Benchmarking of RDBMS and NoSQL performance on unstructured data

New requirements are arising in the database field. Big data has been soaring. The amount of data is ever increasing and becoming more and more varied. Traditional relational database management systems have been a dominant force in the database field but due to the massive growth of unstructured and multiform data, firms are now turning to architectures that have scaleout capabilities using open source software, commodity servers, cloud computing and services like Database as a Service. Due to this, relational databases ought to adopt and meet these new data requirements with easier and faster data processing capabilities and also provide multiple analytical tools that have the possibility of displaying analytics instantly. This study aims to benchmark the performance of relational systems and NoSQL systems on unstructured data.Novos requisitos estão surgindo na área das bases de dados. “Big data” permitiu avanços consideráveis em vários setores. O volume de dados tem aumentado e tornase cada vez mais variado. Os sistemas tradicionais de gestão de base de dados relacionais têm sido uma força dominante na área, mas devido ao crescimento massivo de dados não estruturados e multiformes, as empresas agora recorrem a arquiteturas que possuem recursos escaláveis usando software livre, servidores, computação em nuvem e serviços, tais como “base de dados como um serviço”. Nesse sentido, as bases de dados relacionais devem considerar e adotar novos requisitos de dados com maior agilidade no seu processamento e também fornecer múltiplas ferramentas analíticas com a possibilidade de mostrar análises em tempo real. Este estudo tem como objetivo avaliar o desempenho de sistemas relacionais e sistemas NoSQL em dados não estruturados

Performance Evaluation of Structured and Unstructured Data in PIG/HADOOP and MONGO-DB Environments

The exponential development of data initially exhibited difficulties for prominent organizations, for example, Google, Yahoo, Amazon, Microsoft, Facebook, Twitter and so forth. The size of the information that needs to be handled by cloud applications is developing significantly quicker than storage capacity. This development requires new systems for managing and breaking down data. The term Big Data is used to address large volumes of unstructured (or semi-structured) and structured data that gets created from different applications, messages, weblogs, and online networking. Big Data is data whose size, variety and uncertainty require new supplementary models, procedures, algorithms, and research to manage and extract value and concealed learning from it. To process more information efficiently and skillfully, for analysis parallelism is utilized. To deal with the unstructured and semi-structured information NoSQL database has been presented. Hadoop better serves the Big Data analysis requirements. It is intended to scale up starting from a single server to a large cluster of machines, which has a high level of adaptation to internal failure. Many business and research institutes such as Facebook, Yahoo, Google, and so on had an expanding need to import, store, and analyze dynamic semi-structured data and its metadata. Also, significant development of semi-structured data inside expansive web-based organizations has prompted the formation of NoSQL data collections for flexible sorting and MapReduce for adaptable parallel analysis. They assessed, used and altered Hadoop, the most popular open source execution of MapReduce, for tending to the necessities of various valid analytics problems. These institutes are also utilizing MongoDB, and a report situated NoSQL store. In any case, there is a limited comprehension of the execution trade-offs of using these two innovations. This paper assesses the execution, versatility, and adaptation to an internal failure of utilizing MongoDB and Hadoop, towards the objective of recognizing the correct programming condition for logical data analytics and research. Lately, an expanding number of organizations have developed diverse, distinctive kinds of non-relational databases (such as MongoDB, Cassandra, Hypertable, HBase/ Hadoop, CouchDB and so on), generally referred to as NoSQL databases. The enormous amount of information generated requires an effective system to analyze the data in various scenarios, under various breaking points. In this paper, the objective is to find the break-even point of both Hadoop/Pig and MongoDB and develop a robust environment for data analytics

A framework for multidimensional indexes on distributed and highly-available data stores

Spatial Big Data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of peta bytes of spatial data per year. However, as many authors have pointed out, the lack of specialized frameworks dealing with such kind of data is limiting possible applications and probably precluding many scientific breakthroughs. In this thesis, we describe three HPC scientific applications, ranging from molecular dynamics, neuroscience analysis, and physics simulations, where we experience first hand the limits of the existing technologies. Thanks to our experience, we define the desirable missing functionalities, and we focus on two features that when combined significantly improve the way scientific data is analyzed. On one side, scientific simulations generate complex datasets where multiple correlated characteristics describe each item. For instance, a particle might have a space position (x,y,z) at a given time (t). If we want to find all elements within the same area and period, we either have to scan the whole dataset, or we must organize the data so that all items in the same space and time are stored together. The second approach is called Multidimensional Indexing (MI), and it uses different techniques to cluster and to organize similar data together. On the other side, approximate analytics has been often indicated as a smart and flexible way to explore large datasets in a short period. Approximate analytics includes a broad family of algorithms which aims to speed up analytical workloads by relaxing the precision of the results within a specific interval of confidence. For instance, if we want to know the average age in a group with 1-year precision, we can consider just a random fraction of all the people, thus reducing the amount of calculation. But if we also want less I/O operations, we need efficient data sampling, which means organizing data in a way that we do not need to scan the whole data set to generate a random sample of it. According to our analysis, combining Multidimensional Indexing with efficient data Sampling (MIS) is a vital missing feature not available in the current distributed data management solutions. This thesis aims to solve such a shortcoming and it provides novel scalable solutions. At first, we describe the existing data management alternatives; then we motivate our preference for NoSQL key-value databases. Secondly, we propose an analytical model to study the influence of data models on the scalability and performance of this kind of distributed database. Thirdly, we use the analytical model to design two novel multidimensional indexes with efficient data sampling: the D8tree and the AOTree. Our first solution, the D8tree, improves state of the art for approximate spatial queries on static and mostly read dataset. Later, we enhanced the data ingestion capability or our approach by introducing the AOTree, an algorithm that enables the query performance of the D8tree even for HPC write-intensive applications. We compared our solution with PostgreSQL and plain storage, and we demonstrate that our proposal has better performance and scalability. Finally, we describe Qbeast, the novel distributed system that implements the D8tree and the AOTree using NoSQL technologies, and we illustrate how Qbeast simplifies the workflow of scientists in various HPC applications providing a scalable and integrated solution for data analysis and management.La gestión de BigData con información espacial está considerada como una tendencia esencial en el futuro de las aplicaciones científicas y de negocio. De hecho, se generan cientos de petabytes de datos espaciales por año mediante instrumentos de investigación, dispositivos médicos y redes sociales. Sin embargo, tal y como muchos autores han señalado, la falta de entornos especializados en manejar este tipo de datos está limitando sus posibles aplicaciones y está impidiendo muchos avances científicos. En esta tesis, describimos 3 aplicaciones científicas HPC, que cubren los ámbitos de dinámica molecular, análisis neurocientífico y simulaciones físicas, donde hemos experimentado en primera mano las limitaciones de las tecnologías existentes. Gracias a nuestras experiencias, hemos podido definir qué funcionalidades serían deseables y no existen, y nos hemos centrado en dos características que, al combinarlas, mejoran significativamente la manera en la que se analizan los datos científicos. Por un lado, las simulaciones científicas generan conjuntos de datos complejos, en los que cada elemento es descrito por múltiples características correlacionadas. Por ejemplo, una partícula puede tener una posición espacial (x, y, z) en un momento dado (t). Si queremos encontrar todos los elementos dentro de la misma área y periodo, o bien recorremos y analizamos todo el conjunto de datos, o bien organizamos los datos de manera que se almacenen juntos todos los elementos que comparten área en un momento dado. Esta segunda opción se conoce como Indexación Multidimensional (IM) y usa diferentes técnicas para agrupar y organizar datos similares. Por otro lado, se suele señalar que las analíticas aproximadas son una manera inteligente y flexible de explorar grandes conjuntos de datos en poco tiempo. Este tipo de analíticas incluyen una amplia familia de algoritmos que acelera el tiempo de procesado, relajando la precisión de los resultados dentro de un determinado intervalo de confianza. Por ejemplo, si queremos saber la edad media de un grupo con precisión de un año, podemos considerar sólo un subconjunto aleatorio de todas las personas, reduciendo así la cantidad de cálculo. Pero si además queremos menos operaciones de entrada/salida, necesitamos un muestreo eficiente de datos, que implica organizar los datos de manera que no necesitemos recorrerlos todos para generar una muestra aleatoria. De acuerdo con nuestros análisis, la combinación de Indexación Multidimensional con Muestreo eficiente de datos (IMM) es una característica vital que no está disponible en las soluciones actuales de gestión distribuida de datos. Esta tesis pretende resolver esta limitación y proporciona unas soluciones novedosas que son escalables. En primer lugar, describimos las alternativas de gestión de datos que existen y motivamos nuestra preferencia por las bases de datos NoSQL basadas en clave-valor. En segundo lugar, proponemos un modelo analítico para estudiar la influencia que tienen los modelos de datos sobre la escalabilidad y el rendimiento de este tipo de bases de datos distribuidas. En tercer lugar, usamos el modelo analítico para diseñar dos novedosos algoritmos IMM: el D8tree y el AOTree. Nuestra primera solución, el D8tree, mejora el estado del arte actual para consultas espaciales aproximadas, cuando el conjunto de datos es estático y mayoritariamente de lectura. Después, mejoramos la capacidad de ingestión introduciendo el AOTree, un algoritmo que conserva el rendimiento del D8tree incluso para aplicaciones HPC intensivas en escritura. Hemos comparado nuestra solución con PostgreSQL y almacenamiento plano demostrando que nuestra propuesta mejora tanto el rendimiento como la escalabilidad. Finalmente, describimos Qbeast, el sistema que implementa los algoritmos D8tree y AOTree, e ilustramos cómo Qbeast simplifica el flujo de trabajo de los científicos ofreciendo una solución escalable e integraPostprint (published version

Big Data Management for MMO Games and Integrated Website Implementation

With the popularity and success of massively multiplayer Games (MMOGs), the development of MMOGS has got a quantum leap on game's contents and entertainment which attract huge number of players making MMOGs these years a big business which increased to billions of dollars revenue each year worldwide. But with this number of players and these game contents, the data volume produced from games has rapidly increased and used by simultaneously game players around the world. This data require high performance, fault tolerance and scalability. Considering all these demands the popular used relational database becomes a big challenge and cannot overcomes the challenges and cannot meet the requirements for MMOGS data storage. This paper focus on using big data technology tools to completely meet the requirement of MMO games. My work can be divided into two parts: the first part we proposed Cassandra database for MMO games data storing and the integration of Hadoop with Cassandra nodes for high performance in operations process. The second part: we implement a new MMO website with new payment methods, new advertisement program by friend2019;s invitations and other enhanced function. By implementing this website and comparisons of results of our database management, we show the applicability of our approach as well as the relative performance benefits of designing new games or website using our architecture