CS377: Database Systems Distributed Databases. Li Xiong Department of Mathematics and Computer Science Emory University

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1 CS377: Database Systems Distributed Databases Li Xiong Department of Mathematics and Computer Science Emory University 1

2 Centralized DBMS on a Network Site 1 Site 2 Site 5 Communication Network Site 4 Site 3 2

3 Distributed DBMS Environment Site 1 Site 2 Site 5 Communication Network Site 4 Site 3 3

4 Distributed Database System A distributed database (DDB) is a collection of multiple, logically interrelated databases distributed over a computer network. A distributed database management system (D DBMS) is the software that manages the DDB and provides an access mechanism that makes this distribution transparent to the users. Distributed database system (DDBS) = DDB + D DBMS 4

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6 Distributed Database System The EMPLOYEE, PROJECT, and WORKS_ON tables may be fragmented horizontally and stored with possible replication as shown below. 6

7 Distributed DBMS Promises ❶Transparent management of distributed, fragmented, and replicated data ❷Improved reliability/availability through distributed transactions ❸Improved performance ❹Easier and more economical system expansion 7

8 Distributed DBMS Issues Distributed Database Design How to distribute the database Query Processing Optimize cost = data transmission + local processing 8

9 Distributed DBMS Issues Concurrency Control Synchronization of concurrent accesses Consistency and isolation of transactions' effects Deadlock management Reliability How to make the system resilient to failures Atomicity and durability 9

10 Distributed database design Data distribution Top-down - mostly in designing systems from scratch Bottom-up - when the databases already exist at a number of sites Unit of distribution relation fragments of relations (sub-relations) Data are inherently fragmented, e.g. in locality Allow concurrent execution of a number of transactions that access different portions of a relation 10

11 Example Employee relation E (#,name,loc,sal, ) 40% of queries: 40% of queries: Qa: select * Qb: select * from E from E where loc=sa where loc=sb and and... Motivation: Two sites: Sa, Sb Qa Sa Sb Qb 11

12 Fragmentation Alternatives Horizontal PROJ 1 : projects with budgets less than $200,000 PROJ 2 : projects with budgets greater than or equal to $200,000 PROJ 1 PNO PNAME BUDGET LOC P1 Instrumentation Montreal P2 Database Develop New York PROJ PNO PNAME BUDGET LOC P1 Instrumentation Montreal P2 Database Develop New York P3 CAD/CAM New York P4 Maintenance Paris P5 CAD/CAM Boston PROJ 2 PNO PNAME BUDGET LOC P3 CAD/CAM New York P4 Maintenance Paris P5 CAD/CAM Boston 12

13 Fragmentation Alternatives Vertical PROJ 1 :information about project budgets PROJ 2 :information about project names and locations PROJ PNO PNAME BUDGET LOC P1 Instrumentation Montreal P2 Database Develop New York P3 CAD/CAM New York P4 Maintenance Paris P5 CAD/CAM Boston PROJ 1 PROJ 2 PNO BUDGET PNO PNAME LOC P P P P P P1 Instrumentation Montreal P2 Database Develop. New York P3 CAD/CAM New York P4 Maintenance Paris P5 CAD/CAM Boston 13

14 Data Fragmentation, Replication and Horizontal fragmentation Allocation A horizontal subset of a relation which contain those of tuples which satisfy selection conditions. E.g. Employee relation with selection condition (DNO = 5) Can be specified by a σ Ci (R) operation in the relational algebra. Complete horizontal fragmentation A set of horizontal fragments whose conditions C1, C2,, Cn include all the tuples in R- every tuple in R satisfies (C1 OR C2 OR OR Cn). Disjoint complete horizontal fragmentation: No tuple in R satisfies (Ci AND Cj) where i j. How to reconstruct R from complete horizontal fragments? 14

15 Three common horizontal partitioning techniques Round robin Hash partitioning Range partitioning 15 15

16 Round robin R D0 D1 D2 t1 t1 t2 t2 t3 t4 t4... t5 t3 16

17 Hash partitioning R D0 D1 D2 t1 h(k1)=2 t1 t2 h(k2)=0 t2 t3 h(k3)=0 t3 t4 h(k4)=1 t

18 Range partitioning R D0 D1 D2 t1: A=5 partitioning t1 t2: A=8 vector t2 t3: A=2 4 7 t3 t4: A=3 V0 V1 t

19 Data Fragmentation, Replication and Vertical fragmentation Allocation A vertical subset of a relation that contains a subset of columns. E.g. Employee relation: a vertical fragment of Name, Bdate, Sex Can be specified by a Π Li (R) operation in the relational algebra. Li Each fragment must include the primary key attribute of the parent relation Employee Complete vertical fragmentation A set of vertical fragments whose projection lists L1, L2,, Ln include all the attributes in R but share only the primary key of R. L1 L2... Ln = ATTRS (R) Li Lj = PK(R) for any i j How to reconstruct R from complete vertical fragments? 19

20 Data Fragmentation, Replication and Allocation Mixed (Hybrid) fragmentation A combination of Vertical fragmentation and Horizontal fragmentation. This is achieved by SELECT-PROJECT operations which is represented by Π Li (σ Ci (R)) 20

21 Data Fragmentation, Replication and Fragmentation schema Allocation A definition of a set of fragments (horizontal or vertical or mixed) that can reconstruct the original database Allocation schema Distribution of fragments to sites of distributed databases. It can be fully or partially replicated or can be partitioned Data Replication Full replication: database is replicated to all sites. Partial replication: some selected part is replicated 21

22 Distributed Database System The EMPLOYEE, PROJECT, and WORKS_ON tables may be fragmented horizontally and stored with possible replication as shown below. 22

23 Distributed DBMS Issues Distributed Database Design How to distribute the database Query Processing Optimize cost = data transmission + local processing 23

24 Query Processing in Distributed Databases Cost of transferring data (files and results) over the network is usually high Example: Employee at site 1 and Department at Site 2 Employee at site 1. 10,000 rows. Row size = 100 bytes. Table size = 10 6 bytes. Fname Minit Lname SSN Bdate Address Sex Salary Superssn Dno Department at Site rows. Row size = 35 bytes. Table size = 3,500 bytes. Dname Dnumber Mgrssn Mgrstartdate Q submitted at Site 3: retrieve employee name and department name where the employee works. Π Fname,Lname,Dname (Employee Dno = Dnumber Department) Result has 10,000 tuples and each result tuple is 40 bytes 24

25 Query Processing in Distributed Strategies: Databases 1. Transfer Employee and Department to site 3. Total transfer size 2. Transfer Employee to site 2, execute join at site 2 and send the result to site 3. Total transfer size 3. Transfer Department relation to site 1, execute the join at site 1, and send the result to site 3. Total bytes transferred Optimization criteria: minimizing data transfer. Which strategy? 25

26 Query Processing in Distributed Strategies: Databases 1. Transfer Employee and Department to site 3. Total transfer bytes = 1,000, = 1,003,500 bytes. 2. Transfer Employee to site 2, execute join at site 2 and send the result to site 3. Query result size = 40 * 10,000 = 400,000 bytes. Total transfer size = 400, ,000,000 = 1,400,000 bytes. 3. Transfer Department relation to site 1, execute the join at site 1, and send the result to site 3. Total bytes transferred = 400, = 403,500 bytes. Optimization criteria: minimizing data transfer. Preferred approach: strategy 3. 26

27 Query Processing in Distributed Databases What if Q is submitted at site 2? Example: Employee at site 1 and Department at Site 2 Employee at site 1. 10,000 rows. Row size = 100 bytes. Table size = 10 6 bytes. Fname Minit Lname SSN Bdate Address Sex Salary Superssn Dno Department at Site rows. Row size = 35 bytes. Table size = 3,500 bytes. Dname Dnumber Mgrssn Mgrstartdate Q submitted at Site 2: retrieve employee name and department name where the employee works. Π Fname,Lname,Dname (Employee Dno = Dnumber Department) Result has 10,000 tuples and each result tuple is 40 bytes 27

28 Query Processing in Distributed Databases Semijoin: Objective is to reduce the number of tuples in a relation before transferring it to another site. Example execution of Q: 1. Project the join attributes of Department at site 2, and transfer them to site 1. For Q, 4 * 100 = 400 bytes are transferred 2. Join the transferred file with the Employee relation at site 1, and transfer the required attributes from the resulting file to site 2. For Q, 32 * 10,000 = 320,000 bytes are transferred 3. Execute the query by joining the transferred file with Department and present the result to the user at site 2. Semi-join Left semi-join R S=Π R (R join S). 28

29 Parallel Databases Parallel database Using parallel processers Architectures Shared memory Shared disk Shared nothing Data partitioning (shard) 29