1. Temple University – CIS Dept. CIS661 – Principles of Data Management
V. Megalooikonomou
Concurrency control
(based on slides by C. Faloutsos at CMU and on notes by Silberchatz,Korth, and Sudarshan)

2. General Overview Relational model - SQL
Functional Dependencies & Normalization
Physical Design &Indexing
Query optimization
Transaction processing
concurrency control
recovery

3. Transactions - dfn = unit of work, eg. Atomicity (all or none)
move $10 from savings to checking
Atomicity (all or none)
Consistency
Isolation (as if alone)
Durability
recovery
concurrency control

4. Concurrency – overview
why we want it?
what does it mean ‘correct’ interleaving?
precedence graph
how to achieve correct interleavings automatically?
concurrency control

5. Problem concurrent access to data (consider ‘lost update’ problem)
how to solve it?

6. Lost update problem – no locks
Read(N)
time

7. Solution – part 1 locks! (most popular solution)
lock manager: grants/denies lock requests

8. Lost update problem – with locks
lock manager
lock(N)
Read(N)
N=N-1
Write(N)
Unlock(N)
grants lock
denies lock
lock(N)
time
T2: waits
grants lock to T2
Read(N) ...

9. Locks
but, what if we all just want to read ‘N’?

10. Solution – part 1 Locks and their flavors compatibility matrix
X-locks:
exclusive (or write-) locks
S-locks:
shared (or read-) locks
<and more ... >
compatibility matrix
T2 wants
T1 has S X T F

11. Solution – part 1
A transaction may be granted a lock on an item if the requested lock is compatible with locks already held on the item by other transactions
Any number of transactions can hold shared locks on an item, but if any transaction holds an exclusive on the item no other transaction may hold any lock on the item.
If a lock cannot be granted, the requesting transaction is made to wait till all incompatible locks held by other transactions have been released. The lock is then granted.

12. Solution – part 1 transactions request locks (or upgrades)
lock manager grants or blocks requests
transactions release locks
lock manager updates lock-table

13. Solution – part 2
locks are not enough – eg., ‘inconsistent analysis’

14. ‘Inconsistent analysis’
time
Precedence graph?

15. ‘Inconsistent analysis’ – w/ locks
time T1
L(A)
Read(A)
...
U(A) T2
L(A)
....
L(B)
the problem remains!
Solution??

16. General solution: Protocol(s) Most popular protocol:
A locking protocol is a set of rules followed by all transactions while requesting and releasing locks. Locking protocols restrict the set of possible schedules.
Most popular protocol:
2 Phase Locking (2PL)

17. 2PL Phase 1: Growing Phase Phase 2: Shrinking Phase
This is a protocol which ensures conflict-serializable schedules.
Phase 1: Growing Phase
transaction may obtain locks
transaction may not release locks
Phase 2: Shrinking Phase
transaction may release locks
transaction may not obtain locks
The protocol assures serializability. It can be proved that the transactions can be serialized in the order of their lock points (i.e. the point where a transaction acquired its final lock).

18. 2PL
X-lock version: transactions issue no lock requests, after the first ‘unlock’
THEOREM: if all transactions obey 2PL -> all schedules are serializable

19. 2PL – example ‘inconsistent analysis’ – why not 2PL?
how would it be under 2PL?

20. 2PL – X/S lock version
transactions issue no lock/upgrade request, after the first unlock/downgrade
In general: ‘growing’ and ‘shrinking’ phase

21. 2PL – observations limits concurrency may lead to deadlocks
2PLC (keep locks until ‘commit’)
strict two-phase locking. Here a transaction must hold all its exclusive locks till it commits/aborts.
Rigorous two-phase locking is even stricter: here all locks are held till commit/abort.

22. Concurrency – overview
what does it mean ‘correct’ interleaving?
precedence graph
how to achieve correct interleavings automatically? concurrency control
locks + protocols
2PL, 2PLC
graph protocols
multiple granularity locks
<cc without locks: optimistic cc>

23. Other protocols than 2-PL – graph-based
Assumption: we have prior knowledge about the order in which data items will be accessed
(hierarchical) ordering on the data items, like, eg., pages of a B-tree A C B

24. Other protocols than 2-PL – graph-based
Graph-based protocols are an alternative to two-phase locking
Impose a partial ordering  on the set D = {d1, d2 ,..., dh} of all data items.
If di  dj then any transaction accessing both di and dj must access di before accessing dj.
Implies that the set D may now be viewed as a directed acyclic graph, called a database graph.
The tree-protocol is a simple kind of graph protocol.

25. Eg., tree protocol (X-lock version)
an xact can request any item, on its first lock request
from then on, it can only request items for which it holds the parent lock
it can release locks at any time
it can NOT request an item twice

26. Tree protocol - example
follows tree protocol?
‘correct’?
T T2
L(B) L(D)
L(H)
U(D)
L(E)
U(E)
L(D)
U(B) U(H)
L(G)
U(G) B C D E F G H I

27. Tree protocol
equivalent to 2PL?
deadlocks?
Pros and cons

28. Tree protocol
The tree protocol ensures conflict serializability as well as freedom from deadlock.
Unlocking may occur earlier in the tree-locking protocol than in the two-phase locking protocol.
shorter waiting times, increase in concurrency
protocol is deadlock-free, no rollbacks are required
the abort of a transaction can still lead to cascading rollbacks.
However, in the tree-locking protocol, a transaction may have to lock data items that it does not access.
increased locking overhead, and additional waiting time
potential decrease in concurrency
Schedules not possible under two-phase locking are possible under tree protocol, and vice versa.

29. More protocols lock granularity – field? record? page? table?
Pros and cons?
(Ideally, each transaction should obtain a few locks)

30. Multiple granularity Eg: DB Table1 Table2 record1 record2 record-n
attr2
attr1
attr1

31. Multiple granularity
Allow data items to be of various sizes and define a hierarchy of data granularities
Can be represented graphically as a tree (but don't confuse with tree-locking protocol)
When a transaction locks a node in the tree explicitly, it implicitly locks all the node's descendents in the same mode.
Locking granularity (level in tree where locking is done):
fine granularity (lower in tree): high concurrency, high locking overhead
coarse granularity (higher in tree): low locking overhead, low concurrency

32. what types of locks? X/S locks for leaf level +
‘intent’ locks, for higher levels
IS: intent to obtain S-lock underneath
IX: intent to obtain X-lock underneath
S: shared lock for this level
X: ex- lock for this level
SIX: shared lock here; + IX

33. Protocol each xact obtains appropriate lock at highest level
proceeds to desirable lower levels
intention locks allow a higher level node to be locked in S or X mode without having to check all descendent nodes.

34. Protocol Transaction Ti can lock a node Q, using the following rules:
1. The lock compatibility matrix must be observed.
2. The root of the tree must be locked first, and may be locked in
any mode.
3. A node Q can be locked by Ti in S or IS mode only if the parent of Q is currently locked by Ti in either IX or IS mode.
4. A node Q can be locked by Ti in X, SIX, or IX mode only if the
parent of Q is currently locked by Ti in either IX or SIX mode.
5. Ti can lock a node only if it has not previously unlocked any node
(that is, Ti is two-phase).
6. Ti can unlock a node Q only if none of the children of Q are
currently locked by Ti.
Observe that locks are acquired in root-to-leaf order, whereas they are released in leaf-to-root order.

35. Compatibility matrix
T2 wants
T1 has IS IX S
SIX X t f

36. Conclusions ‘ACID’ for transactions concurrency:
serializability (precedence graph)
one (popular) solution: locks +
2PL(C) protocol
graph protocols; multiple granularity