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Distributed Database SystemsAutumn, 2007
Chapter 7
Overview of Query Processing
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SQL: Non-Procedural Language of RDB
Tuple calculus◦ { t | F(t) } where:
t : tuple variableF(t) : well formed formula
Example◦ Get the No. and name of all managers
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( ) ( ){ }""|, MANAGERTITLEtEMPtENAMEENOt =∧∈
SQL: Non-Procedural Language of RDB
Domain calculus
where:xi : domain variables
: well formed formula
Example
{ x, y | E(x, y, "manager") }
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( ){ } ,,,|,,, 2121 nn xxxFxxx ⋅⋅⋅⋅⋅⋅
( )nxxxF ,,, 21 ⋅⋅⋅
Variables are position sensitive!
SQL: Non-Procedural Language of RDB
SQL is a tuple calculus language
SELECT ENO,ENAMEFROM EMPWHERE TITLE=“manager”
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End user uses non-procedural languages to express queries.
Query Processor
Query processor transforms queries into procedural operations to access data
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Query Processor
Distributed query processor has to deal with
◦query decomposition, and
◦data localization
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7.1 Query Processing Problems
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7.1 Query Processing ProblemsCentralized query processor must◦ transform calculus query into algebra operation, and
◦choose the best execution planExample:
SELECT ENAMEFROM E,GWHERE E.ENO = G.ENOAND RESP=“manager”
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7.1 Query Processing Problems
Relational Algebra 1
Relational Algebra 2
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( )( )GE ManagerRESPENOENAME ""=σπ <>
( )( )GEENOGENOEManagerRESPENAME ×=∧= ..""σπ
Execution plan 2 is better for consuming less resources!
7.1 Query Processing Problems
In DDB, the query processor must consider the communication cost and select the best site!Same query as last example, but G and E are distributed.Simple plan:◦ To transport all segments to query site and
execute there. This causes too much network traffic, very costly.
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7.1 Query Processing Problems
Distributed Query Example◦ Distribution of E and G
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7.1 Query Processing Problems
Distributed Query Example◦ Query
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( )( )GE ManagerREPSPENOENAME ""=σπ <>
7.1 Query Processing Problems
Distributed Query Example◦ Optimized Processing
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7.2 Objectives of Query Processing
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7.2 Objectives of Query Processing
Two-fold objectives:
◦Transformation, and
◦Optimization
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7.2 Objectives of Query Processing
Cost to be considered for optimization:
◦CPU time◦I/O time, and
◦Communication time
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WAN: the last cost is dominant
LAN: all three are equal
7.3 Complexity of Relational Algebra Operations
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7.3 Complexity of Relational Algebra Operations
Measured by n (cardinality) and tuples are sorted on comparison attributes
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O(n)
O(nÿlogn)
O(nÿlogn)
O(n2)
)duplicates(with ,πσ
GROUP ),duplicates(with π
−÷ ,,,, UI<>
×
7.4 Characterization of Query Processor
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7.4.1 Languages
For users:
◦ calculus or algebra based languages.For query processor:
◦map the input into internal form of algebra augmented with communication primitives.
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7.4.2 Types of Optimization
Exhaustive search◦ Workable for small solution space
Heuristics
◦ Perform first, semi-join, etc. for largesolution space
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σ π,
7.4.3 Optimization Timing
Static◦ Do it at compiling time by using statistics,
appropriate for exhaustive search, optimized once, but executed many times.
Dynamic◦ Do it at execution time, accurate, repeated
for every execution, expensive.
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7.4.4 Statistics
Facts of◦ Cardinalities◦ Attribute value distribution◦ Size of relation, etc.
Provided to query optimizer and periodically updated.
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7.4.5 Decision Site
For query optimization, it may be done by◦ Single site – centralized approach, or◦ All the sites involved – distributed, or◦ Hybrid – one site makes major decision in
cooperation with other sites making local decisions
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7.4.6 Exploration of the Network Topology
WAN◦ communication cost is dominant
LAN◦ communication cost is comparable to I/O
cost. Broadcasting capability, star network, satellite network should be considered.
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7.4.7 Exploration of Replicated Fragments
Use replications to minimize communication costs.
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7.4.8 Use of Semi-joins
Reduce the size of operand relations to cut down communication costs when overhead is not significant.
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7.5 Layers of Query Processing
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Generic Laying Scheme for Distributed Query Processing
7.5.1 Query Decomposition
Decompose calculus query into algebra query using global conceptual schema information.
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Step 1 – calculus normalization
Step 2 – semantic analysis to reject incorrect queries
Step 3 – simplification to eliminate redundant components
Step 4 – translation of calculus query into optimized algebra query.
7.5.2 Data Localization
Distributed query is mapped into a fragment query and simplified to produce a good one.
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7.5.3 Global Query Optimization
Find an execution strategy close to optimal.Find the best ordering of operations in the fragment query, including communication operations.Cost function defined in time is required.
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7.5.4 Local Query Optimization
Centralized system algorithms
(to be discussed in chapter 9)
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7.6 Conclusions
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7.6 Conclusions
Query processor – must be able to find good execution plan for a calculus query, s. t. CPU time, I/O time and communication time are minimized.Method: laying of◦ decomposition◦ localization◦ global query optimization◦ local query optimization
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