1. Prepared by: Mudra Patel (113) Pradhyuman Raol(114)
Locking Scheduler
&
Managing Hierarchies of Database Elements
Prepared by: Mudra Patel (113)
Pradhyuman Raol(114)

2. Scheduler
The order in which the individual steps of different transactions occur is regulated by the scheduler.
The general process of assuring that transactions preserve consistency when executing simultaneously is called concurrency control.

3. Role of a Scheduler

4. Architecture of a Locking Scheduler
The transactions themselves do not request locks, or cannot be relied upon to do so. It is the job of the scheduler to insert lock actions into the stream of reads, writes and other actions that access data.
Transactions do not locks. Rather the scheduler releases the locks when the transaction manager tells it that the transaction will commit or abort.

5. Lock Table

6. Lock Table
The lock table is a relation that associates database elements with locking information about that element.
The table is implemented with a hash table using database elements as a hash key.

7. Size of Lock Table
The size of the table is proportional to the number of locked elements only and not to the entire size of the database since any element that is not locked does not appear in the table.

8. Structure of Lock Table Entries

9. Group Mode
The group mode is a summary of the most stringent conditions that a transaction requesting a new lock on an element faces. Rather than comparing the lock request with every lock held by another transaction on the same element, we can simplify the grant/deny decision by comparing the request with only the group mode.

10. Handling Lock Requests
Suppose transaction T requests a lock on A.
If there is no lock-table entry for A, then surely there are no locks on A, so the entry is created and the request is granted.
If the lock-table entry for A exists then we use it to guide the decision about the lock request.

11. Handling Unlocks
If the value of waiting is ‘Yes’ then we need to grant one or more locks from the list of requested locks. The different approaches for this are:
First-come-first-served
Priority to shared locks
Priority to upgrading

12. Managing Hierarchies of Database Elements
It Focus on two problems that come up when there id tree
structure to our data.
Tree Structure : Hierarchy of lockable elements. And How to allow locks on both large elements, like Relations and elements in it such as blocks and tuples of relation, or individual.
Another is data that is itself organized in a tree. A major example would be B-tree index.

13. Locks With Multiple Granularity
“Database Elements” : It is sometime noticeably the various elements which can be used for locking.
Eg: Tuples, Pages or Blocks, Relations etc.
Granularity locks and Types : While putting locks actually when we decide which database element is to be used for locking makes it separates in two types.
Types of granularity locks:
1) Large grained
2) Small grained

14. Example: Bank database
Small granularity locks: Larger concurrency can achieved.
Large granularity locks: Some times saves from unserializable behavior.

15. Warning locks
The solution to the problem of managing locks at different granularities involves a new kind of lock called a “Warning.“
It is helpful in hierarchical or nested structure .
It involves both “ordinary” locks and “warning” locks.
Ordinary locks: Shared(S) and Exclusive(X) locks.
Warning locks: Intention to shared(IS) and Intention to
Exclusive(IX) locks.

16. Warning Protocols
These are the rules to be followed while putting locks on
different elements.
1. To place an ordinary S or X lock on any element. we must
begin at the root of the hierarchy.
2. If we are at the element that we want to lock, we need look
no further. We request lock there only
3. If the element is down in hierarchy then place warning lock
on that node respective of shared and exclusive locks and then
Move on to appropriate child and then try steps 2 or 3 and until
you go to desired node and then request shared or exclusive
lock.

17. Compatibility Matrix IS column: Conflicts only on X lock.
YES NO
N O
IS column: Conflicts only on X lock.
IX column: Conflicts on S and X locks.
S column: Conflicts on X and IX locks.
X column: Conflicts every locks.

18. Warning Protocols Consider the relation:
M o v i e ( t i t l e , year, length, studioName)
Transaction1 (T1):
SELECT *
FROM Movie
WHERE title = 'King Kong';
Transaction2(T2):
UPDATE Movie
SET year = 1939
WHERE title = 'Gone With the Wind';

19. Phantoms and Handling Insertions
When ever some transaction inserts sub elements to the
node being locked then there may be problem like
serializability issues.
Lets have transaction 3 (T3) to be executed:
SELECT SUM(length)
FROM Movie
WHERE studioName = ‘Disney’
But at the same time the transaction t4 inserts the new movie of
‘Disney’ studio. Then what happens if t3 gets executed and t4
afterwards that sum will be incorrect.
But solution could be we could treat the insert or delete transaction like
writing operation with exclusive locks at that time this problem gets
solved.

20. BOOK: DATABASE SYSTEM THE COMPLETE BOOK
References
BOOK: DATABASE SYSTEM THE COMPLETE BOOK
THANK YOU!