A moeba

Abstract

Data partitioning significantly improves the query performance in distributed database systems. A large number of techniques have been proposed to efficiently partition a dataset for a given query workload. However, many modern analytic applications involve ad-hoc or exploratory analysis where users do not have a representative query workload upfront. Furthermore, workloads change over time as businesses evolve or as analysts gain better understanding of their data. Static workload-based data partitioning techniques are therefore not suitable for such settings. In this paper, we describe the demonstration of A moeba , a distributed storage system which uses adaptive multi-attribute data partitioning to efficiently support ad-hoc as well as recurring queries. A moeba applies a robust partitioning algorithm such that ad-hoc queries on all attributes have similar performance gains. Thereafter, A moeba adaptively repartitions the data based on the observed query sequence, i.e., the system improves over time. All along A moeba offers both adaptivity (i.e., adjustments according to workload changes) as well as robustness (i.e., avoiding performance spikes due to workload changes). We propose to demonstrate A moeba on scenarios from an internet-of-things startup that tracks user driving patterns. We invite the audience to interactively fire fast ad-hoc queries, observe multi-dimensional adaptivity, and play with a robust/reactive knob in A moeba . The web front end displays the layout changes, runtime costs, and compares it to Spark with both default and workload-aware partitioning.