V. Megalooikonomou

Concurrency control

(based on slides by C. Faloutsos at CMU and on notes by Silberchatz,Korth, and Sudarshan)

Temple University – CIS Dept.CIS331– Principles of Database Systems

General Overview Relational model - SQL Functional Dependencies &

Normalization Physical Design &Indexing Query optimization Transaction processing

concurrency control recovery

Transactions - dfn= unit of work, e.g.,

move $10 from savings to checking

Atomicity (all or none)ConsistencyIsolation (as if alone)Durability

recovery

concurrency control

Concurrency – overview why we want it? what does it mean ‘correct’

interleaving? precedence graph

how to achieve correct interleavings automatically? concurrency control

Problem concurrent access to data

(consider ‘lost update’ problem) how to solve it?

T1Read(N)

T2

Read(N)N=N-1

N= N-1

Write(N)Write(N)

Read(N)

Lost update problem – no locks

time

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

requests

Lost update problem – with locks

time

T1

lock(N)

Read(N)

N=N-1

Write(N)

Unlock(N)

T2

lock(N)

lock manager

grants lock

denies lock

T2: waits

grants lock to T2Read(N) ...

Locks but, what if we all just want to read

‘N’?

Solution – part 1 Locks and their flavors

X-locks: exclusive (or write-) locks S-locks: shared (or read-) locks <and more ... >

compatibility matrix

T2 wantsT1 has

S X

S T F

X F F

Solution – part 1 transactions request locks (or

upgrades) lock manager grants or blocks

requests transactions release locks lock manager updates lock-table

Solution – part 1 A transaction is granted a lock on an item if the

requested lock is compatible with locks already held on the item

Any number of transactions can hold shared locks on an item

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

Solution – part 2

locks are not enough – e.g., ‘inconsistent analysis’

‘Inconsistent analysis’

T1 Read(A) A=A-10 Write(A)

T2 Read(A) Sum = A

Read(B) Sum += B

Read(B) B=B+10 Write(B)

Precedence graph?

time

‘Inconsistent analysis’ – w/ locks

time T1

L(A)

Read(A)

...

U(A)

T2

L(A)

....

L(B)

....

the problem remains!

Solution??

General solution: Protocol(s)

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)

2PL (2 Phase Locking )

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 The transactions can be serialized in the order of their

lock points (i.e. the point where a transaction acquired its final lock)

2PL

X-lock version: transactions issue no lock requests, after the first ‘unlock’

THEOREM: if all transactions obey 2PL all schedules are serializable

2PL – example

‘inconsistent analysis’ – why not 2PL?

how would it be under 2PL?

2PL – X/S lock version

transactions issue no lock/upgrade request, after the first unlock/downgrade

In general: ‘growing’ and ‘shrinking’ phase

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.

Concurrency – overviewwhat 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>

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, e.g., pages of a B-tree

A

B C

Other protocols than 2-PL – graph-based

Graph-based protocols are an alternative to 2PL

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

E.g., 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

Tree protocol - exampleT1 T2L(B)

L(D)

L(H)U(D)

L(E)U(E)L(D)U(B)

U(H)L(G)U(D)U(G)

G IH

FED

CB

A -2PL?

-follows tree protocol?

-‘correct’?

Tree protocol- equivalent to 2PL?- deadlocks?- Pros and cons

Tree protocol The tree protocol ensures conflict serializability and no

deadlocks 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

More protocols- lock granularity – field? record? page?

table?- Pros and cons?- (Ideally, each transaction should

obtain a few locks)

Multiple granularity Exampl

e:

attr1 attr1attr2

record-nrecord2record1

Table2Table1

DB

Multiple granularity

Allows data items to be of various sizes Defines 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

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

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.

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.

Compatibility matrix T2 wantsT1 has

IS IX S SIX X

IS t t t t f

IX t t f f f

S t f t f f

SIX t f f f f

X f f f f f

Conclusions- ‘ACID’ for transactions- concurrency:

- serializability (precedence graph)- one (popular) solution: locks +- 2PL(C) protocol- graph protocols; multiple granularity