Abstract

We propose a new self-organizing, self-optimizing, meta-data rich storage format for the cloud, called a self-organizing data container (SDC), that enables order-of-magnitude performance improvements in data-intensive applications through instance-optimization, i.e., the adaptation of data representation to exploit both the distribution of the data and the workload operating on it. Unlike existing low-level cloud storage formats like Apache Arrow and Parquet, SDCs capture both data and metadata, like access histories and distributional statistics, and are designed to be flexible enough to encompass a variety of modern high-performance representations for data analytics, including partitioning, replication, indexing, and materialization. We present a preliminary design for SDCs, some motivating experiments, and discuss new challenges they present.