LotusSQL: SQL engine for high-performance big data systems

In recent years, Apache Spark has become the de facto standard for big data processing. SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language (SQL). SparkSQL provides convenient data processing interfaces. Despite its efficient optimizer, SparkSQL stil...

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Bibliographic Details
Published in:Big Data Mining and Analytics Vol. 4; no. 4; pp. 252 - 265
Main Authors: Li, Xiaohan, Yu, Bowen, Feng, Guanyu, Wang, Haojie, Chen, Wenguang
Format: Journal Article
Language:English
Published: Beijing Tsinghua University Press 01.12.2021
Subjects:
Big Data
c++
C++ (programming language)
Data processing
Data systems
Datasets
Native languages
Queries
Query languages
query optimization
Structured Query Language-SQL
Virtual environments
ISSN:2096-0654, 2097-406X
Online Access:Get full text
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Summary:In recent years, Apache Spark has become the de facto standard for big data processing. SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language (SQL). SparkSQL provides convenient data processing interfaces. Despite its efficient optimizer, SparkSQL still suffers from the inefficiency of Spark resulting from Java virtual machine and the unnecessary data serialization and deserialization. Adopting native languages such as C++ could help to avoid such bottlenecks. Benefiting from a bare-metal runtime environment and template usage, systems with C++ interfaces usually achieve superior performance. However, the complexity of native languages also increases the required programming and debugging efforts. In this work, we present LotusSQL, an engine to provide SQL support for dataset abstraction on a native backend Lotus. We employ a convenient SQL processing framework to deal with frontend jobs. Advanced query optimization technologies are added to improve the quality of execution plans. Above the storage design and user interface of the compute engine, LotusSQL implements a set of structured dataset operations with high efficiency and integrates them with the frontend. Evaluation results show that LotusSQL achieves a speedup of up to9×in certain queries and outperforms Spark SQL in a standard query benchmark by more than2×on average.
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ISSN:2096-0654
2097-406X
DOI:10.26599/BDMA.2021.9020009