Algebricks

Borkar, Vinayak; Bu, Yingyi; Carman, E. Preston; Onose, Nicola; Westmann, Till; Pirzadeh, Pouria; Carey, Michael J.; Tsotras, Vassilis J.

doi:10.1145/2806777.2806941

Cited by 16 publications

(1 citation statement)

References 24 publications

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“…The second layer, a query compiler based on Algebricks , is used for parallel query processing. This algebraic layer receives a translated logical SQL++ query plan from the upper layer and performs rule‐based optimization.…”

Section: Preliminaries—apache Asterixdbmentioning

confidence: 99%

Robust and efficient memory management in Apache AsterixDB

Kim

Behm

Blow³

et al. 2020

Softw Pract Exp

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Traditional relational database systems handle data by dividing their memory into sections such as a buffer cache and working memory, assigning a memory budget to each section to efficiently manage a limited amount of overall memory. They also assign memory budgets to memory-intensive operators such as sorts and joins and control the allocation of memory to these operators; each memory-intensive operator attempts to maximize its memory usage to reduce disk I/O cost. Implementing such memory-intensive operators requires a careful design and application of appropriate algorithms that properly utilize memory. Today's Big Data management systems need the ability to handle large amounts of data similarly, as it is unrealistic to assume that truly big data will fit into memory. In this article, we share our memory management experiences in Apache AsterixDB, an open-source Big Data management software platform that scales out horizontally on shared-nothing commodity computing clusters.We describe the implementation of AsterixDB's memory-intensive operators and their designs related to memory management. We also discuss memory management at the global (cluster) level. We conducted an experimental study using several synthetic and real datasets to explore the impact of this work. We believe that future Big Data management system builders can benefit from these experiences.

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Section: Preliminaries—apache Asterixdbmentioning

confidence: 99%

Robust and efficient memory management in Apache AsterixDB

Kim

Behm

Blow³

et al. 2020

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

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UDBMS: Road to Unification for Multi-model Data Management

Liu

et al. 2018

Lecture Notes in Computer Science

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A traditional database systems is organized around a single data model that determines how data can be organized, stored and manipulated. But the vision of this paper is to develop new principles and techniques to manage multiple data models against a single, integrated backend. For example, semi-structured, graph and relational models are examples of data models that may be supported by a new system. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach significantly reduces integration, migration, development, maintenance and operational issues. The problem is challenging: the existing database principles mainly work for a single model and the research on multi-model data management is still at an early stage. In this paper, we envision a UDBMS (Unified Database Management System) for multi-model data management in one platform. UDBMS will provide several new features such as unified data model and flexible schema, unified query processing, unified index structure and cross-model transaction guarantees. We discuss our vision as well as present multiple research challenges that we need to address.

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