Transactions and Concurrency Control

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By:

Prof.manoj kumar padhi

Transactions

Banking transaction for a customer (e.g., at ATM or browser)

Transfer $100 from saving to checking account;

Transfer $200 from money-market to checking account;

Withdraw $400 from checking account.

Transaction (invoked at client): /* Every step is an RPC */

1. savings.withdraw(100) /* includes verification */

2. checking.deposit(100) /* depends on success of 1 */

3. mnymkt.withdraw(200) /* includes verification */

4. checking. deposit(200) /* depends on success of 3 */

5. checking.withdraw(400) /* includes verification */

6. dispense(400)

7. commit

Client

Server

Transaction

Bank Server: Coordinator Interface

• All the following are RPCs from a client to the server
• Transaction calls that can be made at a client, and return values from the server:

openTransaction() -> trans;

starts a new transaction and delivers a unique transaction identifier (TID) trans. This TID will be used in the other operations in the transaction.

closeTransaction(trans) -> (commit, abort);

ends a transaction: a commit return value indicates that the transaction has committed; an abort return value indicates that it has aborted.

abortTransaction(trans);

aborts the transaction.

• TID can be passed implicitly (for other operations between open and close) with CORBA

Bank Server: Account, Branch interfaces

deposit(amount)

deposit amount in the account

withdraw(amount)

withdraw amount from the account

getBalance() -> amount

return the balance of the account

setBalance(amount)

set the balance of the account to amount

create(name) -> account

create a new account with a given name

lookup(name) -> account

return a reference to the account with the given name

branchTotal() -> amount

return the total of all the balances at the branch

Operations of the Branch interface

Operations of the Account interface

Transaction

• Sequence of operations that forms a single step, transforming the server data from one consistent state to another.
• All or nothing principle: a transaction either completes successfully, and the effects are recorded in the objects, or it has no effect at all. (even with multiple clients, or crashes)
• A transactions is indivisible (atomic) from the point of view of other transactions
• No access to intermediate results/states of other transactions
• Free from interference by operations of other transactions

But…

• Transactions could run concurrently, i.e., with multiple clients
• Transactions may be distributed, i.e., across multiple servers

Transaction Failure Modes

Transaction:

1. savings.deduct(100)

2. checking.add(100)

3. mnymkt.deduct(200)

4. checking.add(200)

5. checking.deduct(400)

6. dispense(400)

7. commit

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A failure at these points means the customer loses money; we need to restore old state

A failure at these points does not cause lost money, but old steps cannot be repeated

This is the point of no return

A failure after the commit point (ATM crashes) needs corrective action; no undoing possible.

Transactions in Traditional Databases (ACID)

• Atomicity: All or nothing
• Consistency: if the server starts in a consistent state, the transaction ends the server in a consistent state.
• Isolation: Each transaction must be performed without interference from other transactions, i.e., the non-final effects of a transaction must not be visible to other transactions.
• Durability: After a transaction has completed successfully, all its effects are saved in permanent storage.

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• Atomicity: store tentative object updates (for later undo/redo) – many different ways of doing this
• Durability: store entire results of transactions (all updated objects) to recover from permanent server crashes.

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Concurrent Transactions:Lost Update Problem

• One transaction causes loss of info. for another:

consider three account objects

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Transaction T1 Transaction T2

balance = b.getBalance()

balance = b.getBalance()

b.setBalance(balance*1.1)

b.setBalance(balance*1.1)

a.withdraw(balance* 0.1)

c.withdraw(balance*0.1)

T1/T2’s update on the shared object, “b”, is lost

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100

200

300

a:

b:

c:

280

c:

80

a:

220

b:

220

b:

Conc. Trans.: Inconsistent Retrieval Prob.

• Partial, incomplete results of one transaction are retrieved by another transaction.

Transaction T1 Transaction T2

a.withdraw(100)

total = a.getBalance()

total = total + b.getBalance

b.deposit(100)

total = total + c.getBalance

T1’s partial result is used by T2, giving the wrong result for T2

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100

200

0.00

a:

b:

00

a:

500

200

300

c:

total

300

b:

Concurrency Control: “Serial Equivalence”

• An interleaving of the operations of 2 or more transactions is said to be serially equivalent if the combined effect is the same as if these transactions had been performed sequentially (in some order).

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Transaction T1 Transaction T2

balance = b.getBalance()

b.setBalance(balance*1.1)

balance = b.getBalance()

b.setBalance(balance*1.1)

a.withdraw(balance* 0.1)

c.withdraw(balance*0.1)

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100

200

300

a:

b:

c:

278

c:

a:

242

b:

b:

220

80

== T1 (complete) followed

by T2 (complete)

Checking Serial Equivalence – �Conflicting Operations

• The effect of an operation refers to
• The value of an object set by a write operation
• The result returned by a read operation.
• Two operations are said to be conflicting operations, if their combined effect depends on the order they are executed, e.g., read-write, write-read, write-write (all on same variables). NOT read-read, NOT on different variables.
• Two transactions are serially equivalent if and only if all pairs of conflicting operations (pair containing one operation from each transaction) are executed in the same order (transaction order) for all objects (data) they both access.
• Why? Can start from original operation sequence and swap the order of non-conflicting operations to obtain a series of operations where one transaction finishes completely before the second transaction starts
• Why is the above result important? Because: Serial equivalence is the basis for concurrency control protocols for transactions.

Read and Write Operation Conflict Rules

Concurrency Control: “Serial Equivalence”

• An interleaving of the operations of 2 or more transactions is said to be serially equivalent if the combined effect is the same as if these transactions had been performed sequentially (in some order).

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Transaction T1 Transaction T2

balance = b.getBalance()

b.setBalance(balance*1.1)

balance = b.getBalance()

b.setBalance(balance*1.1)

a.withdraw(balance* 0.1)

c.withdraw(balance*0.1)

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100

200

300

a:

b:

c:

278

c:

a:

242

b:

b:

220

80

== T1 (complete) followed

by T2 (complete)

Pairs of Conflicting Operations

Conflicting Operators Example

Transaction T1 Transaction T2

x= a.read()

a.write(20) y = b.read()

b.write(30)

b.write(x)

z = a.read()

x= a.read()

a.write(20) z = a.read()

b.write(x)

y = b.read()

b.write(30)

Serially equivalent interleaving of operations

(why?)

Inconsistent Retrieval Prob

• Partial, incomplete results of one transaction are retrieved by another transaction.

Transaction T1 Transaction T2

a.withdraw(100)

total = a.getBalance()

total = total + b.getBalance

b.deposit(100)

total = total + c.getBalance

T1’s partial result is used by T2, giving the wrong result for T2

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100

200

0.00

a:

b:

00

a:

500

200

300

c:

total

300

b:

A Serially Equivalent Interleaving of T1 and T2

Implementing Concurrent Transactions

• How can we prevent isolation from being violated?
• Concurrent operations must be consistent:
• If trans.T has executed a read operation on object A, a concurrent trans. U must not write to A until T commits or aborts.
• If trans. T has executed a write operation on object A, a concurrent U must not read or write to A until T commits or aborts.
• How to implement this?

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Concurrency control

• Lost update
• 3 accounts (A, B, C)
• with balances 100, 200, 300
• T1 transfers from A to B, for 10% increase
• T2 transfers from C to B, for 10% increase
• Both T1, T2 read balance of B (200)
• T1 overwrites the update by T2
• Without seeing it

Transactions should not read a “stale” value & use it in

computing a new value

Concurrency control

• Inconsistent retrievals
• T1: transfers 10% of account A to account B
• T2: computes sum of account balances
• T2 computes sum before T1 updates B

Update transactions should not interfere with retrievals.

In general:

Transactions should not violate operation conflict rules.

Concurrency control

Serial equivalence

criterion for correct concurrent execution

T1 serially equivalent with T2 iff:

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All pairs of conflicting operations of the two transactions are executed in the same order at all objects that both transactions access.

3 approaches to CC:

• Locking
• Optimistic CC
• Timestamp ordering

Tx’s wait for one another

OR:

Restart Tx’s after conflicts

have been detected

Recoverability from aborts

• Servers must prevent a aborting Tx from affecting other concurrent Tx’s.
• Dirty reads:
• T2 sees result update by T1 on account A
• T2 performs its own update on A & then commits.
• T1 aborts -> T2 has seen a “transient” value
• T2 is not recoverable
• If T2 delays its commit until T1’s outcome is resolved:
• Abort(T1) -> Abort(T2)
• However, if T3 has seen results of T2:
• Abort(T2) -> Abort(T3) !
• Cascading aborts

Tx’s should only read values written by committed Tx’s

Recoverability from aborts

• Premature writes:
• Assume server implements abort by maintaining the “before” image of all update operations
• T1 & T2 both updates account A
• T1 completes its work before T2
• If T1 commits & T2 aborts, the balance of A is correct
• If T1 aborts & T2 commits, the “before” image that is restored corresponds to the balance of A before T2
• If both T1 & T2 abort, the “before” image that is restored corresponds to the balance of A as set by T1

Tx’s should be delayed until earlier Tx’s that update the

Same objects have been either committed or aborted.

Recoverability from aborts

• Tx’s should delay both their reads & updates in order to avoid interference
• Strict execution -> enforce isolation
• Servers should maintain tentative versions of objects in volatile memory

Tx’s should be delayed until earlier Tx’s that update the

Same objects have been either committed or aborted.

Concurrency Control: Locks

• Transactions:
• Must be scheduled so that their effect on shared data is serially equivalent
• Two types of approach
• Pessimistic 🡪 If something can go wrong, it will

Operations are synchronized before they are carried out

• Optimistic 🡪 In general, nothing will go wrong

Operations are carried out, synchronization at the end of the transaction

• Locks (pessimistic)
• can be used to ensuring serializability
• lock(x), unlock(x)

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Locks: Basics

• Oldest and most widely used CC algorithm
• A process before read/write 🡺 requests the scheduler to grant a lock
• Upon finishing read/write 🡺 the lock is released
• In order to ensure serialized transaction Two Phase Locking (2PL) is used

Locking

• How Locks prevent consistency problems
• Lost update and inconsistent retrieval:
• Causes:
• are caused by the conflict between r_i(x) and w_j(x)
• two transactions read a value and use it to compute new value
• Prevention:
• delay the reads of later transactions until the earlier ones have completed

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• Disadvantage of Locking
• Deadlocks

2PL

• Strict 2PL avoids Cascading Aborts
• A situation where a committed transaction has to be undone because it saw a file it shouldn’t have seen.
• Problems of Locking
• Deadlocks
• Livelocks
• A transaction can’t proceed for an indefinite amount of time while other transactions continue normally. It happens due to unfair locking.
• Lock overhead
• If the system doesn’t allow shared access--wastage of resources
• Avoidance of Cascading Aborts may be costly
• Strict 2PL in fact, reduces the effect of concurrency

Example: Concurrent Transactions

• Exclusive Locks

Transaction T1 Transaction T2

OpenTransaction()

balance = b.getBalance() OpenTransaction()

balance = b.getBalance()

b.setBalance(balance*1.1)

a.withdraw(balance* 0.1)

CloseTransaction()

b.setBalance(balance*1.1) c.withdraw(balance*0.1)

CloseTransaction()

Lock B

Lock A

UnLock B

UnLock A

Lock C

UnLock B

UnLock C

…

WAIT on B

Lock B

…

Basic Locking

• Transaction managers (on server side) set locks on objects they need. A concurrent trans. cannot access locked objects.

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• Two phase locking:
• In the first (growing) phase of the transaction, new locks are only acquired, and in the second (shrinking) phase, locks are only released.
• A transaction is not allowed acquire any new locks, once it has released any one lock.

Basic Locking

• Strict two phase locking:
• Locking on an object is performed only before the first request to read/write that object is about to be applied.
• Unlocking is performed by the commit/abort operations of the transaction coordinator.
• To prevent dirty reads and premature writes, a transaction waits for another to commit/abort

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• However, use of separate read and write locks leads to more concurrency than a single exclusive lock – Next slide

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2P Locking: Non-exclusive lock (per object)

non-exclusive lock compatibility

Lock already Lock requested

set read write

none OK OK

read OK WAIT

write WAIT WAIT

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• A read lock is promoted to a write lock when the transaction needs write access to the same object.
• A read lock shared with other transactions’ read lock(s) cannot be promoted. Transaction waits for other read locks to be released.
• Cannot demote a write lock to read lock during transaction – violates the 2P principle

Two Phase Locking (2PL) Protocols

• In 2PL—All lock operations must precede the first unlock operation
• Two phases
• expanding or growing phase: all locking are done in this phase but no lock release allowed
• shrinking phase: all lock release but no lock acquire

Growing phase

Shrinking phase

Time

No. of

Locks

Locking Procedure in Strict-2P Locking

• When an operation accesses an object:
• if you can, promote a lock (nothing -> read -> write)
• Don’t promote the lock if it would result in a conflict with another transaction’s already-existing lock
• wait until all shared locks are released, then lock & proceed
• When a transaction commits or aborts:
• release all locks that were set by the transaction

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Example: Concurrent Transactions

• Non-exclusive Locks

Transaction T1 Transaction T2

​

OpenTransaction()

balance = b.getBalance() OpenTransaction()

balance = b.getBalance()

b.setBalance(balance*1.1)

​

Commit

R-Lock B

…

R-Lock B

Cannot Promote lock on B, Wait

Promote lock on B

Example: Concurrent Transactions

• What happens in the example below?

Transaction T1 Transaction T2

​

OpenTransaction()

balance = b.getBalance() OpenTransaction()

balance = b.getBalance()

b.setBalance(balance*1.1)

​

b.setBalance=balance*1.1

R-Lock B

…

R-Lock B

Cannot Promote lock on B, Wait

Cannot Promote lock on B, Wait

…

Deadlock with write locks

T locks A and waits for U to release the lock on B, U on the other

hand locks B and waits for T to release the lock on A

🡺 Circular hold and wait 🡺 Deadlock

�The corresponding wait-for graph

Deadlocks

• Necessary conditions for deadlocks
• Non-shareable resources (exclusive lock modes)
• No preemption on locks
• Hold & Wait or Circular Wait

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​

​

​

​

T

U

Wait for

Held by

Held by

Wait for

A

B

T

U

Wait for

Held by

Held by

Wait for

A

B

V

W

...

...

Wait for

Wait for

Held by

Held by

Hold & Wait

Circular Wait

Naïve Deadlock Resolution Using Timeout

Disadvantages?

Strategies to Fight Deadlock

• Lock timeout (costly and open to false positives)
• Deadlock Prevention: violate one of the necessary conditions for deadlock (from 2 slides ago), e.g., lock all objects before transaction starts, aborting entire transaction if any fails
• Deadlock Avoidance: Have transactions declare max resources they will request, but allow them to lock at any time (Banker’s algorithm)
• Deadlock Detection: detect cycles in the wait-for graph, and then abort one or more of the transactions in cycle

Optimistic Concurrency Control �(Kung and Robinson)

• We have seen locking has some problems

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• OCC based on the following simple idea:
• Don’t worry about the conflicts, keep on doing whatever you’re doing, if there’s a problem worry about it later.

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Optimistic Concurrency Control �(Kung and Robinson)

• Algorithm
• Each transaction has the following phases
• Working phase
• Each transaction has a tentative version of each object that it updates
• Tentative version allows the trans. to abort w/o affecting the object
• Validation phase
• transaction is validated to see if any conflicts with other trans.
• Update phase
• if a trans. is validated all tentative objects are made permanent

Optimistic Concurrency Control:�

Validation of transactions

Validation Rules

�Validation of Transactions

Backward validation of transaction T_v

boolean valid = true;

for (int T_i = startTn+1; T_i <= finishTn; T_i++){

if (read set of T_v intersects write set of T_i) valid = false;

}

​

​

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Forward validation of transaction T_v

boolean valid = true;

for (int T_id = active1; T_id <= activeN; T_id++){

if (write set of T_v intersects read set of T_id) valid = false;

}

Summary

• Increasing concurrency important because it improves throughput at server

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• Applications are willing to tolerate temporary inconsistency and deadlocks in turn
• Need to detect and prevent these

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• Driven and validated by actual application characteristics – mostly-read transactions abound