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ISYS224 Database Systems

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ISYS224 Database Systems

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Course Code: ISYS224
University: Macquarie University

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Country: Australia

Question:
In  about  one  page  (single  space),  with  help  of examples  in  the  context  of  the the  Spoc  Bank domain:a) Briefly explain what a transaction is;b) Explain what a schedule is, and how it is different from a transaction;c) Define  and  explain  the  notion  of a conflict  serializable  schedule. Does  a  conflict serializable schedule guarantee database consistency? Explain why or why not.d) Do different conflict-serializable schedules made up of the same set of transactions leave a database in the same state (with same value of the corresponding data items) if they started with the same state of the database? Explain your answer.e) Explain  what  view  serializability  is,  and  why  its  being  a  weaker  notion  than  conflict serializability is also its streng
Answer:

Introduction
The database management system is the software application that allows the database developers to manipulate the original raw database. The developers can store, retrieve or update data in the database along with creating procedures, triggers and events. The database allows the developers to transfer the database through a raw file, individual for different management system.
The report is based on providing the details of the transaction feature of a database management system. The brief details of the transaction properties and features has been provided into the report. The main concern of the report is serializability.
The transition can be considered as the singular logical element of process which evaluates as well as updates the database contents. The transaction has mainly two operations named as read and write. These read and write operation accesses the data stored into the database. The database consistency is a very important factor in maintaining proper functioning of the database at all times. In order to maintain database consistency before a transaction begins or its ends, some properties of tractions are followed. These properties are called ACID properties.
Atomicity: The atomicity refers to the situation when a transaction is either fully completed or rejected by the database. This means that the database will consider all the processes defined a transaction as atomic or one single process. If the transaction is cancelled before it is committed, the database will rollback all the previously executed processes and restore to the stage when the transaction was not began. The abort operation suggests that all the changes will be undone. On the other hand, the commit operation will save all the changes done in transaction part permanently in the database itself.
Consistency: The consistency property is used for maintaining the integrity of the database so that the database can be consistent after and before the transaction. This implies that of consistency is maintained properly then the database will maintain correctness. It suggests that if total amount in all the accounts is 5000 then after all the transactions the total amount should be 5000.
Isolation: The isolation makes sure that simultaneously occurring transactions do not lead the database to inconsistent state. Individual transactions can be executed indecently without any interference. The isolation does not allow any other transaction to see the updated changes in the database until the write operation is committed.  
Durability: The durability property makes sure that after the transactions are done then modifications to the database are done and updates are written to the disk. The updates written into the disks become permanent and can be accessed by all the other transactions.
Schedule 
The schedule is considered as the operations that are executed in series and conveys from one transaction to another transactions. The schedule preserves the operation execution order for each individual transactions. Three types of schedule are available in database transaction such as serial schedule, non-serial schedule and serializable schedule.
The schedule is a part of the transaction that assist in transaction concurrent operation execution. A transaction can consist of only one type of schedule. The schedule is completely based on the transaction type and properties but a transaction is never decided based on schedule.
Conflict serializable schedule 
The conflict serializability states that a schedule can be altered into a serial schedule through adjusting its non-conflicting operations. Two operations of a transaction will be called conflicting operations if both the operations are from different transactions, same data item is considered and at least one of those are write.
Similarity between Conflict serializable schedule and state of database
A schedule is viewed as strife serializable as it can without much of a stretch change or changed into appropriate sort of sequential schedule. It is chiefly gotten by activity of non-struggle. The schedule of the contention serializable is viewed as serializable when struggle is viewed as equivalent to sequential schedule. No, contention serializable schedules are not comprised of comparative arrangement of exchange which leaves the database at the given state. It doesn’t give a similar esteem in the event that they have begun with a similar condition of the given database.
View Serializability 
The view Serializability is view equivalent to serial schedule, it must be a confilict serializable serializable and it does not have any bind write operations.
Conclusion 
From the above discussion it can be stated that the transaction in the database is a very important factor. The database must be following the transaction protocol for maintaining the consistency and other ACID properties of the database. The ACID property is a very important factor database that ensures integrity of the database.
References:
Ahmed, T.M., Bezemer, C.P., Chen, T.H., Hassan, A.E. and Shang, W., 2016, May. Studying the effectiveness of application performance management (APM) tools for detecting performance regressions for web applications: an experience report. In Proceedings of the 13th International Conference on Mining Software Repositories (pp. 1-12). ACM.
Arulraj, J. and Pavlo, A., 2017, May. How to build a non-volatile memory database management system. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1753-1758). ACM.
Coronel, C. and Morris, S., 2016. Database systems: design, implementation, & management. Cengage Learning.
Elmasri, R. and Navathe, S., 2016. Fundamentals of database systems. London: Pearson.
Goel, A.K., Pound, J., Auch, N., Bumbulis, P., MacLean, S., Färber, F., Gropengiesser, F., Mathis, C., Bodner, T. and Lehner, W., 2015. Towards scalable real-time analytics: an architecture for scale-out of OLxP workloads. Proceedings of the VLDB Endowment, 8(12), pp.1716-1727.
Gurajada, A., Gala, D., Zhou, F., Pathak, A. and Ma, Z.F., 2018. BTrim: hybrid in-memory database architecture for extreme transaction processing in VLDBs. Proceedings of the VLDB Endowment, 11(12), pp.1889-1901.
Kim, K., Wang, T., Johnson, R. and Pandis, I., 2016, June. Ermia: Fast memory-optimized database system for heterogeneous workloads. In Proceedings of the 2016 International Conference on Management of Data (pp. 1675-1687). ACM.
Kvet, M., Matiako, K. and Kvet, M., 2014, March. Transaction management in fully temporal system. In Computer Modelling and Simulation (UKSim), 2014 UKSim-AMSS 16th International Conference on (pp. 148-153). IEEE.
Lin, Q., Chang, P., Chen, G., Ooi, B.C., Tan, K.L. and Wang, Z., 2016, June. Towards a non-2pc transaction management in distributed database systems. In Proceedings of the 2016 International Conference on Management of Data (pp. 1659-1674). ACM.
Padhye, V. and Tripathi, A., 2015. Scalable transaction management with snapshot isolation for NoSQL data storage systems. IEEE Transactions on Services Computing, 8(1), pp.121-135.
Pavlo, A., Angulo, G., Arulraj, J., Lin, H., Lin, J., Ma, L., Menon, P., Mowry, T.C., Perron, M., Quah, I. and Santurkar, S., 2017. Self-Driving Database Management Systems. In CIDR.
Zhang, H., Chen, G., Ooi, B.C., Tan, K.L. and Zhang, M., 2015. In-memory big data management and processing: A survey. IEEE Transactions on Knowledge and Data Engineering, 27(7), pp.1920-1948.
Zimmermann, O., 2015. Architectural refactoring: A task-centric view on software evolution. IEEE Software, 32(2), pp.26-29.

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