Consider the fact, that the concepts of nosql databases have been developed and recently, big Internet companies such as Google, Amazon, Yahoo!, and Facebook are using nosql databases. Although the primary focus of No...
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ISBN:
(纸本)9783319163130;9783319163123
Consider the fact, that the concepts of nosql databases have been developed and recently, big Internet companies such as Google, Amazon, Yahoo!, and Facebook are using nosql databases. Although the primary focus of nosql databases is to deal with huge volume of heterogeneous data, these can also be suited for handling moderate volume of data, especially if the data are heterogeneous and there are frequent changes in data. Considering this we consider the development and implementation of an application with moderate volume of heterogeneous data using a nosql database. We perform comparative performance analysis with a relational database system. The experimental evaluations show that nosql databases are also often suitable for handling moderate volume of data.
A large amount of unstructured data arc being continuously generated by mobile applications, e.g., Facebook, Twitter and Foursquare, due to the popularity of smart devices, e.g., iPad, iWatch and smartphone. These dat...
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ISBN:
(纸本)9781509040650
A large amount of unstructured data arc being continuously generated by mobile applications, e.g., Facebook, Twitter and Foursquare, due to the popularity of smart devices, e.g., iPad, iWatch and smartphone. These data are undoubtedly of great value but need to be first well stored and then analyzed. Nevertheless, traditional relational database management system (RDBMS) is not well qualified for this job. As a result, nosql databases are proposed and become very popular. Judging from the present situations. RDMBSs play a better role in managing relational data, and thus application developers have to face the situation of co-existence of both RDBMS and nosql databases, and knowing the details of the underlying data distributions and structures of different databases. To this end, in this paper, we aim to propose a unified, transparent query engine, as a middleware, called "ZQL", by using MySQL and Hive as underlying two exemplary databases, however its usage goes far beyond these two databases. ZQL aims to support application development by hiding the specific details of both nosql databases and RDBMS. Finally, extensive experimental results show the effectiveness and functionalities of the proposed and implemented ZQL middleware.
The aggregation model is the foundation of some of the main nosql databases. This model is characterized because the basic element of management is the concept of aggregate. Furthermore, another characteristic is that...
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ISBN:
(纸本)9781665438278
The aggregation model is the foundation of some of the main nosql databases. This model is characterized because the basic element of management is the concept of aggregate. Furthermore, another characteristic is that it is not necessary to create a definition schema in the database to store information as in the case of relational databases. It is for this reason that in general there are no sophisticated design tools for databases oriented towards aggregates since the schema is implicit in the stored information. However, a design tool allows better management of the information to be stored and especially the maintenance of the database. This article presents a visual tool that allows you to design a nosql database oriented towards aggregates, as well as manage different designs. On the other hand, the tool allows generating instances of the design containing data for a MongoDB-type document database.
Recently nosql databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, ther...
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ISBN:
(纸本)9781509035557
Recently nosql databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, there are more than 225 kinds of nosql databases. However, the overwhelming amount and constantly updated versions of databases make it challenging for people to compare their performance and choose an appropriate one. This paper is trying to evaluate the performance of five nosql clusters (Redis, MongoDB, Couchbase, Cassandra, HBase) by using a measurement tool YCSB (Yahoo! Cloud Serving Benchmark), explain the experimental results by analyzing each database's data model and mechanism, and provide advice to nosql developers and users.
Large-scale web services like LinkedIn serve millions of users across the globe. The user experience depends on high service availability and performance of the services. In such a scenario, capacity measurement is cr...
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ISBN:
(纸本)9781509042845
Large-scale web services like LinkedIn serve millions of users across the globe. The user experience depends on high service availability and performance of the services. In such a scenario, capacity measurement is critical for these cloud services. Resources should be provisioned such that the service can easily handle peak traffic without experiencing bottlenecks or compromising on latency. In addition, accurate understanding of service capacity will lead to systematic provisioning of resources saving millions of dollars in capital investment and better savings in energy. Stateful services like nosql databases are one of the most expensive and critical components in a cloud stack. A clear understanding of the capacity limits of a stateful service will lead to better availability and performance across the stack. However, based on our experience, accurately measuring capacity of nosql databases is much more challenging than regular stateless services. In this work, we present various approaches to accurately measure the capacity of stateful nosql services, their benefits and costs, and discuss in detail about the solution we prefer to use.
The analysis of relational and nosql databases leads to the conclusion that these data processing systems are to some extent complementary. In current Big Data applications, especially where extensive analyses are nee...
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ISBN:
(纸本)9783319754208;9783319754192
The analysis of relational and nosql databases leads to the conclusion that these data processing systems are to some extent complementary. In current Big Data applications, especially where extensive analyses are needed, it turns out that it is non-trivial to design an infrastructure involving data and software of both types. In terms of performance, it may be beneficial to use a polyglot persistence or multi-model approach or even to transform the SQL database schema into nosql and to perform data migration between the relational and nosql database. The aim of the paper is to show these possibilities and some new methods of designing such integrated database architectures.
nosql database (nosql DB) covers the shortage of traditional database and has been widely used in recent years. Currently, researches on nosql DB mainly focus on performance issues;few of them are about energy consump...
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ISBN:
(纸本)9781479950799
nosql database (nosql DB) covers the shortage of traditional database and has been widely used in recent years. Currently, researches on nosql DB mainly focus on performance issues;few of them are about energy consumption (EC) evaluation and optimization. Waiting Energy Consumption (WEC) is another critical reason causing energy waste besides computer idleness. Study the WEC regularities of nosql DB facilitates achieving real "green computing". This paper first analyzes the model and measurement approaches of EC;then designs test cases to study the WEC regularities;finally proposes approaches of "reducing WEC" for EC optimization. Plenty of experiments show that, despite that nosql DB is an application of "green cloud computing", the ECs of selected nosql DBs are widely divergent, and some of them remain to be further optimized.
The analysis of relational and nosql databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so...
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The analysis of relational and nosql databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into nosql and to perform data migration between the relational and nosql databases. Another possibility is to integrate a nosql database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.
The rapid development of computer storage technologies has brought Big Data challenges to digital forensics where the dimensions of digital evidence supports has grown exponentially with heterogeneous data sets struct...
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ISBN:
(纸本)9781479951482
The rapid development of computer storage technologies has brought Big Data challenges to digital forensics where the dimensions of digital evidence supports has grown exponentially with heterogeneous data sets structured, unstructured and semi-structured. Traditional relational database management systems (RDBMS) typically expose a query interface based on SQL (Structured Query Language). However, they are mainly employed for management of structured data and hard to scale to the ever growing size of data sets. This paper reviews nosql (Not Only SQL) database technologies as an alternative to RDBMS for management of Big Data. It introduces MongoDB and Riak as two representative nosql databases and evaluates their performance in data processing using the Amazon EC2 Cloud.
Data conversion has become an emerging topic in BigData era. To face the challenge of rapid data growth, legacy or existing relational databases have the need to convert into nosql column-family database in order to a...
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ISBN:
(纸本)9781509018932
Data conversion has become an emerging topic in BigData era. To face the challenge of rapid data growth, legacy or existing relational databases have the need to convert into nosql column-family database in order to achieve better scalability. The conversion from SQL to nosql databases requires combining small, normalized SQL data tables into larger nosql data tables;a process called denormalization. A challenging issues in data conversion is how to group the denormalized columns in a large data table into "families" in order to ensure the performance of query processing. In this paper, we propose an efficient heuristic algorithm, GPA (Graph-based Partition Algorithm), to address this problem. We use TPC-C and TPC-H benchmarks to demonstrate that, the column-families produced by GPA is very efficient for large scale data processing.
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