PostgreSQL

Résumé

PostgreSQL est un système de gestion de base de données relationnelle et objet (SGBDRO). C'est un outil libre disponible selon les termes d'une licence de type BSD. Ce système est comparable à d'autres systèmes de gestion de base de données, qu'ils soient libres (comme MariaDB et Firebird), ou propriétaires (comme Oracle, MySQL, Sybase, DB2, Informix et Microsoft SQL Server). Comme les projets libres Apache et Linux, PostgreSQL n'est pas contrôlé par une seule entreprise, mais est fondé sur une communauté mondiale de développeurs et d'entreprises. Histoire L'histoire de PostgreSQL remonte à la base de données Ingres, développée à Berkeley par Michael Stonebraker. Lorsque ce dernier décida en 1985 de recommencer le développement de zéro, il nomma le logiciel Postgres, comme raccourci de post-Ingres. Lors de l'ajout des fonctionnalités SQL en 1995, Postgres fut renommé Postgres95. Ce nom fut changé à la fin de 1996 en PostgreSQL. Le projet s'organise de manière à maintenir

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https://fr.wikipedia.org/wiki/PostgreSQL

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One of the most challenging tasks for the database administrator is to physically design the database to attain optimal performance for a given workload. Physical design is hard because it requires the selection of an optimal set of design features from a vast search space. There have been many commercial tools available to automatically suggest the physical design, for a given a set of queries. These tools are, however, based on greedy heuristic pruning, which reduces their usefulness. Furthermore, they are not interactive, as the APIs to simulate the indexes and tables are product specific and hidden from the database administrators. Finally, all these tools are built specifically for commercial systems and there is lack of automated physical designers for open source DBMSs. In this demonstration we introduce –PARINDA - an interactive physical designer for an open source DBMS. Given a workload containing a set of queries, this tool allows the DBA to efficiently simulate various physical design features and get immediate feedback on their effectiveness. It also incorporates recent advances in non-greedy physical design techniques to provide close to optimal suggestions. Although it has been prototyped for several different DBMSs, we demonstrate the usefulness and efficiency of the tool while running on the open source DBMS—PostgreSQL--using large real-world scientific datasets and query workloads.

2010

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DBToaster: A SQL Compiler for High-Performance Delta Processing in Main-Memory Databases

Yanif Ahmad, Christoph Koch

We present DBToaster, a novel query compilation framework for producing high performance compiled query executors that incrementally and continuously answer standing aggregate queries using in-memory views. DBToaster targets applications that require efficient main-memory processing of standing queries (views) fed by high-volume data streams, recursively compiling view maintenance (VM) queries into simple C++ functions for evaluating database updates (deltas). While today's VM algorithms consider the impact of single deltas on view queries to produce maintenance queries, we recursively consider deltas of maintenance queries and compile to thoroughly transform queries into code. Recursive compilation successively elides certain scans and joins, and eliminates signifcant query plan interpreter overheads. In this demonstration, we walk through our compilation algorithm, and show the signifcant performance advantages of our compiled executors over other query processors. We are able to demonstrate 1-3 orders of magnitude improvements in processing times for a nancial application and a data warehouse loading application, both implemented across a wide range of database systems, including PostgreSQL, HSQLDB, a commercial DBMS 'A', the Stanford STREAM engine, and a commercial stream processor 'B'.

2009

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NoDB: Efficient Query Execution on Raw Data Files

Anastasia Ailamaki, Ioannis Alagiannis, Miguel Sérgio De Oliveira Branco

As data collections become larger and larger, data loading evolves to a major bottleneck. Many applications already avoid using database systems, e.g., scientific data analysis and social networks, due to the complexity and the increased data-to-query time. For such applications data collections keep growing fast, even on a daily basis, and we are already in the era of data deluge where we have much more data than what we can move, store, let alone analyze. Our contribution in this paper is the design and roadmap of a new paradigm in database systems, called NoDB, which do not require data loading while still maintaining the whole feature set of a modern database system. In particular, we show how to make raw data files a first-class citizen, fully integrated with the query engine. Through our design and lessons learned by implementing the NoDB philosophy over a modern DBMS, we discuss the fundamental limitations as well as the strong opportunities that such a research path brings. We identify performance bottlenecks specific for in situ processing, namely the repeated parsing and tokenizing overhead and the expensive data type conversion costs. To address these problems, we introduce an adaptive indexing mechanism that maintains positional information to provide efficient access to raw data files, together with a flexible caching structure. Our implementation over PostgreSQL, called PostgresRaw, is able to avoid the loading cost completely, while matching the query performance of plain PostgreSQL and even outperforming it in many cases. We conclude that NoDB systems are feasible to design and implement over modern database architectures, bringing an unprecedented positive effect in usability and performance.

2012

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