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David J. DeWitt
Microsoft Jim Gray Systems Lab
Madison, Wisconsin
dewitt@microsoft.com
© 2010 Microsoft Corporation. All r...
I am running out of things
to talk about
Still no motorcycle to rideacross the stage
My wife decided to show up &
see what...
Day 3Day 2Day 1
The“ImpressIndex”
A Third Keynote?
1
Got to show off
the PDW Appliance
Cool!
My boss’s boss
(Ted Kummert)
...
How About a Quiz to Start!
• Who painted this picture?
o Mondrian?
o Picasso?
o Ingres?
• Actually it was the SQL
Server q...
Who Is This Guy Again?
DeWitt
• Spent 32 years as a computer science professor
at the University of Wisconsin
• Joined Mic...
Today …
I am going to talk about SQL query
optimization
 You voted for this topic on the PASS web site
 Don’t blame me i...
Anonymous Quote
“Query optimization is not rocket
science. When you flunk out of query
optimization, we make you go build
...
The Role of the Query Optimizer
(100,000 ft view)
Query Optimizer
SQL
Statement
Awesome
Query Plan
Magic
Happens
What’s the Magic?
Select o_year,
sum(case
when nation = 'BRAZIL' then volume
else 0
end) / sum(volume)
from
(
select YEAR(...
Hardware
Softwar
e
Queries
Some Historical Background
• Cost-based query optimization was invented by
Pat Selinger as part...
Database System
More Precisely:
The Role of the Query Optimizer
Transform SQL queries into an efficient execution plan
Que...
A First Example
Query
Execution
Engine
Query
Optimizer
Parser
SELECT
Average(Rating)
FROM Reviews
WHERE MID = 932
Reviews
...
Query Plan #1
• Plan starts by scanning the entire
Reviews table
o # of disk I/Os will be equal to the # of pages in the
R...
Query Plan #2
• MID index is used to retrieve only
those rows whose MID field
(attribute) is equal to 932
o Since index is...
Which Plan Will be Faster?
• Query optimizer must pick between the two plans by
estimating the cost of each
• To estimate ...
Query
• Consider the query:
• Optimizer might first enumerate three physical
plans:
Filter
Rating > 9
Sequential
Scan
Revi...
Enumerate logically equivalent plans by applying
equivalence rules
For each logically equivalent plan, enumerate all
alter...
Equivalence Rules
Select and join operators
commute with each other
Select
Select
Customers
Select
Select
Customers
Join
C...
Equivalence Rules (cont.)
Project
[CID, Name]
Customers
Project
[Name]
Project operators
cascade
Project
[Name]
Customers
...
Example of Equivalent Logical Plans
SELECT M.Title, M.Director
FROM Movies M, Reviews R, Customers C
WHERE C.City = “N.Y.”...
Five Logically “Equivalent” Plans
Select Select
Join
Customers Reviews
Project
Join
Movies
Select
Select
Join
Customers Re...
Four More!
Select Select
Join
Customers Reviews
Project
Join
Movies
The “original” plan
Select
CustomersSelect
Reviews
Pro...
9 Logically Equivalent Plans,
In Total
Select Select
Join
Customers Reviews
Project
Join
Movies
Select
Select
Join
Custome...
Enumerate logically equivalent plans by applying
equivalence rules
For each logically equivalent plan, enumerate all
alter...
Physical Plan Example
• Assume that the optimizer has:
o Three join strategies that it can select from:
o nested loops (NL...
Enumerate logically equivalent plans by applying
equivalence rules
For each logically equivalent plan, enumerate all
alter...
Selectivity Estimation
• Task of estimating how many rows will satisfy a predicate such as Movies.MID=932
• Plan quality i...
0
20
40
60
80
100
120
140
160
180
5
52
83
6 10
157
125
17
55
37
56
38
19
48
56
83
43
37
5 7
Histogram Motivation
# of Revi...
Equi-Width Histogram Example
CID Values
Count
Count
1-4 17-2013-169-125-8
Equi-width histogram
Yikes! 8X error!!
0
20
40
6...
0
50
100
150
200
156 157
142 148
161
175
Equi-Height HistogramsCount
Count
Equi-height histogram
Divide ranges so that all...
Example #2: Predicate: CID = 6
Actual Sel. Factor = 157/939 = .167
Estimated Sel. Factor = (157/1)/993 = .167
Example #2: ...
Histogram Summary
• Histograms are a critical tool for estimating
selectivity factors for selection predicates
Errors stil...
Enumerate logically equivalent plans by applying
equivalence rules
For each logically equivalent plan, enumerate all
alter...
Estimating Costs
• Two key costs that the optimizer considers:
o I/O time – cost of reading pages from mass storage
o CPU ...
An Example
• Query:
o SELECT Avg(Rating)
FROM Reviews
WHERE MID = 932
• Two physical query plans:
Reviews
Date CID MID Rat...
Plan #1
Avg_agg
[Cnt, Sum]
Scan
Reviews
Filter
MID = 932
• Filter is applied to 10M
rows
• The optimizer
estimates that 10...
Plan #2
Avg_agg
[Cnt, Sum]
Index Lookup
MID = 932
MID
Index
Reviews
• 100 rows are
estimated to satisfy the
predicate
• Av...
But …
• What if the estimate of the number of rows that
satisfy the predicate MID = 932 is WRONG?
o E.g. 10,000 rows inste...
Estimating Join Costs
• Three basic join methods:
o Nested-loops join
o Sort-merge join
o Hash-join
• Very different perfo...
Sort-Merge Join Algorithm
Sort Reviews on MID column
(unless already sorted)
Sort Movies on MID column
(unless already sor...
Nested-Loops Join
For each page Ri, 1≤ i ≤ |R|, of Reviews
{
Read page Ri from disk
For each Mj, 1≤ j ≤ |M|, of Movies
{
R...
Main Idea: Scan R, and for each tuple in R probe
tuples in M (by probing its index). Output result
Index-Nested Loops
For ...
Estimating Result Cardinalities
• Consider the query
SELECT *
FROM Reviews
WHERE 7/1 < date < 7/31 AND rating > 9
• Assume...
9.9999999999999995E-7 1E-4 0.01 1
1
10
100
1000
10000
Nested Loops Sort Merge Index NL
Selectivity factor of predicate on ...
Multidimensional Histograms
• Used to capture correlation between attributes
• A 2-D example
0
50
100
150
200
250
300
350
...
A Little Bit About Estimating Join Cardinalities
• Question: Given a join of R and S, what is the range of possible result...
• General case: join on {A} (where {A} is key for neither)
o estimate each tuple r of R generates uniform number of matche...
Enumerate logically equivalent plans by applying
equivalence rules
For each logically equivalent plan, enumerate all
alter...
Two Common Query “Shapes”
A
B
Join
Join Join
Join
C
D
F
“Star” Join Queries
A B C D FJoin JoinJoin Join
“Chain” Join Queri...
Pruning the Plan Space
• Consider only left-deep query plans to reduce the search space
A B
C
Join
Join
Join
Join
E
D
Left...
• Optimization is performed in N passes (if N relations are joined):
o Pass 1: Find the best (lowest cost) 1-relation plan...
A
A
SS
A
IS
B
B
SS
C
C
SS
C
IS
D
D
SS
D
IS27 387313 42 9518 All single
relation plans
All tables
First, generate all singl...
B
SS 73
A
SS
A
IS
2713
D
SS42
C
IS
18 All single relation
plans
after pruning
Then, All Two Relation Plans
Two Relation Plans
Starting With A
B
SS 73
A
IS
27
A
SS13
D
SS42
C
IS
18
A
Select Join Join
C
Select
Join
D
B
Select
A
SS
...
Two Relation Plans
Starting With B
Select
A
B
JoinA.A = B.a
B
SS
A
SS
NLJ
B
SS
A
SS
SMJ
B
SS
NLJ
A
IS
B
SS
SMJ
A
IS
Select...
Two Relation Plans
Starting With C
Select
C
B
JoinB.C = C.c NLJ
B
SS
C
IS
B
SS
SMJ
C
IS
A
Select Join Join
C
Select
Join
D...
Two Relation Plans
Starting With D
Select
D
B
JoinB.b = D.b
D
SS
B
SS
NLJ
D
SS
B
SS
SMJ
A
Select Join Join
C
Select
Join
D...
Next, All Three
Relation Plans
A
IS
B
SS
SMJ
D
SS
B
SS
SMJ
Pruned
two relation
plansB
SS
SMJ
C
IS
B
SS
SMJ
A
IS
B
SS
D
SS
...
Next, All Three
Relation Plans
A
IS
B
SS
SMJ
Fully pruned two
relation plans
B
SS
SMJ
C
IS
B
SS
D
SS
SMJ
A
Select Join Joi...
Next, All Three
Relation Plans
A
IS
B
SS
SMJ
Fully pruned
two relation
plans
B
SS
SMJ
C
IS
B
SS
D
SS
SMJ
A
Select Join Joi...
Next, All Three
Relation Plans
A
IS
B
SS
SMJ
Fully pruned two
relation plansB
SS
SMJ
C
IS
B
SS
D
SS
SMJ
A
Select Join Join...
You Have Now Seen the Theory
• But the reality is:
o Optimizer still pick bad plans too frequently for a variety of reason...
Opportunities for Improvement
• Develop tools that give us a better understanding
of what goes wrong
• Improve plan stabil...
Towards a Better
Understanding of QO Behavior
• Picasso Project – Jayant Haritsa, IIT Bangalore
o Bing “Picasso Haritsa” t...
Example: TPC-H Query 8
select
o_year,
sum(case
when nation = 'BRAZIL' then volume
else 0
end) / sum(volume)
from
(
select ...
Resulting Plan Space
• SQL Server 2008 R2
• A total of 90,000 queries
o 300 different values for both L_ExtendedPrice and
...
Estimated Execution Costs
• Plan exhibits what is probably a
“flaw” in the cost model:
o As L_Extended price is increased,...
• Recall this graph of join algorithm performance
• While the two “nested loops” algorithms are faster at low selectivity
...
“Reduced” Plan Diagrams
• Robustness is somehow tied to
the number of plans
o Fewer plans => more robust
plans
• For TPC-H...
How Might We Do
Better?
• At QO time, have the QO annotate compiled query plans with statistics (e.g. expected
cardinaliti...
Helping the Optimizer Learn
OptimizerQuery
Statistics
Statistics
Tracker
Executor Database
Check
Check
C
IS
Check
B
SS
SMJ...
Dynamic Reoptimization
72
OptimizerQuery Executor
Check
Check
C
IS
Check
B
SS
SMJ A
IS
INL
2) Output of SMJ is
materialize...
Key Points To Remember
For The Quiz
• Query optimization is harder than
rocket science
o The other components are trivial ...
And…If You’ve Spent My Entire Keynote
Chatting on Facebook…
At least, check out my lab
“Microsoft Jim Gray Systems Lab”:
Many Thanks To:
Rimma Nehme, Pooja Darera, Il-Sung Lee, Jeff
Naughton, Jignesh Patel, Jennifer Widom and
Donghui Zhang for...
Thanks for inviting me to give a talk again
Finally…
2011
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PASS Summit 2010 Keynote David DeWitt

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Keynote by David DeWitt given at PASS Summit 2010 (on Query Optimization)

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PASS Summit 2010 Keynote David DeWitt

  1. 1. David J. DeWitt Microsoft Jim Gray Systems Lab Madison, Wisconsin dewitt@microsoft.com © 2010 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. Microsoft makes no warranties, express or implied in this presentation. SQL Query Optimization: Why Is It So Hard To Get Right?
  2. 2. I am running out of things to talk about Still no motorcycle to rideacross the stage My wife decided to show up & see what all the fuss was about! (She is probably the only one not tweeting back there) A Third Keynote? Generating all this PowerPoint takes me days and days (unlike my boss, I do not have people to do my slide decks)
  3. 3. Day 3Day 2Day 1 The“ImpressIndex” A Third Keynote? 1 Got to show off the PDW Appliance Cool! My boss’s boss (Ted Kummert) 2 Got to tell you about SQL 11 Awesome! My boss (Quentin Clark) 3 Me (David DeWitt) Possibly IMPRESS YOU? How can I
  4. 4. How About a Quiz to Start! • Who painted this picture? o Mondrian? o Picasso? o Ingres? • Actually it was the SQL Server query optimizer!! o Plan space for TPC-H query 8 as the parameter values for Acct-Bal and ExtendedPrice are varied o Each color represents a different query plan o Yikes! P1 P2 P3 P4 SQL Server
  5. 5. Who Is This Guy Again? DeWitt • Spent 32 years as a computer science professor at the University of Wisconsin • Joined Microsoft in March 2008 o Runs the Jim Gray Systems Lab in Madison, WI o Lab is closely affiliated with the DB group at University of Wisconsin o 3 faculty and 8 graduate students working on projects o Built analytics and semijoin components of PDW V1. Currently working on a number of features for PDW V2 M
  6. 6. Today … I am going to talk about SQL query optimization  You voted for this topic on the PASS web site  Don’t blame me if you didn’t vote and wanted to hear about map-reduce and no-sql database systems instead My hope is that you will leave understanding why all database systems sometimes produce really bad plans Starting with the fundamental principals Query Optimization Map-Reduce
  7. 7. Anonymous Quote “Query optimization is not rocket science. When you flunk out of query optimization, we make you go build rockets.”
  8. 8. The Role of the Query Optimizer (100,000 ft view) Query Optimizer SQL Statement Awesome Query Plan Magic Happens
  9. 9. What’s the Magic? Select o_year, sum(case when nation = 'BRAZIL' then volume else 0 end) / sum(volume) from ( select YEAR(O_ORDERDATE) as o_year, L_EXTENDEDPRICE * (1 - L_DISCOUNT) as volume, n2.N_NAME as nation from PART, SUPPLIER, LINEITEM, ORDERS, CUSTOMER, NATION n1, NATION n2, REGION where P_PARTKEY = L_PARTKEY and S_SUPPKEY = L_SUPPKEY and L_ORDERKEY = O_ORDERKEY and O_CUSTKEY = C_CUSTKEY and C_NATIONKEY = n1.N_NATIONKEY and n1.N_REGIONKEY = R_REGIONKEY and R_NAME = 'AMERICA‘ and S_NATIONKEY = n2.N_NATIONKEY and O_ORDERDATE between '1995-01-01' and '1996-12-31' and P_TYPE = 'ECONOMY ANODIZED STEEL' and S_ACCTBAL <= constant-1 and L_EXTENDEDPRICE <= constant-2 ) as all_nations group by o_year order by o_year Consider Query 8 of the TPC-H benchmark: Plan 1 Plan 2 Plan 3 Plan 4 Plan 5 22millionplans … There about 22 million alternative ways of executing this query! A very big haystack to be searching through The QO must select a plan that runs in seconds or minutes, not days or weeks! Should not take hours or days to pick a plan!
  10. 10. Hardware Softwar e Queries Some Historical Background • Cost-based query optimization was invented by Pat Selinger as part of the IBM System R project in the late 1970s (System R became DB2) • Remains the hardest part of building a DBMS 30+ years later o Progress is hindered by fear of regressions o Far too frequently the QO picks an inefficient plan • Situation further complicated by advances in hardware and the rest of the DBMS software o Hardware is 1000X bigger and faster o DB software is 10X faster o Queries over huge amounts of data are possible IF the QO picks the right plan System R 1000X 10X Huge!
  11. 11. Database System More Precisely: The Role of the Query Optimizer Transform SQL queries into an efficient execution plan Query Execution Engine Query OptimizerParserSQL Query Logical operator tree Physical operator tree Logical operators: what they do e.g., union, selection, project, join, grouping Physical operators: how they do it e.g., nested loop join, sort-merge join, hash join, index join
  12. 12. A First Example Query Execution Engine Query Optimizer Parser SELECT Average(Rating) FROM Reviews WHERE MID = 932 Reviews Date CID MID Rating … … … … Logical operator tree Avg (Rating) Select MID = 932 Reviews Query Plan #1 Avg_agg [Cnt, Sum] Scan Reviews Filter MID = 932 Avg_agg [Cnt, Sum] Index Lookup MID = 932 MID Index Reviews Query Plan #2 or
  13. 13. Query Plan #1 • Plan starts by scanning the entire Reviews table o # of disk I/Os will be equal to the # of pages in the Reviews table o I/Os will be sequential. Each I/O will require about 0.1 milliseconds (0.0001 seconds) • Filter predicate “MID = 932” is applied to all rows • Only rows that satisfy the predicate are passed on to the average computation Avg_agg [Cnt, Sum] Scan Reviews Filter MID = 932
  14. 14. Query Plan #2 • MID index is used to retrieve only those rows whose MID field (attribute) is equal to 932 o Since index is not “clustered”, about one disk I/O will be performed for each row o Each disk I/O will require a random seek and will take about 3 milliseconds (ms) • Retrieved rows will be passed to the average computation Avg_agg [Cnt, Sum] Index Lookup MID = 932 MID Index Reviews
  15. 15. Which Plan Will be Faster? • Query optimizer must pick between the two plans by estimating the cost of each • To estimate the cost of a plan, the QO must: o Estimate the selectivity of the predicate MID=932 o Calculate the cost of both plans in terms of CPU time and I/O time • The QO uses statistics about each table to make these estimates • The “best” plan depends on how many reviews there are for movie with MID = 932 Query Plan #1 Avg_agg [Cnt, Sum] Scan Reviews Filter MID = 932 Avg_agg [Cnt, Sum] Index Lookup MID = 932 MID Index Reviews Query Plan #2 Vs. How many reviews for the movie with MID = 932 will there be? Best Query Plan or? ? ?
  16. 16. Query • Consider the query: • Optimizer might first enumerate three physical plans: Filter Rating > 9 Sequential Scan Reviews Filter 7/1 < Date > 7/31 Rating Index Filter 7/1 < Date < 7/31 Index Lookup Rating > 9 Reviews Filter Rating > 9 Index Lookup 7/1 < Date > 7/31 Reviews Date Index SF = .01 SF = .01 SF = .10 SF = .10 Cost = 11 seconds Cost = 100 seconds Cost = 25 seconds • Then, estimate selectivity factors • Then, calculate total cost • Finally, pick the plan with the lowest cost SELECT * FROM Reviews WHERE 7/1< date < 7/31 AND rating > 9
  17. 17. Enumerate logically equivalent plans by applying equivalence rules For each logically equivalent plan, enumerate all alternative physical query plans Estimate the cost of each of the alternative physical query plans Run the plan with lowest estimated overall cost Query Optimization: The Main Steps ✓ 2 1 3 4
  18. 18. Equivalence Rules Select and join operators commute with each other Select Select Customers Select Select Customers Join Customers Reviews Join Reviews Customers Join Customers Reviews Join Movies Join Customers Join Reviews Movies Join operators are associative
  19. 19. Equivalence Rules (cont.) Project [CID, Name] Customers Project [Name] Project operators cascade Project [Name] Customers Select operator distributes over joins Select Join Customers Reviews Select Join Customers Reviews
  20. 20. Example of Equivalent Logical Plans SELECT M.Title, M.Director FROM Movies M, Reviews R, Customers C WHERE C.City = “N.Y.” AND R.Rating > 7 AND M.MID = R.MID AND C.CID = R.CID • One possible logical plan: Join SelectC.City = “N.Y” Select R.Rating > 7 JoinC.CID = R.CID R.MID = M.MID Customers Reviews Project M.Title, M.Director Movies MID Title Director Earnings 1 2 … CID Name Address City 5 11 … Date CID MID Rating 7/3 11 2 8 7/3 5 2 4 … Find titles and director names of movies with a rating > 7 from customers residing in NYC Customers Reviews Movies
  21. 21. Five Logically “Equivalent” Plans Select Select Join Customers Reviews Project Join Movies Select Select Join Customers Reviews Project Join Movies Select Select Join Customers Reviews Project Join Movies Select Join Customers Reviews Join Movies Select Project The “original” plan Selects distribute over joins rule Join Customers Reviews Join Movies Select Project Select Selects commute rule
  22. 22. Four More! Select Select Join Customers Reviews Project Join Movies The “original” plan Select CustomersSelect Reviews Project Join Movies Join Select Customers Select Reviews Project Join Movies Join Select CustomersSelect Reviews Project Join Movies Join Select Reviews Join Movies Customers Project Join Select Join commutativity rule Select commutativity rule
  23. 23. 9 Logically Equivalent Plans, In Total Select Select Join Customers Reviews Project Join Movies Select Select Join Customers Reviews Project Join Movies Select Select Join Customers Reviews Project Join Movies Select Join Customers Reviews Join Movies Select Project Select Customers Select Reviews Project Join Movies Join Select Customers Select Reviews Project Join Movies Join Select Reviews Join Movies Customers Project Join Select Select CustomersSelect Reviews Project Join Movies Join Join Customers Reviews Join Movies Select Project Select  All 9 logical plans will produce the same result  For each of these 9 plans there is a large number of alternative physical plans that the optimizer can choose from
  24. 24. Enumerate logically equivalent plans by applying equivalence rules For each logically equivalent plan, enumerate all alternative physical query plans Estimate the cost of each of the alternative physical query plans Run the plan with lowest estimated overall cost Query Optimization: The Main Steps ✓ 2 1 3 4 ✓
  25. 25. Physical Plan Example • Assume that the optimizer has: o Three join strategies that it can select from: o nested loops (NL), sort-merge join (SMJ), and hash join (HJ) o Two selection strategies: o sequential scan (SS) and index scan (IS) • Consider one of the 9 logical plans • Here is one possible physical plan Select Select Join Customers Reviews Project Join Movies SS IS HJ Customers Reviews Project NL Movies • There are actually 36 possible physical alternatives for this single logical plan. (I was too lazy to draw pictures of all 36). • With 9 equivalent logical plans, there are 324 = (9 * 36) physical plans that the optimizer must enumerate and cost as part of the search for the best execution plan for the query And this was a VERY simple query! • Later we will look at how dynamic programming is used to explore the space of logical and physical plans w/o enumerating the entire plan space
  26. 26. Enumerate logically equivalent plans by applying equivalence rules For each logically equivalent plan, enumerate all alternative physical query plans Estimate the cost of each of the alternative physical query plans. • Estimate the selectivity factor and output cardinality of each predicate • Estimate the cost of each operator Run the plan with lowest estimated overall cost Query Optimization: The Main Steps ✓ 2 1 3 4 ✓ ✓
  27. 27. Selectivity Estimation • Task of estimating how many rows will satisfy a predicate such as Movies.MID=932 • Plan quality is highly dependent on quality of the estimates that the query optimizer makes 0 1 2 3 4 5 • Histograms are the standard technique used to estimate selectivity factors for predicates on a single table • Many different flavors: o Equi-Width o Equi-Height o Max-Diff o …
  28. 28. 0 20 40 60 80 100 120 140 160 180 5 52 83 6 10 157 125 17 55 37 56 38 19 48 56 83 43 37 5 7 Histogram Motivation # of Reviews for each customer (total of 939 rows) Customer ID (CID) values in Reviews Table Some examples: #1) Predicate: CID = 9 Actual Sel. Factor = 55/939 = .059 #2) Predicate: 2 <= CID <= 3 Actual Sel. Factor = 135/939 = .144 In general, there is not enough space in the catalogs to store summary statistics for each distinct attribute value The solution: histograms
  29. 29. Equi-Width Histogram Example CID Values Count Count 1-4 17-2013-169-125-8 Equi-width histogram Yikes! 8X error!! 0 20 40 60 80 100 120 140 160 180 0 50 100 150 200 250 300 350 146 309 186 206 92 All buckets cover roughly the same key range Example #1: Predicate: CID = 9 Actual Sel. Factor = 55/939= .059 Estimated Sel. Factor = (186/4)/939 = .050 Example #2: Predicate: CID = 5 Actual Sel. Factor = 10/939 = .011 Estimated Sel. Factor = (309/4)/993 =.082
  30. 30. 0 50 100 150 200 156 157 142 148 161 175 Equi-Height HistogramsCount Count Equi-height histogram Divide ranges so that all buckets contain roughly the same number of values 1-5 16-2012-159-117-86 0 20 40 60 80 100 120 140 160 180
  31. 31. Example #2: Predicate: CID = 6 Actual Sel. Factor = 157/939 = .167 Estimated Sel. Factor = (157/1)/993 = .167 Example #2: Predicate: CID = 6 Actual Sel. Factor = 157/939 = .167 Estimated Sel. Factor = (309/4)/993 = .082 Example #1: Predicate: CID = 5 Actual Sel. Factor = 10/939 = .011 Estimated Sel. Factor = (309/4)/993 =.082 Example #1: Predicate: CID = 5 Actual Sel. Factor = 10/939 = .011 Estimated Sel. Factor = (156/5)/993 = .033 Equi-width vs. Equi-Height 1-4 17-2013-169-125-8 Equi-width Equi-height 0 50 100 150 200 156 157 142 148 161 175 0 50 100 150 200 250 300 350 146 309 186 206 92 1-5 16-2012-159-117-86
  32. 32. Histogram Summary • Histograms are a critical tool for estimating selectivity factors for selection predicates Errors still occur, however! • Other statistics stored by the DBMS for each table include # of rows, # of pages, …
  33. 33. Enumerate logically equivalent plans by applying equivalence rules For each logically equivalent plan, enumerate all alternative physical query plans Estimate the cost of each of the alternative physical query plans. • Estimate the selectivity factor and output cardinality of each predicate • Estimate the cost of each operator Run the plan with lowest estimated overall cost Query Optimization: The Main Steps ✓ 2 1 3 4 ✓ ✓
  34. 34. Estimating Costs • Two key costs that the optimizer considers: o I/O time – cost of reading pages from mass storage o CPU time – cost of applying predicates and operating on tuples in memory • Actual values are highly dependent on CPU and I/O subsystem on which the query will be run o Further complicating the job of the query optimizer • For a parallel database system such as PDW, the cost of redistributing/shuffling rows must also be considered o Were you paying attention or updating your Facebook page when I talked about parallel query processing 2 years ago? IO + CPU vs.
  35. 35. An Example • Query: o SELECT Avg(Rating) FROM Reviews WHERE MID = 932 • Two physical query plans: Reviews Date CID MID Rating Plan #1 Avg_agg [Cnt, Sum] Sequential Scan Reviews Filter MID = 932 Avg_agg [Cnt, Sum] Index Lookup MID = 932 MID Index Reviews Plan #2 Which plan is cheaper ??? CostX C ostY
  36. 36. Plan #1 Avg_agg [Cnt, Sum] Scan Reviews Filter MID = 932 • Filter is applied to 10M rows • The optimizer estimates that 100 rows will satisfy the predicate • Table is 100K pages with 100 rows/page • Sorted on date • Average computation is applied to 100 rows • Reviews is scanned sequentially at 100 MB/second • I/O time of scan is 8 seconds • At 0.1 microseconds/row, filter consumes 1 second of CPU time • At 0.1 microseconds/row, avg consumes .00001 seconds of CPU time Optimizer estimates total execution time of 9 seconds Cost of Plan #1
  37. 37. Plan #2 Avg_agg [Cnt, Sum] Index Lookup MID = 932 MID Index Reviews • 100 rows are estimated to satisfy the predicate • Average computation is applied to 100 rows • At 0.1 microseconds/row, average consumes . 00001 seconds of CPU time• 100 rows are retrieved using the MID index • Since table is sorted on date field (and not MID field), each I/O requires a random disk I/O – about .003 seconds per disk I/O • I/O time will be .3 seconds Optimizer estimates total execution time of 0.3 seconds The estimate for Plan #1 was 9 seconds, so Plan #2 is clearly the better choice Cost of Plan #2
  38. 38. But … • What if the estimate of the number of rows that satisfy the predicate MID = 932 is WRONG? o E.g. 10,000 rows instead of 100 rows 10 100 1000 10000 100000 0.01 0.1 1 10 100 1000 Sequential Scan Non-Clustered Index # of rows Time(#sec) Non-clustered Index is better here Sequential scan is better here
  39. 39. Estimating Join Costs • Three basic join methods: o Nested-loops join o Sort-merge join o Hash-join • Very different performance characteristics • Critical for optimizer to carefully pick which method to use when Join SelectC.City = “NY” Select R.Rating > 7 JoinC.CID = R.CID R.MID = M.MID Customers Reviews ProjectM.Title, M.Director Movies
  40. 40. Sort-Merge Join Algorithm Sort Reviews on MID column (unless already sorted) Sort Movies on MID column (unless already sorted) “Merge” two sorted tables: Scan each table sequential in tandem { For current row r of Reviews For current row m of Movies if r.MID = m.MID produce output row Advance r and m cursors } Cost = |R| + |M| I/Os Merge Join Sort Sort Reviews (|R| pages) Movies (|M| pages) Reviews.MID = Movies.MID Cost = 4 * |M| I/Os Total I/O cost = 5*|R| + 5*|M| I/Os Cost = 4 * |R| I/Os Main Idea: Sort R and M on the join column (MID), then scan them to do a ``merge’’ (on join column), and output result tuples.
  41. 41. Nested-Loops Join For each page Ri, 1≤ i ≤ |R|, of Reviews { Read page Ri from disk For each Mj, 1≤ j ≤ |M|, of Movies { Read page Mj from disk For all rows r on page Ri { For all rows m on page Mj { if r.MID = m.MID produce output row } } } } I/O Cost = |R| + |R| * |M| Nested Loops Join Movies (|M| pages) Reviews (|R| pages) Reviews.MID = Movies.MID Main Idea: Scan R, and for each tuple in R probe tuples in M (by scanning it). Output result tuples.
  42. 42. Main Idea: Scan R, and for each tuple in R probe tuples in M (by probing its index). Output result Index-Nested Loops For each page Ri, 1≤ i ≤ |R|, of Reviews { Read page Ri from disk For all rows r on page Ri { Use MID index on Movies to fetch rows with MID attributes = r.MID Form output row for each returned row } } Movies (|M| pages) Nested Loops Join Reviews Reviews.MID = Movies.MID Index Lookup using r.MID MID Index (|R| pages) Sorted on date column Cost = |R| + |R| * (||R||/|R|) * 2 • 2 I/Os: 1 index I/O + 1 movie I/O as Reviews table is sorted on date column • ||R|| is # of rows in R • ||R||/|R| gives the average number of rows of R per page Notice that since Reviews is ordered on the Date column (and not MID), so each row of the Movies table retrieved incurs two random disk I/Os: • one to the index and • one to the table
  43. 43. Estimating Result Cardinalities • Consider the query SELECT * FROM Reviews WHERE 7/1 < date < 7/31 AND rating > 9 • Assume Reviews has 1M rows • Assume following selectivity factors: Sel. Factor # of qualifying rows 7/1 < date < 7/31 0.1 100,000 Review > 9 0.01 10,000 • How many output rows will the query produce? o If predicates are not correlated o .1 * .01 * 1M = 1,000 rows o If predicates are correlated could be as high as o .1 * 1M = 100,000 rows Why does this matter?
  44. 44. 9.9999999999999995E-7 1E-4 0.01 1 1 10 100 1000 10000 Nested Loops Sort Merge Index NL Selectivity factor of predicate on Reviews table Time(#sec) This is Why! Assume that: • Reviews table is 10,000 pages with 80 rows/page • Movies table is 2,000 pages • The primary index on Movies is on the MID column Join R.MID = M.MID Select Reviews Project Movies Rating > 9 and 7/1 < date < 7/31 The consequences of incorrectly estimating the selectivity of the predicate on Reviews can be HUGE INL N L SM Note that each join algorithm has a region where it provides the best performance
  45. 45. Multidimensional Histograms • Used to capture correlation between attributes • A 2-D example 0 50 100 150 200 250 300 350 400 450 500 151 198 229 152 156 303 314 361 392 315 319 466 191 238 269 192 196 343 211 258 289 212 216 363 97 144 175 98 102 249 1-4 5-8 9-12 13-16 17-20 10-20 21-30 31-40 41-50 51-60 61-70
  46. 46. A Little Bit About Estimating Join Cardinalities • Question: Given a join of R and S, what is the range of possible result sizes (in #of tuples)? o Suppose the join is on a key for R and S Students(sid, sname, did), Dorm(did,d.addr) Select S.sid, D.address From Students S, Dorms D Where S.did = D.did What is the cardinality? A student can only live in at most 1 dorm: • each S tuple can match with at most 1 D tuple • cardinality (S join D) = cardinality of S
  47. 47. • General case: join on {A} (where {A} is key for neither) o estimate each tuple r of R generates uniform number of matches in S and each tuple s of S generates uniform number of matches in R, e.g. o SF = min(||R|| * ||S|| / NKeys(A,S) ||S|| * ||R|| / NKeys(A,R)) e.g., SELECT M.title, R.title FROM Movies M, Reviews R WHERE M.title = R.title Movies: 100 tuples, 75 unique titles  1.3 rows for each title Reviews: 20 tuples, 10 unique titles  2 rows for each title Estimating Join Cardinality = 100*20/10 = 200 = 20*100/75 = 26.6
  48. 48. Enumerate logically equivalent plans by applying equivalence rules For each logically equivalent plan, enumerate all alternative physical query plans Estimate the cost of each of the alternative physical query plans. • Estimate the selectivity factor and output cardinality of each predicate • Estimate the cost of each operator Run the plan with lowest estimated overall cost Query Optimization: The Main Steps ✓ 2 1 3 4 ✓ ✓ Enumerate How big is the plan space for a query involving N tables? enumerate It turns out that the answer depends on the “shape” of the query
  49. 49. Two Common Query “Shapes” A B Join Join Join Join C D F “Star” Join Queries A B C D FJoin JoinJoin Join “Chain” Join Queries Number of logically equivalent alternatives # of Tables Star Chain 2 2 2 4 48 40 5 384 224 6 3,840 1,344 8 645,120 54,912 10 18,579,450 2,489,344 In practice, “typical” queries fall somewhere between these two extremes
  50. 50. Pruning the Plan Space • Consider only left-deep query plans to reduce the search space A B C Join Join Join Join E D Left Deep Join Join Join Join ED A B C Bushy Star Join Queries Chain Join Queries # of Tables Bushy Left-Deep Bushy Left Deep 2 2 2 2 2 4 48 12 40 8 5 384 48 224 16 6 3,840 240 1,344 32 8 645,120 10,080 54,912 128 10 18,579,450 725,760 2,489,344 512 These are counts of logical plans only! With: i) 3 join methods ii) n joins in a query There will be 3n physical plans for each logical planExample: For a left-deep, 8 table star join query there will be: i) 10,080 different logical plans ii) 22,044,960 different physical plans!! Solution: Use some form of dynamic programming (either bottom up or top down) to search the plan space heuristically Sometimes these heuristics will cause the best plan to be missed!!
  51. 51. • Optimization is performed in N passes (if N relations are joined): o Pass 1: Find the best (lowest cost) 1-relation plan for each relation. o Pass 2: Find the best way to join the result of each 1-relation plan (as the outer/left table) to another relation (as the inner/right table) to generate all 2-relation plans. o Pass N: Find best way to join result of a (N-1)-relation plan (as outer) to the N’th relation to generate all N-relation plans. • At each pass, for each subset of relations, prune all plans except those o Lowest cost plan overall, plus o Lowest cost plan for each interesting order of the rows • Order by, group by, aggregates etc. handled as the final step Bottom-Up QO Using Dynamic Programming In spite of pruning plan space, this approach is still exponential in the # of tables. Interesting orders include orders that facilitate the execution of joins, aggregates, and order by clauses subsequently by the query
  52. 52. A A SS A IS B B SS C C SS C IS D D SS D IS27 387313 42 9518 All single relation plans All tables First, generate all single relation plans: A Select Join Join C Select Join D B Select An Example: Legend: SS – sequential scan IS – index scan – cost5 Prune
  53. 53. B SS 73 A SS A IS 2713 D SS42 C IS 18 All single relation plans after pruning Then, All Two Relation Plans
  54. 54. Two Relation Plans Starting With A B SS 73 A IS 27 A SS13 D SS42 C IS 18 A Select Join Join C Select Join D B Select A SS B SS NLJ A IS B SS NLJ A IS B SS SMJ A SS B SS SMJJoin Select A B A.a = B.a 1013 822315 293 Single relation plans Prune Let’s assume there are 2 alternative join methods for the QO to select from: 1. NLJ = Nested Loops Join 2. SMJ = Sort Merge Join
  55. 55. Two Relation Plans Starting With B Select A B JoinA.A = B.a B SS A SS NLJ B SS A SS SMJ B SS NLJ A IS B SS SMJ A IS Select D B JoinB.b = D.b Select C B JoinB.C = C.c B SS D SS NLJ B SS D SS SMJ NLJ B SS C IS B SS SMJ C IS A Select Join Join C Select Join D B Select 1013 315 756 293 1520 432 2321 932 Single relation plansB SS 73 A IS 27 A SS13 D SS42 C IS 18 Prune
  56. 56. Two Relation Plans Starting With C Select C B JoinB.C = C.c NLJ B SS C IS B SS SMJ C IS A Select Join Join C Select Join D B Select 6520 932 Single relation plansB SS 73 A IS 27 A SS13 D SS42 C IS 18 Prune
  57. 57. Two Relation Plans Starting With D Select D B JoinB.b = D.b D SS B SS NLJ D SS B SS SMJ A Select Join Join C Select Join D B Select 1520 432 Single relation plans B SS 73 A IS 27 A SS13 D SS42 C IS 18 Prune
  58. 58. Next, All Three Relation Plans A IS B SS SMJ D SS B SS SMJ Pruned two relation plansB SS SMJ C IS B SS SMJ A IS B SS D SS SMJ B SS SMJ C IS A Select Join Join C Select Join D B Select
  59. 59. Next, All Three Relation Plans A IS B SS SMJ Fully pruned two relation plans B SS SMJ C IS B SS D SS SMJ A Select Join Join C Select Join D B Select NLJ C IS A IS B SS SMJ SMJ C IS A IS B SS SMJ D SS NLJ A IS B SS SMJ D SS SMJ A IS B SS SMJ 1) Considering the Two Relation Plans That Started With A
  60. 60. Next, All Three Relation Plans A IS B SS SMJ Fully pruned two relation plans B SS SMJ C IS B SS D SS SMJ A Select Join Join C Select Join D B Select B SS D SS SMJ A SS NLJ B SS D SS SMJ A SS SMJ NLJ A IS B SS D SS SMJ SMJ A IS B SS D SS SMJ NLJ C IS B SS D SS SMJ SMJ C IS B SS D SS SMJ 2) Considering the Two Relation Plans That Started With B
  61. 61. Next, All Three Relation Plans A IS B SS SMJ Fully pruned two relation plansB SS SMJ C IS B SS D SS SMJ A Select Join Join C Select Join D B Select B SS SMJ C IS NLJ A IS SMJ A IS B SS SMJ C IS D SS NLJ C IS B SS SMJ D SS SMJ C IS B SS SMJ 3) Considering the Two Relation Plans That Started With C
  62. 62. You Have Now Seen the Theory • But the reality is: o Optimizer still pick bad plans too frequently for a variety of reasons: o Statistics can be missing, out-of-date, incorrect o Cardinality estimates assume uniformly distributed values but data values are skewed o Attribute values are correlated with one another: • Make = “Honda” and Model = “Accord” o Cost estimates are based on formulas that do not take into account the characteristics of the machine on which the query will actually be run o Regressions happen due hardware and software upgrades What can be done to improve the situation?
  63. 63. Opportunities for Improvement • Develop tools that give us a better understanding of what goes wrong • Improve plan stability • Use of feedback from the QE to QO to improve statistics and cost estimates • Dynamic re-optimization
  64. 64. Towards a Better Understanding of QO Behavior • Picasso Project – Jayant Haritsa, IIT Bangalore o Bing “Picasso Haritsa” to find the project’s web site o Tool is available for SQL Server, Oracle, PostgreSQL, DB2, Sybase • Simple but powerful idea: • For a given query such as – SELECT * from A, B – WHERE A.a = B.b and – A.c <= constant-1 and – B.d <= constant-2 • Systematically vary constant-1 and constant-2 • Obtain query plan and estimated cost from the query optimizer for each combination of input parameters • Plot the results
  65. 65. Example: TPC-H Query 8 select o_year, sum(case when nation = 'BRAZIL' then volume else 0 end) / sum(volume) from ( select YEAR(O_ORDERDATE) as o_year, L_EXTENDEDPRICE * (1 - L_DISCOUNT) as volume, n2.N_NAME as nation from PART, SUPPLIER, LINEITEM, ORDERS, CUSTOMER, NATION n1, NATION n2, REGION where P_PARTKEY = L_PARTKEY and S_SUPPKEY = L_SUPPKEY and L_ORDERKEY = O_ORDERKEY and O_CUSTKEY = C_CUSTKEY and C_NATIONKEY = n1.N_NATIONKEY and n1.N_REGIONKEY = R_REGIONKEY and R_NAME = 'AMERICA‘ and S_NATIONKEY = n2.N_NATIONKEY and O_ORDERDATE between '1995-01-01' and '1996-12-31' and P_TYPE = 'ECONOMY ANODIZED STEEL' and S_ACCTBAL <= constant-1 and L_EXTENDEDPRICE <= constant-2 ) as all_nations group by o_year order by o_year
  66. 66. Resulting Plan Space • SQL Server 2008 R2 • A total of 90,000 queries o 300 different values for both L_ExtendedPrice and S_AcctBal • 204 different plans!! o Each distinct plan is assigned a unique color • Zooming in to the [0,20:0,20] region: Key takeaway: If plan choice is so sensitive to the constants used, it will undoubtedly be sensitive to errors in statistics and cardinality estimates  Intuitively, this seems very bad!
  67. 67. Estimated Execution Costs • Plan exhibits what is probably a “flaw” in the cost model: o As L_Extended price is increased, estimated cost first increases sharply before decreasing • Still a mystery why QO is indeed harder than rocket science!!
  68. 68. • Recall this graph of join algorithm performance • While the two “nested loops” algorithms are faster at low selectivity factors, they are not as “stable” across the entire range of selectivity factors How Might We Do Better? 9.9999999999999995E-7 1E-4 0.01 1 1 10 100 1000 10000 Nested Loops Sort Merge Index NL Selectivity factor of predicate on Reviews table Time(#sec) Join R.MID = M.MID Select Reviews Project Movies Rating > 9 and 7/1 < date < 7/31 INL N L SM
  69. 69. “Reduced” Plan Diagrams • Robustness is somehow tied to the number of plans o Fewer plans => more robust plans • For TPC-H query 8, it is possible to use only 30 plans (instead of 204) by picking more robust plans that are slightly slower (10% max, 2% avg) • Since each plan covers a larger region it will be less sensitive to errors in estimating cardinalities and costs Reduced plan space for TPC-H query 8
  70. 70. How Might We Do Better? • At QO time, have the QO annotate compiled query plans with statistics (e.g. expected cardinalities) and check operators • At runtime, check operators collect the actual statistics and compare actual vs. predicted • Opens up a number of avenues for improving QO performance o Build an optimizer that learns o Do dynamic reoptimization of “in flight” queries INL A IS B SS SMJ C IS Check Check C IS Check B SS SMJ A IS INL 70
  71. 71. Helping the Optimizer Learn OptimizerQuery Statistics Statistics Tracker Executor Database Check Check C IS Check B SS SMJ A IS INL Catalogs Observed StatsOriginal & Observed Optimization of subsequent queries benefits from the observed statistics Query Plan
  72. 72. Dynamic Reoptimization 72 OptimizerQuery Executor Check Check C IS Check B SS SMJ A IS INL 2) Output of SMJ is materialized as Tmp1 in tempdb Tmp1 Database 1) Observed output size of SMJ is 5x larger than expected 3) Remainder of query is returned to the optimizer for reoptimization A IS INL Tmp1 Actual Stats Query Plan
  73. 73. Key Points To Remember For The Quiz • Query optimization is harder than rocket science o The other components are trivial in comparison • Three key phases of QO o Enumeration of logical plan space o Enumeration of alternative physical plans o Selectivity estimation and costing • The QO team of every DB vendor lives in fear of regressions o How exactly do you expect them to make forward progress?
  74. 74. And…If You’ve Spent My Entire Keynote Chatting on Facebook… At least, check out my lab “Microsoft Jim Gray Systems Lab”:
  75. 75. Many Thanks To: Rimma Nehme, Pooja Darera, Il-Sung Lee, Jeff Naughton, Jignesh Patel, Jennifer Widom and Donghui Zhang for their many useful suggestions and their help in debugging these slides
  76. 76. Thanks for inviting me to give a talk again Finally… 2011

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