Architecture Of Database Management System Research Articles

External vs. Internal: An Essay on Machine Learning Agents for Autonomous Database Management Systems

There are many possible ways to configure database management systems (DBMSs) have challenging to manage and set.The problem increased in large-scale deployments with thousands or millions of individual DBMS that each have their setting requirements. Recent research has explored using machine learning-based (ML) agents to overcome this problem's automated tuning of DBMSs. These agents extract performance metrics and behavioral information from the DBMS and then train models with this data to select tuning actions that they predict will have the most benefit. This paper discusses two engineering approaches for integrating ML agents in a DBMS. The first is to build an external tuning controller that treats the DBMS as a black box. The second is to incorporate the ML agents natively in the DBMS's architecture.

В ожидании нативных архитектур СУБД на основе энергонезависимой основной памяти

Many experts in the field of data management believe that the emergence of non-volatile byte-addressable main memory (NVM) available for practical use will lead to the development of a new type of ultra-high-speed database management systems (DBMS) with single-level data storage (native in-NVM DBMS). However, the number of researchers who are actively engaged in research of architectures of native in-NVM DBMS has not increased in recent years. The most active researchers are PhD students that are not afraid of the risks, which, of course, exist in this new area. The second section of the article discusses the state of the art in NVM hardware. The analysis shows that NVM in the DIMM form factor has already become a reality, and that in the near future we can expect the appearance on the market NVM-DIMMs with the speed of conventional DRAM and endurance close to that of hard drives. The third section is devoted to the review of related works, among which the works of young researchers are the most advanced. In the fourth section, we state and justify that the work performed so far in the field of in-NVM DBMS, did not lead to the emergence of a native architecture. This is hampered by the set of limiting factors analyzed by us. In this regard, in the fifth section, we present a sketch of the native architecture of an in-NVM DBMS, the choice of which is influenced only by the goals of simplicity and efficiency. In conclusion, we summarize the article and argues the need for additional research into many aspects of the native architecture of an in-NVM DBMS.

Implementation and verification of high data availability on database

This study provides a low-cost and high-availability database management system architecture for general Small/Medium Enterprises (SMEs) to backup database data access. To prove that the proposed architecture can support the high availability of the database, and can effectively avoid data loss in memory caused by failovers, this study applies the main test method of powering off the virtual machine and verified three cases on two commonly used databases MySQL and PostgreSQL: Case 1 proves that this study combines the database native disaster recovery mechanism to effectively achieve high availability of the database. Case 2 proves that it effectively controls the WAL (Write Ahead Log) of the PostgreSQL database and Redo log mechanism of the MySQL database, so that data correctness is maintained during failovers. Case 3 proves that it can analyze and control the timing of the database in writing data in the cache memory to the hard disk. This study also designed a failover process to avoid data loss during failovers due to no enough time to write the data in the cache memory back to the hard disk; and finally to realize the high-availability of the database management system architecture in a practical way.
 
 Keywords: Database, DBMS, High Availability, Failover, DRBD

Staring into the abyss

Computer architectures are moving towards an era dominated by many-core machines with dozens or even hundreds of cores on a single chip. This unprecedented level of on-chip parallelism introduces a new dimension to scalability that current database management systems (DBMSs) were not designed for. In particular, as the number of cores increases, the problem of concurrency control becomes extremely challenging. With hundreds of threads running in parallel, the complexity of coordinating competing accesses to data will likely diminish the gains from increased core counts. To better understand just how unprepared current DBMSs are for future CPU architectures, we performed an evaluation of concurrency control for on-line transaction processing (OLTP) workloads on many-core chips. We implemented seven concurrency control algorithms on a main-memory DBMS and using computer simulations scaled our system to 1024 cores. Our analysis shows that all algorithms fail to scale to this magnitude but for different reasons. In each case, we identify fundamental bottlenecks that are independent of the particular database implementation and argue that even state-of-the-art DBMSs suffer from these limitations. We conclude that rather than pursuing incremental solutions, many-core chips may require a completely redesigned DBMS architecture that is built from ground up and is tightly coupled with the hardware.

PET

Energy conservation is a growing important issue in designing modern database management system (DBMS). This requires a deep thinking about the tradeoffs between energy and performance. Despite the significant amount of efforts at the hardware level to make the major components consume less energy, we argue for a revisit of the DBMS query processing mechanism to identify and harvest the potential of energy saving. However, the state-of-art architecture of DBMS does not take energy usage into consideration in its design. A major challenge in developing an energy-aware DBMS is to design and implement a cost-based query optimizer that evaluates query plans by both performance and energy costs. By following such a strategy, our previous work revealed the fact that energy-efficient query plans do not necessarily have the shortest processing time. This demo proposal introduces PET -- an energy-aware query optimization framework that is built as a part of the PostgreSQL kernel. PET, via its power cost estimation module and plan evaluation model, enables the database system to run under a DBA-specified energy/performance tradeoff level. PET contains a power cost estimator that can accurately estimate the power cost of query plans at compile time, and a query evaluation engine that the DBA could configure key PET parameters towards the desired tradeoff. The software to be demonstrated will also include workload engine for producing large quantities of queries and data sets. Our demonstration will show how PET functions via a comprehensive set of views from its graphical user interface named PET Viewer . Through such interfaces, a user can achieve a good understanding of the energy-related query optimization and cost-based plan generation. Users are also allowed to interact with PET to experience the different energy/performance tradeoffs by changing PET and workload parameters at query runtime.

Database architectures: Current trends and their relationships to environmental data management

Ever increasing environmental information demands from customers, authorities, and governmental organizations as well as new business control functions are implemented and integrated to environmental information management systems (EIMSs). These systems are often based on traditional file techniques or, more recently, on commercial database management systems (DBMSs). With a production of huge data sets and their processing in real-time applications, the needs for environmental data management have grown significantly. Numerous examples from practice of EIMSs prove that the architecture of DBMS should be open for a permanent evolution. Current trends in database development and an associated research meet these challenges. New information and communication technologies and techniques influence today's DBMSs. They include, among other things, sensor networks, stream processing, processing uncertain and imprecise data, knowledge discovery and intelligent data analysis, as well as wireless broadcast and mobile computing. Both research and practice indicate that the traditional universal DBMS architecture hardly satisfies these trends and new solutions are needed. Rather separate specialized engines connected into networks are beneficial. The paper discusses recent advances in database technologies and attempts to highlight them with respect to requirements of EIMSs.

A coherent architecture for a temporal object data base management system

The last decade has witnessed the emergence of practical general-purpose data base management systems (DBMS) as “state of the art” technology. In consequence, these systems are now widely used in a variety of application environments. However, many such systems were designed to capture current data, i.e., a snapshot of reality, but are insufficient for those applications in which past or future data are also required. Even so, there has been little effort to design temporal DBMS, and currently there are no prominent commercial temporal DBMS. This article identifies, explores, and provides an approach to designing a coherent architecture for a temporal object DBMS (TODBMS). It specifically involves the design, development, and implementation of TODBMS. We introduce the basic features of our system and provide an overview of the functionalities supported. In addition, we identify the major components of TODBMS and define the types of services each component should offer, as well as common abstractions and interfaces for these components. Application environments include management and geographic information systems, data base visualization, and robotics.

Transformation of database populations and operations from the conceptual to the internal level

In this paper we describe a framework for the automatic transformation of databases from the conceptual to the internal level. Our approach fits in with the ANSI/SPARC three level architecture for database management systems. The framework consists of three components: structures (database schemata), populations (database contents) and operations (database manipulations). On the conceptual level we describe these components for modelling techniques, such as ER, NIAM and the Binary Relationship Model. On the internal level we focus on (nested) relational models. For each component on the conceptual level a separate transformation is provided to the internal level. The components are integrated by a central notion: the predicator. Further integration is obtained by means of basic population rules.

A data management extension architecture

A database management system architecture is described that facilitates the implementation of data management extensions for relational database systems. The architecture defines two classes of data management extensions alternative ways of storing relations called relation “storage methods”, and access paths, integrity constraints, or triggers which are “attachments” to relations. Generic sets of operations are defined for storage methods and attachments, and these operations must be provided in order to add a new storage method or attachment type to the system. The data management extension architecture also provides common services for coordination of storage method and attachment execution. This article describes the data management extension architecture along with some implementation issues and techniques.

Towards a new approach to secure database design

The problem of designing and managing a secure database system is considered in this paper. The approach which is proposed is primarily concerned with the security information definition and management in a database environment. A multiphase design methodology is presented reflecting current proposals of database design methodology. In particular four design phases are proposed: requirements analysis of the security system, conceptual, logical and physical design of security information. The content and the solution techniques of each phase are examined. A database management system architecture is also presented which is suitable to control access rights to the database.

Mapping external views to a common data model

In the context of a multilevel database management system architecture, the problem arises of translating, or mapping, operations at the user's representation level (the External Schema) into operations at the system's common logical level (the Conceptual Schema). In order to support different structured user's data models, the need for an unstructured, view-independent and yet carefully defined data model at the Conceptual Schema level is recognized. In this paper a binary model for the Conceptual Schema is illustrated through the specification of a set of primitives, and the elements of a language for the definition of a binary Schema and of the corresponding operations are given. Procedures are then illustrated for translating external into conceptual operations through exploitation of the primitives of the binary model. Two types of mapping specifications are illustrated: the operational mapping, in which the translation is explicitly defined, and the structural mapping, in which only the structural correspondences between elements of the External and the Conceptual Schema are defined. The automatic mapping between n-ary relational views and the binary Conceptual Schema is finally discussed.