Computing & Information Sciences Kansas State University Monday. 20 Oct 2008CIS 560: Database System...

Computing & Information SciencesKansas State University

Monday. 20 Oct 2008CIS 560: Database System Concepts

Lecture 21 of 42

Monday, 20 October 2008

William H. Hsu

Department of Computing and Information Sciences, KSU

KSOL course page: http://snipurl.com/va60

Course web site: http://www.kddresearch.org/Courses/Fall-2008/CIS560

Instructor home page: http://www.cis.ksu.edu/~bhsu

Reading for Next Class:

First half of Chapter 12, Silberschatz et al., 5th edition

XML Query Languages and Document SchemasDiscussion: Indexing



Sorting in XQuery Sorting in XQuery

The order by clause can be used at the end of any expression. E.g. to return customers sorted by name for $c in /bank/customer order by $c/customer_name

return <customer> { $c/* } </customer> Use order by $c/customer_name to sort in descending order Can sort at multiple levels of nesting (sort by customer_name, and by

account_number within each customer) <bank-1> {

for $c in /bank/customer order by $c/customer_namereturn <customer> { $c/* } { for $d in

/bank/depositor[customer_name=$c/customer_name], $a in

/bank/account[account_number=$d/account_number] }order by $a/account_number

return <account> $a/* </account> </customer>

} </bank-1>



Functions and Other XQuery FeaturesFunctions and Other XQuery Features

User defined functions with the type system of XMLSchema function balances(xs:string $c) returns list(xs:decimal*) { for $d in /bank/depositor[customer_name = $c], $a in /bank/account[account_number = $d/account_number] return $a/balance

} Types are optional for function parameters and return values The * (as in decimal*) indicates a sequence of values of that type Universal and existential quantification in where clause predicates

some $e in path satisfies P every $e in path satisfies P

XQuery also supports If-then-else clauses



XSLTXSLT

A stylesheet stores formatting options for a document, usually separately from document E.g. an HTML style sheet may specify font colors and sizes for

headings, etc.

The XML Stylesheet Language (XSL) was originally designed for generating HTML from XML

XSLT is a general-purpose transformation language Can translate XML to XML, and XML to HTML

XSLT transformations are expressed using rules called templates Templates combine selection using XPath with construction of results



XSLT TemplatesXSLT Templates Example of XSLT template with match and select part <xsl:template match=“/bank-2/customer”>

<xsl:value-of select=“customer_name”/> </xsl:template> <xsl:template match=“*”/> The match attribute of xsl:template specifies a pattern in XPath Elements in the XML document matching the pattern are processed

by the actions within the xsl:template element xsl:value-of selects (outputs) specified values (here, customer_name)

For elements that do not match any template Attributes and text contents are output as is Templates are recursively applied on subelements

The <xsl:template match=“*”/> template matches all elements that do not match any other template Used to ensure that their contents do not get output.

If an element matches several templates, only one is used based on a complex priority scheme/user-defined priorities



Creating XML OutputCreating XML Output

Any text or tag in the XSL stylesheet that is not in the xsl namespace is output as is

E.g. to wrap results in new XML elements. <xsl:template match=“/bank-2/customer”>

<customer> <xsl:value-of select=“customer_name”/> </customer>

</xsl;template> <xsl:template match=“*”/>

Example output: <customer> Joe </customer> <customer> Mary </customer>



Creating XML Output (Cont.)Creating XML Output (Cont.)

Note: Cannot directly insert a xsl:value-of tag inside another tag E.g. cannot create an attribute for <customer> in the previous example

by directly using xsl:value-of XSLT provides a construct xsl:attribute to handle this situation

xsl:attribute adds attribute to the preceding elementE.g. <customer> <xsl:attribute name=“customer_id”> <xsl:value-of select = “customer_id”/> </xsl:attribute>

</customer> results in output of the form <customer customer_id=“….”> ….

xsl:element is used to create output elements with computed names



Structural RecursionStructural Recursion Template action can apply templates recursively to the contents of a

matched element

<xsl:template match=“/bank”>

<customers>

<xsl:template apply-templates/>

</customers >

</xsl:template>

<xsl:template match=“/customer”>

<customer>

<xsl:value-of select=“customer_name”/>

</customer>

</xsl:template>

<xsl:template match=“*”/> Example output:

<customers> <customer> John </customer> <customer> Mary </customer> </customers>



Web ServicesWeb Services

The Simple Object Access Protocol (SOAP) standard: Invocation of procedures across applications with distinct databases XML used to represent procedure input and output

A Web service is a site providing a collection of SOAP procedures Described using the Web Services Description Language (WSDL) Directories of Web services are described using the Universal

Description, Discovery, and Integration (UDDI) standard



Chapter 12: Indexing and HashingChapter 12: Indexing and Hashing

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



Basic ConceptsBasic Concepts

Indexing mechanisms used to speed up access to desired data. E.g., author catalog in library

Search Key - attribute to set of attributes used to look up records in a file.

An index file consists of records (called index entries) of the form

Index files are typically much smaller than the original file Two basic kinds of indices:

Ordered indices: search keys are stored in sorted order Hash indices: search keys are distributed uniformly across

“buckets” using a “hash function”.

search-key pointer



Index Evaluation MetricsIndex Evaluation Metrics

Access types supported efficiently. E.g., records with a specified value in the attribute or records with an attribute value falling in a specified range of

values.

Access time Insertion time Deletion time Space overhead



Ordered IndicesOrdered Indices

In an ordered index, index entries are stored sorted on the search key value. E.g., author catalog in library.

Primary index: in a sequentially ordered file, the index whose search key specifies the sequential order of the file. Also called clustering index The search key of a primary index is usually but not necessarily the

primary key.

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.

Indexing techniques evaluated on basis of:



Dense Index FilesDense Index Files

Dense index — Index record appears for every search-key value in the file.



Sparse Index FilesSparse Index Files

Sparse Index: contains index records for only some search-key values. Applicable when records are sequentially ordered on search-key

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.



Example of Sparse Index FilesExample of Sparse Index Files



Multilevel IndexMultilevel Index

If primary index does not fit in memory, access becomes expensive.

To reduce number of disk accesses to index records, 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.



Multilevel Index (Cont.)Multilevel Index (Cont.)



Index Update: DeletionIndex 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.



Index Update: InsertionIndex 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. If a new block is created, 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



Secondary IndicesSecondary Indices

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. Example 1: In the account relation 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 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.



Secondary Index on balance field of account

Secondary Index on balance field of account



Primary and Secondary IndicesPrimary and Secondary Indices

Secondary 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 primary index is efficient, but a sequential

scan using a secondary index is expensive each record access may fetch a new block from disk



B+-Tree Index FilesB+-Tree Index 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: extra insertion and deletion overhead, space overhead.

Advantages of B+-trees outweigh disadvantages, and they are used extensively.

B+-tree indices are an alternative to indexed-sequential files.



B+-Tree Index Files (Cont.)B+-Tree Index Files (Cont.)

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. 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–1) values.

A B+-tree is a rooted tree satisfying the following properties:



B+-Tree Node StructureB+-Tree Node Structure

Typical node

Ki are the search-key values

Pi are 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



Leaf Nodes in B+-TreesLeaf Nodes in B+-Trees

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 primary key.

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

Properties of a leaf node:



Non-Leaf Nodes in B+-TreesNon-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 P1 points are less than K1

For 2 i n – 1, all the search-keys in the subtree to which Pi points have values greater than or equal to Ki–1 and less than Km–1



Example of a B+-treeExample of a B+-tree

B+-tree for account file (n = 3)



Example of B+-treeExample of 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.

B+-tree for account file (n = 5)



Observations about B+-treesObservations about B+-trees

Since the inter-node connections are done by pointers, “logically” close blocks need not be “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).



Queries on B+-TreesQueries on B+-Trees

Find all records with a search-key value of k.1. Start with the root node

1. Examine the node for the smallest search-key value > k.

2. If such a value exists, assume it is Kj. Then follow Pi to the child node

3. Otherwise k Km–1, where there are m pointers in the node. Then follow Pm to the child node.

2. If the node reached by following the pointer above is not a leaf node, repeat step 1 on the node

3. Else we have reached a leaf node. 1. If for some i, key Ki = k follow pointer Pi to the desired record or

bucket.

2. Else no record with search-key value k exists.

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