Distributed scaling out solutions for data management.
Many modern applications (such as large-scale Web-sites, social networks, research projects, business analytics, etc.) have to deal with very large data volumes (also referred to as “big data”) and high read/write loads. These applications require underlying data management systems to scale well in order to accommodate data growth and increasing workloads. High throughput, low latencies and data availability are also very important, as well as data consistency guarantees. Traditional SQL-oriented DBMSs, despite their popularity, ACID transactions and rich features, do not scale well and thus are not suitable in certain cases. A number of new data management systems and approaches have emerged over the last decade intended to resolve scalability issues. This paper reviews several classes of such systems and key problems they are able to solve. A large variety of systems and approaches due to the general trend toward specialization in the field of SMS: every data management system has been adapted to solve a certain class of problems. Thus, the selection of specific solutions due to the specific problem to be solved: the expected load, the intensity ratio of read and write, the form of data storage and query types, the desired level of consistency, reliability requirements, the availability of client libraries for the selected language, etc.
Proceedings of the Institute for System Programming, vol. 24, 2013, pp. 327-358.
ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).
DOI: 10.15514/ISPRAS-2013-24-15Full text of the paper in pdf (in Russian) Back to the contents of the volume