deadlock and locking - dbms

©Silberschatz, Korth and Sudarshan15.1Database System Concepts - 6th Edition
Concurrency Control
Lock-Based Protocols
Timestamp-Based Protocols
Validation-Based Protocols
Multiple Granularity
Multiversion Schemes
Insert and Delete Operations
Concurrency in Index Structures

Lock-Based Protocols
A lock is a mechanism to control concurrent access to a data item
Data items can be locked in two modes :
1. exclusive (X) mode. Data item can be both read as well as
written. X-lock is requested using lock-X instruction.
2. shared (S) mode. Data item can only be read. S-lock is
requested using lock-S instruction.
Lock requests are made to concurrency-control manager. Transaction can
proceed only after request is granted.

Lock-Based Protocols (Cont.)
Lock-compatibility matrix
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.

Lock-Based Protocols (Cont.)
Example of a transaction performing locking:
T2: lock-S(A);
read (A);
unlock(A);
lock-S(B);
read (B);
unlock(B);
display(A+B)
Locking as above is not sufficient to guarantee serializability — if A and B
get updated in-between the read of A and B, the displayed sum would be
wrong.
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.

Pitfalls of Lock-Based Protocols
Consider the partial schedule
Neither T3 nor T4 can make progress — executing lock-S(B) causes T4
to wait for T3 to release its lock on B, while executing lock-X(A) causes
T3 to wait for T4 to release its lock on A.
Such a situation is called a deadlock.
To handle a deadlock one of T3 or T4 must be rolled back
and its locks released.

Pitfalls of Lock-Based Protocols (Cont.)
The potential for deadlock exists in most locking protocols. Deadlocks
are a necessary evil.
Starvation is also possible if concurrency control manager is badly
designed. For example:
A transaction may be waiting for an X-lock on an item, while a
sequence of other transactions request and are granted an S-lock
on the same item.
The same transaction is repeatedly rolled back due to deadlocks.
Concurrency control manager can be designed to prevent starvation.

The Two-Phase Locking Protocol
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).

The Two-Phase Locking Protocol (Cont.)
Two-phase locking does not ensure freedom from deadlocks
Cascading roll-back is possible under two-phase locking. To avoid
this, follow a modified protocol called 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. In this protocol transactions can be serialized in the
order in which they commit.

The Two-Phase Locking Protocol (Cont.)
There can be conflict serializable schedules that cannot be obtained if
two-phase locking is used.
However, in the absence of extra information (e.g., ordering of access
to data), two-phase locking is needed for conflict serializability in the
following sense:
Given a transaction Ti that does not follow two-phase locking, we can
find a transaction Tj that uses two-phase locking, and a schedule for Ti
and Tj that is not conflict serializable.

Lock Conversions
Two-phase locking with lock conversions:
– First Phase:
can acquire a lock-S on item
can acquire a lock-X on item
can convert a lock-S to a lock-X (upgrade)
– Second Phase:
can release a lock-S
can release a lock-X
can convert a lock-X to a lock-S (downgrade)
This protocol assures serializability. But still relies on the programmer to
insert the various locking instructions.

Automatic Acquisition of Locks
A transaction Ti issues the standard read/write instruction, without
explicit locking calls.
The operation read(D) is processed as:
if Ti has a lock on D
then
read(D)
else begin
if necessary wait until no other
transaction has a lock-X on D
grant Ti a lock-S on D;
read(D)
end

Automatic Acquisition of Locks (Cont.)
write(D) is processed as:
if Ti has a lock-X on D
then
write(D)
else begin
if necessary wait until no other trans. has any lock on D,
if Ti has a lock-S on D
then
upgrade lock on D to lock-X
else
grant Ti a lock-X on D
write(D)
end;
All locks are released after commit or abort

Implementation of Locking
A lock manager can be implemented as a separate process to which
transactions send lock and unlock requests
The lock manager replies to a lock request by sending a lock grant
messages (or a message asking the transaction to roll back, in case of
a deadlock)
The requesting transaction waits until its request is answered
The lock manager maintains a data-structure called a lock table to
record granted locks and pending requests
The lock table is usually implemented as an in-memory hash table
indexed on the name of the data item being locked

Lock Table
Black rectangles indicate granted locks,
white ones indicate waiting requests
Lock table also records the type of lock
granted or requested
New request is added to the end of the
queue of requests for the data item, and
granted if it is compatible with all earlier
locks
Unlock requests result in the request
being deleted, and later requests are
checked to see if they can now be
granted
If transaction aborts, all waiting or
granted requests of the transaction are
deleted
lock manager may keep a list of
locks held by each transaction, to
implement this efficiently
granted
waiting
T8
144
T1 T23
14
T23
17 123
T23 T1 T8 T2
1912

Graph-Based Protocols
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.

Tree Protocol
1. Only exclusive locks are allowed.
2. The first lock by Ti may be on any data item. Subsequently, a data Q
can be locked by Ti only if the parent of Q is currently locked by Ti.
3. Data items may be unlocked at any time.
4. A data item that has been locked and unlocked by Ti cannot
subsequently be relocked by Ti

Graph-Based Protocols (Cont.)
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, and increase in concurrency
protocol is deadlock-free, no rollbacks are required
Drawbacks
Protocol does not guarantee recoverability or cascade freedom
 Need to introduce commit dependencies to ensure recoverability
Transactions may have to lock data items that they do 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.

Deadlock Handling
Consider the following two transactions:
T1: write (X) T2: write(Y)
write(Y) write(X)
Schedule with deadlock

Deadlock Handling
System is deadlocked if there is a set of transactions such that every
transaction in the set is waiting for another transaction in the set.
Deadlock prevention protocols ensure that the system will never
enter into a deadlock state. Some prevention strategies :
Require that each transaction locks all its data items before it
begins execution (predeclaration).
Impose partial ordering of all data items and require that a
transaction can lock data items only in the order specified by the
partial order (graph-based protocol).

More Deadlock Prevention Strategies
Following schemes use transaction timestamps for the sake of deadlock
prevention alone.
wait-die scheme — non-preemptive
older transaction may wait for younger one to release data item.
Younger transactions never wait for older ones; they are rolled back
instead.
a transaction may die several times before acquiring needed data
item
wound-wait scheme — preemptive
older transaction wounds (forces rollback) of younger transaction
instead of waiting for it. Younger transactions may wait for older
ones.
may be fewer rollbacks than wait-die scheme.

Deadlock prevention (Cont.)
Both in wait-die and in wound-wait schemes, a rolled back
transactions is restarted with its original timestamp. Older transactions
thus have precedence over newer ones, and starvation is hence
avoided.
Timeout-Based Schemes:
a transaction waits for a lock only for a specified amount of time.
After that, the wait times out and the transaction is rolled back.
thus deadlocks are not possible
simple to implement; but starvation is possible. Also difficult to
determine good value of the timeout interval.

Deadlock Detection
Deadlocks can be described as a wait-for graph, which consists of a
pair G = (V,E),
V is a set of vertices (all the transactions in the system)
E is a set of edges; each element is an ordered pair Ti Tj.
If Ti  Tj is in E, then there is a directed edge from Ti to Tj, implying
that Ti is waiting for Tj to release a data item.
When Ti requests a data item currently being held by Tj, then the edge
Ti Tj is inserted in the wait-for graph. This edge is removed only when
Tj is no longer holding a data item needed by Ti.
The system is in a deadlock state if and only if the wait-for graph has a
cycle. Must invoke a deadlock-detection algorithm periodically to look
for cycles.

Deadlock Detection (Cont.)
Wait-for graph without a cycle Wait-for graph with a cycle

Deadlock Recovery
When deadlock is detected :
Some transaction will have to rolled back (made a victim) to break
deadlock. Select that transaction as victim that will incur minimum
cost.
Rollback -- determine how far to roll back transaction
 Total rollback: Abort the transaction and then restart it.
 More effective to roll back transaction only as far as necessary
to break deadlock.
Starvation happens if same transaction is always chosen as
victim. Include the number of rollbacks in the cost factor to avoid
starvation

Multiple Granularity
Allow data items to be of various sizes and define a hierarchy of data
granularities, where the small granularities are nested within larger
ones
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.
Granularity of locking (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

Example of Granularity Hierarchy
The levels, starting from the coarsest (top) level are
database
area
file
record

Intention Lock Modes
In addition to S and X lock modes, there are three additional lock
modes with multiple granularity:
intention-shared (IS): indicates explicit locking at a lower level of
the tree but only with shared locks.
intention-exclusive (IX): indicates explicit locking at a lower level
with exclusive or shared locks
shared and intention-exclusive (SIX): the subtree rooted by that
node is locked explicitly in shared mode and explicit locking is
being done at a lower level with exclusive-mode locks.
intention locks allow a higher level node to be locked in S or X mode
without having to check all descendent nodes.

Compatibility Matrix with Intention Lock Modes
The compatibility matrix for all lock modes is:

Multiple Granularity Locking Scheme
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.
Lock granularity escalation: in case there are too many locks at a
particular level, switch to higher granularity S or X lock

Timestamp-Based Protocols
Each transaction is issued a timestamp when it enters the system. If an old
transaction Ti has time-stamp TS(Ti), a new transaction Tj is assigned time-
stamp TS(Tj) such that TS(Ti) <TS(Tj).
The protocol manages concurrent execution such that the time-stamps
determine the serializability order.
In order to assure such behavior, the protocol maintains for each data Q two
timestamp values:
W-timestamp(Q) is the largest time-stamp of any transaction that
executed write(Q) successfully.
R-timestamp(Q) is the largest time-stamp of any transaction that
executed read(Q) successfully.

Timestamp-Based Protocols (Cont.)
The timestamp ordering protocol ensures that any conflicting read
and write operations are executed in timestamp order.
Suppose a transaction Ti issues a read(Q)
1. If TS(Ti)  W-timestamp(Q), then Ti needs to read a value of Q
that was already overwritten.
Hence, the read operation is rejected, and Ti is rolled back.
2. If TS(Ti) W-timestamp(Q), then the read operation is executed,
and R-timestamp(Q) is set to max(R-timestamp(Q), TS(Ti)).

Timestamp-Based Protocols (Cont.)
Suppose that transaction Ti issues write(Q).
1. If TS(Ti) < R-timestamp(Q), then the value of Q that Ti is
producing was needed previously, and the system assumed that
that value would never be produced.
Hence, the write operation is rejected, and Ti is rolled back.
2. If TS(Ti) < W-timestamp(Q), then Ti is attempting to write an
obsolete value of Q.
Hence, this write operation is rejected, and Ti is rolled back.
3. Otherwise, the write operation is executed, and W-timestamp(Q)
is set to TS(Ti).

Example Use of the Protocol
A partial schedule for several data items for transactions with
timestamps 1, 2, 3, 4, 5

Correctness of Timestamp-Ordering Protocol
The timestamp-ordering protocol guarantees serializability since all
the arcs in the precedence graph are of the form:
Thus, there will be no cycles in the precedence graph
Timestamp protocol ensures freedom from deadlock as no
transaction ever waits.
But the schedule may not be cascade-free, and may not even be
recoverable.

Recoverability and Cascade Freedom
Problem with timestamp-ordering protocol:
Suppose Ti aborts, but Tj has read a data item written by Ti
Then Tj must abort; if Tj had been allowed to commit earlier, the
schedule is not recoverable.
Further, any transaction that has read a data item written by Tj must
abort
This can lead to cascading rollback --- that is, a chain of rollbacks
Solution 1:
A transaction is structured such that its writes are all performed at
the end of its processing
All writes of a transaction form an atomic action; no transaction may
execute while a transaction is being written
A transaction that aborts is restarted with a new timestamp
Solution 2: Limited form of locking: wait for data to be committed before
reading it
Solution 3: Use commit dependencies to ensure recoverability

Thomas’ Write Rule
Modified version of the timestamp-ordering protocol in which obsolete
write operations may be ignored under certain circumstances.
When Ti attempts to write data item Q, if TS(Ti) < W-timestamp(Q),
then Ti is attempting to write an obsolete value of {Q}.
Rather than rolling back Ti as the timestamp ordering protocol
would have done, this {write} operation can be ignored.
Otherwise this protocol is the same as the timestamp ordering
protocol.
Thomas' Write Rule allows greater potential concurrency.
Allows some view-serializable schedules that are not conflict-
serializable.

View Serializability
Let S and S´ be two schedules with the same set of transactions. S
and S´ are view equivalent if the following three conditions are met,
for each data item Q,
1. If in schedule S, transaction Ti reads the initial value of Q, then in
schedule S’ also transaction Ti must read the initial value of Q.
2. If in schedule S transaction Ti executes read(Q), and that value
was produced by transaction Tj (if any), then in schedule S’ also
transaction Ti must read the value of Q that was produced by the
same write(Q) operation of transaction Tj .
3. The transaction (if any) that performs the final write(Q) operation
in schedule S must also perform the final write(Q) operation in
schedule S’.
As can be seen, view equivalence is also based purely on reads and
writes alone.

View Serializability (Cont.)
A schedule S is view serializable if it is view equivalent to a serial
schedule.
Every conflict serializable schedule is also view serializable.
Below is a schedule which is view-serializable but not conflict
serializable.
What serial schedule is above equivalent to?
Every view serializable schedule that is not conflict serializable has
blind writes.

Test for View Serializability
The precedence graph test for conflict serializability cannot be used
directly to test for view serializability.
Extension to test for view serializability has cost exponential in the
size of the precedence graph.
The problem of checking if a schedule is view serializable falls in the
class of NP-complete problems.
Thus existence of an efficient algorithm is extremely unlikely.
However practical algorithms that just check some sufficient
conditions for view serializability can still be used.

Other Notions of Serializability
The schedule below produces same outcome as the serial
schedule < T1, T5 >, yet is not conflict equivalent or view
equivalent to it.
Determining such equivalence requires analysis of operations
other than read and write.
Operation-conflicts, operation locks

Validation-Based Protocol
Execution of transaction Ti is done in three phases.
1. Read and execution phase: Transaction Ti writes only to
temporary local variables
2. Validation phase: Transaction Ti performs a ``validation test''
to determine if local variables can be written without violating
serializability.
3. Write phase: If Ti is validated, the updates are applied to the
database; otherwise, Ti is rolled back.
The three phases of concurrently executing transactions can be
interleaved, but each transaction must go through the three phases in
that order.
Assume for simplicity that the validation and write phase occur
together, atomically and serially
 I.e., only one transaction executes validation/write at a time.
Also called as optimistic concurrency control since transaction
executes fully in the hope that all will go well during validation

Validation-Based Protocol (Cont.)
Each transaction Ti has 3 timestamps
Start(Ti) : the time when Ti started its execution
Validation(Ti): the time when Ti entered its validation phase
Finish(Ti) : the time when Ti finished its write phase
Serializability order is determined by timestamp given at validation
time, to increase concurrency.
Thus TS(Ti) is given the value of Validation(Ti).
This protocol is useful and gives greater degree of concurrency if
probability of conflicts is low.
because the serializability order is not pre-decided, and
relatively few transactions will have to be rolled back.

Validation Test for Transaction Tj
If for all Ti with TS (Ti) < TS (Tj) either one of the following condition
holds:
finish(Ti) < start(Tj)
start(Tj) < finish(Ti) < validation(Tj) and the set of data items
written by Ti does not intersect with the set of data items read by
Tj.
then validation succeeds and Tj can be committed. Otherwise,
validation fails and Tj is aborted.
Justification: Either the first condition is satisfied, and there is no
overlapped execution, or the second condition is satisfied and
the writes of Tj do not affect reads of Ti since they occur after Ti
has finished its reads.
the writes of Ti do not affect reads of Tj since Tj does not read
any item written by Ti.

Schedule Produced by Validation
Example of schedule produced using validation

Multiversion Schemes
Multiversion schemes keep old versions of data item to increase
concurrency.
Multiversion Timestamp Ordering
Multiversion Two-Phase Locking
Each successful write results in the creation of a new version of the
data item written.
Use timestamps to label versions.
When a read(Q) operation is issued, select an appropriate version of
Q based on the timestamp of the transaction, and return the value of
the selected version.
reads never have to wait as an appropriate version is returned
immediately.

Multiversion Timestamp Ordering
Each data item Q has a sequence of versions <Q1, Q2,...., Qm>. Each
version Qk contains three data fields:
Content -- the value of version Qk.
W-timestamp(Qk) -- timestamp of the transaction that created
(wrote) version Qk
R-timestamp(Qk) -- largest timestamp of a transaction that
successfully read version Qk
when a transaction Ti creates a new version Qk of Q, Qk's W-
timestamp and R-timestamp are initialized to TS(Ti).
R-timestamp of Qk is updated whenever a transaction Tj reads Qk, and
TS(Tj) > R-timestamp(Qk).

Multiversion Timestamp Ordering (Cont)
Suppose that transaction Ti issues a read(Q) or write(Q) operation. Let
Qk denote the version of Q whose write timestamp is the largest write
timestamp less than or equal to TS(Ti).
1. If transaction Ti issues a read(Q), then the value returned is the
content of version Qk.
2. If transaction Ti issues a write(Q)
1. if TS(Ti) < R-timestamp(Qk), then transaction Ti is rolled back.
2. if TS(Ti) = W-timestamp(Qk), the contents of Qk are overwritten
3. else a new version of Q is created.
Observe that
Reads always succeed
A write by Ti is rejected if some other transaction Tj that (in the
serialization order defined by the timestamp values) should read
Ti's write, has already read a version created by a transaction older
than Ti.
Protocol guarantees serializability

Multiversion Two-Phase Locking
Differentiates between read-only transactions and update transactions
Update transactions acquire read and write locks, and hold all locks up
to the end of the transaction. That is, update transactions follow rigorous
two-phase locking.
Each successful write results in the creation of a new version of the
data item written.
each version of a data item has a single timestamp whose value is
obtained from a counter ts-counter that is incremented during
commit processing.
Read-only transactions are assigned a timestamp by reading the current
value of ts-counter before they start execution; they follow the
multiversion timestamp-ordering protocol for performing reads.

Multiversion Two-Phase Locking (Cont.)
When an update transaction wants to read a data item:
it obtains a shared lock on it, and reads the latest version.
When it wants to write an item
it obtains X lock on; it then creates a new version of the item and
sets this version's timestamp to .
When update transaction Ti completes, commit processing occurs:
Ti sets timestamp on the versions it has created to ts-counter + 1
Ti increments ts-counter by 1
Read-only transactions that start after Ti increments ts-counter will see
the values updated by Ti.
Read-only transactions that start before Ti increments the
ts-counter will see the value before the updates by Ti.
Only serializable schedules are produced.

MVCC: Implementation Issues
Creation of multiple versions increases storage overhead
Extra tuples
Extra space in each tuple for storing version information
Versions can, however, be garbage collected
E.g. if Q has two versions Q5 and Q9, and the oldest active
transaction has timestamp > 9, than Q5 will never be required
again

Snapshot Isolation
Motivation: Decision support queries that read large amounts of data
have concurrency conflicts with OLTP transactions that update a few
rows
Poor performance results
Solution 1: Give logical “snapshot” of database state to read only
transactions, read-write transactions use normal locking
Multiversion 2-phase locking
Works well, but how does system know a transaction is read only?
Solution 2: Give snapshot of database state to every transaction,
updates alone use 2-phase locking to guard against concurrent
updates
Problem: variety of anomalies such as lost update can result
Partial solution: snapshot isolation level (next slide)
 Proposed by Berenson et al, SIGMOD 1995
 Variants implemented in many database systems
– E.g. Oracle, PostgreSQL, SQL Server 2005

Snapshot Isolation
A transaction T1 executing with Snapshot
Isolation
takes snapshot of committed data at
start
always reads/modifies data in its own
snapshot
updates of concurrent transactions are
not visible to T1
writes of T1 complete when it commits
First-committer-wins rule:
 Commits only if no other concurrent
transaction has already written data
that T1 intends to write.
T1 T2 T3
W(Y := 1)
Commit
Start
R(X)  0
R(Y) 1
W(X:=2)
W(Z:=3)
Commit
R(Z)  0
R(Y)  1
W(X:=3)
Commit-Req
Abort
Concurrent updates not visible
Own updates are visible
Not first-committer of X
Serialization error, T2 is rolled back

Snapshot Read
Concurrent updates invisible to snapshot read

Snapshot Write: First Committer Wins
Variant: “First-updater-wins”
 Check for concurrent updates when write occurs by locking item
– But lock should be held till all concurrent transactions have finished
 (Oracle uses this plus some extra features)
 Differs only in when abort occurs, otherwise equivalent

Benefits of SI
Reading is never blocked,
and also doesn’t block other txns activities
Performance similar to Read Committed
Avoids the usual anomalies
No dirty read
No lost update
No non-repeatable read
Predicate based selects are repeatable (no phantoms)
Problems with SI
SI does not always give serializable executions
 Serializable: among two concurrent txns, one sees the effects
of the other
 In SI: neither sees the effects of the other
Result: Integrity constraints can be violated

Snapshot Isolation
E.g. of problem with SI
T1: x:=y
T2: y:= x
Initially x = 3 and y = 17
 Serial execution: x = ??, y = ??
 if both transactions start at the same time, with snapshot
isolation: x = ?? , y = ??
Called skew write
Skew also occurs with inserts
E.g:
 Find max order number among all orders
 Create a new order with order number = previous max + 1

Snapshot Isolation Anomalies
SI breaks serializability when txns modify different items, each based on a
previous state of the item the other modified
Not very common in practice
 E.g., the TPC-C benchmark runs correctly under SI
 when txns conflict due to modifying different data, there is usually also
a shared item they both modify too (like a total quantity) so SI will abort
one of them
But does occur
 Application developers should be careful about write skew
SI can also cause a read-only transaction anomaly, where read-only
transaction may see an inconsistent state even if updaters are serializable
We omit details
Using snapshots to verify primary/foreign key integrity can lead to
inconsistency
Integrity constraint checking usually done outside of snapshot

SI In Oracle and PostgreSQL
Warning: SI used when isolation level is set to serializable, by Oracle, and
PostgreSQL versions prior to 9.1
PostgreSQL’s implementation of SI (versions prior to 9.1) described in
Section 26.4.1.3
Oracle implements “first updater wins” rule (variant of “first committer
wins”)
 concurrent writer check is done at time of write, not at commit time
 Allows transactions to be rolled back earlier
 Oracle and PostgreSQL < 9.1 do not support true serializable
execution
PostgreSQL 9.1 introduced new protocol called “Serializable Snapshot
Isolation” (SSI)
 Which guarantees true serializabilty including handling predicate
reads (coming up)

SI In Oracle and PostgreSQL
Can sidestep SI for specific queries by using select .. for update in Oracle
and PostgreSQL
E.g.,
1. select max(orderno) from orders for update
2. read value into local variable maxorder
3. insert into orders (maxorder+1, …)
Select for update (SFU) treats all data read by the query as if it were
also updated, preventing concurrent updates
Does not always ensure serializability since phantom phenomena can
occur (coming up)
In PostgreSQL versions < 9.1, SFU locks the data item, but releases locks
when the transaction completes, even if other concurrent transactions are
active
Not quite same as SFU in Oracle, which keeps locks until all
concurrent transactions have completed

Insert and Delete Operations
If two-phase locking is used :
A delete operation may be performed only if the transaction
deleting the tuple has an exclusive lock on the tuple to be deleted.
A transaction that inserts a new tuple into the database is given an
X-mode lock on the tuple
Insertions and deletions can lead to the phantom phenomenon.
A transaction that scans a relation
 (e.g., find sum of balances of all accounts in Perryridge)
and a transaction that inserts a tuple in the relation
 (e.g., insert a new account at Perryridge)
(conceptually) conflict in spite of not accessing any tuple in
common.
If only tuple locks are used, non-serializable schedules can result
 E.g. the scan transaction does not see the new account, but
reads some other tuple written by the update transaction

Insert and Delete Operations (Cont.)
The transaction scanning the relation is reading information that indicates
what tuples the relation contains, while a transaction inserting a tuple
updates the same information.
The conflict should be detected, e.g. by locking the information.
One solution:
Associate a data item with the relation, to represent the information
about what tuples the relation contains.
Transactions scanning the relation acquire a shared lock in the data
item,
Transactions inserting or deleting a tuple acquire an exclusive lock on
the data item. (Note: locks on the data item do not conflict with locks on
individual tuples.)
Above protocol provides very low concurrency for insertions/deletions.
Index locking protocols provide higher concurrency while
preventing the phantom phenomenon, by requiring locks
on certain index buckets.

Index Locking Protocol
Index locking protocol:
Every relation must have at least one index.
A transaction can access tuples only after finding them through one or
more indices on the relation
A transaction Ti that performs a lookup must lock all the index leaf
nodes that it accesses, in S-mode
 Even if the leaf node does not contain any tuple satisfying the index
lookup (e.g. for a range query, no tuple in a leaf is in the range)
A transaction Ti that inserts, updates or deletes a tuple ti in a relation r
 must update all indices to r
 must obtain exclusive locks on all index leaf nodes affected by the
insert/update/delete
The rules of the two-phase locking protocol must be observed
Guarantees that phantom phenomenon won’t occur

Next-Key Locking
Index-locking protocol to prevent phantoms required locking entire leaf
Can result in poor concurrency if there are many inserts
Alternative: for an index lookup
Lock all values that satisfy index lookup (match lookup value, or
fall in lookup range)
Also lock next key value in index
Lock mode: S for lookups, X for insert/delete/update
Ensures that range queries will conflict with inserts/deletes/updates
Regardless of which happens first, as long as both are concurrent

Concurrency in Index Structures
Indices are unlike other database items in that their only job is to help in
accessing data.
Index-structures are typically accessed very often, much more than
other database items.
Treating index-structures like other database items, e.g. by 2-phase
locking of index nodes can lead to low concurrency.
There are several index concurrency protocols where locks on internal
nodes are released early, and not in a two-phase fashion.
It is acceptable to have nonserializable concurrent access to an
index as long as the accuracy of the index is maintained.
 In particular, the exact values read in an internal node of a
B+-tree are irrelevant so long as we land up in the correct leaf
node.

Concurrency in Index Structures (Cont.)
Example of index concurrency protocol:
Use crabbing instead of two-phase locking on the nodes of the B+-tree, as
follows. During search/insertion/deletion:
First lock the root node in shared mode.
After locking all required children of a node in shared mode, release the lock
on the node.
During insertion/deletion, upgrade leaf node locks to exclusive mode.
When splitting or coalescing requires changes to a parent, lock the parent in
exclusive mode.
Above protocol can cause excessive deadlocks
Searches coming down the tree deadlock with updates going up the tree
Can abort and restart search, without affecting transaction
Better protocols are available; see Section 16.9 for one such protocol, the B-link
tree protocol
Intuition: release lock on parent before acquiring lock on child
 And deal with changes that may have happened between lock release
and acquire

Weak Levels of Consistency
Degree-two consistency: differs from two-phase locking in that S-locks
may be released at any time, and locks may be acquired at any time
X-locks must be held till end of transaction
Serializability is not guaranteed, programmer must ensure that no
erroneous database state will occur]
Cursor stability:
For reads, each tuple is locked, read, and lock is immediately
released
X-locks are held till end of transaction
Special case of degree-two consistency

Weak Levels of Consistency in SQL
SQL allows non-serializable executions
Serializable: is the default
Repeatable read: allows only committed records to be read, and
repeating a read should return the same value (so read locks should
be retained)
 However, the phantom phenomenon need not be prevented
– T1 may see some records inserted by T2, but may not see
others inserted by T2
Read committed: same as degree two consistency, but most
systems implement it as cursor-stability
Read uncommitted: allows even uncommitted data to be read
In many database systems, read committed is the default consistency
level
has to be explicitly changed to serializable when required
 set isolation level serializable

Transactions across User Interaction
Many applications need transaction support across user interactions
Can’t use locking
Don’t want to reserve database connection per user
Application level concurrency control
Each tuple has a version number
Transaction notes version number when reading tuple
 select r.balance, r.version into :A, :version
from r where acctId =23
When writing tuple, check that current version number is same as the
version when tuple was read
 update r set r.balance = r.balance + :deposit
where acctId = 23 and r.version = :version
Equivalent to optimistic concurrency control without validating read set
Used internally in Hibernate ORM system, and manually in many applications
Version numbering can also be used to support first committer wins check of
snapshot isolation
Unlike SI, reads are not guaranteed to be from a single snapshot

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