Query Optimization Lecture # 13 Database Systems Andy Pavlo AP - - PowerPoint PPT Presentation
Query Optimization Lecture # 13 Database Systems Andy Pavlo AP AP Computer Science 15-445/15-645 Carnegie Mellon Univ. Fall 2018 2 ADM IN ISTRIVIA Mid-term Exam is on Wednesday October 17 th See mid-term exam guide for more info.
Database Systems 15-445/15-645 Fall 2018 Andy Pavlo Computer Science Carnegie Mellon Univ.
Lecture # 13
CMU 15-445/645 (Fall 2018)
ADM IN ISTRIVIA
Mid-term Exam is on Wednesday October 17th
→ See mid-term exam guide for more info.
Project #2 – Checkpoint #2 is due Friday October 19th @ 11:59pm.
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CMU 15-445/645 (Fall 2018)
Q UERY O PTIM IZATIO N
Remember that SQL is declarative.
→ User tells the DBMS what answer they want, not how to get the answer.
There can be a big difference in performance based
→ See last week: 1.3 hours vs. 0.45 seconds
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CMU 15-445/645 (Fall 2018)
IBM SYSTEM R
First implementation of a query optimizer. People argued that the DBMS could never choose a query plan better than what a human could write. A lot of the concepts from System R’s optimizer are still used today.
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CMU 15-445/645 (Fall 2018)
Q UERY O PTIM IZATIO N
Heuristics / Rules
→ Rewrite the query to remove stupid / inefficient things. → Does not require a cost model.
Cost-based Search
→ Use a cost model to evaluate multiple equivalent plans and pick the one with the lowest cost.
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CMU 15-445/645 (Fall 2018)
Q UERY PLAN N IN G OVERVIEW
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SQL Query
Parser
Abstract Syntax Tree Annotated AST
Query Plan Cost Model System Catalog Rewriter
(Optional)
Binder Optimizer
Annotated AST Name→Internal ID
CMU 15-445/645 (Fall 2018)
TO DAY'S AGEN DA
Relational Algebra Equivalences Plan Cost Estimation Plan Enumeration Nested Sub-queries Mid-Term Review
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CMU 15-445/645 (Fall 2018)
RELATIO N AL ALGEBRA EQ UIVALEN CES
Two relational algebra expressions are equivalent if they generate the same set of tuples. The DBMS can identify better query plans without a cost model. This is often called query rewriting.
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CMU 15-445/645 (Fall 2018)
PREDICATE PUSH DOWN
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stud student ent enr enrolled lled
s.sid=e.sid grade='A' s.name,e.cid
stud student ent enr enrolled lled
s.sid=e.sid grade='A' s.name,e.cid
SELECT s.name, e.cid FROM student AS s, enrolled AS e WHERE s.sid = e.sid AND e.grade = 'A'
CMU 15-445/645 (Fall 2018)
RELATIO N AL ALGEBRA EQ UIVALEN CES
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name, cid(σgrade='A'(student⋈enrolled)) name, cid(student⋈(σgrade='A'(enrolled)))
SELECT s.name, e.cid FROM student AS s, enrolled AS e WHERE s.sid = e.sid AND e.grade = 'A'
CMU 15-445/645 (Fall 2018)
RELATIO N AL ALGEBRA EQ UIVALEN CES
Selections:
→ Perform filters as early as possible. → Reorder predicates so that the DBMS applies the most selective one first. → Break a complex predicate, and push down
σp1∧p2∧…pn(R) = σp1(σp2(…σpn(R)))
Simplify a complex predicate
→ (X=Y AND Y=3) → X=3 AND Y=3
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CMU 15-445/645 (Fall 2018)
RELATIO N AL ALGEBRA EQ UIVALEN CES
Projections:
→ Perform them early to create smaller tuples and reduce intermediate results (if duplicates are eliminated) → Project out all attributes except the ones requested or required (e.g., joining keys)
This is not important for a column store…
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CMU 15-445/645 (Fall 2018)
PRO J ECTIO N PUSH DOWN
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stud student ent enr enrolled lled
s.sid=e.sid grade='A' s.name,e.cid
stud student ent enr enrolled lled
s.sid=e.sid grade='A' s.name,e.cid
sid,cid
sid,namep
SELECT s.name, e.cid FROM student AS s, enrolled AS e WHERE s.sid = e.sid AND e.grade = 'A'
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Impossible / Unnecessary Predicates
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Source: Lukas Eder
SELECT * FROM A WHERE 1 = 0;X
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Impossible / Unnecessary Predicates
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Source: Lukas Eder
SELECT * FROM A WHERE 1 = 0; SELECT * FROM A WHERE 1 = 1;
X
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Impossible / Unnecessary Predicates
15
Source: Lukas Eder
SELECT * FROM A WHERE 1 = 0; SELECT * FROM A WHERE 1 = 1; SELECT * FROM A;
X
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Impossible / Unnecessary Predicates Join Elimination
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Source: Lukas Eder
SELECT * FROM A WHERE 1 = 0; SELECT A1.* FROM A AS A1 JOIN A AS A2 ON A1.id = A2.id; SELECT * FROM A WHERE 1 = 1; SELECT * FROM A;
X
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Impossible / Unnecessary Predicates Join Elimination
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Source: Lukas Eder
SELECT * FROM A WHERE 1 = 0; SELECT * FROM A WHERE 1 = 1; SELECT * FROM A; SELECT * FROM A;
X
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Ignoring Projections
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Source: Lukas Eder
SELECT * FROM A AS A1 WHERE EXISTS(SELECT * FROM A AS A2 WHERE A1.id = A2.id);
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Ignoring Projections
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Source: Lukas Eder
SELECT * FROM A;
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Ignoring Projections Merging Predicates
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Source: Lukas Eder
SELECT * FROM A WHERE val BETWEEN 1 AND 100 OR val BETWEEN 50 AND 150; SELECT * FROM A;
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
M O RE EXAM PLES
Ignoring Projections Merging Predicates
16
Source: Lukas Eder
SELECT * FROM A WHERE val BETWEEN 1 AND 150; SELECT * FROM A;
CREATE TABLE A ( id INT PRIMARY KEY, val INT NOT NULL );
CMU 15-445/645 (Fall 2018)
RELATIO N AL ALGEBRA EQ UIVALEN CES
Joins:
→ Commutative, associative
R⋈S = S⋈R (R⋈S)⋈T = R⋈(S⋈T) How many different orderings are there for an n- way join?
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CMU 15-445/645 (Fall 2018)
RELATIO N AL ALGEBRA EQ UIVALEN CES
How many different orderings are there for an n- way join? Catalan number ≈4n
→ Exhaustive enumeration will be too slow.
We’ll see in a second how an optimizer limits the search space...
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CO ST ESTIM ATIO N
How long will a query take?
→ CPU: Small cost; tough to estimate → Disk: # of block transfers → Memory: Amount of DRAM used → Network: # of messages
How many tuples will be read/written? What statistics do we need to keep?
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STATISTICS
The DBMS stores internal statistics about tables, attributes, and indexes in its internal catalog. Different systems update them at different times. Manual invocations:
→ Postgres/SQLite: ANALYZE → Oracle/MySQL: ANALYZE TABLE → SQL Server: UPDATE STATISTICS → DB2: RUNSTATS
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CMU 15-445/645 (Fall 2018)
STATISTICS
For each relation R, the DBMS maintains the following information:
→ NR: Number of tuples in R. → V(A,R): Number of distinct values for attribute A.
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DERIVABLE STATISTICS
The selection cardinality SC(A,R) is the average number of records with a value for an attribute A given NR / V(A,R) Note that this assumes data uniformity.
→ 10,000 students, 10 colleges – how many students in SCS?
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SELECTIO N STATISTICS
Equality predicates on unique keys are easy to estimate. What about more complex predicates? What is their selectivity?
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SELECT * FROM people WHERE id = 123 SELECT * FROM people WHERE val > 1000 SELECT * FROM people WHERE age = 30 AND status = 'Lit'
CMU 15-445/645 (Fall 2018)
CO M PLEX PREDICATES
The selectivity (sel) of a predicate P is the fraction of tuples that qualify. Formula depends on type of predicate:
→ Equality → Range → Negation → Conjunction → Disjunction
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CMU 15-445/645 (Fall 2018)
CO M PLEX PREDICATES
The selectivity (sel) of a predicate P is the fraction of tuples that qualify. Formula depends on type of predicate:
→ Equality → Range → Negation → Conjunction → Disjunction
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CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Assume that V(age,people) has five distinct values (0–4) and NR = 5 Equality Predicate: A=constant
→ sel(A=constant) = SC(P) / V(A,R) → Example: sel(age=2) =
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1 2 3 4
count age
SELECT * FROM people WHERE age = 2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Assume that V(age,people) has five distinct values (0–4) and NR = 5 Equality Predicate: A=constant
→ sel(A=constant) = SC(P) / V(A,R) → Example: sel(age=2) =
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1 2 3 4
count age
V(age,people)=5
SELECT * FROM people WHERE age = 2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Assume that V(age,people) has five distinct values (0–4) and NR = 5 Equality Predicate: A=constant
→ sel(A=constant) = SC(P) / V(A,R) → Example: sel(age=2) =
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1 2 3 4
count age
V(age,people)=5 SC(age=2)=1
SELECT * FROM people WHERE age = 2 1/5
CMU 15-445/645 (Fall 2018)
1 2 3 4
count age
SELECTIO N S CO M PLEX PREDICATES
Range Query:
→ sel(A>=a) = (Amax – a) / (Amax – Amin) → Example: sel(age>=2)
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= (4 – 2) / (4 – 0) = 1/2
agemin = 0
SELECT * FROM people WHERE age >= 2
agemax = 4
CMU 15-445/645 (Fall 2018)
1 2 3 4
count age
SELECTIO N S CO M PLEX PREDICATES
Negation Query:
→ sel(not P) = 1 – sel(P) → Example: sel(age != 2)
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SC(age=2)=1
SELECT * FROM people WHERE age != 2
CMU 15-445/645 (Fall 2018)
1 2 3 4
count age
SELECTIO N S CO M PLEX PREDICATES
Negation Query:
→ sel(not P) = 1 – sel(P) → Example: sel(age != 2)
Observation: Selectivity ≈ Probability
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= 1 – (1/5) = 4/5
SC(age!=2)=2 SC(age!=2)=2
SELECT * FROM people WHERE age != 2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Conjunction:
→ sel(P1 ⋀ P2) = sel(P1) ∙ sel(P2) → sel(age=2 ⋀ name LIKE 'A%')
This assumes that the predicates are independent.
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SELECT * FROM people WHERE age = 2 AND name LIKE 'A%'
P1 P2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Conjunction:
→ sel(P1 ⋀ P2) = sel(P1) ∙ sel(P2) → sel(age=2 ⋀ name LIKE 'A%')
This assumes that the predicates are independent.
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SELECT * FROM people WHERE age = 2 AND name LIKE 'A%'
P1 P2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Conjunction:
→ sel(P1 ⋀ P2) = sel(P1) ∙ sel(P2) → sel(age=2 ⋀ name LIKE 'A%')
This assumes that the predicates are independent.
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SELECT * FROM people WHERE age = 2 AND name LIKE 'A%'
P1 P2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Disjunction:
→ sel(P1 ⋁ P2) = sel(P1) + sel(P2) – sel(P1⋁P2) = sel(P1) + sel(P2) – sel(P1) ∙ sel(P2) → sel(age=2 OR name LIKE 'A%')
This again assumes that the selectivities are independent.
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SELECT * FROM people WHERE age = 2 OR name LIKE 'A%'
P1 P2
CMU 15-445/645 (Fall 2018)
SELECTIO N S CO M PLEX PREDICATES
Disjunction:
→ sel(P1 ⋁ P2) = sel(P1) + sel(P2) – sel(P1⋁P2) = sel(P1) + sel(P2) – sel(P1) ∙ sel(P2) → sel(age=2 OR name LIKE 'A%')
This again assumes that the selectivities are independent.
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SELECT * FROM people WHERE age = 2 OR name LIKE 'A%'
P1 P2
CMU 15-445/645 (Fall 2018)
RESULT SIZE ESTIM ATIO N FO R J O IN S
Given a join of R and S, what is the range of possible result sizes in # of tuples? In other words, for a given tuple of R, how many tuples of S will it match?
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RESULT SIZE ESTIM ATIO N FO R J O IN S
General case: Rcols⋂Scols={A} where A is not a key for either table.
→ Match each R-tuple with S-tuples: estSize ≈ NR ∙ NS / V(A,S) → Symmetrically, for S: estSize ≈ NR ∙ NS / V(A,R)
Overall:
→ estSize ≈ NR ∙ NS / max({V(A,S), V(A,R)})
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CMU 15-445/645 (Fall 2018)
CO ST ESTIM ATIO N S
Our formulas are nice but we assume that data values are uniformly distributed.
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5 1 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 Uniform Approximation
Distinct values of attribute # of occurrences
CMU 15-445/645 (Fall 2018)
CO ST ESTIM ATIO N S
Our formulas are nice but we assume that data values are uniformly distributed.
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Bucket #1 Count=8 Bucket #2 Count=4 Bucket #3 Count=1 5 Bucket #4 Count=3 Bucket #5 Count=1 4
5 1 1 5
1
4-6 7-9 1 0-1 2 1 3-1 5
Non-Uniform Approximation
Bucket Ranges
CMU 15-445/645 (Fall 2018)
H ISTO GRAM S WITH Q UAN TILES
A histogram type wherein the "spread" of each bucket is same.
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5 1 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 Equi-width Histogram (Quantiles)
Bucket #1 Count=1 2 Bucket #2 Count=1 2 Bucket #3 Count=9 Bucket #4 Count=1 2
CMU 15-445/645 (Fall 2018)
H ISTO GRAM S WITH Q UAN TILES
A histogram type wherein the "spread" of each bucket is same.
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5 1 1 5 1
6-8 9-1 3 1 4-1 5 Equi-width Histogram (Quantiles)
CMU 15-445/645 (Fall 2018)
SAM PLIN G
Modern DBMSs also collect samples from tables to estimate selectivities. Update samples when the underlying tables changes significantly.
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⋮
1 billion tuples
SELECT AVG(age) FROM people WHERE age > 50
id name age status 1001 Obama 56 Rested 1002 Kanye 40 Weird 1003 Tupac 25 Dead 1004 Bieber 23 Crunk 1005 Andy 37 Lit
CMU 15-445/645 (Fall 2018)
SAM PLIN G
Modern DBMSs also collect samples from tables to estimate selectivities. Update samples when the underlying tables changes significantly.
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⋮
1 billion tuples
sel(age>50) = SELECT AVG(age) FROM people WHERE age > 50
id name age status 1001 Obama 56 Rested 1002 Kanye 40 Weird 1003 Tupac 25 Dead 1004 Bieber 23 Crunk 1005 Andy 37 Lit 1001 Obama 56 Rested 1003 Tupac 25 Dead 1005 Andy 37 Lit
Table Sample
CMU 15-445/645 (Fall 2018)
SAM PLIN G
Modern DBMSs also collect samples from tables to estimate selectivities. Update samples when the underlying tables changes significantly.
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⋮
1 billion tuples
1/3 sel(age>50) = SELECT AVG(age) FROM people WHERE age > 50
id name age status 1001 Obama 56 Rested 1002 Kanye 40 Weird 1003 Tupac 25 Dead 1004 Bieber 23 Crunk 1005 Andy 37 Lit 1001 Obama 56 Rested 1003 Tupac 25 Dead 1005 Andy 37 Lit
Table Sample
CMU 15-445/645 (Fall 2018)
O BSERVATIO N
Now that we can (roughly) estimate the selectivity
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CMU 15-445/645 (Fall 2018)
Q UERY O PTIM IZATIO N
After performing rule-based rewriting, the DBMS will enumerate different plans for the query and estimate their costs.
→ Single relation. → Multiple relations. → Nested sub-queries.
It chooses the best plan it has seen for the query after exhausting all plans or some timeout.
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CMU 15-445/645 (Fall 2018)
SIN GLE- RELATIO N Q UERY PLAN N IN G
Pick the best access method.
→ Sequential Scan → Binary Search (clustered indexes) → Index Scan
Simple heuristics are often good enough for this. OLTP queries are especially easy.
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CMU 15-445/645 (Fall 2018)
O LTP Q UERY PLAN N IN G
Query planning for OLTP queries is easy because they are sargable.
→ Search Argument Able → It is usually just picking the best index. → Joins are almost always on foreign key relationships with a small cardinality. → Can be implemented with simple heuristics.
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CMU 15-445/645 (Fall 2018)
M ULTI- RELATIO N Q UERY PLAN N IN G
As number of joins increases, number of alternative plans grows rapidly
→ We need to restrict search space.
Fundamental decision in System R: only left-deep join trees are considered.
→ Modern DBMSs do not always make this assumption anymore.
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CMU 15-445/645 (Fall 2018)
M ULTI- RELATIO N Q UERY PLAN N IN G
Fundamental decision in System R: Only consider left-deep join trees.
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⨝ ⨝ ⨝
A B C D
⨝ ⨝ ⨝
A B C D
⨝ ⨝ ⨝
A B C D
CMU 15-445/645 (Fall 2018)
M ULTI- RELATIO N Q UERY PLAN N IN G
Fundamental decision in System R: Only consider left-deep join trees.
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⨝ ⨝ ⨝
A B C D
⨝ ⨝ ⨝
A B C D
⨝ ⨝ ⨝
A B C D
CMU 15-445/645 (Fall 2018)
M ULTI- RELATIO N Q UERY PLAN N IN G
Fundamental decision in System R: Only consider left-deep join trees. Allows for fully pipelined plans where intermediate results are not written to temp files.
→ Not all left-deep trees are fully pipelined.
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CMU 15-445/645 (Fall 2018)
M ULTI- RELATIO N Q UERY PLAN N IN G
Enumerate the orderings
→ Example: Left-deep tree #1, Left-deep tree #2…
Enumerate the plans for each operator
→ Example: Hash, Sort-Merge, Nested Loop…
Enumerate the access paths for each table
→ Example: Index #1, Index #2, Seq Scan…
Use dynamic programming to reduce the number of cost estimations.
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CMU 15-445/645 (Fall 2018)
DYN AM IC PRO GRAM M IN G
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R S T
SortMerge Join
R.a=S.a
SortMerge Join
T.b=S.b
Hash Join
T.b=S.b
R ⨝ S T T ⨝ S R R ⨝ S ⨝ T
Hash Join
R.a=S.a
SELECT * FROM R, S, T WHERE R.a = S.a AND S.b = T.b
CMU 15-445/645 (Fall 2018)
DYN AM IC PRO GRAM M IN G
45
R S T
Hash Join
T.b=S.b
R ⨝ S T T ⨝ S R R ⨝ S ⨝ T
Hash Join
R.a=S.a
SELECT * FROM R, S, T WHERE R.a = S.a AND S.b = T.b
CMU 15-445/645 (Fall 2018)
DYN AM IC PRO GRAM M IN G
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R S T
Hash Join
T.b=S.b
R ⨝ S T T ⨝ S R R ⨝ S ⨝ T
Hash Join
R.a=S.a
Hash Join
S.b=T.b
SortMerge Join
S.b=T.b
SortMerge Join
S.a=R.a
Hash Join
S.a=R.a
SELECT * FROM R, S, T WHERE R.a = S.a AND S.b = T.b
CMU 15-445/645 (Fall 2018)
DYN AM IC PRO GRAM M IN G
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R S T
Hash Join
T.b=S.b
R ⨝ S T T ⨝ S R R ⨝ S ⨝ T
Hash Join
R.a=S.a
Hash Join
S.b=T.b
SortMerge Join
S.a=R.a
SELECT * FROM R, S, T WHERE R.a = S.a AND S.b = T.b
CMU 15-445/645 (Fall 2018)
DYN AM IC PRO GRAM M IN G
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R S T
Hash Join
T.b=S.b
R ⨝ S T T ⨝ S R R ⨝ S ⨝ T
SortMerge Join
S.a=R.a
SELECT * FROM R, S, T WHERE R.a = S.a AND S.b = T.b
CMU 15-445/645 (Fall 2018)
CAN DIDATE PLAN EXAM PLE
How to generate plans for search algorithm:
→ Enumerate relation orderings → Enumerate join algorithm choices → Enumerate access method choices
No real DBMSs does it this way. It’s actually more messy…
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SELECT * FROM R, S, T WHERE R.a = S.a AND S.b = T.b
CMU 15-445/645 (Fall 2018)
CAN DIDATE PLAN S
Step #1: Enumerate relation orderings
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⨝ ⨝
T R S
⨝ ⨝
S T R
× ⨝
R S T
⨝ ⨝
R S T
⨝ ⨝
S R T
× ⨝
S T R
CMU 15-445/645 (Fall 2018)
CAN DIDATE PLAN S
Step #1: Enumerate relation orderings
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⨝ ⨝
T R S
⨝ ⨝
S T R
× ⨝
R S T
⨝ ⨝
R S T
⨝ ⨝
S R T
× ⨝
S T R
Prune plans with cross- products immediately!
CMU 15-445/645 (Fall 2018)
CAN DIDATE PLAN S
Step #2: Enumerate join algorithm choices
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⨝ ⨝
R S T
Do this for the other plans.
R S T
NLJ NLJ
R S T
HJ NLJ
R S T
NLJ HJ
R S T
HJ HJ
CMU 15-445/645 (Fall 2018)
CAN DIDATE PLAN S
Step #2: Enumerate join algorithm choices
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⨝ ⨝
R S T
Do this for the other plans.
R S T
NLJ NLJ
R S T
HJ NLJ
R S T
NLJ HJ
R S T
HJ HJ
CMU 15-445/645 (Fall 2018)
CAN DIDATE PLAN S
Step #3: Enumerate access method choices
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R S T
HJ HJ
Do this for the other plans.
HJ HJ SeqScan SeqScan SeqScan HJ HJ SeqScan IndexScan(S.b) SeqScan
CMU 15-445/645 (Fall 2018)
N ESTED SUB- Q UERIES
The DBMS treats nested sub-queries in the where clause as functions that take parameters and return a single value or set of values. Two Approaches:
→ Rewrite to de-correlate and/or flatten them → Decompose nested query and store result to temporary table
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N ESTED SUB- Q UERIES: REWRITE
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SELECT name FROM sailors AS S WHERE EXISTS ( SELECT * FROM reserves AS R WHERE S.sid = R.sid AND R.day = '2018-10-15' ) SELECT name FROM sailors AS S, reserves AS R WHERE S.sid = R.sid AND R.day = '2018-10-15'
CMU 15-445/645 (Fall 2018)
N ESTED SUB- Q UERIES: DECO M PO SE
For each sailor with the highest rating (over all sailors) and at least two reservations for red boats, find the sailor id and the earliest date on which the sailor has a reservation for a red boat.
52
SELECT S.sid, MIN(R.day) FROM sailors S, reserves R, boats B WHERE S.sid = R.sid AND R.bid = B.bid AND B.color = 'red' AND S.rating = (SELECT MAX(S2.rating) FROM sailors S2) GROUP BY S.sid HAVING COUNT(*) > 1
CMU 15-445/645 (Fall 2018)
DECO M PO SIN G Q UERIES
For harder queries, the optimizer breaks up queries into blocks and then concentrates on one block at a time. Sub-queries are written to a temporary table that are discarded after the query finishes.
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CMU 15-445/645 (Fall 2018)
DECO M PO SIN G Q UERIES
54
SELECT S.sid, MIN(R.day) FROM sailors S, reserves R, boats B WHERE S.sid = R.sid AND R.bid = B.bid AND B.color = 'red' AND S.rating = (SELECT MAX(S2.rating) FROM sailors S2) GROUP BY S.sid HAVING COUNT(*) > 1
Nested Block
CMU 15-445/645 (Fall 2018)
DECO M PO SIN G Q UERIES
54
SELECT S.sid, MIN(R.day) FROM sailors S, reserves R, boats B WHERE S.sid = R.sid AND R.bid = B.bid AND B.color = 'red' AND S.rating = (SELECT MAX(S2.rating) FROM sailors S2) GROUP BY S.sid HAVING COUNT(*) > 1
Nested Block
SELECT MAX(rating) FROM sailors
###
CMU 15-445/645 (Fall 2018)
DECO M PO SIN G Q UERIES
54
SELECT S.sid, MIN(R.day) FROM sailors S, reserves R, boats B WHERE S.sid = R.sid AND R.bid = B.bid AND B.color = 'red' AND S.rating = (SELECT MAX(S2.rating) FROM sailors S2) GROUP BY S.sid HAVING COUNT(*) > 1 SELECT MAX(rating) FROM sailors
###
CMU 15-445/645 (Fall 2018)
DECO M PO SIN G Q UERIES
54
SELECT S.sid, MIN(R.day) FROM sailors S, reserves R, boats B WHERE S.sid = R.sid AND R.bid = B.bid AND B.color = 'red' AND S.rating = (SELECT MAX(S2.rating) FROM sailors S2) GROUP BY S.sid HAVING COUNT(*) > 1
Outer Block
SELECT MAX(rating) FROM sailors
###
CMU 15-445/645 (Fall 2018)
CO N CLUSIO N
Filter early as possible. Selectivity estimations
→ Uniformity → Independence → Histograms → Join selectivity
Dynamic programming for join orderings Rewrite nested queries Query optimization is really hard…
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M i d t er m Exam
Who: You What: Midterm Exam When: Wed Oct 17th 12:00pm ‐ 1:20pm Where: Posner Mellon Auditorium Why: https://youtu.be/xgMiaIPxSlc
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CMU 15-445/645 (Fall 2018)
M IDTERM
What to bring:
→ CMU ID → Calculator → One 8.5x11" page of notes (double-sided)
What not to bring:
→ Live animals → Your wet laundry
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CMU 15-445/645 (Fall 2018)
M IDTERM
Covers up to Joins (inclusive).
→ Closed book, one sheet of notes (double-sided) → Please email Andy if you need special accommodations.
https://15445.courses.cs.cmu.edu/fall2018/midter m-guide.html
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CMU 15-445/645 (Fall 2018)
RELATIO N AL M O DEL
Integrity Constraints Relation Algebra
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CMU 15-445/645 (Fall 2018)
SQ L
Basic operations:
→ SELECT / INSERT / UPDATE / DELETE → WHERE predicates → Output control
More complex operations:
→ Joins → Aggregates → Common Table Expressions
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CMU 15-445/645 (Fall 2018)
STO RAGE
Buffer Management Policies
→ LRU / MRU / CLOCK
On-Disk File Organization
→ Heaps → Linked Lists
Page Layout
→ Slotted Pages → Log-Structured
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CMU 15-445/645 (Fall 2018)
H ASH IN G
Static Hashing
→ Linear Probing → Robin Hood → Cuckoo Hashing
Dynamic Hashing
→ Extendible Hashing → Linear Hashing
Comparison with B+Trees
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CMU 15-445/645 (Fall 2018)
TREE IN DEXES
B+Tree
→ Insertions / Deletions → Splits / Merges → Difference with B-Tree → Latch Crabbing / Coupling
Radix Trees Skip Lists
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CMU 15-445/645 (Fall 2018)
SO RTIN G
Two-way External Merge Sort General External Merge Sort Cost to sort different data sets with different number of buffers.
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CMU 15-445/645 (Fall 2018)
Q UERY PRO CESSIN G
Processing Models
→ Advantages / Disadvantages
Join Algorithms
→ Nested Loop → Sort-Merge → Hash
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CMU 15-445/645 (Fall 2018)
N EXT CLASS
Parallel Query Execution
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