Abstract
The increasing demand for the simultaneous transaction and review of the data for either decision making or forecasting has created a need for faster and better Hybrid Transactional/Analytical Processing (HTAP). This paper emphasizes the speedup of Online Analytical Processing (OLAP) operations in an HTAP environment where analytical queries are mainly repetitive and contain non-indexed keys as their predicates. Zone maps and materialized views are popular approaches adopted by more extensive databases to address this issue. However, they are absent in in-memory databases because of space constraints. Instead, in-memory databases load the cache with result pages of frequently accessed queries. Increasing the number of such queries can fill the cache and raise the system’s overhead. This paper presents Query_Dictionary, a hybrid storage solution that leverages the full capabilities of SQLite by retaining less information of repetitive queries in the cache and efficiently accommodating the newly updated data by the end-user. The solution proposes storing page-level metadata query information for a larger result set and row-level information for a smaller result set. It demonstrates Query_Dictionary capabilities on three types of representative queries: single table, binary join, and transactional queries on non-indexed attributes. In comparison with SQLite, the proposed method performs better.
Highlights
I N the modern computing world, Hybrid Transactional/Analytical Processing (HTAP) generally adopts in-memory techniques [1]
A binary search using the indexed key is performed on the index table to fetch the rowid
The obtained rowid is used as a key to perform a binary search on the original table
Summary
I N the modern computing world, HTAP generally adopts in-memory techniques [1]. Storing the entire database structure in the memory is the signature characteristic of inmemory databases. A recent study of modern in-memory database systems shows that index lookup can contribute to 94% of query execution time [3] [4]. OUR CONTRIBUTIONS This paper introduces Query_Dictionary, a hybrid storage solution that accelerates repetitive non-indexed queries with minimal memory footprint It processes most requests in the cache by exploiting page prefetches of only significant pages to simplify data processing. When supplemented with a priori user knowledge about query workloads and datasets, data skipping approaches can considerably improve scan performance than traditional indexing algorithms They minimize the number of filtered records, reducing space and maintenance costs. Lazy maintenance strategies have been found to reduce maintenance costs [16]
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