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An Evaluation of Cassandra for Hadoop

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In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile
applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for “Big Data” research. New generation data can prove costly and unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide

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ABSTRACT

In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile
applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for “Big Data” research. New generation data can prove costly and unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide
a desirable platform for fast and efficient data queries. This leads to increased importance in areas such as cloud applications, e-commerce, social media, bio-informatics, and materials science. In an effort to combine the querying capabilities of conventional database systems and the processing power of the MapReduce model, this paper presents a thorough evaluation of the Cassandra NoSQL database when used in conjunction with the Hadoop MapReduce engine. We characterize the performance for a wide range of representative use cases, and then compare, contrast,and evaluate so that application developers can make informed decisions based upon data size, cluster size, replication factor and partitioning strategy to meet their performance needs,

INTRODUCTION
With the advent of the “Big Data” era, the size and structure of data have become highly dynamic. As application
developers deal with a deluge of data from various sources, they face challenges caused by the data’s lack of structure and schema. As such data grows and is constantly modified via social media, news feeds, and scientific sensor input, the requirements from the storage models have also changed. As the unstructured nature of the data limits the applicability of the traditional SQL model, NoSQL has emerged as an alternative paradigm for this new non-relational data schema.NoSQL frameworks, such as DynamoDB [1], MongoDB [6], BigTable [10] and Cassandra [23], address this “Big Data” challenge by providing horizontal scalability. This, unlike the vertical scalability scheme of traditional databases, results in lower maintenance costs

Publisher: IEEE

year: 2013

By: Elif Dede, Bedri Sendir, Pinar Kuzlu, Jessica Hartog, Madhusudhan Govindaraju

File Information:English Language/8 Page/Size:179 K

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ناشر :IEEE

سال :2013

کاری از : Elif Dede, Bedri Sendir, Pinar Kuzlu, Jessica Hartog, Madhusudhan Govindaraju

اطلاعات فایل :زبان انگلیسی/8 صفحه /حجم :179 K

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