Meet me halfway: split maintenance of continuous views

From Industry 4.0-driven factories to real-time trading algorithms, businesses depend on analytics on high-velocity real-time data. Often these analytics are performed not in dedicated stream processing engines but on views within a general-purpose database to combine current with historical data. However, traditional view maintenance algorithms are not designed with both the volume and velocity of data streams in mind.

In this paper, we propose a new type of view specialized for queries involving high-velocity inputs, called continuous view. The key component of continuous views is a novel maintenance strategy, splitting the work between inserts and queries. By performing initial parts of the view's query for each insert and the remainder at query time, we achieve both high input rates and low query latency. Further, we keep the memory overhead of our views small, independent of input velocity. To demonstrate the practicality of this strategy, we integrate continuous views into our Umbra database system. We show that split maintenance can outperform even dedicated stream processing engines on analytical workloads, all while still offering similar insert rates. Compared to modern materialized view maintenance approaches, such as deferred and incremental view maintenance, that often need to materialize expensive deltas, we achieve up to an order of magnitude higher insert throughput.