Transcript Introduction to Database Systems

Introduction to Database Systems
Chapter 1
Instructor: Johannes Gehrke
[email protected]
Database Management Systems, R. Ramakrishnan and J. Gehrke
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What Is a DBMS?
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A very large, integrated collection of data.
Models real-world enterprise.
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Entities (e.g., students, courses)
Relationships (e.g., Madonna is taking CS564)
A Database Management System (DBMS) is a
software package designed to store and
manage databases.
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Historical Perspective
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Early 1960s
– Integrated data store, first general-purpose DBMS
designed by Charles Bachman at GE
– Formed basis for network data model
– Bachman received Turing Award in 1973 for his
work in database area
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Historical Perspective
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Late 1960s
– IBM developed Information Management System
(IMS), used even today in many major
installations
– IMS formed the basis for hierarchical data model
– American Airlines and IBM jointly developed
SABRE for making airline reservations
– SABRE is used today to populate Web-based
travel services such as Travelocity
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Historical Perspective
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1970
– Edgar Codd, at IBM’s San Jose Research Laboratory,
proposed relational data model.
– It sparked the rapid development of several DBMSs based
on relational model, along with a rich body of theoretical
results that placed the field on a firm foundation.
– Codd won 1981 Turing Award.
– Database systems matured as an academic discipline
– The benefits of DBMS were widely recognized, and the use
of DBMSs for managing corporate data became standard
practice.
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Historical Perspective
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1980s
– Relational data model consolidated its position as
dominant DBMS paradigm, and database systems
continued to gain widespread use
– SQL query language, developed as part of IBM’s
System R project, is now the standard query
language
– SQL was standardized in late 1980s, and current
standard SQL:1999 was adopted by ANSI and ISO
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Historical Perspective
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Late 1980s till 1990s
– Considerable research into more powerful query language
and richer data model, with emphasis on supporting
complex analysis of data from all parts of an enterprise
– Several vendors, e.g., IBM’s DB2, Oracle 8, Informix UDS,
extended their systems with the ability to store new data
types such as images and text, and to ask more complex
queries
– Data warehouses have been developed by many vendors to
consolidate data from several databases, and for carrying
out specialized analysis
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File Systems vs DBMS
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Must write special programs to answer each question
a user may want to ask about data
Must protect data from inconsistent changes made by
different users accessing data concurrently
Must cope with system crashes to ensure data
consistency
Need to enforce security policies in which different
users have permission to access different subsets of
the data
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Why Use a DBMS?
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Data independence (see next page) and
efficient access.
Reduced application development time.
Data integrity and security.
Uniform data administration.
Concurrent access, recovery from crashes.
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Program-data dependence --- Three file
processing systems at Some Company
File descriptions are stored within each application program that
accesses a given file. Any change to a file structure requires changes
to the file descriptions for all programs that access the file.
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Why Study Databases??
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Shift from computation to information
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at the “low end”: scramble to webspace (a mess!)
at the “high end”: scientific applications
Datasets increasing in diversity and volume.
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Digital libraries, interactive video, Human
Genome project, EOS project
... need for DBMS exploding
DBMS encompasses most of CS
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OS, languages, theory, “A”I, multimedia, logic
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Data Models
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A data model is a collection of concepts for
describing data.
A schema is a description of a particular
collection of data, using the a given data
model.
The relational model of data is the most widely
used model today.
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Main concept: relation, basically a table with rows
and columns.
Every relation has a schema, which describes the
columns, or fields.
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Levels of Abstraction
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Many views, single
conceptual (logical) schema
and physical schema.
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View 1
Views describe how users
see the data.
Conceptual schema defines
logical structure
Physical schema describes
the files and indexes used.
View 2
View 3
Conceptual Schema
Physical Schema
* Schemas are defined using DDL; data is modified/queried using DML.
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Example: University Database
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Conceptual schema:
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Physical schema:
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Students(sid: string, name: string, login: string,
age: integer, gpa:real)
Courses(cid: string, cname:string, credits:integer)
Enrolled(sid:string, cid:string, grade:string)
Relations stored as unordered files.
Index on first column of Students.
External Schema (View):
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Course_info(cid:string,enrollment:integer)
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Data Independence
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Applications insulated from how data is
structured and stored.
Logical data independence: Protection from
changes in logical structure of data (the
capacity to change the conceptual schema
without having to change external schemas or
application programs).
* One of the most important benefits of using a DBMS!
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Data Independence (cont.)
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Physical data independence: Protection from
changes in physical structure of data (the
capacity to change the internal schema
without having to change the conceptual (or
external) schemas).
* One of the most important benefits of using a DBMS!
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Concurrency Control
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Concurrent execution of user programs
is essential for good DBMS performance.
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Because disk accesses are frequent, and relatively
slow, it is important to keep the cpu humming by
working on several user programs concurrently.
Interleaving actions of different user programs
can lead to inconsistency: e.g., check is cleared
while account balance is being computed.
DBMS ensures such problems don’t arise: users
can pretend they are using a single-user system.
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Transaction: An Execution of a DB Program
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Key concept is transaction, which is an atomic
sequence of database actions (reads/writes).
Each transaction, executed completely, must
leave the DB in a consistent state if DB is
consistent when the transaction begins.
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Users can specify some simple integrity constraints on
the data, and the DBMS will enforce these constraints.
Beyond this, the DBMS does not really understand the
semantics of the data. (e.g., it does not understand
how the interest on a bank account is computed).
Thus, ensuring that a transaction (run alone) preserves
consistency is ultimately the user’s responsibility!
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Scheduling Concurrent Transactions
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DBMS ensures that execution of {T1, ... , Tn} is
equivalent to some serial execution T1’ ... Tn’.
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Before reading/writing an object, a transaction requests
a lock on the object, and waits till the DBMS gives it the
lock. All locks are released at the end of the transaction.
(Strict 2PL locking protocol.)
Idea: If an action of Ti (say, writing X) affects Tj (which
perhaps reads X), one of them, say Ti, will obtain the
lock on X first and Tj is forced to wait until Ti completes;
this effectively orders the transactions.
What if Tj already has a lock on Y and Ti later requests a
lock on Y? (Deadlock!) Ti or Tj is aborted and restarted!
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Ensuring Atomicity
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DBMS ensures atomicity (all-or-nothing property)
even if system crashes in the middle of a Xact.
Idea: Keep a log (history) of all actions carried out
by the DBMS while executing a set of Xacts:
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Before a change is made to the database, the
corresponding log entry is forced to a safe location.
(WAL protocol; OS support for this is often inadequate.)
After a crash, the effects of partially executed
transactions are undone using the log. (Thanks to WAL, if
log entry wasn’t saved before the crash, corresponding
change was not applied to database!)
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The Log
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The following actions are recorded in the log:
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Ti writes an object: the old value and the new value.
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Log record must go to disk before the changed page!
Ti commits/aborts: a log record indicating this action.
Log records chained together by Xact id, so it’s easy to
undo a specific Xact (e.g., to resolve a deadlock).
Log is often duplexed and archived on “stable” storage.
All log related activities (and in fact, all CC related
activities such as lock/unlock, dealing with deadlocks
etc.) are handled transparently by the DBMS.
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Overview of System Architecture
Database Server
Database Cache
Log Buffer
read
write
begin
Database
Page
commit, rollback
write
Volatile
Memory
Stable
Storage
Stable
Database
fetch
Database
Page
flush
Log Entry
force
Stable
Log
Database Management Systems, R. Ramakrishnan and J. Gehrke
Log Entry
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Databases make these folks happy ...
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End users and DBMS vendors
DB application programmers
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E.g. smart webmasters
Database administrator (DBA)
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Designs logical /physical schemas
Handles security and authorization
Data availability, crash recovery
Database tuning as needs evolve
Must understand how a DBMS works!
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These layers
must consider
concurrency
control and
recovery
Structure of a DBMS
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A typical DBMS has a
Query Optimization
layered architecture.
and Execution
The figure does not
Relational Operators
show the concurrency
Files and Access Methods
control and recovery
components.
Buffer Management
This is one of several
Disk Space Management
possible architectures;
each system has its own
variations.
DB
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Summary
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DBMS used to maintain, query large datasets.
Benefits include recovery from system crashes,
concurrent access, quick application
development, data integrity and security.
Levels of abstraction give data independence.
A DBMS typically has a layered architecture.
DBAs hold responsible jobs
and are well-paid!
DBMS R&D is one of the broadest,
most exciting areas in CS.
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