Concurrency controls (CC) are majorly used for purposes of ensuring the reliability of database systems. When distributed transactions are done concurrently, concurrency must yield similar outcomes as an execution done sequentially. An execution is said to be serializable when its computation reflects a serial execution. When carrying out a serial execution of more than one transaction, all operations of individual transactions will be carried out before moving to the next transaction. This means there will be no conflicting situations. Serializable execution brings about the consistency of the database systems. The concurrency control which is primarily used in historical data management moves to another level when used on the temporal database.
Basically, applications that are time reliant or real-time sensitive are in nature temporal. They are either categorized as time-referenced or time-relevant data. For instance stock exchange and portfolio management for financial applications, airline and hotel reservations within scheduling applications. The aforementioned applications fall within the two categories. The currently existing concurrency control mechanism for database system includes optimistic and pessimistic approaches. For an optimistic approach, in spite of conflicting situation concurrent transaction is allowed to go on with a risk of starting again. While for pessimist approach the transaction is terminated in the event of conflict. In order to ensure consistency of the database system, locking offers an efficient concurrency control. It simply provides concurrency control mechanism locks on data accessibility. Access to the data item is granted instantaneously a lock is attained in a transaction.
When there is a detection of a probable conflict in a transaction, the pessimistic concurrency control will take avoidance measure thus bringing a halt to the transaction. Conversely, an optimistic concurrency will still allow the transactions to take place even when there is foreseeable conflict. In the event that the conflict happens then, the transactions will be started again. The focus is basically to ensure resources are not blocked for longer time intervals. On the other hand, the pessimistic approach has a lot of shortcomings in terms of deadlocks and numerous lockouts.
The optimistic locking provides an alternative solution to the problems. Optimistic locking does not lock records when they are read and proceeds on the assumption that the data being updated has not changed since the reading. Since no locks are taken out during the read, the deadlocks are eliminated since users should never have to wait on each other’s locks. The Oracle database uses optimistic locking by default.
However in an experimental study when the optimistic locking approach for temporal database environment was checked for efficiency and performance it was not up to the mark as per the requirement of temporal database systems and needed improvement.
The historical data can be represented in a systematic manner using the temporal database. The temporal database provides mechanisms to store and manipulate time-varying information.
Temporal databases encompass all database applications that require some aspect of time when organizing their information. So consistency in the temporal database is a critical area needs to be addressed by the database administrator. Oracle introduced Oracle Database 12c on June 25, 2013, which is considered to be the important architectural transformation in the legacy of the world’s leading database in its 25 years with respect to the market presence and dominance.
Oracle 12c supports temporal database consistency through temporal validity support and efficient locking mechanism. Oracle enterprise manager of Oracle 12c provides a graphical view of distributed transaction and various user sessions with locking and unlocking details
In the past two decades, researchers have concentrated on temporal data also known as time referenced with the intention of developing concepts, techniques as well as tools which better suits the management of the temporal data. The most recent research which is based on observations ended up realizing that majority of the time-centric databases that are real-time applications have temporal data. The conventional technology of databases in the banking system lacks adequate support to those databases specifically when it comes to concurrency issues. The system of temporal databases has a difference when compared to conventional databases when it comes to data storage. The difference is arrived at in the representation of data validity in the databases.
Concurrency control is a crucial aspect of a banking system database. Majority of the researchers have tried to develop a variety of protocol with the aim of achieving serializability. The various approaches that are used by these protocols include a timestamp, locking, and versions that are multiple. Majority of the concurrency control schemes that are applied in the banking sector use serializability, which is a common concept. The conflicting processes and functions are usually resolved in these systems through aborting or delaying the processes and the transactions. The locking protocols, techniques of timestamp validation, timestamp themselves and the various versions available are used as the only concurrency control schemes.
The proposals that have been brought on board regarding concurrency control in the databases of a banking system have come up with various classes of concurrency control approaches. There has been a brief survey which aims at designing an algorithm of concurrency control which is more flexible compared to conventional ones.
The development of cloud computing has contributed to issues related to data-intensive services. Cloud computing is termed as an architecture that will be able to accommodate data-intensive and large-scale software (Pokorny 69). NoSQL is the database that can be used in cloud computing architecture to provide a better solution. The need for machines to scale out and enhance the diversity of data retrieval patterns initiated the development of NoSQL databases. According to the available literature, many enterprises are using NoSQL database for data storage. Relational databases developed for structured data and scale-up systems were not effective. Consistency and parallelism of operations are solved through implementation of NoSQL databases. Research indicates that different data types are necessitating enterprises to invest and shift to big data technologies such as NoSQL (Leavitt 13). The NoSQL is believed to provide enhanced scalability, flexibility, and functionality. NoSQL increases performance by allowing many devices to be included in a group. The devices are linked in a distributed form hence the improvement in performance and scalability. The capability to distribute data to various devices is a key aspect of NoSQL databases.
Document store lacks a predefined schema, hence a complex type of NoSQL database. Documents are stored and accessed in the document store database in forms of BSON, JSON and XML formats. Documents in the database are represented by the specific key. Also, data retrieval in the database is accomplished by the use of a query language or an API. Advantages of the document store include intuitive data structures, flexible schema, and applicability in real-time analytics (Katkar 17). The disadvantages are increased hardware demand, dynamic aggregate design, and redundancy storage.
Column family is a type of NoSQL database that store data in collections of columns. Logically clustered columns make up column families that may consist of several columns developed at runtime. A single family column is termed as a map of data. A two-level aggregate arrangement is applied in this kind of database (Kumar et al. 30). Column family can be categorized into Facebook’s, HBase, Yahoo’s PNUTS, and Cassandra. Advantages of column family include distribution, high performance, and enhanced efficiency. The disadvantages are limited query options, incompatibility with early prototypes, and high maintenance effort.
Graph databases entail storage of entities and entity associations. In the graph database, data is stored once but interpreted in diverse ways based on the available relationships. The organization of database influences the above characteristic. Intelligence can be added to a relationship using its specific properties. The graph database best suits inter-linked data such as maps and social networking locations (Kumar et al. 30). Some of the merits include high-performance efficiency, applicability in social networking, and close modeling of networked records. The demerits of graph database are uneven updating, incapability to handle some large volumes of data, and difficulties in data sharing.
Transaction memory is a type of data structure that is locks free and possesses a free mutual exclusion. The state means that in the case of disruption of one process, other processes are not affected (Mahr et al. 39). The transaction memory is a new multiprocessor design architecture. The target of transactional memory is to enhance Lock-Free synchronization. Programmers are allowed by the transaction memory to define a customized read-write operation that applies to many independent words of the memory. Among the best-known lock based technique, deadlocks and convoys, transactional memory is well known.
Transactional memory simply uses many instructions that are executed by a single process satisfying the property of Serializability and Atomicity (Mankin et al. 90). Serializability involves a step of one transaction, which does not interleave another step. Atomicity outlines that, if a process is initiated, it must terminate.
Transaction memory provides the following ways for accessing the memory (Leis et.al 580). Load transaction: The value of the shared memory is read and recorded in a private register. Load-Transaction-Exclusive: It reads the value of shared memory location into a private register, and a location is probably to be changed. Store transaction: it writes a value from the private register to a shared memory location.
The transaction memory manipulates the transaction state through the usage of the following instructions. Commit: This state tries to make the processes’ tentative changes permanent. After a disruption occurs, the unterminated processes are rolled back. Commit returns an indication of either success or failure (Martin, et al. 17). Abort state discards all the updates, which were already written. The validate state tests the current processes and returns true in case the current transaction is not aborted. Finally, the process returns false when the current transaction is aborted.
NoSQL database is implemented in distributed systems to facilitate sharing of data and information. Transactions in the NoSQL database are incorporated using parallel programming. In distributed systems, concurrent activities utilize information or data at the same time (Schoeberl and Hilber 279). It is important to ensure efficiency and sharing for the effective functioning of the processes. NoSQL database systems necessitate for consistency of data and accomplishment of all transactions. Transactional memory enables the database to manage all the processes taking place and eliminate issues such as deadlocks. Transactions included in the NoSQL database need to meet three characteristics. Consistency is the key feature that must be met to ensure that transactions are accurately accomplished (Schoeberl and Hilber 279). Moreover, atomicity and isolation must be achieved for proper utilization of the NoSQL database. The transactional memory plays a major role in the achievement of the three features in all transactions.
Parallel hardware has recently been exploited by distributed systems like NoSQL database systems. Enhancing performance is the key aspect of utilizing distributed systems. Transactional memory enables NoSQL database systems to execute several queries or transactions at the same (Vizzotto et al. 180). When using NoSQL databases, a transactional memory acts like a shared memory. It, therefore, provides the running transactions with relevant resources for their execution. Many queries are able to be executed simultaneously because the transactional memory coordinates the way transactions are added into the system. The strategy for implementing the transactions is based on the availability of the required resources. Moreover, parallelism is achieved in the database systems with the help of transactional memory. Several processes are allowed to make use of the same resources without any issues (Larus et al. 80). The transactional memory manages the resource sharing as if only one transaction was utilizing the shared resource. Although the NoSQL databases may be accessed by many transactions, transactional memory facilitates sharing for effective execution of all computations. On implementing distributed systems, the employment of parallelism leads to the same results as though the processes had been executed one at a time (serialization). In summary, transactional memory enables concurrent processes to use the same database and acquire the same outcomes.
In distributed systems, the key storage memory that aid execution is the transactional memory. Multitasking and error recovery is enabled by the use of transactional memory. Moreover, conflicts occur if transactions depend on a shared memory (transactional memory). The transactional memory controls how transactions are added into the NoSQL database to eliminate conflicts. Also, the transactional memory system detects and determines the conflicts that take place and resolves it (Martin et al. 17). When data is updated or modified in the database, the transactional memory avails it for use by the other transactions. Instant updates are facilitated by the use of the transactional memory hence promoting data consistency. In this case, updates may not be made until a certain transaction is accomplished for consistency. Processes that depend on one another must share their result for the effective and accurate execution of computations in the database.
The transactional memory includes a transactional descriptor that acquires information about all transactions taking place in the NoSQL database system. The memory evaluates the state of each process to ensure that it does not execute before relevant updates are made. Transactions that cause conflicts are eliminated or stopped to avoid the occurrence of the anticipated conflict (Mahr et al. 39). The addition of a new transaction is influenced by the state of the current transactions. For instance, if adding a new transaction causes a conflict, the transactional memory aborts the addition. In summary, transactional memory helps in elimination of conflicts by controlling the way transactions are added in the database system.
Transitional System gives the concept of a transaction within a NoSQL database. During a transaction, there is a sequence of operation that satisfies the ACID property. The property includes the Atomicity. Atomicity refers to a situation whereby a transaction is established. Either all the operations are performed or none is performed (Vizzotto et al 184). Consistency is whereby a system is taken from a single stable state to another stable state. Isolation property entails executing a transaction, which concurrently follows the semantic that define consistency or isolation. Finally, an operation should follow durability property whereby in case a transaction has finished, the terminated process should remain durable in case there is an encountered fault. After adding a transaction to a NoSQL database, the transaction should complete successfully (Commit) otherwise, it should abort.
Conclusions
The transactional system allows concurrent transactions which lead to the access and modification of data in a concurrent way (Sonmez 146). The fault tolerance in NoSQL database is achieved by replication of regions in different servers. The previous approach of NoSQL system uses a consistent update mechanism where it allows a different replica to accept updates. The data replication and data replacement are the common strategies used to add an operation in NoSQL databases. The Middleware layer that occurs between the client and the server leads to an introduction of transactional guarantee (Saha 185). The NoSQL database extends clients interface by issuing commands that end and start a transaction. Once the client establishes a transaction, an operation sequence is carried out that lines with NoSQL API but lies in a transactional context. The NoSQL uses key-value stores that enable storage of value that is entitled to be retrieved my keys. The system van holds both the structured and unstructured data. Hence, by incorporating a Transactional memory to NoSQL databases, it ensures transactions are processed faster or within the minimum time.
References
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Kumar, Rakesh, et al. “Apache Hadoop, NoSQL and NewSQL Solutions of Big Data.” International Journal of Advance Foundation and Research in Science & Engineering (IJAFRSE) 1.6: 28-36.
Larus, James, and Christos Kozyrakis. “Transactional Memory.” Communications of the ACM 51.7 (2008): 80-8. EBSCOhost; asf. Web.
Leavitt, N. Will NoSQL Databases Live Up to their Promise?. 43 Vol., 2010: 12-14.
Mahr, Philipp, Alexander Heine, and Christophe Bobda. “On-Chip Transactional Memory System for FPGAs using TCC Model.” Proceedings of the 6th FPGAworld Conference (2009): 39. EBSCOhost; edb. Web.
Mankin, Jennifer, David Kaeli, and John Ardini. “Software Transactional Memory for Multicore Embedded Systems.” Languages, Compilers, Tools & Theory for Embedded Systems (2009): 90.
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Pokorny, Jaroslav. “NoSQL Databases: A Step to Database Scalability in Web Environment.” International Journal of Web Information Systems 9.1 (2013): 69.
Saha, B., A. Adl-Tabatabai, and Q. Jacobson. “Architectural Support for Software Transactional Memory.” 2006 39th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’06) (2006): 185. EBSCOhost; edb. Web.
Schoeberl, M., and P. Hilber. “Design and Implementation of Real-Time Transactional Memory.” 2010 International Conference on Field Programmable Logic & Applications (FPL) (2010): 279.
Sonmez, N. [et al ]. From Plasma to Beefarm: Design Experience of an FPGA-Based Multicore Prototype. Springer VerlagOAIster; OCLC; EBSCOhost; edsoai. Web.
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