Popular NoSQL Databases Research Articles

NoSQL Databases: Modern Data Systems for Big Data Analytics

Because of the massive utilization of the world wide web and the drastic use of electronic gadgets to access the online world, there is an exponential growth in the information produced by these hardware gadgets. The data produced by different sources, such as smart transportation, healthcare, and e-commerce, are large, complex, and heterogeneous. Therefore, storing and querying this data, coined "Big Data," is challenging. This paper compares relational databases with a few of the popular NoSQL databases. The performance of various databases in executing join queries, filter queries, and aggregate queries on large datasets are compared on a single node and multinode clusters. The experimental results demonstrate the suitability of NoSQL databases for Big Data Analytics and for supporting large userbase interactive web applications.

Enabling Attribute-based Access Control in NoSQL Databases.

NoSQL databases are being increasingly used for efficient management of high volumes of unstructured data in applications like information retrieval, natural language processing, social computing, etc. However, unlike traditional databases, data protection measures such as access control for these databases are still in their infancy, which could lead to significant vulnerabilities and security/privacy issues as their adoption increases. Attribute-based Access Control (ABAC), which provides a flexible and dynamic solution to access control, can be effective for mediating accesses in typical usage scenarios for NoSQL databases. In this paper, we propose a novel methodology for enabling ABAC in NoSQL databases. Specifically we consider MongoDB, which is one of the most popular NoSQL databases in use today. We present an approach to both specify ABAC access control policies and to enforce them when an actual access request has been made. MongoDB Wire Protocol is used for extracting and processing appropriate information from the requests. We also present a method for supporting dynamic access decisions using environmental attributes and handling of ad-hoc access requests through digitally signed user attributes. Results from an extensive set of experiments on the Enron corpus as well as on synthetically generated data demonstrate the scalability of our approach. Finally, we provide details of our implementation on MongoDB and share a Github repository so that any organization can download and deploy the same for enabling ABAC in their own MongoDB installations.

Examining and juxtaposing the two NoSQL databases MongoDB and CouchDB

Relational databases have been an essential component of technology for many years. It has been, and in some cases, still is, the foundation of both large and small organizations. However, a few flaws in relational databases gave rise to NoSQL databases. NoSQL is not a particularly new technology, but it is one that is rapidly expanding. The structured query language is also known as NoSQL. Today, a variety of NoSQL databases are accessible depending on the user's needs. This review combines numerous comparisons of these databases based on various attributes. A few terms relating to NoSQL databases are also clarified and brought to light. Additionally, a detailed comparison of the two most popular NoSQL databases is evaluated.NoSQL is a component of a very narrow ecosystem with fewer applications than relational databases. Relational databases are fairly straightforward, which makes them easy to use, but the non-uniformity of the expanding data causes an issue with them. Organizations manage millions of pieces of data, which makes using a relational database challenging. Additionally, the IoT is right outside our door, and NoSQL is better for IoT applications. NoSQL was developed to address this non-uniformity and the rapidly expanding data sets. NoSQL is mostly utilized in distributed, cloud, and IoT systems. NoSQL databases eschew rigid schemas and prioritize availability, scalability, and fault tolerance as key properties.

Strategies for the Storage of Large LiDAR Datasets—A Performance Comparison

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.

NagareDB: A Resource-Efficient Document-Oriented Time-Series Database

The recent great technological advance has led to a broad proliferation of Monitoring Infrastructures, which typically keep track of specific assets along time, ranging from factory machinery, device location, or even people. Gathering this data has become crucial for a wide number of applications, like exploration dashboards or Machine Learning techniques, such as Anomaly Detection. Time-Series Databases, designed to handle these data, grew in popularity, becoming the fastest-growing database type from 2019. In consequence, keeping track and mastering those rapidly evolving technologies became increasingly difficult. This paper introduces the holistic design approach followed for building NagareDB, a Time-Series database built on top of MongoDB—the most popular NoSQL Database, typically discouraged in the Time-Series scenario. The goal of NagareDB is to ease the access to three of the essential resources needed to building time-dependent systems: Hardware, since it is able to work in commodity machines; Software, as it is built on top of an open-source solution; and Expert Personnel, as its foundation database is considered the most popular NoSQL DB, lowering its learning curve. Concretely, NagareDB is able to outperform MongoDB recommended implementation up to 4.7 times, when retrieving data, while also offering a stream-ingestion up to 35% faster than InfluxDB, the most popular Time-Series database. Moreover, by relaxing some requirements, NagareDB is able to reduce the disk space usage up to 40%.

Improving security in NoSQL document databases through model-driven modernization

NoSQL technologies have become a common component in many information systems and software applications. These technologies are focused on performance, enabling scalable processing of large volumes of structured and unstructured data. Unfortunately, most developments over NoSQL technologies consider security as an afterthought, putting at risk personal data of individuals and potentially causing severe economic loses as well as reputation crisis. In order to avoid these situations, companies require an approach that introduces security mechanisms into their systems without scrapping already in-place solutions to restart all over again the design process. Therefore, in this paper we propose the first modernization approach for introducing security in NoSQL databases, focusing on access control and thereby improving the security of their associated information systems and applications. Our approach analyzes the existing NoSQL solution of the organization, using a domain ontology to detect sensitive information and creating a conceptual model of the database. Together with this model, a series of security issues related to access control are listed, allowing database designers to identify the security mechanisms that must be incorporated into their existing solution. For each security issue, our approach automatically generates a proposed solution, consisting of a combination of privilege modifications, new roles and views to improve access control. In order to test our approach, we apply our process to a medical database implemented using the popular document-oriented NoSQL database, MongoDB. The great advantages of our approach are that: (1) it takes into account the context of the system thanks to the introduction of domain ontologies, (2) it helps to avoid missing critical access control issues since the analysis is performed automatically, (3) it reduces the effort and costs of the modernization process thanks to the automated steps in the process, (4) it can be used with different NoSQL document-based technologies in a successful way by adjusting the metamodel, and (5) it is lined up with known standards, hence allowing the application of guidelines and best practices.

A data recovery technique for Redis using internal dictionary structure

Since database systems are vulnerable to attacks the research in database forensics is gaining more importance. Database systems are used to store sensitive and confidential information. The loss of vital information from database can prove to be costly for any organization which may happen due to accidental or purposeful data deletion. Data recovery research in NoSQL databases is extremely important due to rapid growth of big data applications. The key-value database Redis, is one of the popular NoSQL database systems used by several applications. This article presents a data recovery technique for Redis database using internal dictionary structure. Redis is in-memory database and hence it does not leave any kind of signature of data deletion on the disk except log file. The proposed technique is based on tracing the memory addresses of dictionary data structure where key-value pairs are stored. The performance of this technique is evaluated by conducting several experiments on a sample dataset. The technique is found to be effective for the recovery of string, list, hash, set and sorted set data types.

Performance Benchmarking for NoSQL Database Management Systems

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.

A Comparative Study of NOSQL System Vulnerabilities with Big Data

With the emerging third wave in the development of the Internet, the past year has witnessed huge data exposure resulting in cyber-attacks that have increased four times as that of the previous year’s record. In this digital era, businesses are making use of NoSQL technologies for managing such Big Data. However, the NoSQL database systems come with inherent security issues, which pose a major challenge to many organisations worldwide. There is a paucity of research studies for exposing the security threats and vulnerabilities of NoSQL technologies comprehensively. This paper presents an in-depth study of NoSQL security issues by performing a detailed comparative study of the security vulnerabilities identified in NoSQL database systems. A set of key security features offered by the four commonly used NoSQL database systems, namely Redis, Cassandra, MongoDB and Neo4j are analysed with an aim to identify their strengths and weaknesses. The vulnerabilities associated with built-in security, encryption, authentication/authorization and auditing that impact Big Data management are compared among these popular NoSQL database systems and the risk levels are identified. In addition, illustrations of possible injection attacks experimented with these NoSQL systems are provided. Finally, a high-level framework is proposed for NoSQL databases with considerations for security measures in Big Data deployments. The discussion forms a significant technical contribution for learners, application developers and Big Data deployers paving way for a better awareness and management of the NoSQL systems in an organization.

Using Orthogonal Defect Classification to characterize NoSQL database defects

NoSQL databases are increasingly used for storing and managing data in business-critical Big Data systems. The presence of software defects (i.e., bugs) in these databases can bring in severe consequences to the NoSQL services being offered, such as data loss or service unavailability. Thus, it is essential to understand the types of defects that frequently affect these databases, allowing developers take action in an informed manner (e.g., redirect testing efforts). In this paper, we use Orthogonal Defect Classification (ODC) to classify a total of 4096 software defects from three of the most popular NoSQL databases: MongoDB, Cassandra, and HBase. The results show great similarity for the defects across the three different NoSQL systems and, at the same time, show the differences and heterogeneity regarding research carried out in other domains and types of applications, emphasizing the need for possessing such information. Our results expose the defect distributions in NoSQL databases, provide a foundation for selecting representative defects for NoSQL systems, and, overall, can be useful for developers for verifying and building more reliable NoSQL database systems.

Secure search for encrypted personal health records from big data NoSQL databases in cloud

As the healthcare industry adopts the use of cloud to store personal health record (PHR), there is a need to ensure that we maintain the ability to perform efficient search on encrypted data (stored in the cloud). In this paper, we propose a secure searchable encryption scheme, which is designed to search on encrypted personal health records from a NoSQL database in semi-trusted cloud servers. The proposed scheme supports almost all query operations available in plaintext database environments, especially multi-dimensional, multi-keyword searches with range query. Specifically, in the proposed scheme, an Adelson-Velsky Landis (AVL) tree is utilized to construct the index, and an order-revealing encryption (ORE) algorithm is used to encrypt the AVL tree and realize range query. As document-based databases are probably the most popular NoSQL database, due to their flexibility, high efficiency, and ease of use, MongoDB, a document-based NoSQL database, is chosen to store the encrypted PHR data in our scheme. Experimental results show that the scheme can achieve secure and practical searchable encryption for PHRs. A comparison of the range query demonstrates that the time overhead of our ORE-based scheme is 25.5% shorter than that of the mOPE-based Arx (an encrypted database system) scheme.

Comparison of MongoDB and Cassandra Databases for Spectrum Monitoring As-a-Service

Due to the growing number of devices accessing the Internet through wireless networks, the radio spectrum has become a highly contended resource. The availability of low cost radio spectrum monitoring sensors enables a geographically distributed, real-time observation of the spectrum to spot inefficiencies and to develop new strategies for its utilization. The potentially large number of sensors to be deployed and the intrinsic nature of data make this task a Big Data problem. In this work we design, implement, and validate a hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture. This system offers Spectrum Sensing as a Service to let end users easily access and process radio spectrum data. To minimize the latency of services offered by the platform, we fine tune the data processing chain. From the analysis of sensor data characteristics, we design the data models for MongoDB and Cassandra, two popular NoSQL databases. A MapReduce job for spectrum visualization has been developed to show the potential of our approach and to identify the challenges in processing spectrum sensor data. We experimentally evaluate and compare the performance of the two databases in terms of application processing time for different types of queries applied on data streams with heterogeneous generation rate. Our experiments show that Cassandra outperforms MongoDB in most cases, with some exceptions depending on data stream rate.

Consistency Models of NoSQL Databases

Internet has become so widespread that most popular websites are accessed by hundreds of millions of people on a daily basis. Monolithic architectures, which were frequently used in the past, were mostly composed of traditional relational database management systems, but quickly have become incapable of sustaining high data traffic very common these days. Meanwhile, NoSQL databases have emerged to provide some missing properties in relational databases like the schema-less design, horizontal scaling, and eventual consistency. This paper analyzes and compares the consistency model implementation on five popular NoSQL databases: Redis, Cassandra, MongoDB, Neo4j, and OrientDB. All of which offer at least eventual consistency, and some have the option of supporting strong consistency. However, imposing strong consistency will result in less availability when subject to network partition events.

The IoT Applications and Consideration for NoSQL Database

A large amount of data are generated every moment by connected objects creating Internet of Things (IoT). These data are difficult to handle using traditional databases leading to use NoSQL databases. These latter have achieved a large popularity thanks to their high performance, flexibility in scaling, and high availability. But, which NoSQL database is the most suitable for IoT applications? In order to answer this question, we need a significant knowledge about the domain requirements. For this reason, we give in the next subsection, a set of IoT data management requirements. In this paper, i first give a set of data management requirements for IoT applications, i propose then a comparative study of five of the most popular NoSQL databases namely, Redis, Cassandra, MongoDB, Couchbase and Neo4j, regarding to data management requirements, in order to find the most suitable NoSQL database for IoT applications.

Leveraging Conceptual Data Modelsto Ensure the Integrity of CassandraDatabases

The use of NoSQL databases for cloud environments has been increasing due to their performance advantages when working with big data. One of the most popular NoSQL databases used for cloud services is Cassandra, in which each table is created to satisfy one query. This means that as the same data could be retrieved by several queries, these data may be repeated in several different tables. The integrity of these data must be maintained in the application that works with the database, instead of in the database itself as in relational databases. In this paper, we propose a method to ensure the data integrity when there is a modification of data by using a conceptual model that is directly connected to the logical model that represents the Cassandra tables. This method identifies which tables are affected by the modification of the data and also proposes how the data integrity of the database may be ensured. We detail the process of this method along with two examples where we apply it in two insertions of tuples in a conceptual model. We also apply this method to a case study where we insert several tuples in the conceptual model, and then we discuss the results. We have observed how in most cases several insertions are needed to ensure the data integrity as well as needing to look for values in the database in order to do it.

Memory Hierarchy Characterization of NoSQL Applications through Full-System Simulation

In this work, we conduct a detailed memory characterization of a representative set of modern data-management software (Cassandra, MongoDB, OrientDB and Redis) running an illustrative NoSQL benchmark suite (YCSB). These applications are widely popular NoSQL databases with different data models and features such as in-memory storage. We compare how these data-serving applications behave with respect to other well-known benchmarks, such as SPEC CPU2006, PARSEC and NAS Parallel Benchmark. The methodology employed for evaluation relies on state-of-the-art full-system simulation tools, such as gem5. This allows us to explore configurations unattainable using performance monitoring units in actual hardware, being able to characterize memory properties. The results obtained suggest that NoSQL application behavior is not dissimilar to conventional workloads. Therefore, some of the optimizations present in state-of-the-art hardware might have a direct benefit. Nevertheless, there are some common aspects that are distinctive of conventional benchmarks that might be sufficiently relevant to be considered in architectural design. Strikingly, we also found that most database engines, independently of aspects such as workload or database size, exhibit highly uniform behavior. Finally, we show that different data-base engines make highly distinctive demands on the memory hierarchy, some being more stringent than others.

Enhancing NoSQL datastores with fine-grained context-aware access control: a preliminary study on MongoDB

NoSQL datastores are getting increasing attention by companies and organisation for the ease and efficiency of handling high volumes of heterogeneous and unstructured data. Nowadays, as majority of these systems are available as cloud based services, this potentially favours their use even among small companies that could not afford the management of server farms for local cluster based solutions. However, besides all their benefits in terms of performance, availability and scalability as well as support for advanced analysis forms, NoSQL datastores also have some weaknesses, such as poor natively provided support for data protection. Recent surveys show that several companies consider the poor support for security features of NoSQL databases as a valid reason not to use them (Intel Co., 2013). In this paper, we do a first step to overcome these weaknesses by first proposing a roadmap to enhance the data protection functionalities of NoSQL datastores. Then, we illustrate our preliminary experience of designing an enhanced access control mechanism for MongoDB (http://www.mongodb.org), which, according to recent surveys (DB-Engines Ranking, 2017) ranks as the most popular NoSQL database.

Enhancing NoSQL datastores with fine-grained context-aware access control: a preliminary study on MongoDB

NoSQL datastores are getting increasing attention by companies and organisation for the ease and efficiency of handling high volumes of heterogeneous and unstructured data. Nowadays, as majority of these systems are available as cloud based services, this potentially favours their use even among small companies that could not afford the management of server farms for local cluster based solutions. However, besides all their benefits in terms of performance, availability and scalability as well as support for advanced analysis forms, NoSQL datastores also have some weaknesses, such as poor natively provided support for data protection. Recent surveys show that several companies consider the poor support for security features of NoSQL databases as a valid reason not to use them (Intel Co., 2013). In this paper, we do a first step to overcome these weaknesses by first proposing a roadmap to enhance the data protection functionalities of NoSQL datastores. Then, we illustrate our preliminary experience of designing an enhanced access control mechanism for MongoDB (http://www.mongodb.org), which, according to recent surveys (DB-Engines Ranking, 2017) ranks as the most popular NoSQL database.

Fine-Grained Access Control Within NoSQL Document-Oriented Datastores

The recent years have seen the birth of several NoSQL datastores, which are getting more and more popularity for their ability to handle high volumes of heterogeneous and unstructured data in a very efficient way. In several cases, NoSQL databases proved to outclass in terms of performance, scalability, and ease of use relational database management systems, meeting the requirements of a variety of today ICT applications. However, recent surveys reveal that, despite their undoubted popularity, NoSQL datastores suffer from some weaknesses, among which the lack of effective support for data protection appears among the most serious ones. Proper data protection mechanisms are therefore required to fill this void. In this work, we start to address this issue by focusing on access control and discussing the definition of a fine-grained access control framework for document-oriented NoSQL datastores. More precisely, we first focus on issues and challenges related to the definition of such a framework, considering theoretical, implementation, and integration aspects. Then, we discuss the reasons for which state-of-the-art fine-grained access control solutions proposed for relational database management systems cannot be used within the NoSQL scenario. We then introduce possible strategies to address the identified issues, which are at the basis of the framework development. Finally, we shortly report the outcome of an experience where the proposed framework has been used to enhance the data protection features of a popular NoSQL database.

A MongoDB-Based Management of Planar Spatial Data with a Flattened R-Tree

This paper addresses how to manage planar spatial data using MongoDB, a popular NoSQL database characterized as a document-oriented, rich query language and high availability. The core idea is to flatten a hierarchical R-tree structure into a tabular MongoDB collection, during which R-tree nodes are represented as collection documents and R-tree pointers are expressed as document identifiers. By following this strategy, a storage schema to support R-tree-based create, read, update, and delete (CRUD) operations is designed and a module to manage planar spatial data by consuming and maintaining flattened R-tree structure is developed. The R-tree module is then seamlessly integrated into MongoDB, so that users could manipulate planar spatial data with existing command interfaces oriented to geodetic spatial data. The experimental evaluation, using real-world datasets with diverse coverage, types, and sizes, shows that planar spatial data can be effectively managed by MongoDB with our flattened R-tree and, therefore, the application extent of MongoDB will be greatly enlarged. Our work resulted in a MongoDB branch with R-tree support, which has been released on GitHub for open access.