Performance Prediction for Concurrent Workloads in Distributed Database Systems

Abstract

In order to store and process data at large-scale, distributed databases are built to partition data and process it in parallel on distributed nodes in a cluster. When the database concurrently execute heterogeneous query workloads, performance prediction is needed. However, running queries in a distributed database heavily touches upon the network overhead as the data transmission between cluster nodes. Hence, in this work, we take network latency into account when predict concurrent query performance. We propose a linear regression model to estimate the interaction when execute concurrent query for analytical workloads in distributed database system. Since network latency and overheads of local processing are the two most significant factors for query execution, we analyze the query behavior with multivariate regression on both of them at different degree of concurrency. In addition, we use sampling techniques to obtain various query mixes as concurrency level increasing. The experiments for evaluation the performance of our prediction model are conducted over a PostgreSQL database cluster with a representative analytical workloads of TPC-H, the experimental results demonstrates that the query latency predictions of our model can minimize the relative error within 14 % on average.