Transcript Concurrency Control

Concurrency Control
In
Dynamic Database Systems
Laurel Jones
Overview
Quick Review of what we already know
Some Concurrency Control Model for
Static Databases
Concurrency Control Models for Dynamic
Databases
Basic Definitions
Concurrency Control:

Software included within the DBMS that
ensures data integrity in a multiuser database
Transaction:


An event that has a start and stop
Modifies the data
Basic Definitions con’t
Locking:



A lock is a variable associated with a data item that
describes the status of the item with respect to
possible operations that can be applied to it.
Binary Locks: mutually exclusive
Multiple Mode Lock:

Shared/Exclusive or Read/Write
Granularity:



The size of a data item
Fine (small) – field of some record
Course (large) – whole record or disk block
Basic Definitions con’t
Serial / Serializability / Serializable



Serial – all the operations of a given
transaction are executed consecutively
Serializability – which schedules are correct
when transactions have interleaving of their
operations
Serializable – refers to a schedule of
transactions that is equivalent to some serial
schedule of those same transactions
Example
Using binary locks or
read/write locks does
NOT guarantee
serializability of
schedules on its own.
To guarantee
serializability, we must
follow an additional
protocol concerning the
positioning of locking and
unlocking operations in
every transaction.
Protocols to Maintain Serialization
Two-Phase Locking (2PL)
Timestamps Ordering
Mulitversion Concurrency
Multiversion based on Timestamp
Multiversion 2PL with certify locks
Optimistic Concurrency Control (OCC)
Two-Phase Locking Protocol
(2PL)
All locking operations (read or write) precede the
first unlock operation in the transaction.


Expanding Phase – new locks acquired but none
released
Shrinking Phase – existing locks can be released but
no new locks can be acquired.
Conservative - Strict - Rigorous

Conservative – locks all items accessed before
transaction begins execution.
All but Conservative have deadlock problems
Dealing with Deadlock
Timestamps
Timeouts – aborts transaction
Wait-for Graphs
Optimistic Concurrency Control
(OCC)
No checking for serialization is done while
the transaction is executing
During transaction execution, all updates
are applied to local copies of the data
items that are kept for the transaction
During a validation phase the transactions
updates are check to see if they violate
serializability
Three Phases of Optimistic CC
Read Phase: A transaction can read values of
committed data items from the database.
However, updates are applied only to local
copies (versions) of the data items kept in the
transaction workspace
Validation Phase: Checking is performed to
ensure that serializability will not be violated if
the transaction updates are applied to the
database
Write Phase: If the validation phase is
successful, the transaction updates are applied
to the database; otherwise the updates are
discarded and the transaction is restarted
Optimistic CC con’t
The idea behind OCC is to do all the
checks at once
If there is little interference between
transactions, most will be validated
successfully.
Extra Requirements:



Local Copy
Transaction Timestamps
Must keep track of write_set & read_set
OCC Validation Phase
During Validation the current transaction is
checked against other transactions that are
either in the validation phase or have been
committed
Given the current transaction Ti and the
transactions it is being checked against Tj :



Tj completes its write phase before Ti starts read phase.
Ti completes its write phase after Tj completes its write phase and Ti’s read_set
has no items in common with Tj’s write_set.
Ti read_set & write_set have no common items to Tj’s write_set, and Tj
completes its read phase before Ti completes its read phase.
These are checked in order and if at least one is
true the transaction validates otherwise it is
restarted.
What is a Dynamic Database?
Real-Time DB

Timing Constraints called “deadlines”
Soft/Firm vs Hard
Firm transactions must give in to hard transations
due to their stringent timing requirement.
Active DB Systems:


A DB that is capable of initiation actions
Event – Condition – Action (triggering)
Put these together and you get:
Real-Time Active Database
Systems (RTABS)
“The application domains for such
databases are numerous – network
management, manufacturing process
control, intelligent highway systems and
air traffic control.”
Let’s take a look at a real world example:



Traffic Collision Avoidance System
July 1, 2002
Timeliness and correctness of data is crucial
The Importance of Concurrency
Control in Dynamic Databases
Many spin-offs of 2PL and OCC
“In firm or hard real-time scenarios (i.e.
where late transactions are worthless),
optimistic concurrency control (OCC)
outperforms locking over a large spectrum
of system loading and resource contention
conditions.” Datta & Son
Modifying OCC
“It has been repeatedly shown that it is the
number of restarts that determines the
performance of real-time CC algorithms.”
Datta & Son
This has given rise to:





OCC-FV (forward validation)
OCC-BV (backward validation)
OCC-DA (dynamic adjust)
OCC-BC (broadcast commit)
OCC-APFO (Adaptive Priority Fan Out)
Decreasing Restarts in OCC
Dynamically Adjusting the serialization


Backward Validation (BV) – check against
committed transactions
Forward Validation (FV) – check against
concurrent executing transactions
When Transactions Conflict
Irreconcilably
Which one do you restart?

Deadlines
Transaction A might be targeted to restart but if it is
restarted it has no hope of making its deadline. If
transaction B will have time to complete restart it
instead.
Firm/soft transactions continually blocked by hard

Triggering
If transaction A is targeted to restart but it has
triggered several other transactions while B does
not you might want to restart B instead.
Deadlines of triggered transactions.
Adaptive Priority Fan Out
OCC-APFO
In this model the “Fan Out” refers to
adjusting for the number of “triggered”
transactions
A Concurrency Priority Index (CPI) which
takes into account the deadlines and fanout of the transactions
Adaptive Priority Fan Out
OCC-APFO con’t
This model also takes into consideration
that if a specific transaction is targeted for
restart it still may not have enough time to
meet it’s deadline.
“OCC-APFO attempts to satisfy this goal
(of reducing restarts) by restarting
validating transactions only when it feels
that the restarted transaction is very likely
to commit eventually” Datta & Son
In Conclusion
There are many different models used to ensure
concurrency control in Dynamic Databases.
OCC-APFO is one model that seemed to take in
a variety variables when trying to reduce
restarts:



Dynamic serialization adjustments
Deadlines
Triggered Transaction
How many, and their deadlines

Will the restarted transaction be able to commit
Back to reality (TCAS vs. Humans (ATC))