Distributed Databases and Its Twelve Objectives CS157B Name: Yingying Wu Professor: Sin-Min Lee Reference Book: An introduction to Database Systems By C.J.Date

Definition of Distributed Database: A distributed database system consists of a collection of sites, connected together via some kind of communication network, in which: A distributed database system consists of a collection of sites, connected together via some kind of communication network, in which: a. Each site is a full database system site in its own right. a. Each site is a full database system site in its own right. b. The sites have agreed to work together so that a user at any site can access data anywhere in the network exactly as if the data were all stored at the user’s own site. b. The sites have agreed to work together so that a user at any site can access data anywhere in the network exactly as if the data were all stored at the user’s own site.

Communication network New York Shanghai London San Francisco A typical distributed database system:

The Fundamental Principle of Distributed Database “To the user, a distributed system should look exactly like a nondistributed system.”

What is the 12 objectives? Local autonomy Local autonomy No reliance on a central site No reliance on a central site Continuous operation Continuous operation Location independence Location independence Fragmentation independence Fragmentation independence Replication independence Replication independence Distributed query processing Distributed query processing Distributed transaction management Distributed transaction management Hardware independence Hardware independence Operating system independence Operating system independence Network independence Network independence DBMS independence DBMS independence

Why study the 12 objectives? --Useful as --Useful as A basis for understanding distributed technology in general A basis for understanding distributed technology in general A framework for characterizing the functionality of specific distributed systems. A framework for characterizing the functionality of specific distributed systems.

Objective 1 Local Autonomy All operations at a given site are controlled by that site. All operations at a given site are controlled by that site. No site X should depend on some other site Y for its successful operation. No site X should depend on some other site Y for its successful operation. -- Otherwise site Y is down might mean that site X is unable to run even if there is nothing wrong with site X itself. -- Otherwise site Y is down might mean that site X is unable to run even if there is nothing wrong with site X itself.

Objective 2 No Reliance on a Central Site All sites must be treated as equals. All sites must be treated as equals. There must not be any reliance on a central “master” site for some central service—for example, centralized transaction management. There must not be any reliance on a central “master” site for some central service—for example, centralized transaction management. Two reasons: 1. The central site might be a bottleneck. 2. If the central site went down, the whole system would be down.

Objective 3 Continuous Operation Provide greater reliability and greater availability – it is the advantage of distributed systems in general. Provide greater reliability and greater availability – it is the advantage of distributed systems in general. Unplanned shutdowns are undesirable, but hard to prevent entirely. Unplanned shutdowns are undesirable, but hard to prevent entirely. Planned shutdowns should never be required. Planned shutdowns should never be required.

Objective 4 Location Independence Also known as location transparency. Also known as location transparency. Users should not have to know where data is physically stored, but rather should be able to behave -- as if the data were all stored at their own local site. Users should not have to know where data is physically stored, but rather should be able to behave -- as if the data were all stored at their own local site.

Objective 5 Fragmentation Independence A system supports data fragmentation if a given base relation can be divided into pieces or fragments for physical storage purposes. A system supports data fragmentation if a given base relation can be divided into pieces or fragments for physical storage purposes. Two benefits: Two benefits: 1. most operations are local 1. most operations are local 2. reduce network traffic 2. reduce network traffic

An example of fragmentation Define two fragments: Define two fragments: FRAGMENT EMP AS FRAGMENT EMP AS N_EMP AT SITE ‘New York’ WHERE DEPT# = DEPT#(‘D1’) N_EMP AT SITE ‘New York’ WHERE DEPT# = DEPT#(‘D1’) OR DEPT# = DEPT#(‘D3’) OR DEPT# = DEPT#(‘D3’) S_EMP AT SITE ‘Shanghai’ WHERE DEPT# = DEPT#(‘D2’) S_EMP AT SITE ‘Shanghai’ WHERE DEPT# = DEPT#(‘D2’) EMP#DEPT#SALARY E1D140K E2D142K E3D230K E4D235K E5D348K User perception EMPEMP#DEPT#SALARYE1D140K E2D142K E5D348K EMP#DEPT#SALARYE3D230K E4D245K New York N_EMP Shanghai S_EMP

Objective 6 Replication Independence A system supports data replication if a given base relation or fragment can be represented in storage by many distinct copies or replicas, stored at many distinct sites. A system supports data replication if a given base relation or fragment can be represented in storage by many distinct copies or replicas, stored at many distinct sites. Ideally should be “transparent to the user”. Ideally should be “transparent to the user”. Desirable for two reasons: Desirable for two reasons: 1. Applications can operate on local copies instead of remote sites. 1. Applications can operate on local copies instead of remote sites. 2. At least one copy available 2. At least one copy available

An example of replication EMP#DEPT#SALARY E1D140K E2D142K E5D348K EMP#DEPT#SALARYE3D230K E4D235K EMP#DEPT#SALARYE3D230K E4D235K EMP#DEPT#SALARYE1D140K E2D142K E5D348K REPLICATE N_EMP AS REPLICATE N_EMP AS SN_EMP AT SITE ‘Shanghai’; SN_EMP AT SITE ‘Shanghai’; REPLICATE S_EMP AS REPLICATE S_EMP AS NS_EMP AT SITE ‘New York’; NS_EMP AT SITE ‘New York’; New York N_EMP Shanghai NS_EMP (S_EMP Replica) S_EMP SN_EMP (N_EMP Replica)

Objective 7 Distributed Query Processing A relational distributed system is likely to outperform a nonrelational one by orders of magnitude. A relational distributed system is likely to outperform a nonrelational one by orders of magnitude. The query that involves several sites, there will be many possible ways of moving data around the system. The query that involves several sites, there will be many possible ways of moving data around the system.

Example: Consider Query “Get supplier numbers for London suppliers of red parts” Database (suppliers-and-parts, simplified): S {S#, CITY} 10,000 stored tuples at site A P {P#, COLOR} 100,000 stored tuples at site B SP {S#, P#} 1,000,000 stored tuples at site A Assume every stored tuple is 25 bytes(200 bits)long. Query (“Get supplier numbers for London suppliers of red parts”): ( ( S JOIN SP JOIN P )WHERE CITY = ‘London’ AND COLOR = COLOR (‘Red’) ) { S# } Estimated cardinalities of certain intermediate results: Number of red parts = 10 Number of shipments by London suppliers = 100,000 Communication assumptions: Data rate = 50,000 bits per second Access delay = 0.1 second

We now briefly examine three possible strategies for processing this query, and for each strategy calculate the total communication time T from the formula: ( total access delay ) + (total data volume / data rate) 1.Move parts to site A and process the query at A. T1 = ( * 200 ) / = 400 seconds approx. (6.67minutes) 2. Move suppliers and shipments to site B and process the query at B. T2 = ( ( ) * 200 ) / = 4040 seconds approx. (1.12 hours) 3. Restrict parts at site B to those that are red and move the result to site A. Complete the processing at site A. T3 = (10 * 200 ) / = 0.1 second approx.

Objective 8 Distributed Transaction Management Recovery Recovery The system must ensure that the set of agents for that transaction either all commit in unison or all roll back in unison. The system must ensure that the set of agents for that transaction either all commit in unison or all roll back in unison. Achieved by two-phase commit protocol. Achieved by two-phase commit protocol. Concurrency Concurrency Typically based on locking. Typically based on locking.

t1t4t5t6 t2 t3t7t8 t9 GETREADYGETREADY OKOK DOITDOIT DONEITDONEIT Coordinator Participant Two-phase commit: Force decision Log entry-end ph.1,start ph.2 “In doubt” Forces a log entry for agent

Objective 9 Hardware Independence Real world involves a multiplicity of different machines—IBM machines, HP machines, PCs and workstations of various kinds. Real world involves a multiplicity of different machines—IBM machines, HP machines, PCs and workstations of various kinds. Need to be able to integrate the data on all of those systems. Need to be able to integrate the data on all of those systems. Desirable to be able to run the same DBMS on different hardware platform. Desirable to be able to run the same DBMS on different hardware platform.

Objective 10 Operating System Independence Be able to run the same DBMS on different operating system platforms. Be able to run the same DBMS on different operating system platforms. Have (e.g.) an OS/390 version and a UNIX version and a Windows version all participate in the same distributed system. Have (e.g.) an OS/390 version and a UNIX version and a Windows version all participate in the same distributed system.

Objective 11 Network Indepence Desirable to be able to support a variety of disparate communication networks also. Desirable to be able to support a variety of disparate communication networks also.

Objective 12 DBMS Independence All needed is that the DBMS instances at different sites all support the same interface– they don’t necessarily all of the same DBMS software. All needed is that the DBMS instances at different sites all support the same interface– they don’t necessarily all of the same DBMS software. For example, if Ingres and Oracle both supported the official SQL standard, the Ingres site and the Oracle site might be able to talk to each other in a distributed database system. For example, if Ingres and Oracle both supported the official SQL standard, the Ingres site and the Oracle site might be able to talk to each other in a distributed database system.

GATEWAY Ingres (SQL) Oracle (SQL) Ingres database Oracle database Ingres user Distributed Ingres database A hypothetical Ingres–provided gateway to Oracle: Site XSite Y

Thank you!