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Database , 6 Query Introduction
1.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/1 Outline âą Introduction âą Background âą Distributed Database Design âą Database Integration âą Semantic Data Control âą Distributed Query Processing ⥠Overview ⥠Query decomposition and localization ⥠Distributed query optimization âą Multidatabase Query Processing âą Distributed Transaction Management âą Data Replication âą Parallel Database Systems âą Distributed Object DBMS âą Peer-to-Peer Data Management âą Web Data Management âą Current Issues
2.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/2 Query Processing in a DDBMS high level user query query processor Low-level data manipulation commands for D-DBMS
3.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/3 Query Processing Components âą Query language that is used ⥠SQL: âintergalactic dataspeakâ âą Query execution methodology ⥠The steps that one goes through in executing high-level (declarative) user queries. âą Query optimization ⥠How do we determine the âbestâ execution plan? âą We assume a homogeneous D-DBMS
4.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/4 SELECT ENAME FROM EMP,ASG WHERE EMP.ENO = ASG.ENO AND RESP = "Manager" Strategy 1 ENAME( RESP=âManagerâ EMP.ENO=ASG.ENO(EMPĂASG)) Strategy 2 ENAME(EMP âENO ( RESP=âManagerâ (ASG)) Strategy 2 avoids Cartesian product, so may be âbetterâ Selecting Alternatives
5.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/5 What is the Problem? Site 1 Site 2 Site 3 Site 4 Site 5 EMP1= ENOâ€âE3â(EMP) EMP2= ENO>âE3â(EMP)ASG2= ENO>âE3â(ASG)ASG1= ENOâ€âE3â(ASG) Result Site 5 Site 1 Site 2 Site 3 Site 4 ASG1 EMP1 EMP2ASG2Site 4Site 3 Site 1 Site 2 Site 5 EMPâ 1=EMP1 âENO ASGâ 1 ' 2 EMPEMPresult ' 1 1Manager""RESP1 ASGÏASG ' 2Manager""RESP2 ASGÏASG ' ' 1 ASG ' 2 ASG ' 1 EMP ' 2 EMP result= (EMP1 Ă EMP2)âENOÏRESP=âManagerâ(ASG1Ă ASG2) EMPâ 2=EMP2 âENO ASGâ 2
6.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/6 Cost of Alternatives âą Assume ⥠size(EMP) = 400, size(ASG) = 1000 ⥠tuple access cost = 1 unit; tuple transfer cost = 10 units âą Strategy 1 ⥠produce ASG': (10+10) tuple access cost 20 ⥠transfer ASG' to the sites of EMP: (10+10) tuple transfer cost 200 ⥠produce EMP': (10+10) tuple access cost 2 40 ⥠transfer EMP' to result site: (10+10) tuple transfer cost 200 Total Cost 460 âą Strategy 2 ⥠transfer EMP to site 5: 400 tuple transfer cost 4,000 ⥠transfer ASG to site 5: 1000 tuple transfer cost 10,000 ⥠produce ASG': 1000 tuple access cost 1,000 ⥠join EMP and ASG': 400 20 tuple access cost 8,000 Total Cost 23,000
7.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/7 Query Optimization Objectives âą Minimize a cost function I/O cost + CPU cost + communication cost These might have different weights in different distributed environments âą Wide area networks ⥠communication cost may dominate or vary much ⊠bandwidth ⊠speed ⊠high protocol overhead âą Local area networks ⥠communication cost not that dominant ⥠total cost function should be considered âą Can also maximize throughput
8.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/8 Complexity of Relational Operations âą Assume ⥠relations of cardinality n ⥠sequential scan Operation Complexity Select Project (without duplicate elimination) O(n) Project (with duplicate elimination) Group O(n log n) Join Semi-join Division Set Operators O(n log n) Cartesian Product O(n2)
9.
Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/9 Query Optimization Issues â Types Of Optimizers âą Exhaustive search ⥠Cost-based ⥠Optimal ⥠Combinatorial complexity in the number of relations âą Heuristics ⥠Not optimal ⥠Regroup common sub-expressions ⥠Perform selection, projection first ⥠Replace a join by a series of semijoins ⥠Reorder operations to reduce intermediate relation size ⥠Optimize individual operations
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Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/10 Query Optimization Issues â Optimization Granularity âą Single query at a time ⥠Cannot use common intermediate results âą Multiple queries at a time ⥠Efficient if many similar queries ⥠Decision space is much larger
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Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/11 Query Optimization Issues â Optimization Timing âą Static ⥠Compilation ïš optimize prior to the execution ⥠Difficult to estimate the size of the intermediate results error propagation ⥠Can amortize over many executions ⥠R* âą Dynamic ⥠Run time optimization ⥠Exact information on the intermediate relation sizes ⥠Have to reoptimize for multiple executions ⥠Distributed INGRES âą Hybrid ⥠Compile using a static algorithm ⥠If the error in estimate sizes > threshold, reoptimize at run time ⥠Mermaid
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Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/12 Query Optimization Issues â Statistics âą Relation ⥠Cardinality ⥠Size of a tuple ⥠Fraction of tuples participating in a join with another relation âą Attribute ⥠Cardinality of domain ⥠Actual number of distinct values âą Common assumptions ⥠Independence between different attribute values ⥠Uniform distribution of attribute values within their domain
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Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/13 Query Optimization Issues â Decision Sites âą Centralized ⥠Single site determines the âbestâ schedule ⥠Simple ⥠Need knowledge about the entire distributed database âą Distributed ⥠Cooperation among sites to determine the schedule ⥠Need only local information ⥠Cost of cooperation âą Hybrid ⥠One site determines the global schedule ⥠Each site optimizes the local subqueries
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Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/14 Query Optimization Issues â Network Topology âą Wide area networks (WAN) â point-to-point ⥠Characteristics ⊠Low bandwidth ⊠Low speed ⊠High protocol overhead ⥠Communication cost will dominate; ignore all other cost factors ⥠Global schedule to minimize communication cost ⥠Local schedules according to centralized query optimization âą Local area networks (LAN) ⥠Communication cost not that dominant ⥠Total cost function should be considered ⥠Broadcasting can be exploited (joins) ⥠Special algorithms exist for star networks
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Distributed DBMS ©
M. T. Ăzsu & P. Valduriez Ch.6/15 Distributed Query Processing Methodology Calculus Query on Distributed Relations CONTROL SITE LOCAL SITES Query Decomposition Data Localization Algebraic Query on Distributed Relations Global Optimization Fragment Query Local Optimization Optimized Fragment Query with Communication Operations Optimized Local Queries GLOBAL SCHEMA FRAGMENT SCHEMA STATS ON FRAGMENTS LOCAL SCHEMAS
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