Query Optimizer

(Chapter 9.0 - 9.6)

Optimization

• Minimizes uses of resources by choosing best set of alternative query access plans

• considers  I/O cost, CPU cost

• gathers statistics - may become out of date (DB2 – RUNSTATS command) – E.g. Selectivity of values - 1/domain - used

to determine number of tuples of each value

Filter Factor FF (selectivity)

• Fraction of rows with specified values(s) for specific attribute that result from the predicate restriction

• FF(c)= # records satisfying condition ctotal# of records in relation

• Estimate attribute with i distinct values as:– Assume |R| is #rows in table R

( |R|/i) / |R|  = 1/col_cardinality e.g. (10,000/2)/10,000 = 1/2

              

Filter Factor FF

• FF tells how many tuples satisfy predicate - hopefully only need to access those tuples + index

• Statistical assumptions - uniform distribution of column values, independent join distribution of values from any 2 columns

Assumptions

• Attribute values independent

• Conjunctive select (independent)                 C1 and C2                 FF(C1) * FF(C2)   

• e.g.  1/2 (gender) * 1/4 (class) = 1/8 freshman in CS are female

Information for Optimization

1. SYSCOLUMNS     col_name, table_name, #of values, High, Low

2. Statistics on columns that deviate strongly from the uniform assumption

3. Cluster Ratio how well clustering property holds for rows with respect to a given index           if 100% clustered

with updates, becomes less clustered              if clustering ratio 80% or more, use sequential

prefetch

Examples of FF

• if SQL statement specified: – col = const,

• DB2 assumes FF is 1/col_cardinality

– col between const1 and const2• DB2 assumes FF=(const2 - const1)/(High - Low)

• For some predicates, FF not predictable by simple formula

Explain Plan

• You can have access to query plan with

  EXPLAIN PLAN statement for SQL_query in ORACLE

gives access type (index) col

Using Indexes

• System must decide if to use index

• What if more than one index, which one?

• What if composite index?

Plans using Indexes

Can use an index if index matches select condition in where clause:

1. A matching index scan - only have to access a limited number of contiguous leaf entries to access data

2. Predicate screening – index entries to eliminate RIDs3. Non-matching index scan – use index to identify RIDs4. Index-only retrieval – don’t have to access data, RIDs5. Multiple index retrieval – use >1 index to identify RIDs

Indexes – Matching index scan

A matching index scan is a single-step query planOnly have to access contiguous leaf nodes

Example: Assume a table T1 with multiple indexes on columns C1, C2 and C3

1. Single where clause and (one) index matches Select * from T1

where C1=10

search B+-tree to leaf level for leftmost entry having specified values

useful for =, between

Index Scan 2. If multiple where clauses and all '='

Select * from T1 where C1=10 and C2=5 and C3=1

  a)  if there is a composite index and a selectcondition matches all index columns

only have to read contiguous leaf pages  FF = FF(P1) * FF(P2) * ...

b)   if there is a separate index for each clause

      must choose one of the indexes

Index Scan– Predicate screening

3. If all select conditions match composite index columns and some selects are a range Select * from T1 where C1=10 and C2

between 5 and 50 and C3 like ‘A%’

- Access contiguous leaf pages, but not all results on contiguous leaf pages - Must examine index entries to determine if in the result

called predicate screening

Predicate screening

• discard RIDs based on values (for index)

• will access fewer tuples because RIDs used instead to eliminate potential tuples

Index Scan

4. If select conditions match some index columns of composite index

          Select * from T1 where C1=10 and C2=30 and C6=20

- a matching scan can be used if at least one of the columns in select is first column of index – must eliminate tuples with what indexes you can, then

examine the tuples

Rules for predicate matching

Decide how many attributes to match in a composite index after the first column, so can read in a small contiguous range of leaf entries in B+-tree to get RIDs

• Match first column of composite index then: – look at index columns from left to right – Match ends when no predicate found – If range (<=, like, between) for a column, match

terminates thereafter

  If a range, easier to scan all entries for range - treat rest of entries as screening predicates

Non-matching index scan

• Not always used by DBMSs• attributes in where clause don't include initial

attribute of index

          Select * from T1

where C2=30 and C3=15

search leaf entries of index and compare values for entries

must read in all leaf pages to find C2, C3 values       e.g. 50 index pages vs 500,000 data pages

Index only retrieval

• elements retrieved in select clause are attributes of compose index

• don't need to access rows (actual data)     Select C1, C3 from T1

where C1=5 and C3 between 2 and 5

       Select count(*) from T1

Multiple Index Access

• If conjunctive conditions in where clause (and), can use >1 index – Extract RIDs from each index satisfying

matching predicate – Intersect lists of RIDs (and them) from each

index – Final list - satisfies all predicates indexed – If disjunctive conditions (or)

         Union the two lists of RIDs

Some Query optimizer rules for using RID-lists (then use list prefetch)

   1.  predicted active resulting RIDs must not be  > 50% of RID pool

2.  Limit to any single RID list the size of the RID memory pool (16M RIDs)

3.  RID list cannot be generated by screening predicates

Rules for multiple indexes

Optimizer determines diminishing returns using multiple index access

1.  List indexes with matching predicates in where clause

2.  Place indexes in order by increasing filter factor3.  For successive indexes, extract RID list only if

reduced cost for final row returned     e.g. no sense reading 100's of pages of a new index to get number of rows to only 1 tuple

List Prefetch – for accessing rows with RID list

• Assume once a list of RIDs is created, the system can order pages to minimize disk I/O– E.g. elevator algorithm for disk request

scheduling

Problem: Using RID lists with Multiple IndexesProspects Table : 50M rows - 10 row per

pageIndexes:

• zipcode – 100,000 values (100 entries per page)

• hobby – 100 values (1000 entries per page• page – 50 values (1000 entries per page• incomeclass – 10 values (1000 entries per

page)

Problem cont’d

Select name, stradr from prospectswhere zipcode between 02159 and 02658and age = 40 and hobby = ‘chess’ and incomeclass = 10;

FF in ascending order:1. FF(zipcode) = 500/100,000 = 1/2002. FF(hobby) = 1/1003. FF(age) = 1/504. FF(incomeclass) = 1/10

Problem cont’d

Rows in table is: 5,000,000Data rows read if use indexes: (1) 50,000,000/200 = 250,000 (1,2) 250,000/100 = 2500 (1,2,3) 2500/50 = 50 (1,2,3,4) 50/10 = 5 How much time will this take? Is it cost effective

to use all of these indexes? see textbook Pg. 579

Problem cont’d I/O costs

• Cost:– RIO is 1/80– Sequential Prefetch 1/800– List Prefetch 1/200

• Note: textbook assumes if read <= 3 pages use RIO

Problem cont’d

Table scan5,000,000/800 = 6250

Using index 1:index: (500,000/200 + 3)/800 = 4data: 250,000/200 = 1250

Using indexes 1&2:index (50,000/100)/800 = 0.625data: 2500/200 = 12.5

Problem cont’d

Using indexes 1,2,3:

index (50,000/50)/800 = 1.25

data: 50/200 = 0.25

Using indexes 1,2,3,4:

index (50,000/10)/800 = 6.25

data: 5/200 = 0.025

Problem cont’dIndex used

Data rows

I/O cost

Index I/O cost

Cost Increase if use index

None 50M

6250 s

1 250,000

1250 s

4 s Decrease 6250 to 1250 s

With 4 additional s

1,2 2500

12.5 s

4 + 0.625 s Decrease 1250 to 12.5 s

With 0.625 additional s

1,2,3 50

0.25 s

4 + 0.625 + 1.25

Decrease 12.5 to .25 s

With 1.25 additional s

1,2,3,4 5

0.025 s

4 + 0.625 + 1.25 + 6.25 s

Decrease 0.25 to 0.025 s

With 6.25 additional s