a specified value in an attribute » Range searches – records with an attribute value falling within a specified range. • Insertion time • Deletion time • Space overhead
- 6. Types of Indices • The records in a file may be unordered or ordered sequentially by some search key. • A file whose records are unordered is called a heap file. • If an index contains the actual data records or the records are sorted by search key in a separate file, the index is called clustering (otherwise non-clustering). • In an ordered index, index entries are sorted on the search key value. Other index structures include trees and hash tables. • A primary index is an index on a set of fields that includes the primary key. Any other index is a secondary index.
- 7. Dense Index Files • Dense index – index record appears for every search-key value in the file. Brighton Downtown Miami Perryridge Redwood Round Hill Brighton A-217 750 Downtown A-101 500 Downtown A-110 600 Miami A-215 700 Perryridge A-102 400 Perryridge A-201 900 Perryridge A-218 700 Redwood A-222 700 Round Hill A-305 350
- 8. Sparse Index Files • A clustering index may be sparse. • Index records for only some search-key values. • To locate a record with search-key value k we: » Find index record with largest search-key value < k » Search file sequentially starting at the record to which the index record points • Less space and less maintenance overhead for insertions and deletions. • Generally slower than dense index for locating records. • Good tradeoff: sparse index with an index entry for every block in file, corresponding to least search-key value in the block.
- 9. Example of Sparse Index Files Brighton Miami Redwood Brighton A-217 750 Downtown A-101 500 Downtown A-110 600 Miami A-215 700 Perryridge A-102 400 Perryridge A-201 900 Perryridge A-218 700 Redwood A-222 700 Round Hill A-305 350
- 10. Multilevel Index • If an index does not fit in memory, access becomes expensive. • To reduce number of disk accesses to index records, treat the index kept on disk as a sequential file and construct a sparse index on it. » outer index – a sparse index on main index » inner index – the main index file • If even outer index is too large to fit in main memory, yet another level of index can be created, and so on. • Indices at all levels must be updated on insertion or deletion from the file.
- 11. M M Multilevel Index (Cont.) M M M M M Index Block 0 Data Block 0 Index Block 1 Data Block 1 outer index inner index M M M M
- 12. Index Update: Deletion • If deleted record was the only record in the file with its particular search-key value, the search-key is deleted from the index also. • Single-level index deletion: » Dense indices – deletion of search-key is similar to file record deletion. » Sparse indices – if an entry for the search key exists in the index, it is deleted by replacing the entry in the index with the next search- key value in the file (in search-key order). If the next search-key value already has an index entry, the entry is deleted instead of being replaced.
- 13. Index Update: Insertion • Single-level index insertion: » Perform a lookup using the search-key value appearing in the record to be inserted. » Dense indices – if the search-key value does not appear in the index, insert it. » Sparse indices – if index stores an entry for each block of the file, no change needs to be made to the index unless a new block is created. In this case, the first search-key value appearing in the new block is inserted into the index. • Multilevel insertion (as well as deletion) algorithms are simple extensions of the single-level algorithms
- 14. Non-clustering Indices • Frequently, one wants to find all the records whose values in a certain field satisfy some condition, and the file is not ordered on the field. » Example 1: In the account database stored sequentially by account number, we may want to find all accounts in a particular branch. » Example 2: As above, but where we want to find all accounts with a specified balance or range of balances. • We can have a non-clustering index with an index record for each search-key value. The index record points to a bucket that contains pointers to all the actual records with that particular search-key value.
- 15. Secondary Index on balance field of account Brighton A-217 750 Downtown A-101 500 Downtown A-110 600 Miami A-215 700 Perryridge A-102 400 Perryridge A-201 900 Perryridge A-218 700 Redwood A-222 700 Round Hill A-305 350 350 400 500 600 700 750 900
- 16. Clustering and Non-clustering • Non-clustering indices have to be dense. • Indices offer substantial benefits when searching for records. • When a file is modified, every index on the file must be updated. Updating indices imposes overhead on database modification. • Sequential scan using clustering index is efficient, but a sequential scan using a non-clustering index is expensive – each record access may fetch a new block from disk.
- 17. B+ - Tree Index Files B+ - Tree indices are an alternative to indexed-sequential files. • Disadvantage of indexed-sequential files: performance degrades as file grows, since many overflow blocks get created. Periodic reorganization of entire file is required. • Advantage of B+ - tree index files: automatically reorganizes itself with small, local, change, in the face of insertions and deletions. Reorganization of entire file is not required to maintain performance. • Disadvantage of B+ -tree: extra insertion and deletion overhead, space overhead. • Advantages of B+-trees outweigh disadvantages, and they are used extensively.
- 18. B+ -Tree Index Files (Cont.) A B+ -tree is a rooted tree satisfying the following properties: • All paths from root to leaf are of the same length • Each node that is not a root or a leaf has between n/2 and n children where n is the maximum number of pointers per node. • A leaf node has between (n – 1)/2 and n – 1 values • Special cases: if the root is not a leaf, it has at least 2 children. If the root is a leaf (that is, there are no other nodes in the tree), it can have between 0 and n values.
- 19. • Typical node » Ki are the search-key values » Piare pointers to children (for non-leaf nodes) or pointers to records or buckets of records (for leaf nodes). • The search-keys in a node are ordered K1 < K2 < K3 < … < Kn-1 B+ -Tree Node Structure P1 K1 P2 … Pn-1 Kn-1 Pn
- 20. Leaf Nodes in B+ -Trees Properties of a leaf node: • For i = 1,2,…, n-1, pointer Pi either points to a file record with search- key value Ki, or to a bucket of pointers to file records, each record having search-key value Ki. Only need bucket structure if search-key does not form a superkey and the index is non-clustering. • If Li, Lj are leaf nodes and i < j, Li’s search-key values are less than Lj’s search-key values • Pn points to next leaf node in search-key order Brighton Downtown Brighton A-212 750 Downtown A-101 500 Downtown A-110 600 leaf node account file
- 21. Non-Leaf Nodes in B+ -Trees • Non leaf nodes form a multi-level sparse index on the leaf nodes. For a non-leaf node with m pointers: » All the search-keys in the subtree to which Pi points are less than Ki » All the search-keys in the subtree to which Pi points are greater than or equal to Ki−1 P1 K1 P2 … Pn-1 Kn-1 Pn
- 22. Examples of a B+ -tree Perryridge RedwoodMiami Brighton Downtown Redwood Round HillMiami Perryridge B+ -tree for account file (n=3)
- 23. Example of a B+ -tree • Leaf nodes must have between 2 and 4 values ( (n−1)/2 and n − 1, with n=5). • Non-leaf nodes other than root must have between 3 and 5 children ( n/2 and n with n = 5). • Root must have at least 2 children Perryridge Perryridge Redwood Round HillBrighton Downtown Miami B+ -tree for account file (n=5)
- 24. Observations about B+ -trees • Since the inter-node connections are done by pointers, there is no assumption that in the B+-tree, the “logically” close blocks are “physically” close. • The non-leaf levels of the B+-tree form a hierarchy of sparse indices. • The B+-tree contains a relatively small number of levels (logarithmic in the size of the main file), thus searches can be conducted efficiently. • Insertions and deletions to the main file can be handled efficiently, as the index can be restructured in logarithmic time (as we shall see).
- 25. Queries on B+ -Trees • Find all records with a search-key value of k. » Start with the root node » Examine the node for the smallest search-key value > k. » If such a value exists, assume it is Ki. Then follow Pi to the child node » Otherwise k ≥ Km-1, where there are m pointers in the node, Then follow Pm to the child node. » If the node reached by following the pointer above is not a leaf node, repeat the above procedure on the node, and follow the corresponding pointer. » Eventually reach a leaf node. If key Ki = k, follow pointer Pi to the desired record or bucket. Else no record with search-key value k exists.
- 26. Queries on B+ -Trees (Cont.) • In processing a query, a path is traversed in the tree from the root to some leaf node. • If there are K search-key values in the file, the path is no longer than logn/2(K). • A node is generally the same size as a disk block, typically 4 kilobytes, and n is typically around 100 (40 bytes per index entry). • With 1 million search key values and n = 100, at most log50(1,000,000) = 4 nodes are accessed in a lookup. • Contrast this with a balanced binary tree with 1 million search key values — around 20 nodes are accessed in a lookup » above difference is significant since every node access may need a disk I/O, costing around 20 millisecond!
- 27. Updates on B+-Trees: Insertion • Find the leaf node in which the search-key value would appear • If the search-key value is already there in the leaf node, record is added to file and if necessary pointer is inserted into bucket. • If the search-key value is not there, then add the record to the main file and create bucket if necessary. Then: » if there is room in the leaf node, insert (search-key value, record/bucket pointer) pair into leaf node at appropriate position. » if there is no room in the leaf node, split it and insert (search-key value, record/bucket pointer) pair as discussed in the next slide.
- 28. Updates on B+ -Trees: Insertion (Cont.) • Splitting a node: » take the n(search-key value, pointer) pairs (including the one being inserted) in sorted order. Place the first n/2 in the original node, and the rest in a new node. » Let the new node be p, and let k be the least key value in p. Insert (k, p) in the parent of the node being split. If the parent is full, split it and propagate the split further up. • The splitting of nodes proceeds upwards till a node that is not full is found. In the worst case the root node may be split increasing the height of the tree by 1.
- 29. Updates on B+-Trees: Insertion(Cont.) Perryridge RedwoodMiami Brighton Downtown Redwood Round HillMiami Perryridge Perryridge RedwoodDowntown Miami Brighton Clearview Downtown Miami Perryridge Redwood Round Hill
- 30. Updates on B+-Trees:Deletion • Find the record to be deleted, and remove it from the main file and from the bucket (if present) • Remove (search-key value, pointer) from the leaf node if there is no bucket or if the bucket has become empty • If the node has too few entries due to the removal, and the entries in the node and a sibling fit into a single node, then » Insert all the search-key values in the two nodes into a single node (the one on the left), and delete the other node. » Delete the pair (Ki–1, Pi), where Pi is the pointer to the deleted node, from its parent, recursively using the above procedure.
- 31. Updates on B+-Trees: Deletion • Otherwise, if the node has too few entries due to the removal, and the entries in the node and a sibling don’t fit into a single node, then » Redistribute the pointers between the node and a sibling such that both have at least the minimum number of entries » Update the corresponding search-key value in the parent of the node. • The node deletions may cascade upwards till a node which has n/2 or more pointers is found. If the root node has only one pointer after deletion, it is deleted and the sole child becomes the root.
- 32. Examples of B+ -Tree Deletion • The removal of the leaf node containing “Downtown” did not result in its parent having too little pointers. So the cascaded deletions stopped with the deleted leaf node’s parent. Perryridge RedwoodMiami Brighton Clearview Miami Perryridge Redwood Round Hill Result after deleting “Downtown” from account
- 33. Examples of B+-Tree Deletion (Cont.) • The deleted “Perryridge” node’s parent became too small, but its sibling did not have space to accept one more pointer, so redistribution is performed. Observe that the root node’s search-key value changes as a result. Redwood Miami Brighton Clearview Miami Redwood Round HillDowntown Downtown Deletion of “Perryridge” instead of “Downtown”
- 34. B+ -Tree File Organization • Index file degradation problem is solved by using B+ -Tree indices. Data file degradation problem is solved by using B+ -Tree File Organization. • The leaf nodes in a B+-tree file organization store records, instead of pointers. • Since records are larger than pointers, the maximum number of records that can be stored in a leaf node is less than the number of pointers in a nonleaf node. • Leaf nodes are still required to be half full. • Insertion and deletion are handled in the same way as insertion and deletion of entries in a B+ -tree index. • Good space utilization is important since records use more space than pointers. To improve space utilization, involve more sibling nodes in redistribution during splits and merges.
- 35. B-Tree Index Files • Similar to B+-tree, but B-tree allows search-key values to appear only once; eliminates redundant storage of search keys. • Search keys in nonleaf nodes appear nowhere else in the B-tree; an additional pointer field for each search key in a nonleaf node must be included. • Generalized B-tree leaf node • Nonleaf node — pointers Bi are the bucket or file record pointers. P1 K1 P2 … Pn-1 Kn-1 Pn P1 B1 K1 P2 B2 K2 … Pm-1 Bm-1 Km-1 Pm
- 36. B-Tree Index Files (Cont.) • Advantages of B-Tree indices: » May use less tree nodes than a corresponding B+ -Tree. » Sometimes possible to find search-key value before reaching leaf node. • Disadvantages of B-Tree indices: » Only small fraction of all search-key values are found early » Non-leaf nodes are larger, so fan-out is reduced. Thus B-Trees typically have greater depth than corresponding B+ -Tree. » Insertion and deletion more complicated than in B+ -Trees » Implementation is harder than B+ -Trees. • Typically, advantages of B-Trees do not outweigh disadvantages.
- 37. Static Hashing • A bucket is a unit of storage containing one or more records (a bucket is typically a disk block). In a hash file organization we obtain the bucket of a record directly from its search-key value using a hash function. • Hash function h is a function from the set of all search-key values K to the set of all bucket addresses B. • Hash function is used to locate records for access, insertion, and deletion. • Records with different search-key values may be mapped to the same bucket; thus entire bucket has to be searched sequentially to locate a record.
- 38. Hash Functions • Worst hash function maps all search-key values to the same bucket; this makes access time proportional to the number of search-key values in the file. • An ideal hash function is uniform, i.e. each bucket is assigned the same number of search-key values from the set of all possible values. • Ideal hash function is random, so each bucket will have the same number of records assigned to it irrespective of the actual distribution of search-key values in the file. • Typical hash functions perform computation on the internal binary representation of the search-key. For example, for a string search-key, the binary representations of all the characters in the string could be added and the sum modulo number of buckets could be returned.
- 39. Example of Hash File Organization Brighton A-217 750 Round Hill A-305 350 Redwood A-222 700 Perryridge A-102 400 Perryridge A-201 900 Perryridge A-218 700 Miami A-215 700 Downtown A-101 500 Downtown A-110 600 bucket 0 bucket 1 bucket 2 bucket 8 bucket 7 bucket 6 bucket 5 bucket 4 bucket 3 bucket 9
- 40. Example of Hash File Organization (Cont.) • Hash file organization of account file, using branch-name as key. • (See figure in previous slide.) • There are 10 buckets. • The binary representation of the ith character is assumed to be the integer i. • The hash function returns the sum of the binary representations of the characters modulo 10.
- 41. Handling of Bucket Overflows • Bucket overflow can occur because of » Insufficient buckets » Skew in distribution of records. This can occur due to two reasons: * multiple records have same search-key value * chosen hash function produces non-uniform distribution of key values • Although the probability of bucket overflow can be reduced, it can not be eliminated; it is handled by using overflow buckets. • Overflow chaining — the overflow buckets of a given bucket are chained together in a linked list • Above scheme is called closed hashing. An alternative, called open hashing, is not suitable for database applications.
- 42. Hash Indices • Hashing can be used not only for file organization, but also for index-structure creation. A hash index organizes the search keys, with their associated record pointers, into a hash file structure. • Hash indices are always secondary indices — if the file itself is organized using hashing, a separate primary hash index on it using the same search-key is unnecessary. However, the term hash index is used to refer to both secondary index structures and hash organized files.
- 43. Example of Hash Index A-215 A-305 A-101 A-110 A-217 A-102 A-218 A-222 A-201 Brighton A-217 750 Downtown A-101 500 Downtown A-110 600 Miaimi A-215 700 Perryridge A-102 400 Perryridge A-201 900 Perryridge A-218 700 Redwood A-222 700 Round Hill A-305 350 bucket 0 bucket 1 bucket 2 bucket 5 bucket 4 bucket 6 bucket 3
- 44. Deficiencies of Static Hashing • In static hashing, function h maps search-key values to a fixed set B of bucket addresses. » Databases grow with time. If initial number of buckets is too small, performance will degrade due to too much overflows. » If file size at some point in the future is anticipated and number of buckets allocated accordingly, significant amount of space will be wasted initially. » If database shrinks, again space will be wasted. » One option is periodic, re-organization of the file with a new hash function, but it is very expensive. • These problems can be avoided by using techniques that allow the number of buckets to be modified dynamically.
- 45. Dynamic Hashing • Good for database that grows and shrinks in size • Allows the hash function to be modified dynamically • Extendable hashing – one form of dynamic hashing » Hash function generates values over a large range — typically b-bit integers, with b = 32. » At any time use only the last i bits of the hash function to index into a table of bucket addresses, where: 0 ≤ i ≤ 32 » Initially i = 0 » Value of i grows and shrinks as the size of the database grows and shrinks. » Actual number of buckets is < 2i , and this also changes dynamically due to merging and splitting of buckets.
- 46. General Extendable Hash Structure ..00 ..01 ..10 ..11 i i1 i3 i2 hash suffix bucket 1 bucket 2 bucket 3 bucket address table In this structure, i2 = i3 = i, whereas i1 = i – 1
- 47. Use of Extendable Hash Structure • Multiple entries in the bucket address table may point to a single bucket. Each bucket j stores a value ij; all the entries that point to the same bucket have the same values on the last ij bits of the hash suffix. • To locate the bucket containing search-key Kj: 1. Compute h(Kj) = X 2. Use the last i bits of X as a displacement into bucket address table, and follow the pointer to appropriate bucket. • To insert a record with search-key value Ki, follow same procedure as look-up and locate the bucket, say j. If there is room in the bucket j insert record in the bucket. Else the bucket must be split and insertion re-attempted. (See next slide.)
- 48. Updates in Extendable Hash Structure To split a bucket j when inserting record with search-key value Ki: » If i > ij(more than one pointer to bucket j) » allocate a new bucket z, and set ij and jz to the old ij+1. » Make the second half of the bucket address table entries pointing to j to point to z » remove and reinsert each record in bucket j. » recompute new bucket for Ki and insert record in the bucket (further splitting is required if the bucket is still full) • If i = ij (only one pointer to bucket j) » increment i and double the size of the bucket address table. » replace each entry in the table by two entries that point to the same bucket. » recompute new bucket address table entry for Ki. Now i > ij, so use the first case above.
- 49. Update in Extendable Hash Structure (Cont.) • When inserting a value, if the bucket is full after several splits (that is, i reaches some limit b) create an overflow bucket instead of splitting bucket entry value further. • To delete a key value, locate it in its bucket and remove it. The bucket itself can be removed if it becomes empty (with appropriate updates to the bucket address table). • Merging of buckets and decreasing bucket address table size is also possible.
- 50. Use of Extendable Hash Structure: Example branch-name Brighton Downtown Miami Perryridge Redwood Round Hill h(branch-name) 0110 1101 1111 1011 0010 1100 0011 0000 0010 0011 1010 0000 1100 0110 1001 0001 1110 0111 1110 1101 1011 1111 0011 0011 1011 0001 0010 0100 1001 0011 0110 1111 0011 0101 1010 0110 1100 1001 1110 1110 1111 1000 0011 1111 1001 1100 0000 1101 0 0 bucket 1 Initial Hash Structure, Bucket size = 2 bucket address table
- 51. Example (2) • Insert account records from branches: 1) Brighton 2) Downtown 3) Downtown 4) Miami 5) Perryridge 6) Redwood 7) Roundhill 8) Redwood 9) Downtown
- 52. Example (3) Brighton A-217 750 Downtown A-101 500 Downtown A-110 600 Miami A-215 700 2 2 2 1 hash suffix bucket address table Hash Structure after four insertions ..00 ..01 ..10 ..11
- 53. Example (4) Brighton A-217 750 Downtown A-101 500 Downtown A-110 600 Miami A-102 400 Perryridge A-201 900 3 3 3 2 Hash Structure after all insertions bucket address table ..000 ..001 ..010 ..011 ..100 ..101 ..110 ..111 Round Hill A-432 500 3 Redwood A-222 700 Redwood A-722 750 2 Downtown A-611 820 3
- 54. Comparison of Ordered Indexing and Hashing Issues to consider: • Cost of periodic re-organization • Relative frequency of insertions and deletions • Is it desirable to optimize average access time at the expense of worst-case access time? • Expected type of queries: » Hashing is generally better at retrieving records having a specified value of the key. » If range queries are common, ordered indices are to be preferred
- 55. Index Definition in SQL • Create an index create index <index-name> on <relation-name> (<attribute-list>) E.g.: create index b-index on branch(branch-name) • Use create unique index to indirectly specify and enforce the condition that the search key is a candidate key. • To drop an index drop index <index-name>
- 56. Multiple-Key Access • Use multiple indices for certain types of queries. • Example: select account-number from account where branch-name = “Perryridge” and Balance = 1000 • Possible strategies for processing query using indices on single attributes: 1. Use index on branch-name to find Perryridge records; test balance = 1000. 2. Use index on balance to find accounts with balances of $1000; test branch-name = “Perryridge.” 3. Use branch-name index to find pointers to all records pertaining to the Perryridge branch. Similarly use index on balance. Take intersection of both sets of pointers obtained.
- 57. Indices on Multiple Attributes Suppose we have an ordered index on combined search-key (branch-name, balance). • With the where clause where branch-name = “Perryridge” and balance = 1000 the index on the combined search-key will fetch only records that satisfy both conditions. Using separate indices is less efficient we may fetch many records (or pointers) that satisfy only one of the conditions. • Can also efficiently handle where branch-name = “Perryridge” and balance < 1000 • But cannot efficiently handle where branch-name < “Perryridge” and balance = 1000 May fetch many records that satisfy the first but not the second condition.
- 58. Thank you