Gotowe bibliografie tematyczne / Cassandra database system

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Gotowa bibliografia na temat „Cassandra database system”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Cassandra database system"

Andor, C. F. "Performance Benchmarking for NoSQL Database Management Systems". Studia Universitatis Babeș-Bolyai Informatica 66, nr 1 (1.07.2021): 23. http://dx.doi.org/10.24193/subbi.2021.1.02.

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NoSQL database management systems are very diverse and are known to evolve very fast. With so many NoSQL database options available nowadays, it is getting harder to make the right choice for certain use cases. Also, even for a given NoSQL database management system, performance may vary significantly between versions. Database performance benchmarking shows the actual performance for different scenarios on different hardware configurations in a straightforward and precise manner. This paper presents a NoSQL database performance study in which two of the most popular NoSQL database management systems (MongoDB and Apache Cassandra) are compared, and the analyzed metric is throughput. Results show that Apache Cassandra outperformes MongoDB in an update heavy scenario only when the number of operations is high. Also, for a read intensive scenario, Apache Cassandra outperformes MongoDB only when both number of operations and degree of parallelism are high.

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Gorbenko, Anatoliy, i Olga Tarasyuk. "EXPLORING TIMEOUT AS A PERFORMANCE AND AVAILABILITY FACTOR OF DISTRIBUTED REPLICATED DATABASE SYSTEMS". RADIOELECTRONIC AND COMPUTER SYSTEMS, nr 4 (27.11.2020): 98–105. http://dx.doi.org/10.32620/reks.2020.4.09.

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A concept of distributed replicated data storages like Cassandra, HBase, MongoDB has been proposed to effectively manage the Big Data sets whose volume, velocity, and variability are difficult to deal with by using the traditional Relational Database Management Systems. Trade-offs between consistency, availability, partition tolerance, and latency are intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP theorem in qualitative terms, it is still necessary to quantify how different consistency and timeout settings affect system latency. The paper reports results of Cassandra's performance evaluation using the YCSB benchmark and experimentally demonstrates how to read latency depends on the consistency settings and the current database workload. These results clearly show that stronger data consistency increases system latency, which is in line with the qualitative implication of the CAP theorem. Moreover, Cassandra latency and its variation considerably depend on the system workload. The distributed nature of such a system does not always guarantee that the client receives a response from the database within a finite time. If this happens, it causes so-called timing failures when the response is received too late or is not received at all. In the paper, we also consider the role of the application timeout which is the fundamental part of all distributed fault tolerance mechanisms working over the Internet and used as the main error detection mechanism here. The role of the application timeout as the main determinant in the interplay between system availability and responsiveness is also examined in the paper. It is quantitatively shown how different timeout settings could affect system availability and the average servicing and waiting time. Although many modern distributed systems including Cassandra use static timeouts it was shown that the most promising approach is to set timeouts dynamically at run time to balance performance, availability and improve the efficiency of the fault-tolerance mechanisms.

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Wang, Bo-Qian, Qi Yu, Xin Liu, Li Shen i Zhi-ying Wang. "A System Performance Estimation Model for Cassandra Database". International Journal of Database Theory and Application 9, nr 3 (31.03.2016): 123–36. http://dx.doi.org/10.14257/ijdta.2016.9.3.14.

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P, aralakshmi, Hima S i Surekha Mariam Varghese. "Cassandra a distributed NoSQL database for Hotel Management System". International Journal on Cybernetics & Informatics 5, nr 2 (30.04.2016): 109–16. http://dx.doi.org/10.5121/ijci.2016.5212.

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Béjar-Martos, Juan A., Antonio J. Rueda-Ruiz, Carlos J. Ogayar-Anguita, Rafael J. Segura-Sánchez i Alfonso López-Ruiz. "Strategies for the Storage of Large LiDAR Datasets—A Performance Comparison". Remote Sensing 14, nr 11 (31.05.2022): 2623. http://dx.doi.org/10.3390/rs14112623.

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The widespread use of LiDAR technologies has led to an ever-increasing volume of captured data that pose a continuous challenge for its storage and organization, so that it can be efficiently processed and analyzed. Although the use of system files in formats such as LAS/LAZ is the most common solution for LiDAR data storage, databases are gaining in popularity due to their evident advantages: centralized and uniform access to a collection of datasets; better support for concurrent retrieval; distributed storage in database engines that allows sharding; and support for metadata or spatial queries by adequately indexing or organizing the data. The present work evaluates the performance of four popular NoSQL and relational database management systems with large LiDAR datasets: Cassandra, MongoDB, MySQL and PostgreSQL. To perform a realistic assessment, we integrate these database engines in a repository implementation with an elaborate data model that enables metadata and spatial queries and progressive/partial data retrieval. Our experimentation concludes that, as expected, NoSQL databases show a modest but significant performance difference in favor of NoSQL databases, and that Cassandra provides the best overall database solution for LiDAR data.

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Gorbenko, Anatoliy, Andrii Karpenko i Olga Tarasyuk. "Performance evaluation of various deployment scenarios of the 3-replicated Cassandra NoSQL cluster on AWS". RADIOELECTRONIC AND COMPUTER SYSTEMS, nr 4 (29.11.2021): 157–65. http://dx.doi.org/10.32620/reks.2021.4.13.

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A concept of distributed replicated NoSQL data storages Cassandra-like, HBase, MongoDB has been proposed to effectively manage Big Data set whose volume, velocity and variability are difficult to deal with by using the traditional Relational Database Management Systems. Tradeoffs between consistency, availability, partition tolerance and latency is intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP and PACELC theorems in qualitative terms, it is still necessary to quantify how different consistency settings, deployment patterns and other properties affect system performance.This experience report analysis performance of the Cassandra NoSQL database cluster and studies the tradeoff between data consistency guaranties and performance in distributed data storages. The primary focus is on investigating the quantitative interplay between Cassandra response time, throughput and its consistency settings considering different single- and multi-region deployment scenarios. The study uses the YCSB benchmarking framework and reports the results of the read and write performance tests of the three-replicated Cassandra cluster deployed in the Amazon AWS. In this paper, we also put forward a notation which can be used to formally describe distributed deployment of Cassandra cluster and its nodes relative to each other and to a client application. We present quantitative results showing how different consistency settings and deployment patterns affect Cassandra performance under different workloads. In particular, our experiments show that strong consistency costs up to 22 % of performance in case of the centralized Cassandra cluster deployment and can cause a 600 % increase in the read/write requests if Cassandra replicas and its clients are globally distributed across different AWS Regions.

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Aniceto, Rodrigo, Rene Xavier, Valeria Guimarães, Fernanda Hondo, Maristela Holanda, Maria Emilia Walter i Sérgio Lifschitz. "Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency". International Journal of Genomics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/502795.

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Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and another NoSQL database approach, MongoDB.

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Stetsyk, Oleksii, i Svitlana Terenchuk. "COMPARATIVE ANALYSIS OF NOSQL DATABASES ARCHITECTURE". Management of Development of Complex Systems, nr 47 (27.09.2021): 78–82. http://dx.doi.org/10.32347/2412-9933.2021.47.78-82.

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This article is devoted to the study of problematic issues due to the growing scale and requirements for modern high-load distributed systems. The relevance of the work is ensured by the fact that an important component of each such system is a database. The paper highlights the main problems associated with the use of relational databases in many high-load distributed systems. The main focus is on the study of such properties as data consistency, availability, and stability of the system. Basic information about the architecture and purpose of non-relational databases with a wide column, databases of key-value type, and document-oriented databases is provided. The advantages and disadvantages of non-relational databases of different types are shown, which are manifested in solving different problems depending on the purpose and features of the system. The choice of non-relational databases of different types for comparative analysis is substantiated. Databases such as Cassandra, Redis, and Mongo, which have long been used in high-load distributed systems and have already proven themselves among users, have been studied in detail. The main task addressed in this article was to find an answer to the question of the feasibility of using non-relational databases of the architecture of Cassandra, Redis, and Mongo depending on the characteristics of the system, or record information. Based on the analysis, options for using these databases for systems with a high number of requests to read or write information are proposed.

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Milošević, Danijela, Selver Pepić, Muzafer Saračević i Milan Tasić. "Weighted Moore–Penrose generalized matrix inverse: MySQL vs. Cassandra database storage system". Sādhanā 41, nr 8 (sierpień 2016): 837–46. http://dx.doi.org/10.1007/s12046-016-0523-6.

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Deng, Lu, Wei Hong Han, Dong Liu i Ying Xiong. "The Model of Fuzzy Retrieval Based on External Index". Applied Mechanics and Materials 380-384 (sierpień 2013): 1605–8. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1605.

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With the rapid development of the Internet, database is getting more and more attention for its advantages of high concurrency, high scalability and high availability. In order to solve the problem that how to retrieving the data in the database efficiently and accurately, a model of fuzzy retrieval based on external index is proposed in this paper. By this external index, the efficiency of the NoSQL databases retrieval whose column not be appointed is greatly improved. As an example, the Cassandra database is adopted to store the data and the external index is stored in the national databases. A community information management system is utilized to show the feasibility of the model. The results show that the model can save a lot of time in retrieval whose column not be appointed. Moreover, this model can also be used to some other NoSQL databases.

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Rozprawy doktorskie na temat "Cassandra database system"

Brander, Thomas, i Christian Dakermandji. "En jämförelse mellan databashanterare med prestandatester och stora datamängder". Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188199.

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Företaget Nordicstation hanterar stora datamängder åt Swedbank där datalagringen sker i relationsdatabasen Microsoft SQL Server 2012 (SQL Server). Då det finns andra databashanterare designade för stora datavolymer är det oklart om SQL Server är den optimala lösningen för situationen. Detta examensarbete har tagit fram en jämförelse med hjälp av prestandatester, beträffande exekveringstiden av databasfrågor, mellan databaserna SQL Server, Cassandra och NuoDB vid hanteringen av stora datamängder. Cassandra är en kolumnbaserad databas designad för hantering av stora datavolymer, NuoDB är en minnesdatabas som använder internminnet som lagringsutrymme och är designad för skalbarhet. Resultaten togs fram i en virtuell servermiljö med Windows Server 2012 R2 på en testplattform skriven i Java. Jämförelsen visar att SQL Server var den databas mest lämpad för gruppering, sortering och beräkningsoperationer. Däremot var Cassandra bäst i skrivoperationer och NuoDB presterade bäst i läsoperationer. Analysen av resultatet visade att mindre access till disken ger kortare exekveringstid men den skalbara lösningen, NuoDB, lider av kraftiga prestandaförluster av att endast konfigureras med en nod. Nordicstation rekommenderas att uppgradera till Microsoft SQL Server 2014, eller senare, där möjlighet finns att spara tabeller i internminnet.
The company Nordicstation handles large amounts of data for Swedbank, where data is stored using the relational database Microsoft SQL Server 2012 (SQL Server). The existence of other databases designed for handling large amounts of data, makes it unclear if SQL Server is the best solution for this situation. This degree project describes a comparison between databases using performance testing, with regard to the execution time of database queries. The chosen databases were SQL Server, Cassandra and NuoDB. Cassandra is a column-oriented database designed for handling large amounts of data, NuoDB is a database that uses the main memory for data storage and is designed for scalability. The performance tests were executed in a virtual server environment with Windows Server 2012 R2 using an application written in Java. SQL Server was the database most suited for grouping, sorting and arithmetic operations. Cassandra had the shortest execution time for write operations while NuoDB performed best in read operations. This degree project concludes that minimizing disk operations leads to shorter execution times but the scalable solution, NuoDB, suffer severe performance losses when configured as a single-node. Nordicstation is recommended to upgrade to Microsoft SQL Server 2014, or later, because of the possibility to save tables in main memory.

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Vellanki, Mohit. "Performance Evaluation of Cassandra in a Virtualized Environment". Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14032.

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Context. Apache Cassandra is an open-source, scalable, NoSQL database that distributes the data over many commodity servers. It provides no single point of failure by copying and storing the data in different locations. Cassandra uses a ring design rather than the traditional master-slave design. Virtualization is the technique using which physical resources of a machine are divided and utilized by various virtual machines. It is the fundamental technology, which allows cloud computing to provide resource sharing among the users. Objectives. Through this research, the effects of virtualization on Cassandra are observed by comparing the virtual machine arrangement to physical machine arrangement along with the overhead caused by virtualization. Methods. An experiment is conducted in this study to identify the aforementioned effects of virtualization on Cassandra compared to the physical machines. Cassandra runs on physical machines with Ubuntu 14.04 LTS arranged in a multi node cluster. Results are obtained by executing the mixed, read only and write only operations in the Cassandra stress tool on the data populated in this cluster. This procedure is repeated for 100% and 66% workload. The same procedure is repeated in virtual machines cluster and the results are compared. Results. Virtualization overhead has been identified in terms of CPU utilization and the effects of virtualization on Cassandra are found out in terms of Disk utilization, throughput and latency. Conclusions. The overhead caused due to virtualization is observed and the effect of this overhead on the performance of Cassandra has been identified. The consequence of the virtualization overhead has been related to the change in performance of Cassandra.

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Ayyagari, Nitin Reddy. "Databases For Mediation Systems : Design and Data scaling approach". Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1028.

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Context: There is continuous growth in data generation due to wide usage of modern communication systems. Systems have to be designed which can handle the processing of these data volumes efficiently. Mediation systems are meant to serve this purpose. Databases form an integral part of the mediation systems. Suitability of the databases for such systems is the principle theme of this work. Objectives: The objective of this thesis is to identify the key requirements for databases that can be used as part of Mediation systems, gain a thorough understanding of various features, the data models commonly used in databases and to benchmark their performance. Methods: Previous work that has been carried out on various databases is studied as a part of literature review. Test bed is set up as a part of experiment and performance metrics such as throughput and total time taken were measured through a Java based client. Thorough analysis has been carried out by varying various parameters like data volumes, number of threads in the client etc. Results: Cassandra has a very good write performance for event and batch operations. Cassandra has a slightly better read performance when compared to MySQL Cluster but this differentiation withers out in case of fewer number of threads in the client. Conclusions: On evaluation of MySQL Cluster and Cassandra we conclude that they have several features that are suitable for mediation systems. On the other hand, Cassandra does not guarantee ACID transactions while MySQL Cluster has good support. There is need for further evaluation on new generation databases which are not mature enough as of now.

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Landbris, Johan. "A Non-functional evaluation of NoSQL Database Management Systems". Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-46804.

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NoSQL is basically a family name for all Database Management Systems (DBMS) that is not Relational DBMS. The fast growth of all social networks has led to a huge amount of unstructured data that NoSQL DBMS is supposed to handle better than Relational DBMS. Most comparisons performed are between Relational DBMS and NoSQL DBMS. In this paper, the comparison is about non-functional properties for different types of NoSQL DBMS instead. Three of the most common NoSQL types are Document Stores, Key-Value Stores and Column Stores. The most used DBMS of those types are MongoDB, Redis and Apache Cassandra. After working with the databases and performing YCSB Benchmarking the conclusion is that if the database should handle an enormous amount of data, Cassandra is most probably best choice. If speed is the most important property and if all data fits within the memory; Redis is probably the most well suited database. If the database needs to be flexible and versatile, MongoDB is probably the best choice.

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Bergman, Sara. "Permissioned Blockchains and Distributed Databases : A Performance Study". Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-152230.

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Blockchain technology is a booming new field in both computer science and economicsand other use cases than cryptocurrencies are on the rise. Permissioned blockchains are oneinstance of the blockchain technique. In a permissioned blockchain the nodes which validatesnew transactions are trusted. Permissioned blockchains and distributed databasesare essentially two different ways for storing data, but how do they compare in terms ofperformance? This thesis compares Hyperledger Fabric to Apache Cassandra in four experimentsto investigate their insert and read latency. The experiments are executed usingDocker on an Azure virtual machine and the studied systems consist of up to 20 logicalnodes. Latency measurements are performed using varying network size and load. Forsmall networks, the insert latency of Cassandra is twice as high as that of Fabric, whereasfor larger networks Fabric has almost twice as high insert latencies as Cassandra. Fabrichas around 40 ms latency for reading data and Cassandra between 150 ms to 250 ms, thusit scales better for reading. The insert latency of different workloads is heavily affected bythe configuration of Fabric and by the Docker overhead for Cassandra. The read latency isnot affected by different workloads for either system.

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Singla, Priyanka. "Handling Overloads with Social Consistency". Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5401.

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Cloud computing applications have dynamic workloads, and they often observe spikes in the incoming traffic which might result in system overloads. System overloads are generally handled by various load balancing techniques like replication and data partitioning. These techniques are effective when the incoming bursty traffic is dominated by reads and writes to partitionable data, but they become futile against bursts of writes to a single hot object. Further, the systems which use these load balancing techniques, to provide good performance, often adopt a variant of eventual consistency and do not provide strong guarantees to applications, and programmers. In this work, we propose a new client based consistency model, called social consistency, as a solution to this single object overload problem. Along with handling overloads, the proposed model also provides a stronger set of guarantees within subsets of nodes (socially related), and provides eventual consistency across different subsets. We argue that by using this approach, we can in practice ensure reasonably good consistency among the clients and a concomitant increase in performance. We further describe the design of a prototype system, BLAST, which implements this model. It dynamically adjusts resource utilisation in response to changes in the workload thus ensuring nearly constant latency, and throughput, which scales with the offered load. In particular, the workload spikes for a single hot object are handled by cloning the object and partitioning the clients according to their social connectivity, binding the partitions to different clones, where each partition has a unique view of the object. The clones and the client partitions are recombined when the spike subsides. We compare the performance of BLAST to Cassandra database system, and our experiments show that BLAST handles 1.6X (by performing one split) and 2.4X (by performing three splits) more workload. We also evaluate BLAST against another load balancing system and show that BLAST provides 37% better Quality of Experience (QoE) to the clients.

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Huang, Cheng-Chieh, i 黃正杰. "A Hybrid Storage System For Cassandra Databases". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/t7ur25.

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碩士
國立臺灣科技大學
電子工程系
105
Compared with traditional hard disks (HDD), solid-state drive (SSD) can offer more I/O efficiency, especially when applications need high I/O performance. We can improve I/O efficiency by moving frequently-used data to SSD. However, SSD is more expensive then HDD and we want to use limited SSD space to get reasonable I/O performance. To achieve this goal, we design a hybrid storage system for Cassandra databases. In this thesis, we modify a NoSQL Cassandra database by implementing a hybrid storage system. We place high-priority data in SSD with fast access property and put low-priority data in HDD with low cost. The experimental results also show that our proposed method can achieve the goal.

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Selander, Nizar. "Resource utilization comparison of Cassandra and Elasticsearch". Thesis, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18665.

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Elasticsearch and Cassandra are two of the widely used databases today withElasticsearch showing a more recent resurgence due to its unique full text searchfeature, akin to that of a search engine, contrasting with the conventional querylanguage-based methods used to perform data searching and retrieval operations. The demand for more powerful and better performing yet more feature rich andflexible databases has ever been growing. This project attempts to study how the twodatabases perform under a specific workload of 2,000,000 fixed sized logs and underan environment where the two can be compared while maintaining the results of theexperiment meaningful for the production environment which they are intended for. A total of three benchmarks were carried, an Elasticsearch deployment using defaultconfiguration and two Cassandra deployments, a default configuration a long with amodified one which reflects a currently running configuration in production for thetask at hand. The benchmarks showed very interesting performance differences in terms of CPU,memory and disk space usage. Elasticsearch showed the best performance overallusing significantly less memory and disk space as well as CPU to some degree. However, the benchmarks were done in a very specific set of configurations and a veryspecific data set and workload. Those differences should be considered whencomparing the benchmark results.

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Książki na temat "Cassandra database system"

Cassandra: The Definitive Guide. Cambridge: O'Reilly Media, Incorporated, 2010.

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Hewitt, Eben. Cassandra: The Definitive Guide. O'Reilly Media, Incorporated, 2010.

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Cassandra: The Definitive Guide. O'Reilly Media, Incorporated, 2022.

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Cassandra: The Definitive Guide. O'Reilly Media, Incorporated, 2022.

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Brown, Mat. Learning Apache Cassandra. Packt Publishing, Limited, 2015.

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Części książek na temat "Cassandra database system"

Wahid, Abdul, i Kanupriya Kashyap. "Cassandra—A Distributed Database System: An Overview". W Advances in Intelligent Systems and Computing, 519–26. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1951-8_47.

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Anand, Saurabh, Pallavi Singh i B. M. Sagar. "Working with Cassandra Database". W Advances in Intelligent Systems and Computing, 531–38. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7563-6_55.

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Mozaffari, Maryam, Anton Dignös, Hind Hamrouni i Johann Gamper. "NoSQL Schema Extraction from Temporal Conceptual Model: A Case for Cassandra". W New Trends in Database and Information Systems, 280–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42941-5_25.

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Prakash, Deepika. "Towards Convergence in Information Systems Design". W Novel Approaches to Information Systems Design, 247–63. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2975-1.ch011.

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Three technologies—business intelligence, big data, and machine learning—developed independently and address different types of problems. Data warehouses have been used as systems for business intelligence, and NoSQL databases are used for big data. In this chapter, the authors explore the convergence of business intelligence and big data. Traditionally, a data warehouse is implemented on a ROLAP or MOLAP platform. Whereas MOLAP suffers from having propriety architecture, ROLAP suffers from the inherent disadvantages of RDBMS. In order to mitigate the drawbacks of ROLAP, the authors propose implementing a data warehouse on a NoSQL database. They choose Cassandra as their database. For this they start by identifying a generic information model that captures the requirements of the system to-be. They propose mapping rules that map the components of the information model to the Cassandra data model. They finally show a small implementation using an example.

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Huacarpuma, Ruben C., Daniel da C. Rodrigues, Antonio M. Rubio Serrano, João Paulo C. Lustosa da Costa, Rafael T. de Sousa Júnior, Lizane Leite, Edward Ribeiro, Maristela Holanda i Aleteia P. F. Araujo. "Evaluating NoSQL Databases for Big Data Processing within the Brazilian Ministry of Planning, Budget, and Management". W Big Data, 1110–28. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch050.

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The Brazilian Ministry of Planning, Budget, and Management (MP) manages enormous amounts of data that is generated on a daily basis. Processing all of this data more efficiently can reduce operating costs, thereby making better use of public resources. In this chapter, the authors construct a Big Data framework to deal with data loading and querying problems in distributed data processing. They evaluate the proposed Big Data processes by comparing them with the current centralized process used by MP in its Integrated System for Human Resources Management (in Portuguese: Sistema Integrado de Administração de Pessoal – SIAPE). This study focuses primarily on a NoSQL solution using HBase and Cassandra, which is compared to the relational PostgreSQL implementation used as a baseline. The inclusion of Big Data technologies in the proposed solution noticeably increases the performance of loading and querying time.

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Huacarpuma, Ruben C., Daniel da C. Rodrigues, Antonio M. Rubio Serrano, João Paulo C. Lustosa da Costa, Rafael T. de Sousa Júnior, Lizane Leite, Edward Ribeiro, Maristela Holanda i Aleteia P. F. Araujo. "Evaluating NoSQL Databases for Big Data Processing within the Brazilian Ministry of Planning, Budget, and Management". W Artificial Intelligence Technologies and the Evolution of Web 3.0, 230–47. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8147-7.ch011.

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Streszczenia konferencji na temat "Cassandra database system"

Adyatma, Mohammad Rafi, i Fazat Nur Azizah. "Library Development for Join Operation in Cassandra Database Management System". W 2022 International Conference on Data and Software Engineering (ICoDSE). IEEE, 2022. http://dx.doi.org/10.1109/icodse56892.2022.9972027.

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Araujo, Jose Maria A., Alysson Cristiano E. de Moura, Silvia Laryssa B. da Silva, Maristela Holanda, Edward de Oliveira Ribeiro i Gladston Luiz da Silva. "Comparative Performance Analysis of NoSQL Cassandra and MongoDB Databases". W 2021 16th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2021. http://dx.doi.org/10.23919/cisti52073.2021.9476319.

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Gomes, Carlos, Eduardo Tavares i Meuse Nogueira De O. Junior. "Energy Consumption Evaluation of NoSQL DBMSs". W XV Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2016.9729.

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Streszczenie:

Over the years, NoSQL Database Management Systems (DBMS) have been adopted as an alternative to the constraints of relational/SQL DBMSs. In order to demonstrate their feasibility, works have evaluated NoSQL DBMSs regarding some performance metrics, but energy consumption has not been assessed. Indeed, energy consumption is an issue that should not be neglected due to the rise of energy costs and environmental sustainability. This paper presents a peformance and energy consumption evaluation of NoSQL DBMSs, more specifically, Cassandra (column), MongoDB (document-oriented), Redis (key-value). Experiments are based on YCSB benchmark, and results demonstrate energy consumption can vary significantly among the assessed DBMSs for different commands (e.g., read) and workloads.

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Marungo, Fumbeya. "A Primer on NoSQL Databases for Enterprise Architects: The CAP Theorem and Transparent Data Access with MongoDB and Cassandra". W Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2018. http://dx.doi.org/10.24251/hicss.2018.583.

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