2. SQL: Non-Procedural Language of RDB
Tuple calculus
◦ { t | F(t) } where:
t : tuple variable
F(t) : well formed formula
Example
◦ Get the No. and name of all managers
2
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"
"
|
, MANAGER
TITLE
t
EMP
t
ENAME
ENO
t
3. 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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,
,
,
|
,
,
, 2
1
2
1 n
n x
x
x
F
x
x
x
n
x
x
x
F ,
,
, 2
1
Variables are position sensitive!
4. SQL: Non-Procedural Language of RDB
SQL is a tuple calculus language
SELECT ENO,ENAME
FROM EMP
WHERE TITLE=“manager”
4
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End user uses non-procedural languages
to express queries.
5. Query Processor
Query processor transforms queries into
procedural operations to access data
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6. Query Processor
Distributed query processor has to deal
with
◦query decomposition, and
◦data localization
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8. 7.1 Query Processing Problems
Centralized query processor must
◦transform calculus query into
algebra operation, and
◦choose the best execution plan
Example:
SELECT ENAME
FROM E,G
WHERE E.ENO = G.ENO
AND RESP=“manager”
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9. 7.1 Query Processing Problems
Relational Algebra 1
Relational Algebra 2
9
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G
E Manager
RESP
ENO
ENAME "
"
G
E
ENO
G
ENO
E
Manager
RESP
ENAME
.
.
"
"
Execution plan 2 is better for consuming
less resources!
10. 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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11. 7.1 Query Processing Problems
Distributed Query Example
◦ Distribution of E and G
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12. 7.1 Query Processing Problems
Distributed Query Example
◦ Query
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G
E Manager
REPSP
ENO
ENAME "
"
13. 7.1 Query Processing Problems
Distributed Query Example
◦ Optimized Processing
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15. 7.2 Objectives of Query Processing
Two-fold objectives:
◦Transformation, and
◦Optimization
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16. 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
17. 7.3 Complexity of Relational Algebra Operations
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18. 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(nlogn)
O(nlogn)
O(n2)
)
duplicates
(with
,
GROUP
),
duplicates
(with
,
,
,
,
20. 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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21. 7.4.2 Types of Optimization
Exhaustive search
◦ Workable for small solution space
Heuristics
◦ Perform first, semi-join, etc. for large
solution space
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,
22. 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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23. 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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24. 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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25. 7.4.6 Exploration of the NetworkTopology
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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26. 7.4.7 Exploration of Replicated Fragments
Use replications to minimize
communication costs.
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27. 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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28. 7.5 Layers of Query Processing
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30. 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.
31. 7.5.2 Data Localization
Distributed query is mapped into
a fragment query and simplified
to produce a good one.
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32. 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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33. 7.5.4 Local Query Optimization
Centralized system algorithms
(to be discussed in chapter 9)
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35. 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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