Atomicity, Consistency, Isolation, Durability Research Articles

Concurrency versus consistency in NoSQL databases

<p>With the advent of cloud services, the proliferation of data has reached unprecedented levels. The load distribution across multiple servers, driven by web and mobile applications, has become a defining characteristic of contemporary data management. In contrast to this surge in data complexity, traditional relational databases have proven inadequate in handling vast amounts of unstructured data due to their inherent focus on structured data models. Additionally, the concept of clustering, vital for efficient unstructured data management, eluded relational databases, rendering them ill-equipped for customized clustering techniques and the optimal execution of queries. SQL (Structured Query Language) databases earlier emerged as a groundbreaking solution, introducing the relational database model that organized data into structured tables. They employed ACID (atomicity, consistency, isolation, durability) properties to maintain data integrity and enabled intricate querying through SQL. However, as applications grew in complexity, SQL databases encountered hurdles in handling various data types, rapid data expansion, and concurrent workloads. The limitations of SQL databases propelled the rise of NoSQL (Not Only Structured Query Language) databases, which prioritized adaptability, scalability, and performance. NoSQL databases embraced diverse data models such as documents, key-values, column families, and graphs, enabling effective management of structured, semi-structured, and unstructured data. The transition to NoSQL databases was justified by several factors; horizontally scaled across nodes, handling extensive read-write operations effectively, Agile development of accommodating changing data structures without schema constraints, optimization for specific tasks, providing low-latency access and high throughput, dynamic schemas aligned with modern iterative development, promoting adaptability, and adeptly managed diverse data types, spanning text, geospatial, time-series, and multimedia data. These databases are purposefully designed to accommodate the escalating demands of data storage. Notably, this data emanates from diverse nodes and is susceptible to concurrent access by numerous users. However, a critical challenge surfaces as the data present on one node may diverge from its counterpart on another node replica. In this context, the simultaneous execution of database operations, while preserving the integrity of the data, emerges as a pivotal concern. Maintaining data consistency amid concurrent access hinges upon the synchronization of operations across all replica nodes. Achieving this synchronization necessitates the adoption of a robust concurrency control technique. Concurrency control acts as the linchpin for upholding accuracy and reliability within a system where operations unfold concurrently. Hence, the focal point of this investigation lies in examining the assorted concurrency control methodologies employed by NoSQL systems. The objective is to dissect the intricate interplay between concurrency and consistency, shedding light on the strategies these systems employ to strike an optimal balance between the two. In summation, as the landscape of data management witnesses an era of exponential growth catalyzed by cloud services, the dynamics of load distribution and unstructured data have necessitated a departure from traditional relational databases. NoSQL databases have risen to the fore, demonstrating the ability to grapple with these challenges. However, the quest for concurrent data access without compromising data consistency propels the exploration of various concurrency control methods. The aim of this study is to look at some of the different concurrency control approaches employed by NoSQL systems, highlighting how they priorities concurrency and consistency.</p>

Exploring Blockchains Interoperability: A Systematic Survey

The next-generation blockchain ecosystem is expected to integrate both homogeneous and heterogeneous distributed ledgers. These systems require operations across multiple blockchains to enrich advanced functionalities for future applications. However, the development of blockchain interoperability involves much more complexity regarding the variety of underlying architectures. Guaranteeing the properties of ACID (Atomicity, Consistency, Isolation, Durability) across diverse blockchain systems remains challenging. To clear the fog, this article accordingly provides a comprehensive review of the current progress of blockchain interoperability. We explore the general principles and procedures for interoperable blockchain systems to highlight their design commons. Then, we survey practical instances and compare state-of-the-art systems to present their unique features between distinct solutions. Finally, we discuss critical challenges and point out potential research directions. We believe our work can provide an intuitive guideline for newcomers and also promote rapid development in terms of blockchain interoperability.

Performance Impact of Type-I Virtualization on a NewSQL Relational Database Management System

For more than 40 years, the relational database management system (RDBMS) and the atomicity, consistency, isolation, durability (ACID) transaction guarantees provided through its use have been the standard for data storage. The advent of Big Data created a need for new storage approaches that led to NoSQL technologies, which rely on basic availability, soft-state, eventual consistency (BASE) transactions. Over the last decade, NewSQL RDBMS technology has emerged, providing the benefits of RDBMS ACID transaction guarantees and the performance and scalability of NoSQL databases. The reliance on virtualization in IT has continued to grow, but an investigation of current academic literature identified a void regarding the performance impact of virtualization of NewSQL databases. To help address the lack of research in this area, a quantitative experimental study was designed and carried out to answer the central research question, "What is the performance impact of Type-I virtualization on a NewSQL RDBMS?" VMware ESXi virtualization software, NuoDB RDBMS, and OLTP-Bench software were used to execute a mixed-load benchmark. Performance metrics were collected comparing bare metal and virtualized environments, and the data analyzed statistically to evaluate five hypotheses related to CPU utilization, memory utilization, disk and network input-output (I/O) rates, and database transactions per second. Findings indicated a negative performance impact on CPU and memory utilization, as well as network I/O rates. Performance improvements were noted in disk I/O rates and database transactions-per-second.

Статический анализ зависимостей для семантической валидации данных

Modern information systems manipulate data models containing millions of items, and the tendency is to make these models even more complex. One of the most crucial aspects of modern concurrent engineering environments is their reliability. The principles of ACID (atomicity, consistency, isolation, durability) are aimed at providing it, but directly following them leads to serious performance drawbacks on large-scale models, since it is necessary to control the correctness of every performed transaction. In the paper, a method for incremental validation of object-oriented data is presented. Assuming that a submitted transaction is applied to originally consistent data, it is guaranteed that the final data representation is also consistent if only the spot rules are satisfied. To identify data items subject to spot rule validation, a bipartite data-rule dependency graph is formed. To automatically build the dependency graph a static analysis of the model specifications is proposed to apply. In the case of complex object-oriented models defining hundreds and thousands of data types and semantic rules, the static analysis seems to be the only way to realize the incremental validation and to make possible to manage the data in accordance with the ACID principles.

Data Storage Management in Cloud Environments

Storage as a Service (StaaS) is a vital component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Application providers deploy these storage classes across different cloud-based data stores not only to tackle the challenges arising from reliance on a single cloud-based data store but also to obtain higher availability, lower response time, and more cost efficiency. Hence, in this article, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data management cost. Finally, we map various cloud-based data stores projects to our proposed taxonomy to validate the taxonomy and identify areas for future research.

Predictive Performance Comparison Analysis of Relational & NoSQL Graph Databases

From last three decades, the relational databases are being used in many organizations of various natures such as Education, Health, Business and in many other applications. Traditional databases show tremendous performance and are designed to handle structured data with ACID (Atomicity, Consistency, Isolation, Durability) property to manage data integrity. In the current era, organizations are storing more data i.e. videos, images, blogs, etc. besides structured data for decision making. Similarly, social media and scientific applications are generating large amount of semi-structured data of varied nature. Relational databases cannot process properly and manage such large amount of data efficiently. To overcome this problem, another paradigm NoSQL databases is introduced to manage and process massive amount of unstructured data efficiently. NoSQL databases are divided into four categories and each category is used according to the nature and need of the specific problem. In this paper we will compare Oracle relational database and NoSQL graph database using optimized queries and physical database tuning techniques. The comparison is two folded: in the first iteration we compare various kinds of queries such as simpler query, database tuning of Oracle relational database such as sub databases and perform these queries in our desired environments. Secondly, for this comparison we will perform predictive analysis for the results obtained from our experiments.

What Can NoSQL Serve an Enterprise

Strengths and limitations of relational database model and SQL databases are rethought. Universality of SQL knowledge, “universal database” engine and symmetric querying are found to be the main strengths of SQL databases. Need of centralized database and thus limited data volume manageable, relative stability of database schema and limited set of query types processed efficiently are seen as the main limitations. As well, limitation of ACID (atomicity, consistency, isolation, durability) architecture is expounded; this found to be the poor performance in case of distributed data processing. Formed types of NoSQL databases are listed, with their features and scope of strength outlined. Finally, recommendations in what cases different types of NoSQL stores to apply are summarized.

Improving Database System Performance by Applying NoSQL

Internet accessibility has been growing due to the diffusion of smartphones in today's society. Therefore, people can generate data anywhere and are confronted with the challenge that they should process a large amount of data. Since the appearance of relational database management system (RDBMS), most of the recent information systems are built by utilizing it. RDBMS uses foreign-keys to avoid data duplication. The transactions in the database use attributes, such as atomicity, consistency, isolation, durability (ACID), which ensures that data integrity and processing results are stably managed. The characteristic of RDBMS is that there is high data reliability. However, this results in performance degradation. Meanwhile, from among these information systems, some systems only require high-performance rather than high reliability. In this case, if we only consider performance, the use of NoSQL provides many advantages. It is possible to reduce the maintenance cost of the information system that continues to increase in the use of open source software based NoSQL. And has a huge advantage that is easy to use NoSQL. Therefore, in this study, we prove that the leverage of NoSQL will ensure high performance than RDBMS by applying NoSQL to database systems that implement RDBMS.

TRAID: Exploiting Temporal Redundancy and Spatial Redundancy to Boost Transaction Processing Systems Performance

In the past few years, more storage system applications have employed transaction processing techniques to ensure data integrity and consistency. Logging is one of the key requirements to ensure transaction Atomicity, Consistency, Isolation, Durability (ACID) properties and data recoverability in transaction processing systems (TPS). Recently, emerging complex I/O bound transactions have resulted in substantially more log content and higher log flushing latency. The latency will delay transaction commit and decrease the overall throughput of the TPS. On the other hand, RAID is widely used as the underlying storage system for Databases to guarantee system reliability and availability with high I/O performance. In this paper, we observe the overlap between the redundancies in the underlying RAID storage system and database logging system, and propose a novel reliable storage architecture called Transactional RAID (TRAID). TRAID deduplicates this overlap by only logging one compact version (XOR results) of recovery references for the updating data. It minimizes the amount of log content and thereby boosts the overall transaction processing performance. At the same time, TRAID guarantees the same RAID reliability, as well as recovery correctness and ACID semantics as current TPS setups. We experiment on two open-source database systems: Berkeley DB and PostgreSQL, with three different workloads: standard OLTP benchmark TPC-C, customized TPC-C with strong access locality, and customized TPC-C with write-intensive property. Then we test TRAID performance with Group Commit” enabled. Finally, we evaluate the recovery efficiency of TRAID. Our extensive results demonstrate that for throughput, TRAID outperforms RAID by 43.24-69.5 percent for various workloads; it also saves on log space by 28.57-35.48 percent, and outperforms RAID by about 20 percent in throughput with Group Commit” enabled. At last, we show that TRAID outperforms RAID from 28.7 to 35.7 percent during the recovery.

The CORBA Activity Service Framework for supporting extended transactions

AbstractAlthough it has long been realized that ACID (atomicity, consistency, isolation, durability) transactions by themselves are not adequate for structuring long‐lived applications and much research work has been done on developing specific extended transaction models, no middleware support for building extended transactions is currently available and the situation remains that a programmer often has to develop application specific mechanisms. The CORBA Activity Service Framework described in this paper is a way out of this situation. The design of the service is based on the insight that the various extended transaction models can be supported by providing a general purpose event signalling mechanism that can be programmed to enable activities—application specific units of computations—to coordinate each other in a manner prescribed by the model under consideration. The different extended transaction models can be mapped onto specific implementations of this framework, permitting such transactions to span a network of systems connected indirectly by some distribution infrastructure. The framework described in this paper is an overview of the OMG's (Object Management Group) Additional Structuring Mechanisms for the OTS standard. Through a number of examples the paper shows that the framework has the flexibility to support a wide variety of extended transaction models. Although the framework is presented here in CORBA specific terms, the main ideas are sufficiently general, so that it should be possible to use them in conjunction with other middleware. Copyright © 2003 John Wiley & Sons, Ltd.

Nomadic transaction management

The distinguishing characteristic for nomadic (mobile) computing is the wireless communication medium. This makes it possible for a fixed computer system to support a mobile user. The general nomadic multidatabase management system (NMDBMS) discussed in this article is a collection of autonomous databases connected to a fixed network. The respective DBMSs retain complete control over their data. Each database may be viewed as an independent site in the network. These databases operate in different environments. Thus, a NMDBMS may be viewed as a multidatabase system that supports mobile users. Users of the independent databases, called internal users, access these databases through their respective DBMSs. The execution of local transactions submitted by these local users is transparent to any global process. Users accessing more than one database, called external users, submit global transactions to the NMDBMS. The NMDBMS is a set of software modules existing on the fixed network that cooperate with each other. Together they project the illusion of a single database to the external user. A global transaction consists of a set of sub-transactions that need to be executed at different sites. The global transaction manager (GTM), a software component of the NMDBMS, manages the executions of the global transactions. Global transactions are allowed only limited access to the individual databases. Consistency and reliability can be achieved by enforcing the ACID (atomicity, consistency, isolation, durability) properties.

An object-oriented transaction model

In object-oriented database systems (OODBSs), the traditional transaction models are no longer suitable because of the difference between the object-oriented data model (OODM) and the conventional data models (e.g. relational data model). In this paper, transaction models for advanced database applications are reviewed and their shortcomings are analyzed. Exchangeability of operations is proposed instead of commuativity and recoverability for using more semantics in transaction management. As a result, an object-oriented transaction model (in short, OOTM) is presented. It is not modeled for some special application, but directly based on object-oriented paradigms. A transaction is regarded as an interpretation of a method. Each transaction (even subtransactions) keeps relative ACID (Atomicity, Consistency, Isolation, Durability) properties, therefore the special problems appearing in OODBSs such as “long transactions”, “visibility of inconsistent database state” can be solved.