1. Chapter 8: Concurrency Control on Relational Databases
최병준

2. Outline Goal and Overview
Examine Three Approaches to Concurrency Control on Relational Databases:
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
Concurrency Control on Relational Databases

3. Goal and Overview Semantic approach to Concurrency Control
Possible to exploit semantic knowledge at higher abstraction level
Three Approaches to Concurrency Control on Relational Databases
Predicate-Oriented Concurrency Control
Relational Update Transactions
Exploiting Transaction Program Knowledge
Hardly of practical relevance
Efficient implementation is difficult to achieve
Semantics is more complex than syntax
Concurrency Control on Relational Databases

4. 1. Predicate-Oriented Concurrency Control
Relational Databases
Database consists of tables
Relation (r) : table
Schema (R) : table header
Attributes (A1, A2, … , An) : columns
Finite domain dom(Ai)
Tuples : elements of r ⊆ dom(A1) x … dom(An)
Operations on tables and databases are
Queries (select-from-where expressions)
Insertions
Deletions
Modifications
Two Locking for transactional access on relational database
Lock entire relations
Prevents simultaneously accessing an subset of tuples from relation, unnecessary delays
Lock individual tuples
Large number of locks may result (lock administration problem, phantom problem)
Concurrency Control on Relational Databases

5. 1. Predicate-Oriented Concurrency Control
Phantom problem
Example 8.1
Update transaction t:
(a) Delete From Emp Where Department = ‘Service’
And Position = ‘Manager’
(b) Insert Into Emp Values (‘Smith’, ‘Service’, ‘Manager’, 40000)
(c) Update Emp Set Department = ‘Sales’ Where Department = ‘Service’ And Position ≠ ‘Manager’
(d) Insert Into Emp Values (‘Stone’, ‘Service’, ‘Clerk’, 13000)
Retrieval transaction q:
Select Name, Position, Salary From Emp
Where Department = ‘Service’
Observations:
- Interleaving q with t leads to inconsistent read known as “phantom problem”
- Locking existing records cannot prevent this problem
☞ Phantom problem :
The query could no longer lock, because tuples have been deleted or are not yet existing but will be inserted in the future
Concurrency Control on Relational Databases

6. 1. Predicate-Oriented Concurrency Control
Predicate Locking
Hyperplane
Associate with each operation on table R(A1, ..., An) a set C of conditions that covers a set H(C) of – existing or conceivable – tuples with
Each operation locks its H(C)
Compatibility
Predicate locks on predicates Ct and Ct‘ on behalf of transactions t and t‘ in modes mt and mt‘ are compatible if
t = t‘ or
both mt and mt‘ are read (shared) mode or
H(Ct) ∩ H(Ct‘) = Φ
Concurrency Control on Relational Databases

7. 1. Predicate-Oriented Concurrency Control
Example 8.2
Testing whether H(Ct) ∩ H(Ct‘) = Φ is NP-complete
For preventing the phantom problem it is sufficient that
queries lock predicates and
insert, update, and delete operations lock individual records, and
compatibility is checked by testing that an update-affected record does not satisfy any of the query predicate locks
Concurrency Control on Relational Databases

8. 2. Relational Update Transactions
Transactions are sequences of insert, delete, or modify operations (in the style of SQL updates)
Define notions of serializability along the lines of the classical ones
The semantic information available on transaction effects can be exploited to allow more concurrency
Definition 8.1 (IDM Transaction):
An IDM transaction over a database schema D is a finite sequence of update
operations (insertions, deletions, modifications) over D.
If is an IDM transaction over a given database, the effect of ,
, is defined as
☞ Effect : Semantics of the update operations
Concurrency Control on Relational Databases

9. 2. Relational Update Transactions
Transaction Equivalence
Commutativity Rules
Two update operations commute if they refer to distinct tuples
Definition 8.2 (Transaction Equivalence):
Two IDM transactions over the same database schema are equivalent, written
, if , i.e., and have the same effect.
Concurrency Control on Relational Databases

10. 2. Relational Update Transactions
Simplification Rules
Simplification rules can be used for transaction optimization.
Concurrency Control on Relational Databases

11. 2. Relational Update Transactions
Final State Serializability
Improve histories by removing redundant updates or simplifying individual transactions.
Definition 8.3 (Final State Serializability):
A history s for a set of IDM transactions is final state
serializable if for some serial history for T.
Let FSRIDM denote the class of all final state serializable histories (for ).
Concurrency Control on Relational Databases

12. 2. Relational Update Transactions
Testing Membership in FSRIDM
Thus, “exact“ testing is no easier than for page model transactions when semanti c information is present.
Theorem 8.1:
The problem of testing whether a given history is in FSRIDM is NP complete.
Concurrency Control on Relational Databases

13. 2. Relational Update Transactions
Conflict Serializability
Definition 8.4 (Conflict Serializability):
A history for a set of n transactions is conflict serializable if the equivalence
of to a serial history can be proven using the commutativity rules alone.
Let CSRIDM denote the class of all conflict serializable histories (for ).
Definition 8.5 (Conflict Graph):
Let T be a set of IDM transactions and a history for The conflict graph
of s is defined by: is in if for transactions and in , , there is an update in and an update in s.t And
is not equivalent to (i.e., does not hold).
Theorem 8.2:
Let s be a history for a set T of transactions. Then s is in CSRIDM iff G(s) is
acyclic.
Concurrency Control on Relational Databases

14. 2. Relational Update Transactions
Example 8.6
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
m1(2;3) m2(3;2)
m2(1;2) m1(2;3)
s = m2(1;2) m1(2;3) m2(3;2) = m(1;2) m(3;2)
t1 t2 = m1(2;3) m2(1;2) m2(3;2)
= m(2;3)m(3;2) m(1;2)
= m(3;2) m(1;2)
Consequence: CSRIDM is a strict subset of FSRIDM
Concurrency Control on Relational Databases

15. 2. Relational Update Transactions
Extended Conflict Serializability
Conflict serializability : only take local conflicts into account
Final state serializability : look at entire context at once
Extended conflicts serializability : between single updates u from one transaction and prefixes of other transactions occurring in a given history prior to u
Definition 8.6 (Extended Conflict Graph / Serializability):
Let be a history for a set of transactions.
(1) The extended conflict graph of is defined by:
is in if there is an update in s.t and does not commute with the projection of onto .
(2) is extended conflict serializable if is acyclic.
Let ECSRIDM denote the class of all extended conflict-serializable histories.
Concurrency Control on Relational Databases

16. 2. Relational Update Transactions
Example 8.7 t1 t2
m2(1;2) m1(2;3)
m1(2;3) m2(3;2)
☞ Example 8.6
s ∈ CSRIDM s ∈ FSRIDM
G(s) is cyclic, but
s ∈ ECSRIDM
Theorem 8.3:
CSRIDM ⊂ ECSRIDM ⊂ FSRIDM
Concurrency Control on Relational Databases

17. 3. Exploiting Transaction Program Knowledge
Transaction Chopping
Short transactions need less locks
Short transactions cause potentially less lock contention
Long transactions can be broken into smaller pieces
Assumption:
all potentially concurrent app programs are known in advance and
their structure and resulting access patterns can be precisely analyzed
Definition 8.8 (Transaction Chopping):
A chopping of transaction is a decomposition of into pieces s.t.
every step of is contained in exactly one piece and the step order is preserved.
Definition 8.10 (Correct Chopping):
A chopping of is correct if every execution of the transaction
pieces is conflict-equivalent to a serial history of under a protocol with
• transaction pieces obey the execution precedences of the original programs.
• each piece is executed as a unit under a CSR scheduler.
Concurrency Control on Relational Databases

18. 3. Exploiting Transaction Program Knowledge
Short Transactions Are Good
Concurrency Control on Relational Databases

19. 3. Exploiting Transaction Program Knowledge
Chopping Graph
Definition 8.9 (Chopping Graph):
For a chopping of transaction set the chopping graph is
an undirected graph s.t.
• the nodes of are the transaction pieces
• for two pieces , from different transactions contains a
edge between and if and contain conflicting operations
• for two pieces , from the same transaction contains an edge
Theorem 8.5:
A chopping is correct if the associated chopping graph does not contain
an sc cycle (i.e., a cycle that involves at least one edge and at least one edge.
Concurrency Control on Relational Databases

20. 3. Exploiting Transaction Program Knowledge
Chopping Graph
Concurrency Control on Relational Databases

21. 3. Exploiting Transaction Program Knowledge
Applicability of Chopping
Directly applicable to straight-line, parameter-less SQL programs with predicate locking.
Needs to conservatively derive covering program for parameterized SQL, if-then-else and loops, and needs to be conservative about c edges
Concurrency Control on Relational Databases