C ONCURRENCY C ONTROL ON R ELATIONAL D ATABASES Seminar: Transaction Processing (Bachelor) SS 2009...

CONCURRENCY CONTROL ON RELATIONAL DATABASES

Seminar: Transaction Processing (Bachelor)

SS 2009

Dennis Stratmann

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sOUTLINE

Goal and Overview Examine Three Approaches to CC on RD:

1. Predicate-Oriented Concurrency Control 2. Relational Update Transactions

Syntax and Semantics Histories and Final State Serializability Conflict Serializability Extended Conflict Serializability

3. Exploiting Transaction Program Knowledge Transaction Chopping Applicability of Chopping

Summary Questions & Answers

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GOAL AND OVERVIEW

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sGOAL AND OVERVIEW

Semantic approach to Concurrency Control Possible to exploit semantic knowledge at

higher abstraction level

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sGOAL AND OVERVIEW

Three Approaches to CC on RD: Predicate-Oriented Concurrency Control Relational Update Transactions Exploiting Transaction Program Knowledge

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THREE APPROACHES TO CC ON RD:

1. Predicate-Oriented Concurrency Control

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s1. PREDICATE-ORIENTED CC ON RD

Relational Database

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s1. PREDICATE-ORIENTED CC ON RD

Lock entire Relation

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s1. PREDICATE-ORIENTED CC ON RD

Lock individual Tuples

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1. PREDICATE-ORIENTED CC ON RD

Phantom Problem: Transaction 1:

Transaction 2:

Emp Name Department

Position Salary

Jones Service Clerk 20 000

Meier Service Clerk 22 000

Paulus Service Manager 42 000

Smyth Toys Cashier 25 000

Brown Sales Clerk 28 000

Albert Sales Manager 38 000

DELETE FROM Emp WHERE Department = ‘Service’ AND Position = ‘Manager’

INSERT INTO Emp VALUES (‘Smith’, ‘Service’, ‘Manager’, 40000)

SELECT Name, Position, SalaryFROM EmpWHERE Department = ‘Service’

UPDATE EmpSET Department = ‘Sales’WHERE Department = ‘Service’AND Position <> ‘Manager’

INSERT INTO EmpVALUES (‘Sone’, ‘Serivce’, ‘Clerk’, 13000)

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s1. PREDICATE-ORIENTED CC ON RD

Predicate Locking

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s1. PREDICATE-ORIENTED CC ON RD

Transaction 1:

Ca: Department = ‘Service‘ Position = ‘Manager‘

Cb: Name = ‘Smith‘ Department = ‘Service‘ Position = ‘Manager‘ Salary = ‘40000‘

Cc1: Department = ‘Service‘ Position ‘Manager‘

Cc2: Department = ‘Sales‘ Position ‘Manager‘

Cd: Name = ‘Stone‘ Department = ‘Service‘ Position = ‘Clerk‘ Salary = ‘13000‘

DELETE FROM Emp WHERE Department = ‘Service’ AND Position = ‘Manager’

INSERT INTO Emp VALUES (‘Smith’, ‘Service’, ‘Manager’, 40000)

UPDATE EmpSET Department = ‘Sales’WHERE Department = ‘Service’AND Position <> ‘Manager’

INSERT INTO EmpVALUES (‘Sone’, ‘Serivce’, ‘Clerk’, 13000)

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s1. PREDICATE-ORIENTED CC ON RD

Transaction 2:

Cq: Department = ‘Service‘

Transaction 3:

Cp: Department = ‘Sales‘

Example: H(Ca) H(Cq) ∅ H(Cb) H(Cq) ∅ H(Cc1) H(Cq) ∅ H(Cc2) H(Cq) = ∅ H(Cd) H(Cq) ∅

SELECT Name, Position, SalaryFROM EmpWHERE Department = ‘Service’

SELECT Name, Position, SalaryFROM EmpWHERE Department = ‘Sales’

H(Ca) H(Cp) = ∅ H(Cb) H(Cp) = ∅ H(Cc1) H(Cp) = ∅ H(Cc2) H(Cp) ∅ H(Cd) H(Cp) = ∅

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s1. PREDICATE-ORIENTED CC ON RD

Scheduler

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THREE APPROACHES TO CC ON RD:

2. Relational Update Transactions

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2. RELATIONAL UPDATE TRANSACTIONSSYNTAX AND SEMANTICS

IDM Transaction Model Insertion: iR(C) Deletion: dR(C) Modify:

mR(C1;C2)

Semantics is called effect

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s2. RELATIONAL UPDATE TRANSACTIONS

SYNTAX AND SEMANTICS

Transaction Equivalence Two IDM Transactions t and t’ are equivalent,

if eff(t) = eff(t’) Written: t t’

Commutativity Rules Simplification Rules

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s2. RELATIONAL UPDATE TRANSACTIONS

HISTORIES AND FINAL STATE SERIALIZABILITY

A history s is serial, if all Transactions appear strictly one after the other

A history s is Final State Serializable if s s’ for some history s’

FSRIDM denotes class of all Final State Serializable histories

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s2. RELATIONAL UPDATE TRANSACTIONS

HISTORIES AND FINAL STATE SERIALIZABILITY

Example:

t1

= d(3)m(1;2)m(3;4)

t2

= d(3)m(2;3)

s = d2

(3)d1

(3)m1

(1;2)m2

(2;3)m1

(3;4)

s’

= d(3)m(1;4)m(2;4)m(3;4)

eff(s) = eff(s’

) s s’

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s2. RELATIONAL UPDATE TRANSACTIONS

HISTORIES AND FINAL STATE SERIALIZABILITYt1

= d(3)m(1;2)m(3;4)

t2

= d(3)m(2;3)

s / t1t2 or s / t2t1 s FSRIDM

t1

t1‘ = d(3)m(1;2)

s‘

= d2

(3)d1

(3)m1

(1;2)m2

(2;3) t1

t2

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s2. RELATIONAL UPDATE TRANSACTIONS

CONFLICT SERIALIZABILITY

A history s for a set T of n transactions is conflict serializable, if s tp(1)...tp(n) using only the Commutativity Rules

CSRIDM denotes class of all Conflict Serializable histories

Conflict Graph G(s) = (T,E) History s CSRIDM, if G(s) is acyclic

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s2. RELATIONAL UPDATE TRANSACTIONS

CONFLICT SERIALIZABILITY

Consider s = m2(1;2) m1(2;3) m2(3;2)

G(s) is cyclic, so s is not in CSRIDM

On the other hand, s m1(2;3) m2(1;2) m2(3;2) t1 t2

s is in FSRIDM

CSRIDM FSRIDM

t1

t2

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s2. RELATIONAL UPDATE TRANSACTIONS

EXTENDED CONFLICT SERIALIZABILITY

Sometimes, the context in which a conflict occurs can make a difference:Example: Let

s = d1(0) m1(0;1) m2(1;2) m1(2;3)

G(s) is cyclic, but s m2(1;2) d1(0) m1(0;1) m1(2;3) t2 t1

Intuitively the conflict involving m1(0;1) does not exist (due to d1(0) ) !

A history s for a set T of n transactions is extended conflict serializable, if Extended Conflict Graph EG(s) = (T,E) is acyclic

ECSRIDM denotes class of all Extended Conflict Serializable histories

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s2. RELATIONAL UPDATE TRANSACTIONS

EXTENDED CONFLICT SERIALIZABILITY

CSRIDM ECSRIDM FSRIDM

FSRIDM

CSRIDM

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THREE APPROACHES TO CC ON RD:

3. Exploiting Transaction Program Knowledge

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

Transaction Chopping Short Transactions need less locks Short Transactions cause potentially less lock contention

Chopping depends on concurrent Transactions

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

Definition of Transaction Chopping Every database operation invoked by the

chopped transactions is contained in exactly one piece, and the order of operation invocations is preserved

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

Deadlock / Rollback situation

Atomicity of original Transaction needs to be preserved

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3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

Three types of Transactions:

1. A Transaction updating a single Customer’s Account as well as the corresponding Branch

2. A Transaction reading a Customer’s Account Balance

3. A Transaction comparing the grand Total of all Account Balances with the Sum of the Branch Balances

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

t1 = r1(A1)w1(A1)r1(B1)w1(B1)

t2 = r2(A3)w2(A3)r2(B1)w2(B1)

t3 = r3(A4)w3(A4)r3(B2)w3(B2)

t4 = r4(A2)

t5 = r5(A4)

t6 = r6(A1)r6(A2)r6(A3)r6(B1)r6(A4)r6(A5)r6(B2)

SELECT Balance INTO :oldbalance FROM Accounts WHERE AccountNo = A1;UPDATE Accounts SET Balance = :newbalance WHERE AccountNo = A1;SELECT Total INTO :oldtotal FROM Branches WHERE BranchNo = B1;UPDATE Branches SET TOTAL = :newtotal WHERE Branches = B1;

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

Test if a given chopping is correct with a Chopping Graph Example

t6 = r6(A1)r6(A2)r6(A3)r6(B1)r6(A4)r6(A5)r6(B2)

Chop into two pieces:

t61 = r61(A1)r61(A2)r61(A3)r61(B1)

t62 = r62(A4)r62(A5)r62(B2)

Corresponding Chopping Graph:

s: sibling

c: conflict

A chopping is correct if the associated graph does not contain an sc cycle.

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

TRANSACTION CHOPPING

Further in the example:t1 = r1(A1)w1(A1)r1(B1)w1(B1)

Chop into two pieces:

t11 = r1(A1)w1(A1)

t12 = r1(B1)w1(B1)

Corresponding Chopping Graph:

Making chopping finer can introduce sc cycles

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

APPLICABILITY OF CHOPPING

Applicability of Chopping To apply chopping algorithm, semantic knowledge is necessary Semantic knowledge derived form predicates

Real World Example:

High Level conflict Record-Level conflict

SELECT AccountNo, Balance FROM AccountsWHERE City = ‘Konstanz’

UPDATE Accounts SET Balance = Balance * 1.05WHERE City = ‘Stuttgart’

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

APPLICABILITY OF CHOPPING

Gain chopping relevant information from parameterized SQL statements:

The chopping method is in limited settings ready for practical use, if No control-flow branching is used No loops are used No If-then-else constructs are used

SELECT AccountNo, Balance FROM AccountsWHERE AccountType = ‘savings’ AND City = :x

UPDATE Accounts SET Balance = Balance * 1.05WHERE AccountType = ‘checking’ AND City = :y

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s3. EXPLOITING TRANSACTION PROGRAM KNOWLEDGE

APPLICABILITY OF CHOPPING

Rewrite SQL statement to a parameter-less statementSELECT AccountNo, Balance FROM AccountsWHERE AccountType = ‘savings’ AND City = :x;

If not found thenSELECT AccountNo, Balance FROM AccountsWHERE AccountTyoe = ‘checking’ AND City = :x;fi;

SELECT AccountNo, Balance FROM AccountsWHERE AccountType = ‘savings’;SELECT AccountNo, Balance FROM AccountsWHERE AccountTyoe = ‘checking’;

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SUMMARY

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sSUMMARY

Predicate-Oriented Concurrency Control•Trade-off between locking entire relation and locking individual tuples•NP complete to test Satisfiability

Relational Update Transactions•Commutativity based Serializability (FSRIDM)•Conflict Serializability ([E]CSRIDM)

Exploiting Transaction Program Knowledge •Decomposition of Transactions into small pieces•Only in limited settings ready for practical use

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THANKS FOR LISTENING

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QUESTIONS & ANSWERS