Consistency Models of NoSQL Databases

Relational database is widely used in most of the application to store and retrieve data. They work best when they handle a limited set of data. Handling real time huge volume of data like internet was inefficient in relation database systems. To overcome this problem the "NO-SQL" or "Not Only SQL" Database came into existence. This paper discusses about problems with relation databases and how different types of NOSQL Databases are used to efficiently handle the real world problems.

Big Data requirements are motivating new database management models that can process billions of data requests per second, and established relational models have been changing to keep pace. Until recently, relational database-management systems(RDBMSs) have been main stay for managing data and a lifeline for businesses that can handle millions of website requests daily. These system's popularity stems in part from their use of SQL, a declarative query language that lets users at all levels of database-management expertise manipulate and retrieve data. An RDBMS typically incorporates support for data-integrity enforcement; user authentication; role-based access control; data backup and recovery; and atomicity, consistency, isolation, and durability(ACID) transaction properties. Although RDBMSs are perfect fit in many scenarios, they are very less suitable for the big data requirements typical in modern applications. To serve modern applications, an array of alternative database-management systems has recently emerged with varied abilities for handling data volume and throughput. The main objective of this paper is to present modern database management system and how it meets current performance and scalability needs of modern applications.

International Journal of Computer Applications, 2015

The rapid acceptance of social networking sites like Facebook, Twitter etc and with the huge use of mobile phones, the data volume is increasing at the rate of terabytes. As the data size and variety is increasing dramatically, databases intend to be more schema-less and less strict, in order to achieve higher scalability. Their queries tend to be quick responding, which grants them very fast writes and even faster reads. All these could not be managed with our Relational Databases or SQL. The ease of scalability and improved security has increased the popularity of Not-Only SQL. But they are not as reliable or effective as Relational Databases. Here, in this paper, an extensive study of the pros and cons of SQL and NoSQL, their relevance in cloud and at last, an evaluation of some NoSQL databases is also done. General Terms NoSQL databases, Cloud and Big data.

Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages. Table 2 Partitioning, replication, consistency, and concurrency control capabilities NoSQL Data Stores Partitioning Replication Consistency Concurrency control Key-value stores Redis Not available (planned for Redis Cluster release). It can be implemented by a client or a proxy. Master-slave, asynchronous replication. Eventual consistency. Strong consistency if slave replicas are solely for failover.

Quantitative Evaluation of Systems, 2015

The promise of high scalability and availability has prompted many companies to replace traditional relational database management systems (RDBMS) with NoSQL key-value stores. This comes at the cost of relaxed consistency guarantees: key-value stores only guarantee eventual consistency in principle. In practice, however, many key-value stores seem to offer stronger consistency. Quantifying how well consistency properties are met is a non-trivial problem. We address this problem by formally modeling key-value stores as probabilistic systems and quantitatively analyzing their consistency properties by both statistical model checking and implementation evaluation. We present for the first time a formal probabilistic model of Apache Cassandra, a popular NoSQL key-value store, and quantify how much Cassandra achieves various consistency guarantees under various conditions. To validate our model, we evaluate multiple consistency properties using two methods and compare them against each other. The two methods are: (1) an implementationbased evaluation of the source code; and (2) a statistical model checking analysis of our probabilistic model.

International Journal of Advanced Computer Science and Applications

Due to the proliferation of big data with large volume, velocity, complexity, and distribution among remote servers, it became obvious that traditional relational databases are unsuitable for meeting the requirements of such data. This led to the emergence of a novel technology among organizations and business enterprises; NoSQL datastores. Today such datastores have become popular alternatives to traditional relational databases, since their schema-less data models can manipulate and handle a huge amount of structured, semistructured and unstructured data, with high speed and immense distribution. Those data stores are of four basic types, and numerous instances have been developed under each type. This implies the need to understand the differences among them and how to select the most suitable one for any given data. Unfortunately, research efforts in the literature either consider differences from a theoretical point of view (without real use cases), or address performance issues such as speed and storage, which is insufficient to give researchers deep insight into the mapping of a given data structure to a given NoSQL datastore type. Hence, this paper provides a qualitative comparison among three popular datastores of different types (Redis, Neo4j, and MongoDB) using a real use case of each type, translated to the others. It thus highlights the inherent differences among them, and hence what data structures each of them suits most.

NoSQL is a free and open-source, scattered, extensive column store database management system intended to handle large amounts of data across many product servers, providing high obtainability and accessibility with no single point of failure. It is the easiest truly big-data database that can scale and replicate data globally in a master-less configuration. A NoSQL database delivers a mechanism for storage and recovery of data that is demonstrated in means other than the tabular relations used in relational databases. NoSQL databases usually understood by engineers as 'not only SQL databases' neither 'no SQL', it is an alternative to the most widely used relational databases. As the given name proposed, it is a substitute for SQL that uses in such a way that the SQL is co-existed. A relational database management system (RDBMS) is a database management system based on the relational model of data and it is a completely structured way of storing data. But NoSQL is an unstructured way of storing data. Is NoSQL an RDBMS? This is an article we will discuss various features of NoSQL, various advantages over RDBMS and the future of NoSQL. This article also reviews the basics of NoSQL and its usages.

Data storage management plays a vital role in cloud computing environment. The researchers are still trying to make the cloud data storage system to be more mature. And there are two major requirements of datastores in cloud computing environment: (1) high until almost ultimate scalability and low administration overhead. As a result, scalable, distributed databases to store peta bytes of data becomes a necessity. In this case, NoSQL databases are the most suitable solutions for cloud computing. Three properties of distributed databases that are commonly desired are: CAP (consistency, availability, partition tolerance). It would be ideal if distributed databases can deliver all three guarantees. However, providing all three attributes in NoSQL is still a challenge.This paper intends to examine how NoSQL works and to improve the availability in NoSQL databases that are suitable for cloud computing environment.

Computer Science - Research and Development, 2016

Today, data is generated and consumed at unprecedented scale. This has lead to novel approaches for scalable data management subsumed under the term NoSQL database systems to handle the everincreasing data volume and request loads. However, the heterogeneity and diversity of the numerous existing systems impede the well-informed selection of a data store appropriate for a given application context. Therefore, this article gives a top-down overview of the eld: Instead of contrasting the implementation specics of individual representatives, we propose a comparative classication model that relates functional and non-functional requirements to techniques and algorithms employed in NoSQL databases. This NoSQL Toolbox allows us to derive a simple decision tree to help practitioners and researchers lter potential system candidates based on central application requirements.

International Journal of Advanced Research in Computer Science, 2017

Now a day the technology is growing rapidly stimulating and generating whopping amount of data. Every day people and companies generate huge amounts of data and this data may be unstructured, semi-structured and structured. That’s why we need to design databases which can store this type of data in huge volumes. The name of this database is NoSQL databases. NoSQL database solves this type of problems. NoSQL database is being used widely and it is a commonly known as engines well scale. Therefore, it is useful to investigate how different factors, such as workload, data size and number of simultaneous sessions influence scaling capabilities. In this paper we describe the brief introduction of NoSQL and its categories and also what the benefits of NoSQL are and why we are using now. Keywords: NoSQL, Graph DB, Key value DB, Column DB, Document DB