Database Resource Allocation Based on Resilient Intermediates
Martin L. Kersten, Ying Zhang, Pavlos Katsogridakis, Panagiotis Koutsourakis, Joeri van Ruth · 2018
Scale-out of big data analytics applications often does not pay off due to the poor performance in response time and the increasing bill due to a longer execution time on a resource limited machine. To enable a stable DBMS workload environment it helps to maintain several virtual machines with difference resource configurations (CPU, memory, disk, etc) hosting part of the database, so that users can send their tasks to those machines that have the best price/performance characteristics. This, however, requires a method to decide which VM should be used for a given query. When choosing the VM, the memory usage of a query is a particularly important factor, especially for the main-memory (optimised) DBMSs which are generally used for analytical queries today. In this paper, we introduce MALCOM, a memory footprint predictor for queries based on resilient intermediates in MonetDB. Unlike traditional cost-based approaches, MALCOM uses an empirical approach (i.e. using the memory usage information of queries executed in the past) to incrementally update its model to improve its predictions. Our preliminary experiment results show that this approach is robust against varying data distributions.