DATABASE SYSTEMS - 10p Course No. 2AD235 Spring 2002

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DATABASE SYSTEMS - 10pCourse No. 2AD235 Spring
2002

• A second course on development of database
  systems
• Kjell OrsbornUppsala Database
  LaboratoryDepartment of Information Technology,
  Uppsala University, Uppsala, Sweden

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Introduction to Distributed DBMSs(Elmasri/Navathe
ch. 24)

• Distributed DBMS (ch. 24.4 and 24.5 are omitted)
• Kjell Orsborn Uppsala Database
  Laboratory,Department of Information Technology,
  Uppsala University, Uppsala, Sweden

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Distributed DBMSs

• A distributed database (DDB) is a collection of
  several logically interrelated databases
  distributed over a computer network including a
  number of computers (nodes).
• A distributed database mangement system (DDBMS)
  is a software system that permits management of
  DDBs and that makes the distribution transparent
  for the user.
• A DDB is not
• a collection of files (need structure and DB
  manager)
• a client-server interface to a database
• data on one node, clients on other nodes in
  network
• (almost) every centralized DBMS has client-server
  interface

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Background

• What is a Distributed System?
• A Distributed System is a number of autonomous
  computers communicating over a network with
  software for integrated tasks.
• Examples of Distributed Systems
• SUNs Network File System (NFS), distributed file
  system

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Distributed DBMSs . . .
Distributed database over several nodes in a
network
Centralized database in a network
Node 1
Node 5
Node 1
Node 2
Node 5
Node 2
communication network
communication network
Node 4
Node 4
Node 3
Node 3
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Centralized Database Server

• Stream (row-by-row) based client-server
  interfaces
• DBMS specific interfaces
• Compiler integrated interfaces (embedded SQL)
• ODBC SQL-based standardized subroutine call
  library (Microsoft)
• JDBC ODBC for Java (not Microsoft)

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Distributed Databases

• Database seen as one unit queries and updates to
  ONE database.
• Data in database transparently distributed over
  many DB nodes.
• Manual partitioning or fragmentation of data
  tables.
• DBMS automatically optimizes queries and updates
  to distributed database.

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Multi-Databases

• Database seen as several heterogeneous units
• Multi-database query language needed to combine
  data from the databases.
• Primitives needed to integrate (combine, fuse)
  data from the databases.
• Special query optimization techniques to deal
  with heterogneity and dynamism.

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Example of Multi-Database

• Automatic Teller Machines, ATMs

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Fragmentation of data

• data fragmentation ( data partitioning)
• division of data sets (e.g. a relation) into
  several pieces - fragments transparently stored
  on several different nodes
• increased accessability and performance
• several types of fragmentation
• horisontal fragmentation
• vertical fragmentation
• mixed fragmentation
• good when nodes far apart

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Replication of data

• copies of the same data on several nodes
• increased reliability and access performance
• more complex updating, transactions handling,
  recovery.
• updates must be propagated to each replica!
• special procedures after failures to restore
  consistency
• more problematic transaction synchronization!
• types of replication
• full replication (whole db at each node)
• no replication (each fragment only at one node)
• partial replication (certain fragments
  replicated)
• not necessary to replicate all tables
• full replication often not realistic!

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Transparency in a DDBMS

• By transparency we here mean the hiding of basic
  implementation details from one abstraction level
  to another.
• Data independence
• logical data independence
• physical data independence
• Network transparency
• protect user from operational details of network
• hides the existence of a network
• no machine names in database table references
• location transparency
• naming transparency
• Replication transparency
• user should not experience data replicas
• automatic handling of updates, such as replica
  propagation
• automatic handling of node crasches
• Fragmentation transparency
• hides the existence of fragments
• e.g. that a logical relation is horizontally
  fragmented into local physical tables
• handling of transformation of global queries to
  fragmented queries

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Advantages of Distributed DBMSs . . .

• Data sharing
• uniform interface and sharing of data through the
  DDBMS
• natural to distribute certain database
  applications
• Increased reliability
• redundance increase security and accessability
• crashes less severe (if application not dependent
  of non-local data)
• Local independence
• allows sharing of data but keeps local control of
  data
• Improved performance
• avoid unnecessary data transfer
• Expandibility
• easy to add new nodes (not always linear scale up
  due to central directory)
• Local autonomy
• local control
• local policies

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Problems with Distributed DBMSs . . .

• Complexity
• database administration becomes more complex
  (such as recovery)
• increased complexity of system design,
  implementation and maintenance
• Security
• keep security in a network harder
• Networking a known problem
• Distributed administration
• less control and more meetings
• Cost
• hardware - software - development/maintenance

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Problems with Distributed DBMSs . . .

• Distributed schema management
• schema is accessed whenever SQL query issued!
• global directory gt Central Database becomes hot
  spot
• local directories gt Data replication
• gt Since schema is not updated often but need to
  be accessed very often it is normally fully
  replicated by the DDBMS.
• Distributed concurrency control
• consistency of replicas mutual consistency
• Distributed deadlock management
• Reliability of DDBMS
• consistency of replicas
• bring up (fragmented) database at failed sites
• OS Support
• multiple layers of network software

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Additional functionality required by DDBMS

• Access of physically divided databases - schema
  management
• Handling of distribution and replication of data
• which copy of data should for example be used
• Handling of consistency of replicated data
• Handling of distributed queries
• Handling of distributed transactions (over
  several network nodes)
• Handling of recovery/restart from crashes (of
  nodes) and new types of errors such as
  communication errrors/failures.

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Distributed database design

• Goal
• to minimize the combined cost of maintaining
  data, recieve efficient communication and good
  performance for transactions.
• Problems
• where (on which node/nodes) shall data and
  applications be placed
• partitioning of data (split data into distributed
  partitions)
• replication of data (copies of data on several
  nodes)
• NP-complete optimization problem.
• distributed query processing
• automatically done by distributed query processor
  of DDBMS
• analyze query --gt distributed execution plan
• factors
• data replication
• data availability
• communication costs