Partial Aggregation Using Hibernate

Burzańska

Krukowski

2016

Self Cite

<p class="artSummaryshort">Object Relational Mappings reduce a gap between Relational Databases and programming languages. However, only the simplest operations are covered by the ORM frameworks. Most facilities provided by DBMSs are not usable via ORM. Among such features are recursive queries, introduced in SQL:99 standard. This paper presents integration of Recursive Common Table Expressions with Doctrine Query Language - a part of Doctrine ORM framework for PHP.</p>

Section: Conclusion and Related Workmentioning

confidence: 99%

Extending DQL with Recursive Facilities

Burzańska

Krukowski

2016

Self Cite

“…In our research, we exploit this opportunity. We have prepared proof-of-concept augments to Hibernate ORM that realize recursive queries [9] and partial aggregation [10]. In particular, we experimented with adding recursion on top of database systems that do not implement it directly [11].…”

Section: Introductionmentioning

confidence: 99%

On Materializing Paths for Faster Recursive Querying

Boniewicz

Advances in Intelligent Systems and Computing

Stencel

2014

Self Cite

Abstract. Recursive data structures are often used in business applications. They store data on e.g. corporate hierarchies, product categories and bill-of-material. Therefore, recursive queries as introduced by SQL:1999 or formerly implemented by Oracle constitute a useful facility for application programmers. Unfortunately, recursive queries are not implemented by a number of database systems with MySQL as the most profound example. If an application has such a database as the backend storage, recursive queries will be usually hard-coded at the client side. This is not efficient. In this paper we propose using redundant data structures to answer recursive queries quicker. Such structures must be synchronized in response to updates of data. This means a significant processing overhead for updates. We present experimental evaluation to show loses and gains caused by our solution for various usage scenarios. They prove the feasibility of our proposal. We show our proof-of-concept implementation as a part of the Hibernate framework. Thus, application programmers are separated from all internals of necessary database objects and triggers. These are created automatically by Hibernate generators.

“…In the article [7] we presented the idea of materializing partial aggregations in order to accelerate analytic queries. The inherent cost of this idea is attributed to the need of database triggers that keep the materializations up to date in real time.…”

Section: Introductionmentioning

confidence: 99%

“…The solutions presented in [7] suffer from a noteworthy deficiency. The application programmer is obliged to cater for additional database objects that store materialized data.…”

Section: Introductionmentioning

confidence: 99%

Query Rewriting Based on Meta-Granular Aggregation

Fundamenta Informaticae

Stencel

2014

Self Cite

Analytic database queries are exceptionally time consuming. Decision support systems employ various execution techniques in order to accelerate such queries and reduce their resource consumption. Probably the most important of them consists in materialization of partial results. However, any introduction of derived objects into the database schema increases the cost of software development, since programmers must take care of their usage and synchronization. In this article we consider using partial aggregations materialized in additional tables. The idea is based on the concept of metagranules that represent the information on grouping and used aggregations. Metagranules have a natural partial order that guides the optimisation process. We present solutions to two problems. Firstly, we assume that a set of stored metagranules is given and we optimize a query. We present a novel query rewriting method to make analytic queries use the information stored in metagranules. We also describe our proof-of-concept implementation of this method and perform an extensive experimental evaluation using databases of the size up to 0.5 TiB and 6 billions rows. Secondly, we assume that a database workload is given and we want to select the optimal set of metagranules to materialize. Although each metagranule accelerates some queries, it also imposes a significant overhead on updates. Therefore, we propose a cost model that includes both benefits for queries and penalties for updates. We experiment with the complete search in the space of sets of metagranules to find the optimum. Finally, we empirically verify identified optimal sets against database instances up to 0.5 TiB with billions of rows and hundreds millions of aggregated rows.