Plan d'exécution

Résumé

En informatique, et plus précisément dans le domaine des bases de données, un plan d'exécution est un schéma pas à pas des opérations d'accès aux données d'un système de gestion de base de données SQL. Principe L'optimisation de requête consiste à étudier la meilleure manière d'exécuter une requête SQL. Comme les optimiseurs automatiques ne sont pas parfaits, les administrateurs de bases de données sont parfois amenés à examiner et préciser le plan d'exécution le plus rapide, par exemple en définissant l'algorithme de jointure à utiliser en tenant compte du volume des données manipulées. Les mots clés à inclure dans les requêtes SQL pour influencer le plan d'exécution sont appelés les hints (ou indicateurs). Pour visualiser le rendu, certains SGBD permettent de représenter les plans d'exécution sous forme de texte ou de graphique. En ligne de commande, il est possible d'utiliser l'instruction SQL EXPLAIN pour afficher le plan d'exécution, notamment sur un SELECT. Exe

Source officielle

https://fr.wikipedia.org/wiki/Plan_d'ex%C3%A9cution

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Efficient Massively Parallel Join Optimization for Large Queries

Anastasia Ailamaki, Bikash Chandra, Riccardo Mancini, Srinivas Karthik Venkatesh

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, finding the optimal join order becomes a bottleneck in query optimization. Due to the exponential nature of join order optimization, optimizers resort to heuristic solutions after a threshold number of tables. Our objective is two fold: (a) reduce the optimization time for generating optimal plans; and (b) improve the quality of the heuristic solution. In this paper, we propose a new massively parallel algorithm, MPDP, that can efficiently prune the large search space (via a novel plan enumeration technique) while leveraging the massive parallelism offered by modern hardware (Eg: GPUs). When evaluated on real-world benchmark queries with PostgreSQL, MPDP is at least an order of magnitude faster compared to state-of-the-art techniques for large analytical queries. As a result, we are able to increase the heuristic-fall-back limit from 12 relations to 25 relations with same time budget in PostgreSQL. Also, in order to handle queries with even larger number of tables, we augment MPDP to a well known heuristic, IDP2 (iterative DP version 2) and a novel heuristic UnionDP. By systematically exploring a much larger search space, these heuristics provides query plans that are up to 7 times cheaper as compared to the state-of-the-art techniques while being faster to compute.

2022

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Efficient GPU-accelerated Join Optimization for Complex Queries

Anastasia Ailamaki, Bikash Chandra, Riccardo Mancini, Srinivas Karthik Venkatesh

Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processing, hardware accelerators like GPUs and code generation techniques. However, optimization of such queries remains a challenge. Existing optimal solutions either cannot be effectively parallelized, or are inefficient while doing a lot of unnecessary work. In this demonstration, we present our system, GPU-QO, which aims to demonstrate query optimization techniques for large analytical queries using GPUs. We first demonstrate Massively Parallel Dynamic Programming (MPDP) – a novel query optimization technique that can run on GPUs to generate optimal plans in a (massively) parallel and efficient manner. We then showcase IDP2-MPDP and UnionDP – two heuristic techniques, again using GPUs, that can even optimize queries containing 1000s of joins. Furthermore, we compare our techniques with current state-of-the-art solutions, and demonstrate how our techniques can reduce optimization time for optimal solutions by nearly two orders of magnitude and produce much better query plans for heuristics (up to 7x).

IEEE2022

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To benefit from the cloud’s higher elasticity and price-efficiency, most modern data-lake engines support S3-like cloud object storage (COS) services as their optional or preferred underlying storage. Meanwhile, the widespread column stores, such as Parquet, are applied in these data lakes to improve analytical performance. However, as these column stores were designed for on-premise HDFS, they often suffer from the high latency of COS and deliver sub-optimal query performance. We observe that by optimizing the storage layout and data access pattern, we can effectively hide and mitigate the high latency. In this paper, we present Pixels, a column store optimized for the cloud that solves the problem by (1) the workload-driven storage layout optimization within and across the row group boundaries; (2) the I/O scheduling concerning the optimized storage layout and the performance characteristics of COS. They collectively improve the analytical performance in a transparent way that does not affect data ingestion and query execution in data lakes. Evaluations show that Pixels outperforms the state-of- the-art column store on COS by more than one order of magnitude on real-world workload and by 1.93x on TPC-H. Moreover, the performance of Pixels is also portable to HDFS.

IEEE2022

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