| Autor: |
Kloibhofer, Sebastian, Makor, Lukas, Leopoldseder, David, Bonetta, Daniele, Stadler, Lukas, Mössenböck, Hanspeter |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
The Art, Science, and Engineering of Programming, 2023, Vol. 7, Issue 3, Article 9 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.22152/programming-journal.org/2023/7/9 |
| Popis: |
Columnar databases are an established way to speed up online analytical processing (OLAP) queries. Nowadays, data processing (e.g., storage, visualization, and analytics) is often performed at the programming language level, hence it is desirable to also adopt columnar data structures for common language runtimes. While there are frameworks, libraries, and APIs to enable columnar data stores in programming languages, their integration into applications typically requires developer interference. In prior work, researchers implemented an approach for *automated* transformation of arrays into columnar arrays in the GraalVM JavaScript runtime. However, this approach suffers from performance issues on smaller workloads as well as on more complex nested data structures. We find that the key to optimizing accesses to columnar arrays is to identify queries and apply specific optimizations to them. In this paper, we describe novel compiler optimizations in the GraalVM Compiler that optimize queries on columnar arrays. At JIT compile time, we identify loops that access potentially columnar arrays and duplicate them in order to specifically optimize accesses to columnar arrays. Additionally, we describe a new approach for creating columnar arrays from arrays consisting of complex objects by performing **multi-level storage transformation**. We demonstrate our approach via an implementation for JavaScript `Date` objects. [ full abstract at https://doi.org/10.22152/programming-journal.org/2023/7/9 ] |
| Databáze: |
arXiv |
| Externí odkaz: |
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