Several emerging evolving graph application workloads demand support for efficient ad-hoc analytics—the ability to perform ad-hoc queries on arbitrary time windows of the graph. Existing systems face limitations when used for such tasks. We present Tegra , a system that enables efficient ad-hoc window operations on evolving graphs. Tegra enables efficient access to the state of the graph at arbitrary windows, and significantly accelerates ad-hoc window queries by using a compact in-memory representation for both graph and intermediate computation state. For this, it leverages persistent data-structures to build a versioned, distributed graph state store, and couples it with an incremental computation model which can leverage these compact states. For users, it exposes these compact states using Timelapse, a natural abstraction. These techniques enable Tegra to significantly outperform existing evolving graph analysis techniques significantly for ad-hoc window operation queries.
Tegra: Efficient Ad-Hoc Analytics on Time-Evolving Graphs
Anand Padmanabha Iyer