Kyrix: Interactive Pan/Zoom Visualizations at Scale

Abstract

Pan and zoom are basic yet powerful interaction techniques for exploring large datasets. However, existing zoomable UI toolkits such as Pad++ and ZVTM do not provide the backend database support and data‐driven primitives that are necessary for creating large‐scale visualizations. This limitation in existing general‐purpose toolkits has led to many purpose‐built solutions (e.g. Google Maps and ForeCache) that address the issue of scalability but cannot be easily extended to support visualizations beyond their intended data types and usage scenarios. In this paper, we introduce Kyrix to ease the process of creating general and large‐scale web‐based pan/zoom visualizations. Kyrix is an integrated system that provides the developer with a concise and expressive declarative language along with a backend support for performance optimization of large‐scale data. To evaluate the scalability of Kyrix, we conducted a set of benchmarked experiments and show that Kyrix can support high interactivity (with an average latency of 100 ms or below) on pan/zoom visualizations of 100 million data points. We further demonstrate the accessibility of Kyrix through an observational study with 8 developers. Results indicate that developers can quickly learn Kyrix's underlying declarative model to create scalable pan/zoom visualizations. Finally, we provide a gallery of visualizations and show that Kyrix is expressive and flexible in that it can support the developer in creating a wide range of customized visualizations across different application domains and data types.

Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

“…However, this threshold may be unrealistic for datasets larger than one million points, so we instead set a more modest worst-case threshold of 10 frames per second (or 100 ms), consistent with the type1 latency constraint discussed in §2.2. Note that this threshold is five times lower than most thresholds reported in previous visualization work [8,16,53,63]. As mentioned in §4, live updates are a critical feature for crossfilter use cases, and 500 ms is simply too slow to maintain even our 10 frames per second worst-case threshold.…”

“…Type2 latency is often used as a threshold to evaluate data exploration systems such as Tableau [60], Spotfire [58], and Kyrix [63]. However, these applications do not challenge modern databases as much.…”

“…such that the interface can quickly provide a visual response [39,42,56]. To meet this growing demand for interactive and real-time performance, the database and visualization communities have developed a variety of techniques, including approximate query processing [2,14], online aggregation/progressive visualization [3,19,26], data cubes [8,36,38], spatial indexing [63], speculative query execution [8,30], and lineage tracking [53].…”

Data Civilizer 2.0

“…For instance, the one we use in our demo is 30TB. Because we wanted to enable interactive visualizations at scale, we integrated Kyrix [11], a state-of-the-art visualization system for massive datasets into DC2. With Kyrix, users can write simple code to build intuitive visualization applications that support panning and zooming.…”

Physical Visualization Design

“…At the extremes, visualization libraries like Vega-lite [9] help accelerate client-side visualization design, whereas database tuners [1,3] optimize the physical data layouts but are agnostic to the application interface design. Scalable visualization frameworks like Kyrix [10], Falcon [8], and Polaris [7] make specific architectural decisions that limit the designer to a subset of designs or interactions that the architecture can efficiently express, e.g., pan/zoom or brushed linking.…”