COP4540 Database Management System Final Review

1
COP4540 Database Management SystemFinal Review

• Reviewed by Ramakrishna.
• Parts of this are taken from Fernando Farfans
  presentation

2
AGENDA

• Exercises to do.
• Ch6 JDBC
• Ch7 3-Tier Architecture
• Ch8. Storage and Indexing
• Ch10. Tree-Structured Indexing
• Ch7. XML Documents
• Ch27. XQUERY Querying XML Data

3
Exercises to do.

• Remember to practice even numbered exercises of
  the book
• Solutions are available online at
  http//www.cs.wisc.edu/dbbook/openAccess/thirdEdi
  tion/supporting_material.htm
• Important Recommended exercises 8.3, 8.4, 8.5,
  8.7, 8.10, 8.11

4
JDBC

• What is JDBC ? Explain its purpose.
• JDBC is Java DataBase Connectivity used to enable
  integration of SQL with a general purpose
  programming language.
• Explain JDBC Architecture
• 4 components
• Application,
• Driver Manager,
• Data Source Specific Drivers,
• Data Sources (stored in MySql,Oracle,DB2,MSSQL,Acc
  ess and so on).

5
JDBC

• Explain the individual steps required to submit a
  query to a data source and to retrieve results in
  JDBC ?
• JDBC Driver Management (class.forName(..)),
• Establishing Connection (Connection Object),
• Executing SQL Statements
• Statement,
• PreparedStatement,
• CallableStatement.
• Retrieving results ( Examining ResultSets).

6
Stored Procedures

• Explain the term stored procedure, and give
  examples why stored procedures are useful.
• Stored procedures are programs that run on the
  database server and can be called with a single
  SQL statement.
• Runs inside the process space of the database
  server.
• Stored procedures can also be used to reduce
  network communication.
• They are locally-executed and results are
  packaged in to one big result.
• Different users can re-use the stored procedure.

7
3-Tier Application Architecture

• What is a 2-tier architecture ? Explain different
  types of 2-tier architectures.
• Its a client-server architecture.

Client
Application Logic
Network
DBMS
Client
Architecture 1 Thin Clients ( web browsers)
8
3-Tier Application Architecture

• Architecture 2 Thick Clients

Client
Application Logic
Network
DBMS
Client
Application Logic

• Disadvantages ?
• No central place to update
• Need to trust client

9
3-tier architecture

• What are the advantages of 3-tier architecture ?
• Heterogeneous Systems
• Thin Clients
• Integrated Data Access
• Scalable

Client (user interface)
Network
Network
DBMS (database)
Client (web browser)
Application Logic (in C or Java..)
10
CH8. OVERVIEW OF STORAGE AND INDEXING

• DBMS stores vast quantities of data
• Data must persist across program executions
• Data is stored on external storage devices.
• The unit of information read from or written to
  disk is a page.
• The cost of page I/O dominates the cost of
  typical database operations.
• Input from disk to main memory
• Output from main memory to disk

11
CH8. OVERVIEW OF STORAGE AND INDEXING

• Simplest file structure is an unordered file, or
  heap file.
• An index is a data structure to optimize certain
  kinds of retrieval operations.
• CLUSTERED - When a file is organized so that the
  ordering of data records is the same as the
  ordering of data entries in some index
• UNCLUSTERED - It is un-clustered otherwise
• There is UTMOST one clustered index and several
  un-clustered ones for a table.

12
Example

• Employee (Eid integer, name String, age
  integer..)
• Assume data records sorted by name
• Assume an index on name
• Index on name ? CLUSTERED
• Assume an index on age
• Index on age ? UNCLUSTERED

13
Example Using Indexes

• Employee Data Records Sorted by Name
• Page 1
• (1,Alex,30)
• (2,Amy,21)
• Page 2
• (3, Bob,31)
• (4, Brenda,21)
• Select from Employee where name like A
• Use index on name ? Retrieve Page 1.
• Select from Employee where age 21
• Use index on age ? Retrieve Page 1 Page 2.

14
Clustered and UnClustered Indexes

• Retrieval using a Clustered index
• Data retrieval using minimum number of Data page
  I/Os.
• Retrieval using an Unclustered index
• In worst case, 1 page I/O for every qualifying
  tuple.

15
Another Example

• Consider Select E.dno from Employee E where
  E.age gt 40
• Assume B Tree index on age.
• Is it better to use this index ? Or just do a
  segment scan ?
• Answer Depends on several factors
• Depends on Selectivity of the condition.
• Whether the index is clustered or unclustered.

16
CH8. OVERVIEW OF STORAGE AND INDEXING

• 8.3 Consider a relation stored as a randomly
  ordered file for which the only index is an
  unclustered index on a field called sal. If you
  want to retrieve all records with sal gt 20, is
  using the index always the best alternative?
  Explain.
• No. In this case, the index is unclustered, each
  qualifying data entry could contain an rid that
  points to a distinct data page, leading to as
  many data page I/Os as the number of data entries
  that match the range query. In this situation,
  using index is actually worse than file scan.

17
CH8. OVERVIEW OF STORAGE AND INDEXING

• 8.5 Explain the difference between Hash indexes
  and B-tree indexes. In particular, discuss how
  equality and range searches work.
• Hash Index Hashing function. Quickly maps a
  search key value to a bucket. Inserts/Deletes are
  simple. Linked list for collisions. Good at
  equality searches. Terrible for range queries.
• B-tree Index Hierarchical search data
  structure. Maintenance is costly. Costly for
  individual record lookup. Efficient for range
  queries.

18
CH8. OVERVIEW OF STORAGE AND INDEXING

• Consider the following relation
• Answer Update the salary of an employee using
  the employee id.

19
CH8. OVERVIEW OF STORAGE AND INDEXING

• Update the age or employee id for some
  department id

Update the salaries of all employees in some
department
20
Constraints
21
Constraints
22
Constraints
23
Constraints
Assume tables are already created.
ALTER TABLE ENROLLED add constraint cons1
check((select count() from ENROLLED group by
cname)gt5) ALTER TABLE ENROLLED add
constraint cons2 check((select count() from
ENROLLED group by cname)lt30)
24
Constraints
25
Views
26
Views
27
Tree-Structured Indexing

• Explain ISAM and B Trees ? What are the
  differences, advantages and disadvantages ?
• ISAM Indexes Sequential access Method
• Effective for static files
• Unsuitable for dynamically changing files
• B Tree
• Dynamic index structure
• Adjusts well to changes
• Does well for both range and equality selections

28
Tree-Structured Indexing

• Insertions and deletions in ISAM
• Happen only in leaf pages
• Insertions adding records to the overflow chain
• Search inefficient as the chain grows
• In B tree even after several insertions and
  deletions,
• The tree is kept balanced
• Height of the tree ? length of the path from root
  to leaf

29
Query Evaluation

• Consider a relation R(a,b,c,d,e) containing
  5,000,000 records, where each data page of the
  relation holds 10 records. R is organized as a
  sorted file with secondary indexes. Assume that
  R.a is a candidate key for R, with values lying
  in the range 0 to 4,999,999, and that R is stored
  in R.a order. For each of the following
  relational algebra queries, state which of the
  following three approaches is most likely to be
  the cheapest
• Access the sorted file for R directly.
• Use a (clustered) B tree index on attribute
  R.a.
• Use a linear hashed index on attribute R.a.

30
Query Evaluation
31
CH7. XML DOCUMENTS

• 7.1 When is an XML document well-formed? When is
  an XML document valid?
• An XML document is valid if it has an associated
  DTD and the document follows the rules of the
  DTD. An XML document is well-formed if it follows
  three guidelines
• It starts with an XML declaration.
• It contains a root element that contains all
  other elements.
• All elements are properly nested.

32
CH27. XQUERY

• ltbookstoregt ltbook category"COOKING"gt lttitle
  lang"en"gtEveryday Italianlt/titlegt ltauthorgtGiada
  De Laurentiislt/authorgt ltyeargt2005lt/yeargt ltpri
  cegt30.00lt/pricegt lt/bookgt ltbook
  category"CHILDREN"gt lttitle lang"en"gtHarry
  Potterlt/titlegt ltauthorgtJ K. Rowlinglt/authorgt lt
  yeargt2005lt/yeargt ltpricegt29.99lt/pricegt lt/bookgt
  ltbook category"WEB"gt lttitle lang"en"gtXQuery
  Kick Startlt/titlegt ltauthorgtJames
  McGovernlt/authorgt ltauthorgtPer
  Bothnerlt/authorgt ltauthorgtKurt
  Caglelt/authorgt ltauthorgtJames Linnlt/authorgt ltau
  thorgtVaidyanathan Nagarajanlt/authorgt ltyeargt2003lt
  /yeargt ltpricegt49.99lt/pricegt lt/bookgt ltbook
  category"WEB"gt lttitle lang"en"gtLearning
  XMLlt/titlegt ltauthorgtErik T. Raylt/authorgt ltyear
  gt2003lt/yeargt ltpricegt39.95lt/pricegt lt/bookgtlt/boo
  kstoregt

33
CH27. XQUERY

• Querydoc("books.xml")/bookstore/book/title
• Resultlttitle lang"en"gtEveryday
  Italianlt/titlegtlttitle lang"en"gtHarry
  Potterlt/titlegtlttitle lang"en"gtXQuery Kick
  Startlt/titlegtlttitle lang"en"gtLearning
  XMLlt/titlegt

34
CH27. XQUERY

• Querydoc("books.xml")/bookstore/bookpricelt30
• Resultltbook category"CHILDREN"gt lttitle
  lang"en"gtHarry Potterlt/titlegt ltauthorgtJ K.
  Rowlinglt/authorgt ltyeargt2005lt/yeargt ltpricegt29.99lt
  /pricegtlt/bookgt

35
CH27. XQUERY

• Queryfor x in doc("books.xml")/bookstore/book
  where x/pricegt30 order by x/title return
  x/title
• Resultlttitle lang"en"gtLearning
  XMLlt/titlegtlttitle lang"en"gtXQuery Kick
  Startlt/titlegt

36
CH27. XQUERY

• Queryltulgt for x in doc("books.xml")/bookstor
  e/book/title order by x return ltligtxlt/ligt
  lt/ulgt
• Resultltulgt ltligtlttitle lang"en"gtEveryday
  Italianlt/titlegtlt/ligt ltligtlttitle lang"en"gtHarry
  Potterlt/titlegtlt/ligt ltligtlttitle
  lang"en"gtLearning XMLlt/titlegtlt/ligt ltligtlttitle
  lang"en"gtXQuery Kick Startlt/titlegtlt/ligtlt/ulgt