Relational databases have gone through a phase of architectural transition from a monolithic to a distributed architecture to take full advantage of cloud technology. These distributed databases can leverage remote storage to maintain larger amounts of data than monolithic databases at the cost of increased latency. At ByteDance, we have built a distributed database called veDB based on the popular compute-storage separation architecture, however we have observed the system is unable to provide both low latency and high throughput required by some business critical applications, such as batched order processing.In this paper we present our novel approaches to tackle this problem. We have modified our system’s storage to utilize persistent memory (PMem) coupled with a remote direct memory access (RDMA) network to reduce read/write latency and increase the throughput. We also propose a query push-down framework to push partial computations to the PMem storage layer to accelerate analytical queries and reduce the impact of the transaction workload in the computation layer. Our experiments show that our methods improve the throughput by up to 1.5× and reduce latency by up to 20× for standard benchmarks and real-world applications.

The growth in observational data volumes over the past decade has occurred alongside a need to make sense of the phenomena that underpin them. Visualization is a key component of the data wrangling process that precedes the analyses that informs these insights. The crux of this study is interactive visualizations of spatiotemporal phenomena from voluminous datasets. Spatiotemporal visualizations of voluminous datasets introduce challenges relating to interactivity, overlaying multiple datasets and dynamic feature selection, resource capacity constraints, and scaling. In this study we describe our methodology to address these challenges. We rely on a novel mix of algorithms and systems innovations working in concert to ensure effective apportioning and amortization of workloads and enable interactivity during visualizations. In particular our research prototype, Iris, leverages sketching algorithms, effective query predicate generation and evaluation, avoids performance hotspots, harnesses coprocessors for hardware acceleration, and convolutional neural network based encoders to render visualizations while preserving responsiveness and interactivity. We also report on several empirical benchmarks that demonstrate the suitability of our methodology to preserve interactivity while utilizing resources effectively to scale.

There has been a substantial growth in remotely sensed hyperspectral satellite imagery. These data offer opportunities to understand phenomena and inform decision making. The nature of these collections introduces challenges stemming from their volumes, variety, and spatiotemporal resolutions. The crux of this study is to facilitate effective training of deep learning models over satellite data collections. We describe our novel embeddings (multidimensional latent space representations) based approach to effectively support model training, refinement, and inferences. We rigorously explore several aspects relating to embeddings, including their dimensionality, single vs multiple bands, and preservation of inter-band metrics. We also incorporate support for transfer learning over spatiotemporal scopes to address issues relating to cold start and alleviate resource pressure. Our methodology addresses disk, network, CPU/GPU, and accuracy implications of several aspects relating to model construction. Our empirical benchmarks assess the suitability of our methodology using the MODIS and Sentinel-2 satellite data. We demonstrate that our methodology reduces storage requirements by more than 10,000x and reduces model construction times by 75%.

Exploring a voluminous graph dataset visually is a challenging task due to the sheer amount of data and the lack of structure to rely on during the navigation. Indra, our framework for large-scale graph data, provides responsive visual analytics over large-scale graph datasets. In this study, we propose a novel graph indexing scheme that pivots the view of the graph to a hierarchical structure while preserving the semantic importance of vertices within the user's analysis scenario. Indra allows users to compare and track multiple aspects of sub-graphs by supporting linked multi-views and multi-resolution operations such as drill-in and roll-ups. We have performed a set of empirical benchmarks profiling Indra and these demonstrate that several operations are executed with sub-second latency to effectively support interactive visual analytics.