Updating a cracked database

Abstract

A cracked database is a datastore continuously reorganized based on operations being executed. For each query, the data of interest is physically reclustered to speed-up future access to the same, overlapping or even disjoint data. This way, a cracking DBMS self-organizes and adapts itself to the workload.So far, cracking has been considered for static databases only. In this paper, we introduce several novel algorithms for high-volume insertions, deletions and updates against a cracked database. We show that the nice performance properties of a cracked database can be maintained in a dynamic environment where updates interleave with queries. Our algorithms comply with the cracking philosophy, i.e., a table is informed on pending insertions and deletions, but only when the relevant data is needed for query processing just enough pending update actions are applied.We discuss details of our implementation in the context of an open-source DBMS and we show through a detailed experimental evaluation that our algorithms always manage to keep the cost of querying a cracked datastore with pending updates lower than the non-cracked case.

An experimental evaluation and analysis of database cracking

“…This research group has proposed several different indexing techniques to address different dimensions of database cracking, including updates [14], tuple reconstruction [15], convergence [16], concurrency control [8,9], and robustness [11]. In this paper, we critically review database cracking in several aspects.…”

“…Thus, several researchers have proposed adaptive indexing techniques to cope with these requirements. In particular, Database Cracking has emerged as an attractive approach for adaptive indexing in recent years [8,11,[14][15][16][17][18]. Since the release of our conference paper [25] on which this work builds upon, three more studies have been published [2,9,23].…”

Benchmarking Adaptive Indexing

“…Updates and their efficient integration into the data structure are covered in [11]. Multi-column indexes to support selections and tuple reconstructions are covered in [9].…”

“…As the authors of two different approaches to adaptive indexing [6,7,9,10,11] and long-time admirers of previous self-tuning approaches [1,2,3,12,13] we propose here a framework for benchmarking adaptive indexing systems. One design goal is that the framework should be able to measure the incremental costs and benefits of reorganization actions in terms of how these are distributed along the lifetime of a workload.…”

“…It first observes the workload and then identifies and constructs the most promising indexes [2,12,13]. Unlike all of these methods, adaptive indexing (bottom row) creates and refines indexes automatically and incrementally, as a side effect of query execution [6,7,9,10,11].…”