![Types of Locks
Types of Locks
The types of locks are as follows −
Shared Lock [Transaction can read only the data item
values]
Exclusive Lock [Used for both read and write data item
values]
-Manisha Kapila](https://image.slidesharecdn.com/concurrencycontrol-250503131125-bde18db4/85/Concurrency-Control-power-point-presentations-ppt-6-320.jpg)
Concurrency Control power point presentations.ppt
- 2. When multiple transactions execute concurrently in an uncontrolled or unrestricted manner, then it might lead to several problems. Such problems are called as concurrency problems. For example, if we take ATM machines and do not use concurrency, multiple persons cannot draw money at a time in different places. This is where we need concurrency. The main objective of concurrency control is to allow many users perform different operations at the same time. -Manisha Kapila
- 3. The concurrency control protocols ensure the atomicity, consistency, isolation, durability and serializability of the concurrent execution of the database transactions. Concurrency control can be divided into two protocols: Lock –based protocol Timestamp Based protocol -Manisha Kapila
- 4. Lock-based protocol Lock is a mechanism which is important in a concurrency control. It controls concurrent access to a data item. It assures that one process should not retrieve or update a record which another process is updating. -Manisha Kapila
- 5. For e.g. In traffic, there are signals which indicate stop or go . When one signal is allowed to pass at a time, then other signals are locked. Similarly, in database transaction only one transaction is performed at a time and other transaction are locked. -Manisha Kapila
- 6. Types of Locks Types of Locks The types of locks are as follows − Shared Lock [Transaction can read only the data item values] Exclusive Lock [Used for both read and write data item values] -Manisha Kapila
- 7. Shared Lock(S) A shared lock is also called a Read-only lock. With the shared lock, the data item can be shared between transactions. This is because you will never have permission to update data on the data item. For example, consider a case where two transactions are reading the account balance of a person. The database will let them read by placing a shared lock. However, if another transaction wants to update that account’s balance, shared lock prevent it until the reading process is over. -Manisha Kapila
- 8. Exclusive Lock(X) In the exclusive lock, the data item can be both reads as well as written by the transaction. This lock is exclusive, and in this lock, multiple transactions do not modify the same data simultaneously. For example, when a transaction needs to update the account balance of a person. You can allows this transaction by placing X lock on it. Therefore, when the second transaction wants to read or write, exclusive lock prevent this operation. -Manisha Kapila
- 9. One-phase Locking Protocol One-phase Locking Protocol In this method, each transaction locks an item before use and releases the lock as soon as it has finished using it. This locking method provides for maximum concurrency but does not always enforce serializability. -Manisha Kapila
- 10. Two-phase Locking Protocol(2PL) Two-phase Locking Protocol(2PL) The two-phase locking protocol divides the execution phase of the transaction into three parts. In the first part, when the execution of the transaction starts, it seeks permission for the lock it requires. In the second part, the transaction acquires all the locks. The third phase is started as soon as the transaction releases its first lock. In the third phase, the transaction cannot demand any new locks.It only releases the acquired locks. -Manisha Kapila
- 11. Two-phase Locking Protocol(2PL) Two-phase Locking Protocol(2PL) Growing Phase: New Locks on data item may be acquired but none can be released. Shrinking Phase: Existing lock may be released but no new locks can be acquired. -Manisha Kapila
- 12. Timestamp-Based Protocol Timestamp-Based Protocol The most commonly used concurrency protocol is the timestamp based protocol. The timestamp ordering protocol is used to order the transaction based on their timestamps. The order of transaction means in ascending order. The priority of the older transaction is higher that’s why it executes first. To determine the timestamp of the transaction, this protocol use system time . -Manisha Kapila
- 13. Let's assume there are two transactions T1 and T2. Suppose the transaction T1 has entered the system at 007 times and transaction T2 has entered the system at 009 times. T1 has the higher priority, so it executes first as it is entered the system first. The timestamp ordering protocol also maintains the timestamp of last 'read' and 'write' operation on a data. -Manisha Kapila
- 14. Timestamp based ordering follow three rules to enforce Timestamp based ordering follow three rules to enforce serializability − serializability − Access Rule − When two transactions try to access the same data item simultaneously, for conflicting operations, priority is given to the older transaction. This causes the younger transaction to wait for the older transaction to commit first. Late Transaction Rule − If a younger transaction has written a data item, then an older transaction is not allowed to read or write that data item. This rule prevents the older transaction from committing after the younger transaction has already committed. -Manisha Kapila
- 15. Younger Transaction Rule − A younger transaction can read or write a data item that has already been written by an older transaction. -Manisha Kapila
- 16. Distributed Databases Distributed Databases A distributed database is basically a database that is not limited to one system, it is spread over different sites, i.e, on multiple computers or over a network of computers. A distributed database system is located on various sites that don’t share physical components. This may be required when a particular database needs to be accessed by various users globally. It needs to be managed such that for the users it looks like one single database. -Manisha Kapila
- 17. -Manisha Kapila
- 18. Features of Distributed Databases Features of Distributed Databases Location independency Distributed query processing Distributed transaction management Seamless integration Transaction processing -Manisha Kapila
- 19. Types of Distributed Databases Types of Distributed Databases There are two types of distributed databases: Homogenous Heterogeneous -Manisha Kapila
- 20. Homogenous Homogenous A homogenous distributed database is a network of identical databases stored on multiple sites. The sites have the same operating system, DDBMS, and data structure, making them easily manageable. Homogenous databases allow users to access data from each of the databases seamlessly. -Manisha Kapila
- 21. Heterogeneous Heterogeneous Heterogeneous distributed database system is a network of two or more databases with different types of DBMS software, which can be stored on one or more machines. In this system data can be accessible to several databases in the network with the help of generic connectivity (ODBC and JDBC). -Manisha Kapila
- 22. Distributed Database Storage Distributed database storage is managed in two ways: Replication Fragmentation -Manisha Kapila
- 23. Replication Replication In database replication, the systems store copies of data on different sites. If an entire database is available on multiple sites, it is a fully redundant database. The advantage of database replication is that it increases data availability on different sites and allows for parallel query requests to be processed. -Manisha Kapila
- 24. Advantages of Data Replication Reliability − In case of failure of any site, the database system continues to work since a copy is available at another site(s). Reduction in Network Load − Since local copies of data are available, query processing can be done with reduced network usage. Quicker Response − Availability of local copies of data ensures quick query processing and consequently quick response time. Simpler Transactions − Transactions require less number of joins of tables located at different sites and minimal coordination across the network. Thus, they become simpler in nature. -Manisha Kapila
- 25. Disadvantages of Data Replication Increased Storage Requirements Maintaining multiple − copies of data is associated with increased storage costs. The storage space required is in multiples of the storage required for a centralized system. Increased Cost and Complexity of Data Updating − Each time a data item is updated, the update needs to be reflected in all the copies of the data at the different sites. This requires complex synchronization techniques and protocols. -Manisha Kapila
- 26. Fragmentation Fragmentation When it comes to fragmentation of distributed database storage, the relations are fragmented, which means they are split into smaller parts. Each of the fragments is stored on a different site, where it is required. The advantage of fragmentation is that there are no data copies, which prevents data inconsistency. -Manisha Kapila
- 27. Two Types of Data Fragmentation Two Types of Data Fragmentation Vertical fragmentation Vertical fragmentation is a subset of attributes. Basically, vertical fragmentation splits tables by columns Horizontal Fragmentation Horizontal Fragmentation is a subset of tuples (rows). Horizontal Fragmentation splits tables by rows. -Manisha Kapila
- 28. -Manisha Kapila
- 29. Advantages of Fragmentation Since data is stored close to the site of usage, efficiency of the database system is increased. Local query optimization techniques are sufficient for most queries since data is locally available. Since irrelevant data is not available at the sites, security and privacy of the database system can be maintained. -Manisha Kapila
- 30. Disadvantages of Fragmentation When data from different fragments are required, the access speeds may be very high. In case of recursive fragmentations, the job of reconstruction will need expensive techniques. Lack of back-up copies of data in different sites may render the database ineffective in case of failure of a site. -Manisha Kapila
- 31. Distributed DBMS - Commit Protocols In a local database system, for committing a transaction, the transaction manager has to only convey the decision to commit to the recovery manager. However, in a distributed system, the transaction manager should convey the decision to commit to all the servers in the various sites where the transaction is being executed and uniformly enforce the decision. -Manisha Kapila
- 32. When processing is complete at each site, it reaches the partially committed transaction state and waits for all other transactions to reach their partially committed states. When it receives the message that all the sites are ready to commit, it starts to commit. In a distributed system, either all sites commit or none of them does. The different distributed commit protocols are − One-phase commit Two-phase commit Three-phase commit -Manisha Kapila
- 33. Distributed One-phase Commit Distributed one-phase commit is the simplest commit protocol. Let us consider that there is a controlling site and a number of slave sites where the transaction is being executed. The steps in distributed commit are- After each slave has locally completed its transaction, it sends a “DONE” message to the controlling site. -Manisha Kapila
- 34. The slaves wait for “Commit” or “Abort” message from the controlling site. This waiting time is called window of vulnerability. When the controlling site receives “DONE” message from each slave, it makes a decision to commit or abort. This is called the commit point. Then, it sends this message to all the slaves. On receiving this message, a slave either commits or aborts and then sends an acknowledgement message to the controlling site. -Manisha Kapila
- 35. Distributed Two-phase Commit Distributed two-phase commit reduces the vulnerability of one-phase commit protocols. The steps performed in the two phases are as follows − Phase 1: Prepare Phase Phase 2:Commit/Abort Phase -Manisha Kapila
- 36. Phase 1: Prepare Phase Phase 1: Prepare Phase After each slave has locally completed its transaction, it sends a “DONE” message to the controlling site. When the controlling site has received “DONE” message from all slaves, it sends a “Prepare” message to the slaves. The slaves vote on whether they still want to commit or not. If a slave wants to commit, it sends a “Ready” message. A slave that does not want to commit sends a “Not Ready” message. This may happen when the slave has conflicting concurrent transactions or there is a timeout. -Manisha Kapila
- 37. Phase 2: Commit/Abort Phase Phase 2: Commit/Abort Phase After the controlling site has received “Ready” message from all the slaves − ◦ The controlling site sends a “Global Commit” message to the slaves. ◦ The slaves apply the transaction and send a “Commit ACK” message to the controlling site. ◦ When the controlling site receives “Commit ACK” message from all the slaves, it considers the transaction as committed. -Manisha Kapila
- 38. After the controlling site has received the first “Not Ready” message from any slave − ◦ The controlling site sends a “Global Abort” message to the slaves. ◦ The slaves abort the transaction and send a “Abort ACK” message to the controlling site. ◦ When the controlling site receives “Abort ACK” message from all the slaves, it considers the transaction as aborted. -Manisha Kapila
- 39. Distributed Three-phase Commit The steps in distributed three-phase commit are as follows − Phase 1: Prepare Phase: The steps are same as in distributed two- phase commit. Phase 2: Prepare to Commit Phase: The controlling site issues an “Enter Prepared State” broadcast message. The slave sites vote “OK” in response. Phase 3: Commit / Abort Phase: The steps are same as two- phase commit except that “Commit ACK”/”Abort ACK” message is not required. -Manisha Kapila