1. File Processing : Query Processing
2018, Spring
Pusan National University
Ki-Joune Li

2. Basic Concepts of Query
Retrieve records satisfying predicates
Types of Query
Operators
Aggregate Query
Sorting

3. Relational Operators : Select
Selection (condition)
Retrieve records satisfying predicates
Example
Find Student where Student.Score > 3.5
score>3.5(Student)
Index or Hash
Predicate
Select

4. Relational Operators : Project
Project (attributes)
Extract interesting attributes
Example
Find Student.name where score > 3.5
name(acore>3.5(Student))
Full Scan
Interesting attributes to get
Extract

5. Cartesian Product Cartesian Product () Join ( ) Two Tables : R1  R2
Produce all cross products
Join ( )
r11 …
r21
r22
r2n
r12
r1m
r11
r12 …
r1m R1
r21
r22 …
r2n R2  =

6. Join
Join ( )
Select combined records of cartesian product with same value of a common attribute (Natural Join)
Example
Student (StudentName, AdvisorProfessorID, Department, Score)
Professor(ProfessorName, ProfessorID, Department)
Student AdivsorProfessorID=ProfessorID Professor
=  AdivsorProfessorID=ProfessorID(Student Professor)
Double Scan : Expensive Operation

7. Relational Algebra Relational Algebra Operand : Table (Relation)
Operator : Relational Operator (, , , etc)
Example: SQL and relational algebra
find Student.Name from Student, Professor where Student.Score > 3.5 and Student.AdvisorProfessorID=Professor.ID and Professor.Department=‘CSE’
student.name(score>3.5(Student) Department=‘CSE’ (Professor) )
Relational Algebra Specifies the sequence of operations   

8. Query Processing Mechanism
Query Processing Steps
1. Parsing and translation
2. Optimization
3. Evaluation

9. Parsing and Translation
Parsing Query Statement (e.g. in SQL)
Translation into relational algebra
Equivalent Expression
For a same query statement
several relation algebraic expressions are possible
Example
name(balance  2500(account )) name (balance  2500(name, balance (account )))
Different execution schedules
Query Execution Plan (QEP)
Determined by relational algebra
Several QEPs may be produced by Parsing and Translation

10. Query Optimization Choose ONE QEP among QEPs based on
Execution Cost of each QEP, where cost means execution time
How to find cost of each QEP ?
Real Execution
Exact but Not Feasible
Cost Estimation
Types of Operations
Number of Records
Selectivity
Distribution of data

11. Cost Model : Basic Concepts
Cost Model : Number of Block Accesses
Cost
C = Cindex + Cdata
where Cindex : Cost for Index Access
Cdata : Cost for Data Block Retrieval
Cindex vs. Cdata ?
Cindex : depends on index
Cdata
depends on selectivity
Random Access or Sequential Access
Selectivity
Number (or Ratio) of Objects Selected by Query

12. Cost Model : Type of Operations
Cost model for each type of operations
Select
Project
Join
Aggregate Query
Query Processing Method for each type of operations
Index/Hash or Not

13. Cost Model : Number of Records
Nrecord  Nblocks
Number of Scans
Single Scan
O(N) : Linear Scan
O(logN ) : Index
Multiple Scans
O(NM ) : Multiple Linear Scans
O(N logM ) : Multiple Scans with Index

14. Selectivity Selectivity Affects on Cdata Random Access
Scattered on several blocks
Nblock  Nselected
Sequential Access
Contiguously stored on blocks
Nblock = Nselected / Bf

15. Selectivity Estimation
Depends on Data Distribution
Example
Q1 : Find students where 60 < weight < 70
Q2 : Find students where 80 < weight < 90
How to find the distribution
Parametric Method
e.g. Gaussian Distribution
No a priori knowledge
Non-Parametric Method
e.g. Histogram
Smoothing is necessary
Wavelet, Discrete Cosine 30 40 50 60 70 80 90
100
Frequency

16. Select : Linear Search Algorithm : linear search
Scan each file block and test all records to see whether they satisfy the selection condition.
Cost estimate (number of disk blocks scanned) = br
br denotes number of blocks containing records from relation r
If selection is on a key attribute (sorted), cost = (br /2)
stop on finding record
Linear search can be applied regardless of
selection condition or
ordering of records in the file, or
availability of indices

17. Select : Range Search Algorithm : primary index, comparison
Relation is sorted on A
For A  V (r)
Step 1: use index to find first tuple  v and
Step 2: scan relation sequentially
For AV (r)
just scan relation sequentially till first tuple > v; do not use index
Algorithm : secondary index, comparison
Step 1: use index to find first index entry  v and
Step 2: scan index sequentially to find pointers to records.
scan leaf nodes of index finding pointers to records, till first entry > v

18. Select : Range Search Comparison between Secondary Index
Searching with Index and
Linear Search
Secondary Index
retrieval of records that are pointed to
requires an I/O for each record
Linear file scan may be cheaper
if records are scattered on many blocks
clustering is important for this reason

19. Select : Complex Query Conjunction : 1  2 . . . n(r)
Algorithm : selection using one index
Step 1: Select a condition of i (i (r) )
Step 2: Test other conditions on tuple after fetching it into memory buffer.
Algorithm : selection using multiple-key index
Use appropriate multiple-attribute index if available.
Algorithm : selection by intersection of identifiers
Step 1: Requires indices with record pointers.
Step 2: Intersection of all the obtained sets of record pointers.
Step 3: Then fetch records from file
Disjunction : 1 2 . . . n (r)
Algorithm : Disjunctive selection by union of identifiers

20. Join Operation Several different algorithms to implement joins
Nested-loop join
Block nested-loop join
Indexed nested-loop join
Merge-join
Hash-join
Choice based on cost estimate
Examples use the following information
Number of records of (S)customer: 10, (R)depositor: 5000
Number of blocks of customer: depositor: 100
Blocking factors of customer : depositor:

21. Nested-Loop Join
Algorithm NLJ the theta join r  s For each tuple tr in r do begin For each tuple ts in s do begin test pair (tr,ts) to see if they satisfy the join condition  if they do, add tr • ts to the result. End End
r : outer relation, s : inner relation.
No indices, any kind of join condition.
Expensive

22. Example: Nested-Loop JoinB1 s1 B1 r1 s2 r2 … …
r50
s250 B2
r51 B2
s251
r52 … …
s500
r100 … …
B400
s9751
B100
r4951
s9752
r4952 … …
s10000
r5000

23. Nested-Loop Join : Performance
Worst case
the estimated cost is nr  bs + br disk accesses, if not enough memory only to hold one block of each relation,
Example
5000  = 2,000,100 disk accesses with depositor as outer relation, and
1000  = 1,000,400 disk accesses with customer as the outer relation.
If the smaller relation fits entirely in memory,
use that as the inner relation.
Reduces cost to br + bs disk accesses.
If smaller relation (depositor) fits entirely in memory, cost estimate will be 500 disk accesses.

24. Block Nested-Loop Join
Algoritm BNLJ
For each block Br of r do
Get Block Br For each block Bs of s do
Get Block Bs For each tuple tr in Br do For each tuple ts in Bs do Check if (tr, ts) satisfy the join condition if they do, add tr • ts to the result. End End End End
No disk access required
Disk access happens here
No disk access required

25. Example: Block-Oriented Nested-Loop Join… …
r50
s250 B2
r51 B2
s251
r52 … …
s500
r100 … …
B400
s9751
B100
r4951
s9752
r4952 … …
s10000
r5000

26. Block Nested-Loop Join : Performance
Worst case
Estimate: br  bs + br block accesses.
Each block in the inner relation s is read once for each block in the outer relation (instead of once for each tuple in the outer relation)
Improvements : If M blocks can be buffered
use (M-2) disk blocks as blocking unit for outer relations,
use remaining two blocks to buffer inner relation and output
Then the cost becomes br / (M-2)  bs + br

27. Indexed Nested-Loop Join
Index lookups can replace file scans if
join is an equi-join or natural join and
an index is available on the inner relation’s join attribute
Can construct an index just to compute a join.
Algorithm INLJ
For each block Br of r do
Get Block Br For each tuple tr in Br do Search Index (IDXr , tr.key)
if found, add tr • ts to the result. End
End

28. Indexed Nested-Loop Join : Performance
Worst case
buffer has space for only one page of r,
Cost of the join: br + nr  c
Where c is the cost of traversing index and fetching matching tuple
Number of matching tuples may be greater than one.
If indices are available on join attributes of both r and s,
use the relation with fewer tuples as the outer relation

29. Example of Nested-Loop Join Costs
Assume depositor customer,
with depositor as the outer relation.
customer have a primary B+-tree index on the join attribute customer-name, which contains 20 entries in each index node.
customer has 10,000 tuples,
the height of the tree is 4, and
one more access is needed to find the actual data
Depositor has 5000 tuples
Cost of block nested loops join
400* = 40,100 disk accesses assuming worst case memory
Cost of indexed nested loops join
* 5 = 25,100 disk accesses.

30. Hash-Join Applicable for equi-joins and natural joins.
A hash function h is used to partition tuples of both relations
h : A→ { 0, 1, ..., n }
r0, r1, . . ., rn : partitions of r tuples
s0, s1. . ., sn : partitions of s tuples
r tuples in ri need only to be compared with s tuples in si .