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Presentation on theme: "INDEXING AND HASHING."— Presentation transcript:


2 Basic Concepts Index: an ordered list of words or phrases used for easy access to desired information or data. Indexing mechanisms are used for quick access to desired information or data. Example: author/title catalog in a Library. Search key: an attribute or set of attributes used to look up records in a file. An attribute or set of attributes used to look up records in a file is called a search key. Note that this definition of key differs from that used in primary key, candidate key, and super key. This duplicate meaning for key is (unfortunately) well established in practice. Using our notion of a search key, we see that if there are several indices on a file, there are several search keys.

3 Basic Concepts (continued)
An index file consists of records (entries), which are of the form below: Index files are usually and typically much smaller than the original file. There are two basic kinds of indices: Ordered indices and Hash indices Search Key Pointer

4 Basic Concepts (continued)
Ordered indices: Based on a sorted ordering of values-search keys are stored in a sorted order. Hash indices: Based on a uniform distribution of values across a range of buckets-search keys are distributed uniformly across buckets using a hash function. The bucket to which a value is assigned is determined by a hash function.

5 Techniques for Ordered indexing and Hashing (INDEX EVALUATION METRICS)
Each of the techniques to be discussed are best suited to a particular database application, so no one technique can be said to be the best. Each technique must be evaluated on the basis of the following factors: Access Types: Access types supported efficiently. Access types can include finding records with a specified attribute value and records whose attribute value fall within a specified range. We often want to have more than one index for a file. For example, we may wish to search for a book by author, by subject, or by title.

Access Time: Time it takes to find a particular data item/set of items using a particular technique. Insertion Time: Time it takes to insert a new data item. Deletion Time: Time it takes to delete a data item. Space Overhead: The additional space occupied by an index structure. Insertion time- This value includes the time it takes to find the correct place to insert the new data item, as well as the time it takes to update the index structure. Deletion time- This value includes the time it takes to find the item to be deleted, as well as the time it takes to update the index structure. Space overhead- Provided that the amount of additional space is moderate, it is usually worthwhile to sacrifice the space to achieve improved performance.

7 Ordered Indices Ordered Index: Index entries are stored on the search key value. Example: an author catalog in a Library. Primary Index: In a sequentially ordered file, the index whose search key specifies the sequential order of the file is primary index also known as clustering index. The search key of a primary index is usually but not necessarily the primary key. Ordered indices: To gain fast random access to records in a file, we can use an index structure. Each index structure is associated with a particular search key. Just like the index of a book or a library catalog, an ordered index stores the values of the search keys in sorted order, and associates with each search key the records that contain it. The records in the indexed filemay themselves be stored in some sorted order, just as books in a library are stored according to some attribute such as the Dewey decimal number. A file may have several indices, on different search keys. If the file containing the records is sequentially ordered, a clustering index is an index whose search key also defines the sequential order of the file. Clustering indices are also called primary indices; the term primary index may appear to denote an index on a primary key, but such indices can in fact be built on any search key. The search key of a clustering index is often the primary key, although that is not necessarily so.

8 Ordered Indices Secondary index: an index whose search key specifies an order different from the sequential order of the file. Also called non-clustering index. Index-sequential file: ordered sequential file with a primary index. The terms “clustered” and “nonclustered” are often used in place of “clustering” and “nonclustering.”

9 Dense and Sparse Indices
Dense Index Files: In a dense index, an index entry or record appears for every search-key value in the file. In a dense clustering index, the index record contains the search-key value and a pointer to the first data record with that search-key value. The rest of the records with the same search-key value would be stored sequentially after the first record, since, because the index is a clustering one, records are sorted on the same search key. An index entry/index record, consists of a search-key value and pointers to one or more records with that value as their search-key value. The pointer to a record consists of the identifier of a disk block and an offset within the disk block to identify the record within the block.

10 Dense Index Files (continued)
In a dense nonclustering index, the index must store a list of pointers to all records with the same search-key value.

11 Dense Index File

12 Sparse Index Files Sparse Index: In a sparse index, an index entry appears for only some of the search-key values. Sparse indices can be used only if the relation is stored in sorted order of the search key, that is, if the index is a clustering index. As is true in dense indices, each index entry contains a search-key value and a pointer to the first data record with that search-key value. To locate a record, we find the index entry with the largest search-key value that is less than or equal to the search-key value for which we are looking/searching. We start at the record pointed to by that index entry, and follow the pointers in the file until we find the desired record. Consider a (printed) dictionary. The header of each page lists the first word alphabetically on that page. The words at the top of each page of the book index together form a sparse index on the contents of the dictionary pages.

13 Sparse Index: contains index records for only some searchkey values.
Applicable when records are sequentially ordered on searchkey. To locate a record with searchkey value K we: Find index record with largest searchkey value < K. Search file sequentially starting at the record to which the index record points.

14 Sparse Index File

15 Comparing Sparse index to Dense index files:
Sparse index files have less space and less maintenance overhead for insertions and deletions than Dense indices. Sparse indices are generally slower than dense indices for locating records.

16 Good tradeoff: sparse index with an index entry for every block in file, corresponding to least searchkey value in the block.

17 Multilevel Index If primary index does not fit in memory, access becomes expensive. Solution: treat primary index kept on disk as a sequential file and construct a sparse index on it. outer index – a sparse index of primary index inner index – the primary 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.

18 Multilevel Index File

19 Index Update: Record Deletion
If deleted record was the only record in the file with its particular searchkey value, the searchkey is also deleted from the index. Single level index deletion: Dense indices: Deletion of searchkey: similar to file record deletion. Sparse indices: If deleted key value exists in the index, the value is replaced by the next searchkey value in the file (in searchkey order). If the next searchkey value already has an index entry, the entry is deleted instead of being replaced.

20 Index Update: Record Insertion
Single level index insertion: Perform a lookup using the key value from inserted record Dense indices – if the searchkey 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. If a new block is created, the first searchkey value appearing in the new block is inserted into the index. Multilevel insertion/deletion algorithms are simple extensions of the single level algorithms.

21 Primary and Secondary Indices
Indices offer substantial benefits when searching for records BUT Updating indices imposes overhead on database modification because when a file is modified, every index on the file must be updated. Sequential scan using primary index is efficient, but a sequential scan using a secondary index is expensive. Each record access may fetch a new block from disk. Block fetch requires about 5 to 10 micro seconds, versus about 100 nanoseconds for memory access. Frequently, one wants to find all the records whose values in a certain field, which is not the search-key of the primary index satisfy some condition. For example: In the account database stored sequentially by account number, we may want to find all accounts in a particular branch. But what if we would also want to find all accounts with a specified balance or range of balances, then we need secondary indices.

22 Secondary indices have to be dense.
Index record points to a bucket that contains pointers to all the actual records with that particular searchkey value. Secondary indices have to be dense. Secondary index on balance field of account We can have a secondary index with an index record for each search-key value; index record points to a bucket that contains pointers to all the actual records with that particular search-key value.

23 Indices on Multiple Keys
In general a search key can have more than one attribute. A search key containing more than one attribute is referred to as a composite search key. The structure of the index is the same as that of any other index, the only difference being that the search key is not a single attribute, but rather is a list of attributes. The search key can be represented as a tuple of values, of the form (a1, , an), where the indexed attributes are A1, , An.

24 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, changes, in the face of insertions and deletions. Reorganization of entire file is not required to maintain performance. Disadvantage of B+trees: There is extra insertion and deletion overhead, space overhead. Advantages of B+trees outweigh disadvantages B+trees are used extensively B+-tree index is an index structure that takes the form of a balanced tree in which every path from the root of the tree to a leaf of the tree is of the same length. Each non-leaf node in the tree has between [n/2] and n children, where n is fixed for a particular tree. The B+-tree index structure is the most widely used of several index structures that maintain their efficiency despite insertion and deletion of data. The main disadvantage of the index-sequential file organization is that performance degrades as the file grows, both for index lookups and for sequential scans

25 B+ Tree node Structure A B+tree index is a multilevel index, but it has a structure that differs from that of the multilevel index-sequential file. A typical node of a B+ Tree Ki are the searchkey values Pi are pointers to children (for non-leaf nodes) or pointers to records or buckets of records (for leaf nodes). The searchkeys in a node are ordered: K1 < K2 < K3 < < Kn–1 P1 K1 P2 . . . Pn-1 Kn-1 Pn Details on B+ Tree File Organization, Queries and Updates (Insertion/Deletion) will be discussed in our next class, by our lecturer Dr. Nazife Dimililer.

26 B-Tree Index Files B-Tree Index Files are similar to B+-Tree index files but the primary difference or distinction is that: B-tree indices eliminates redundant storage of search-key values. B-tree allows search-key values to appear only once (if they are unique), unlike a B+-tree, where a search key value may appear in a non-leaf node, in addition to appearing in a leaf node.

27 Multiple-Key Access For certain types of queries it is advantageous to use multiple indices if they are available. Example: select account_number from account where branch_name = “Perryridge” and balance = 1000 There are three possible strategies for processing query using indices on single attributes: Use index on branch_name to find accounts with branch name Perryridge; 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. The third strategy is the only one of the three that takes advantage of the existence of multiple indices. However, even this strategy may be a poor choice if all of the following hold: • There are many records pertaining to Perryridge Branch. • There are many records pertaining to accounts with a balance of $1,000. • There are only a few records pertaining to both the Perryridge Branch and accounts with a salary of $1,000. If these conditions hold, we must scan a large number of pointers to produce a small result. An index structure called a “bitmap index” can in some cases greatly speed up the intersection operation used in the third strategy.

28 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 searchkey value using a hash function. Hash function h is a function from the set of all searchkey values K to the set of all bucket addresses B. Hash function is used to locate records for access, insertion as well as deletion. Records with different searchkey values may be mapped to the same bucket; thus entire bucket has to be searched sequentially to locate a record. One disadvantage of sequential file organization is that we must access an index structure to locate data, or must use binary search, and that results in more I/O operations. File organizations based on the technique of hashing allow us to avoid accessing an index structure. Hashing also provides a way of constructing indices.

29 Example of Hash File Organization
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 E.g. h(Perryridge) = 5 h(Round Hill) = 3 h(Brighton) = 3 In our description of hashing, we shall use the term bucket to denote a unit of storage that can store one or more records. A bucket is typically a disk block, but could be chosen to be smaller or larger than a disk block. Formally, let K denote the set of all search-key values, and let B denote the set of all bucket addresses. A hash function h is a function from K to B. Let h denote a hash function.

30 Hash file organization of account file, using branch_name as key.
The hash function returns the sum of the binary representations of the characters modulo 10. E.g. h(Perryridge) = 5 h(Round Hill) = 3 h(Brighton) = 3

31 Hash Functions Worst hash function maps all searchkey values to the same bucket; this makes access time proportional to the number of searchkey values in the file. An ideal hash function is uniform, i.e., each bucket is assigned the same number of searchkey 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 searchkey values in the file. Typical hash functions perform computation on the internal binary representation of the searchkey. For example, for a string searchkey, the binary representations of all the characters in the string could be added and the sum modulo the number of buckets could be returned. Hash functions

32 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 searchkey value chosen hash function produces non-uniform distribution of key values Although the probability of bucket overflow can be reduced, it cannot be eliminated; it is handled by using overflow buckets.

33 Handling of Bucket Overflows (Continued)
Overflow chaining: this occurs when 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, which does not use overflow buckets, is not suitable for database applications. Despite allocation of a few more buckets than required, bucket overflow can still occur. We handle bucket overflow by using overflow buckets. If a record must be inserted into a bucket b, and b is already full, the system provides an overflow bucket for b, and inserts the record into the overflow bucket. If the overflow bucket is also full, the system provides another overflow bucket, and so on. All the overflow buckets of a given bucket are chained together in a linked list. Overflow handling using such a linked list is called overflow chaining.

34 Example of Overflow Chaining
Overflow Chaining in a hash structure. The form of hash structure above is sometimes referred to as closed hashing. Under an alternative approach, called open hashing, the set of buckets is fixed, and there are no overflow chains. Instead, if a bucket is full, the system inserts records in some other bucket in the initial set of buckets B. One policy is to use the next bucket (in cyclic order) that has space; this policy is called linear probing. Other policies, such as computing further hash functions, are also used. Open hashing has been used to construct symbol tables for compilers and assemblers, but closed hashing is preferable for database systems. The reason is that deletion under open hashing is troublesome and in a database system, it is important to be able to handle deletion as well as insertion.

35 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. Actually, hash indices are always secondary indices; If the file itself is organized using hashing, a separate primary hash index on it using the same searchkey is unnecessary. However, we use the term hash index to refer to both secondary index structures and hash organized files. 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 pointers, into a hash file structure. We construct a hash index as follows. We apply a hash function on a search key to identify a bucket, and store the key and its associated pointers in the bucket (or in overflow buckets).

36 Example of Hash Index

37 Deficiencies of Static Hashing
In static hashing, function h maps searchkey values to a fixed set of B of bucket addresses. Databases grow or shrink with time. If initial number of buckets is too small, and file grows, performance will degrade due to too much overflows. If space is allocated for anticipated growth, a significant amount of space will be wasted initially (and buckets will be underfull). If database shrinks, again space will be wasted. One solution: periodic reorganization of the file with a new hash function. Expensive, disrupts normal operations Better solution: allow the number of buckets to be modified dynamically

38 Dynamic Hashing Most databases grow larger over time. If we are to use static hashing for such a database, we have three classes of options: Choose a hash function based on the current file size. This option will result in performance degradation as the database grows. Choose a hash function based on the anticipated size of the file at some point in the future. Although performance degradation is avoided, a significant amount of space may be wasted initially. Periodically reorganize the hash structure in response to file growth. Such a reorganization involves choosing a new hash function, recomputing thehash function on every record in the file, and generating new bucket assignments. This reorganization is a massive, time-consuming operation, it is necessary to forbid access to the file during reorganization. Several dynamic hashing techniques allow the hash function to be modified dynamically to accommodate the growth or shrinkage of the database. In this section we describe one form of dynamic hashing, called extendable hashing.

39 Dynamic Hashing Dynamic hashing is good for database that grows and shrinks in size and it allows the hash function to be modified dynamically. Extendable hashing is 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 a prefix of the hash function to index into a table of bucket addresses. Let the length of the prefix be i bits, 0 ≤ i ≤ 32. Bucket address table size = 2i. Initially i = 0 Value of i grows and shrinks as the size of the database grows and shrinks. Multiple entries in the bucket address table may point to a bucket Thus, actual number of buckets is < 2i The number of buckets also changes dynamically due to coalescing and splitting of buckets.

40 General Extendable hash Structure
In this structure, i2 = i3 = i, whereas i1 = i – 1

41 Comparison of Ordered Indexing and Hashing
Cost of periodic reorganization Relative frequency of insertions and deletions Is it desirable to optimize average access time at the expense of worstcase 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 IN PRACTICE: PostgreSQL supports hash indices, but discourages use due to poor performance. Oracle supports static hash organization, but not hash indices. SQLServer supports only B+trees.

42 Bitmap Indices Bitmap indices are a specialized type of index designed for easy and efficient querying on multiple keys, although each bitmap index is built on a single key. For bitmap indices to be used, records in a relation must be numbered sequentially, starting from 0 (zero). Bitmap Indices are applicable on attributes that take on a relatively small number of distinct values. Example: gender, country, state, … Income-level (income broken up into a small number of levels such as 09999, , ,50000infinity) Bitmap indices provide a very compact representation for indexing attributes with very few distinct values. Intersection operations are extremely fast on bitmaps, making them ideal for supporting queries on multiple attributes.

43 Bitmap Index Structure
In its simplest form, a bitmap index on the attribute A of relation r consists of one bitmap for each value that A can take. Each bitmap has as many bits as the number of records in the relation. The ith bit of the bitmap for value vj is set to 1 if the record numbered i has the value vj for attribute A. All other bits of the bitmap are set to 0. A bitmap is simply an array of bits. Given a number n, it must be easy to retrieve the record numbered n. Retrieval is particularly easy to achieve if records are fixed in size, and allocated on consecutive blocks of a file. The record number can then be translated easily into a block number & a number that identifies the record within the block.

44 Bitmap Indices (Continued)
In a bitmap for value v, the bit for a record is 1 if the record has the value v for the attribute, but is 0 if the record has no value v for the attribute.

45 Bitmap Indices (Continued)
Bitmap indices are useful for queries on multiple attributes and not particularly useful for single attribute queries. Queries are answered using bitmap operations Intersection (and) Union (or) Complementation (not) Each operation takes two bitmaps of the same size and applies the operation on corresponding bits to get the result bitmap. Example: AND = OR = NOT = Bitmap indices generally very small compared with the relation size. For example: if record is 100 bytes, space for a single bitmap is 1/800 of space used by the relation. If number of distinct attribute values is 8, bitmap is only 1% of the relation size.

46 Index Definition in SQL
The SQL standard does not provide any way for the database user or administrator to control what indices are created and maintained in the database system. Indices are not required for correctness, since they are redundant data structures. However, indices are important for efficient processing of transactions, including both update transactions and queries. Indices are also important for efficient enforcement of integrity constraints. In principle, a database system can decide automatically what indices to create. However, because of the space cost of indices, as well as the effect of indices on update processing, it is not easy to automatically make the right choices about what indices to maintain. Therefore, most SQL implementations provide the programmer control over creation and removal of indices via data-definition language commands. We illustrate the syntax of these commands in the next slide. Although the syntax that we show is widely used and supported by many database systems, it is not part of the SQL standard. The SQL standard does not support control of the physical database schema; it restricts itself to the logical database schema.

47 Index Definition in SQL (Continued)
We create an index with the create index command, as shown in the SQL command below: create index <index-name> on <relation-name> (<attribute-list>); To define an index named dept_index on the instructor relation with dept_name as the search key; Example: create index dept_index on instructor (dept_name); Use create unique index to indirectly specify and enforce the condition that the search key is a candidate key. To drop an index, the index name we specified for an index is required. Example: drop index <index-name> Most database systems allow specification of type of index, and clustering. The attribute-list is the list of attributes of the relations that form the search key for the index. If we wish to declare that the search key is a candidate key, we add the attribute unique to the index definition. Use the command: create unique index dept_index on instructor (dept_name); declares dept_name to be a candidate key for instructor (which is probably not what we actually would want for our university database). If, at the time we enter the create unique index command, dept name is not a candidate key, the system will display an error message, and the attempt to create the index will fail. If the index-creation attempt succeeds, any subsequent attempt to insert a tuple that violates the key declaration will fail. Note that the unique feature is redundant if the database system supports the unique declaration of the SQL standard.



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