Relational database

A relational database is a (most commonly digital) database based on the relational model of data, as proposed by E. F. Codd in 1970. A system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using SQL (Structured Query Language) for querying and updating the database. History The term "relational database" was first defined by E. F. Codd at IBM in 1970. Codd introduced the term in his research paper "A Relational Model of Data for Large Shared Data Banks". In this paper and later papers, he defined what he meant by "relational". One well-known definition of what constitutes a relational database system is composed of Codd's 12 rules. However, no commercial implementations of the relational model conform to all of Codd's rules, so the term has gradually come to describe a broader class of database systems, which at a minimum:

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Graph integration of structured, semistructured and unstructured data for data journalism

Angelos Christos Anadiotis, Jingmao You

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments. (C) 2021 Elsevier Ltd. All rights reserved.

PERGAMON-ELSEVIER SCIENCE LTD2022

Query Optimization in Oracle 12c Database In-Memory

Ramachandra Satyanarayana Valluri

Traditional on disk row major tables have been the dominant storage mechanism in relational databases for decades. Over the last decade, however, with explosive growth in data volume and demand for faster analytics, has come the recognition that a different data representation is needed. There is widespread agreement that in-memory column-oriented databases are best suited to meet the realities of this new world. Oracle 12c Database In-memory, the industry’s first dual-format database, allows existing row major on-disk tables to have complementary in-memory columnar representations. The new storage format brings new data processing techniques and query execution algorithms and thus new challenges for the query optimizer. Execution plans that are optimal for one format may be sub-optimal for the other. In this paper, we describe the changes made in the query optimizer to generate execution plans optimized for the specific format – row major or columnar – that will be scanned during query execution. With enhancements in several areas – statistics, cost model, query transformation, access pat h and join optimization, parallelism, and cluster - awareness – the query optimizer plays a significant role in unlocking the full promise and performance of Oracle Database In-Memory.

2015

Scaling up Mixed Workloads: a Battle of Data Freshness, Flexibility, and Scheduling

Anastasia Ailamaki, Iraklis Psaroudakis

The common "one size does not fit all" paradigm isolates transactional and analytical workloads into separate, specialized database systems. Operational data is periodically replicated to a data warehouse for analytics. Competitiveness of enterprises today, however, depends on real-time reporting on operational data, necessitating an integration of transactional and analytical processing in a single database system. The mixed workload should be able to query and modify common data in a shared schema. The database needs to provide performance guarantees for transactional workloads, and, at the same time, efficiently evaluate complex analytical queries. In this paper, we share our analysis of the performance of two main-memory databases that support mixed workloads, SAP HANA and HyPer, while evaluating the mixed workload CH-benCHmark. By examining their similarities and differences, we identify the factors that affect performance while scaling the number of concurrent transactional and analytical clients. The three main factors are (a) data freshness, i.e., how recent is the data processed by analytical queries, (b) flexibility, i.e., restricting transactional features in order to increase optimization choices and enhance performance, and (c) scheduling, i.e., how the mixed workload utilizes resources. Specifically for scheduling, we show that the absence of workload management under cases of high concurrency leads to analytical workloads overwhelming the system and severely hurting the performance of transactional workloads.

Springer International Publishing AG2014

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SQL

Structured Query Language (SQL) (ˌɛsˌkjuːˈɛl S-Q-L, sometimes ˈsiːkwəl "sequel" for historical reasons) is a domain-specific language used in programming and designed for man

Database

In computing, a database is an organized collection of data (also known as a data store) stored and accessed electronically through the use of a database management system. Small databases can be st

Relational model

The relational model (RM) is an approach to managing data using a structure and language consistent with first-order predicate logic, first described in 1969 by English computer scientist Edgar F. Co