Parallel and Distributed Databases <ul><li>CS263 Lecture 16 </li></ul>
 
<ul><li>LECTURE PLAN </li></ul><ul><ul><ul><li>Parallel DBMS - What and Why? </li></ul></ul></ul><ul><ul><ul><li>What is a...
PARALLEL DATABASE SYSTEM
<ul><li>More and More Data! </li></ul><ul><li>We have databases that hold a high amount of </li></ul><ul><li>data, in the ...
<ul><li>Improves Response Time. </li></ul><ul><li>INTERQUERY PARALLELISM   </li></ul><ul><li>It is possible to process a n...
<ul><li>Speed-Up. </li></ul><ul><li>As you multiply resources by a certain factor, the time taken </li></ul><ul><li>to exe...
Linear speed-up (ideal)‏ Number of CPUs Number of transactions/second Sub-linear speed-up 1000/Sec 5 CPUs 2000/Sec 10 CPUs...
Number of CPUs, Database size Number of transactions/second Linear scale-up (ideal)‏ PARALLEL DBMSs SCALE-UP 10 CPUs 2 GB ...
MEMORY Shared Memory – Parallel Database Architecture X CPU CPU CPU CPU CPU CPU X X X
Shared Disk – Parallel Database Architecture CPU CPU CPU CPU CPU CPU M M M M M M X X X
Shared Nothing – Parallel Database Architecture CPU M CPU M CPU M CPU M CPU M
MAINFRAME DATABASE SYSTEM
SPECIALISED NETWORK CONNECTION TERMINALS MAINFRAME COMPUTER PRESENTATION LOGIC BUSINESS LOGIC DATA LOGIC DUMB DUMB DUMB
CLIENT/SERVER DATABASE SYSTEM
<ul><li>CLIENT/SERVER DBMS </li></ul><ul><ul><ul><li>Manages user interface </li></ul></ul></ul><ul><ul><ul><li>Accepts us...
<ul><li>CLIENT/SERVER DBMS </li></ul><ul><ul><ul><li>Accepts database requests </li></ul></ul></ul><ul><ul><ul><li>Process...
    CLIENT/SERVER DBMS ARCHITECTURE PRESENTATION LOGIC BUSINESS LOGIC DATA LOGIC (FAT CLIENT)‏ SERVER   DBMS NETWORK...
D/BASE SERVER DBMS       CLIENT/SERVER DBMS ARCHITECTURE PRESENTATION LOGIC BUSINESS LOGIC DATA LOGIC (THIN CLIENT)‏...
LAN CLIENT CLIENT LAN CLIENT CLIENT CLIENT CLIENT LAN CLIENT CLIENT LAN CLIENT Leyton CLIENT CLIENT CLIENT Stratford DBMS ...
DISTRIBUTED DATABASE SYSTEM
<ul><ul><ul><li>A distributed database system is a collection of  </li></ul></ul></ul><ul><ul><ul><li>logically related da...
WIDE  AREA  NETWORK LAN CLIENT CLIENT CLIENT CLIENT DBMS DISTRIBUTED DATABASE ARCHITECTURE LAN CLIENT CLIENT CLIENT CLIENT...
D/BASE SERVER #1 DBMS CLIENT#1 D/BASE SERVER #2 DBMS CLIENT#2 CLIENT#3 M:N CLIENT/SERVER DBMS ARCHITECTURE NOT TRANSPARENT...
DB Computer Network Site 2 Site 1 GSC DDBMS DC LDBMS GSC DDBMS DC LDBMS  =   Local DBMS   DC  = Data Communications  GSC  ...
<ul><li>Reduced Communication Overhead </li></ul><ul><li>Most data access is local, less expensive and performs  </li></ul...
<ul><li>Expandability   </li></ul><ul><li>It is easier to accommodate increasing the size of the </li></ul><ul><li>global ...
<ul><ul><li>A distributed system looks exactly like  </li></ul></ul><ul><ul><li>a non-distributed system to the user! </li...
<ul><ul><ul><li>Data Allocation   </li></ul></ul></ul><ul><ul><ul><li>Data Fragmentation   </li></ul></ul></ul><ul><ul><ul...
<ul><ul><li>Locality of reference   </li></ul></ul><ul><ul><ul><li>Is the data near to the sites that need it? </li></ul><...
<ul><ul><li>CENTRALISED   </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performance Communic...
<ul><ul><li>PARTITIONED/FRAGMENTED   </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performan...
<ul><ul><li>COMPLETE REPLICATION   </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performance...
<ul><ul><li>SELECTIVE REPLICATION   </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performanc...
<ul><ul><li>Usage   </li></ul></ul><ul><ul><ul><li>Applications are usually interested in ‘views’ not whole relations . </...
Horizontal Fragmentation: Consists of a Restriction on a Relation. e.g.,   (    branch = ‘Stratford’  Account)‏ DISTRIBUT...
STRATFORD BRANCH BARKING BRANCH DISTRIBUTED DATABASES HORIZONTAL DATA FRAGMENTATION STRATFORD STRATFORD STRATFORD 333.00 K...
KJTR78 KHA456T 0208-500-5821 STRATFORD KHAN 456 ZZEE56 GRA324S 0208-545-7528 BARKING GRAY 324 XXYY22 JON200T 0208-500-9000...
KJTR78 ZZEE56 XXYY22 KHA456T 456 GRA324S 324 JON200T 200 PASSWORD LOGIN-ID S# STUDENT ADMINISTRATION NETWORK ADMINISTRATIO...
DISTRIBUTED DATABASES DISTRIBUTED CATALOG MANAGEMENT <ul><li>Centralised Global Catalog </li></ul><ul><li>One site  mainta...
DISTRIBUTED DATABASES DISTRIBUTED CATALOG MANAGEMENT <ul><li>Replicated Global Catalog </li></ul><ul><li>Each site maintai...
DISTRIBUTED DATABASES DISTRIBUTED TRANSACTIONS Stratford DB Barking DB Leyton DB Stratford DBMS Stratford Client Stratford...
TWO-PHASE COMMIT (2PC) - OK
TWO-PHASE COMMIT (2PC) - ABORT ‘ Global Abort’
<ul><li>Architectural complexity. </li></ul><ul><li>Cost. </li></ul><ul><li>Security. </li></ul><ul><li>Integrity control ...
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Database ppt

  1. 1. Parallel and Distributed Databases <ul><li>CS263 Lecture 16 </li></ul>
  2. 3. <ul><li>LECTURE PLAN </li></ul><ul><ul><ul><li>Parallel DBMS - What and Why? </li></ul></ul></ul><ul><ul><ul><li>What is a Client/Server DBMS? </li></ul></ul></ul><ul><ul><ul><li>Why do we need Distributed DBMSs? </li></ul></ul></ul><ul><ul><ul><li>Date’s rules for a Distributed DBMS </li></ul></ul></ul><ul><ul><ul><li>Benefits of a Distributed DBMS </li></ul></ul></ul><ul><ul><ul><li>Issues associated with a Distributed DBMS </li></ul></ul></ul><ul><ul><ul><li>Disadvantages of a Distributed DBMS </li></ul></ul></ul>
  3. 4. PARALLEL DATABASE SYSTEM
  4. 5. <ul><li>More and More Data! </li></ul><ul><li>We have databases that hold a high amount of </li></ul><ul><li>data, in the order of 10 12 bytes: </li></ul><ul><li>10,000,000,000,000 bytes ! </li></ul><ul><li>Faster and Faster Access! </li></ul><ul><li>We have data applications that need to process </li></ul><ul><li>data at very high speeds: </li></ul><ul><li>10,000s transactions per second ! </li></ul>SINGLE-PROCESSOR DBMS AREN’T UP TO THE JOB! PARALLEL DBMSs WHY DO WE NEED THEM?
  5. 6. <ul><li>Improves Response Time. </li></ul><ul><li>INTERQUERY PARALLELISM </li></ul><ul><li>It is possible to process a number of transactions in </li></ul><ul><li>parallel with each other. </li></ul><ul><li>Improves Throughput . </li></ul><ul><li>INTRAQUERY PARALLELISM </li></ul><ul><li>It is possible to process ‘sub-tasks’ of a transaction in </li></ul><ul><li>parallel with each other. </li></ul>PARALLEL DBMSs BENEFITS OF A PARALLEL DBMS
  6. 7. <ul><li>Speed-Up. </li></ul><ul><li>As you multiply resources by a certain factor, the time taken </li></ul><ul><li>to execute a transaction should be reduced by the same factor: </li></ul><ul><li>10 seconds to scan a DB of 10,000 records using 1 CPU </li></ul><ul><li>1 second to scan a DB of 10,000 records using 10 CPUs </li></ul><ul><li>Scale-up . </li></ul><ul><li>As you multiply resources the size of a task that can be executed </li></ul><ul><li>in a given time should be increased by the same factor. </li></ul><ul><li>1 second to scan a DB of 1,000 records using 1 CPU </li></ul><ul><li>1 second to scan a DB of 10,000 records using 10 CPUs </li></ul>PARALLEL DBMSs HOW TO MEASURE THE BENEFITS
  7. 8. Linear speed-up (ideal)‏ Number of CPUs Number of transactions/second Sub-linear speed-up 1000/Sec 5 CPUs 2000/Sec 10 CPUs 16 CPUs 1600/Sec PARALLEL DBMSs SPEED-UP
  8. 9. Number of CPUs, Database size Number of transactions/second Linear scale-up (ideal)‏ PARALLEL DBMSs SCALE-UP 10 CPUs 2 GB Database Sub-linear scale-up 1000/Sec 5 CPUs 1 GB Database 900/Sec
  9. 10. MEMORY Shared Memory – Parallel Database Architecture X CPU CPU CPU CPU CPU CPU X X X
  10. 11. Shared Disk – Parallel Database Architecture CPU CPU CPU CPU CPU CPU M M M M M M X X X
  11. 12. Shared Nothing – Parallel Database Architecture CPU M CPU M CPU M CPU M CPU M
  12. 13. MAINFRAME DATABASE SYSTEM
  13. 14. SPECIALISED NETWORK CONNECTION TERMINALS MAINFRAME COMPUTER PRESENTATION LOGIC BUSINESS LOGIC DATA LOGIC DUMB DUMB DUMB
  14. 15. CLIENT/SERVER DATABASE SYSTEM
  15. 16. <ul><li>CLIENT/SERVER DBMS </li></ul><ul><ul><ul><li>Manages user interface </li></ul></ul></ul><ul><ul><ul><li>Accepts user data </li></ul></ul></ul><ul><ul><ul><li>Processes application/business logic </li></ul></ul></ul><ul><ul><ul><li>Generates database requests (SQL)‏ </li></ul></ul></ul><ul><ul><ul><li>Transmits database requests to server </li></ul></ul></ul><ul><ul><ul><li>Receives results from server </li></ul></ul></ul><ul><ul><ul><li>Formats results according to application logic </li></ul></ul></ul><ul><ul><ul><li>Present results to the user </li></ul></ul></ul>CLIENT PROCESS
  16. 17. <ul><li>CLIENT/SERVER DBMS </li></ul><ul><ul><ul><li>Accepts database requests </li></ul></ul></ul><ul><ul><ul><li>Processes database requests </li></ul></ul></ul><ul><ul><ul><ul><li>Performs integrity checks </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Handles concurrent access </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Optimises queries </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Performs security checks </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Enacts recovery routines </li></ul></ul></ul></ul><ul><ul><ul><li>Transmits result of database request to client </li></ul></ul></ul>SERVER PROCESS
  17. 18.     CLIENT/SERVER DBMS ARCHITECTURE PRESENTATION LOGIC BUSINESS LOGIC DATA LOGIC (FAT CLIENT)‏ SERVER   DBMS NETWORK  Data Request  Data Response CLIENT#1 CLIENT#2 CLIENT#3 D/BASE
  18. 19. D/BASE SERVER DBMS       CLIENT/SERVER DBMS ARCHITECTURE PRESENTATION LOGIC BUSINESS LOGIC DATA LOGIC (THIN CLIENT)‏ PL/SQL NETWORK  Data Request  Data Response CLIENT#1 CLIENT#2 CLIENT#3
  19. 20. LAN CLIENT CLIENT LAN CLIENT CLIENT CLIENT CLIENT LAN CLIENT CLIENT LAN CLIENT Leyton CLIENT CLIENT CLIENT Stratford DBMS WIDE AREA NETWORK Barking Leytonstone DISTRIBUTED PROCESSING ARCHITECTURE CLIENT CLIENT CLIENT CLIENT
  20. 21. DISTRIBUTED DATABASE SYSTEM
  21. 22. <ul><ul><ul><li>A distributed database system is a collection of </li></ul></ul></ul><ul><ul><ul><li>logically related databases that co-operate in a </li></ul></ul></ul><ul><ul><ul><li>transparent manner . </li></ul></ul></ul><ul><ul><ul><li>Transparent implies that each user within the </li></ul></ul></ul><ul><ul><ul><li>system may access all of the data within all of the </li></ul></ul></ul><ul><ul><ul><li>databases as if they were a single database </li></ul></ul></ul><ul><ul><ul><li>There should be ‘location independence’ i.e.- as </li></ul></ul></ul><ul><ul><ul><li>the user is unaware of where the data is located it </li></ul></ul></ul><ul><ul><ul><li>is possible to move the data from one physical </li></ul></ul></ul><ul><ul><ul><li>location to another without affecting the user. </li></ul></ul></ul>DISTRIBUTED DATABASES WHAT IS A DISTRIBUTED DATABASE?
  22. 23. WIDE AREA NETWORK LAN CLIENT CLIENT CLIENT CLIENT DBMS DISTRIBUTED DATABASE ARCHITECTURE LAN CLIENT CLIENT CLIENT CLIENT DBMS Leytonstone CLIENT CLIENT CLIENT DBMS Stratford CLIENT CLIENT CLIENT DBMS Barking CLIENT CLIENT CLIENT Leyton CLIENT
  23. 24. D/BASE SERVER #1 DBMS CLIENT#1 D/BASE SERVER #2 DBMS CLIENT#2 CLIENT#3 M:N CLIENT/SERVER DBMS ARCHITECTURE NOT TRANSPARENT! NETWORK
  24. 25. DB Computer Network Site 2 Site 1 GSC DDBMS DC LDBMS GSC DDBMS DC LDBMS = Local DBMS DC = Data Communications GSC = Global Systems Catalog DDBMS = Distributed DBMS COMPONENTS OF A DDBMS
  25. 26. <ul><li>Reduced Communication Overhead </li></ul><ul><li>Most data access is local, less expensive and performs </li></ul><ul><li>better . </li></ul><ul><li>Improved Processing Power </li></ul><ul><li>Instead of one server handling the full database, we now </li></ul><ul><li>have a collection of machines handling the same database. </li></ul><ul><li>Removal of Reliance on a Central Site </li></ul><ul><li>If a server fails, then the only part of the system that is </li></ul><ul><li>affected is the relevant local site. The rest of the system </li></ul><ul><li>remains functional and available. </li></ul>DISTRIBUTED DATABASES ADVANTAGES
  26. 27. <ul><li>Expandability </li></ul><ul><li>It is easier to accommodate increasing the size of the </li></ul><ul><li>global (logical) database. </li></ul><ul><li>Local autonomy </li></ul><ul><li>The database is brought nearer to its users. This can effect </li></ul><ul><li>a cultural change as it allows potentially greater control </li></ul><ul><li>over local data . </li></ul>DISTRIBUTED DATABASES ADVANTAGES
  27. 28. <ul><ul><li>A distributed system looks exactly like </li></ul></ul><ul><ul><li>a non-distributed system to the user! </li></ul></ul><ul><ul><li>Local autonomy </li></ul></ul><ul><ul><li>No reliance on a central site </li></ul></ul><ul><ul><li>Continuous operation </li></ul></ul><ul><ul><li>Location independence </li></ul></ul><ul><ul><li>Fragmentation independence </li></ul></ul><ul><ul><li>Replication independence </li></ul></ul><ul><ul><li>Distributed query independence </li></ul></ul><ul><ul><li>Distributed transaction processing </li></ul></ul><ul><ul><li>Hardware independence </li></ul></ul><ul><ul><li>Operating system independence </li></ul></ul><ul><ul><li>Network independence </li></ul></ul><ul><ul><li>Database independence </li></ul></ul>DISTRIBUTED DATABASES DATE’S TWELVE RULES FOR A DDBMS
  28. 29. <ul><ul><ul><li>Data Allocation </li></ul></ul></ul><ul><ul><ul><li>Data Fragmentation </li></ul></ul></ul><ul><ul><ul><li>Distributed Catalogue Management </li></ul></ul></ul><ul><ul><ul><li>Distributed Transactions </li></ul></ul></ul><ul><ul><ul><li>Distributed Queries – (see chapter 20)‏ </li></ul></ul></ul>DISTRIBUTED DATABASES ISSUES
  29. 30. <ul><ul><li>Locality of reference </li></ul></ul><ul><ul><ul><li>Is the data near to the sites that need it? </li></ul></ul></ul><ul><ul><li>Reliability and availability </li></ul></ul><ul><ul><ul><li>Does the strategy improve fault tolerance and accessibility? </li></ul></ul></ul><ul><ul><li>Performance </li></ul></ul><ul><ul><ul><li>Does the strategy result in bottlenecks or under-utilisation of resources? </li></ul></ul></ul><ul><ul><li>Storage costs </li></ul></ul><ul><ul><ul><li>How does the strategy effect the availability and cost of data storage? </li></ul></ul></ul><ul><ul><li>Communication costs </li></ul></ul><ul><ul><ul><li>How much network traffic will result from the strategy? </li></ul></ul></ul>DISTRIBUTED DATABASES DATA ALLOCATION METRICS
  30. 31. <ul><ul><li>CENTRALISED </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performance Communication Costs Lowest Lowest Lowest Unsatisfactory Highest DISTRIBUTED DATABASES DATA ALLOCATION STRATEGIES
  31. 32. <ul><ul><li>PARTITIONED/FRAGMENTED </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performance Communication Costs High Low (item) – High (system)‏ Lowest Satisfactory Low DISTRIBUTED DATABASES DATA ALLOCATION STRATEGIES
  32. 33. <ul><ul><li>COMPLETE REPLICATION </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performance Communication Costs Highest Highest Highest High High (update) – Low (read)‏ DISTRIBUTED DATABASES DATA ALLOCATION STRATEGIES
  33. 34. <ul><ul><li>SELECTIVE REPLICATION </li></ul></ul>Locality of Reference Reliability/Availability Storage Costs Performance Communication Costs High Average Satisfactory Low Low (item) – High (system)‏ DISTRIBUTED DATABASES DATA ALLOCATION STRATEGIES
  34. 35. <ul><ul><li>Usage </li></ul></ul><ul><ul><ul><li>Applications are usually interested in ‘views’ not whole relations . </li></ul></ul></ul><ul><ul><li>Efficiency </li></ul></ul><ul><ul><ul><li>It’s more efficient if data is close to where it is frequently used. </li></ul></ul></ul><ul><ul><li>Parallelism </li></ul></ul><ul><ul><ul><li>It is possible to run several ‘sub-queries’ in tandem. </li></ul></ul></ul><ul><ul><li>Security </li></ul></ul><ul><ul><ul><li>Data not required by local applications is not stored at the local </li></ul></ul></ul><ul><ul><ul><li>site. </li></ul></ul></ul>DISTRIBUTED DATABASES WHY FRAGMENT DATA?
  35. 36. Horizontal Fragmentation: Consists of a Restriction on a Relation. e.g., (  branch = ‘Stratford’ Account)‏ DISTRIBUTED DATABASES HORIZONTAL DATA FRAGMENTATION 333.00 STRATFORD KHAN 456 500.00 BARKING ONO 400 340.14 BARKING GREEN 350 23.17 STRATFORD SMITH 345 200.00 BARKING GRAY 324 1000.00 STRATFORD JONES 200 BALANCE BRANCH CUSTOMER ACCOUNT
  36. 37. STRATFORD BRANCH BARKING BRANCH DISTRIBUTED DATABASES HORIZONTAL DATA FRAGMENTATION STRATFORD STRATFORD STRATFORD 333.00 KHAN 456 23.17 SMITH 345 1000.00 JONES 200 BALANCE BRANCH CUSTOMER ACCT NO. BARKING BARKING BARKING 500.00 ONO 400 340.14 GREEN 350 200.00 GRAY 324 BALANCE BRANCH CUSTOMER ACCT NO.
  37. 38. KJTR78 KHA456T 0208-500-5821 STRATFORD KHAN 456 ZZEE56 GRA324S 0208-545-7528 BARKING GRAY 324 XXYY22 JON200T 0208-500-9000 STRATFORD JONES 200 PASSWORD LOGIN PHONE NO SITE NAME S# Vertical Fragmentation: Consists of a Projection on a Relation. e.g., (  S#, NAME, SITE, PHONE NO Student)‏ DISTRIBUTED DATABASES VERTICAL DATA FRAGMENTATION
  38. 39. KJTR78 ZZEE56 XXYY22 KHA456T 456 GRA324S 324 JON200T 200 PASSWORD LOGIN-ID S# STUDENT ADMINISTRATION NETWORK ADMINISTRATION DISTRIBUTED DATABASES VERTICAL DATA FRAGMENTATION STRATFORD BARKING STRATFORD KHAN 456 GRAY 324 0208-500-5821 0208-545-7528 0208-500-9000 JONES 200 PHONE NO. SITE NAME S#
  39. 40. DISTRIBUTED DATABASES DISTRIBUTED CATALOG MANAGEMENT <ul><li>Centralised Global Catalog </li></ul><ul><li>One site maintains the full global catalog. All changes to </li></ul><ul><li>any local system catalog have to be propagated to the site </li></ul><ul><li>maintaining the global catalog. Bad performance, single </li></ul><ul><li>point of failure , compromises site autonomy . </li></ul><ul><li>Dispersed Catalog </li></ul><ul><li>There is no physical global catalog . Each time a remote </li></ul><ul><li>data item is required, the catalogues from ALL other sites </li></ul><ul><li>are examined for the item. This has severe performance </li></ul><ul><li>penalties . </li></ul>
  40. 41. DISTRIBUTED DATABASES DISTRIBUTED CATALOG MANAGEMENT <ul><li>Replicated Global Catalog </li></ul><ul><li>Each site maintains its own global catalog. Although this </li></ul><ul><li>greatly speeds up remote data location, it is very </li></ul><ul><li>inefficient to maintain . A detail of every data item added, </li></ul><ul><li>changed or deleted locally has to be propagated to ALL </li></ul><ul><li>other sites . </li></ul><ul><li>Local-Master Catalog </li></ul><ul><li>Each site maintains both its local system catalog as well </li></ul><ul><li>as a catalog of all of its data items that are replicated at </li></ul><ul><li>other sites. This avoids compromising site autonomy , is </li></ul><ul><li>fairly efficient , and is not a single point of failure . </li></ul>
  41. 42. DISTRIBUTED DATABASES DISTRIBUTED TRANSACTIONS Stratford DB Barking DB Leyton DB Stratford DBMS Stratford Client Stratford Client Stratford Client Barking DBMS Leyton DBMS Global Transaction (a) Debit Stratford A/C £500 (b) Credit Barking A/C £350 (c) Credit Leyton A/C £150 (a)‏ (b)‏ (c)‏ X ATOMIC DISTRIBUTED TRANSACTION
  42. 43. TWO-PHASE COMMIT (2PC) - OK
  43. 44. TWO-PHASE COMMIT (2PC) - ABORT ‘ Global Abort’
  44. 45. <ul><li>Architectural complexity. </li></ul><ul><li>Cost. </li></ul><ul><li>Security. </li></ul><ul><li>Integrity control more difficult. </li></ul><ul><li>Lack of standards. </li></ul><ul><li>Lack of experience. </li></ul><ul><li>Database design more complex. </li></ul>DISTRIBUTED DATABASES DISADVANTAGES OF DDBMSs
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