Enabling efficient and general subpopulation analytics in multidimensional data streams

Abstract

Today's large-scale services ( e.g. , video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer general and accurate analytics in real time at reasonable costs. The root cause is the combinatorial explosion of data subpopulations and the diversity of summary statistics we need to monitor simultaneously. We present Hydra, an efficient framework for multidimensional analytics that presents a novel combination of using a "sketch of sketches" to avoid the overhead of monitoring exponentially-many subpopulations and universal sketching to ensure accurate estimates for multiple statistics. We build Hydra as an Apache Spark plugin and address practical system challenges to minimize overheads at scale. Across multiple real-world and synthetic multidimensional datasets, we show that Hydra can achieve robust error bounds and is an order of magnitude more efficient in terms of operational cost and memory footprint than existing frameworks (e.g., Spark, Druid) while ensuring interactive estimation times.

TrustSketch: Trustworthy Sketch-based Telemetry on Cloud Hosts

“…For instance, UnivMon [68] needs to update multiple counters and maintain multiple priority queues, resulting in an unbearable resource load for the NIC. Similarly, Hydra [70] uses up to tens of MBs for multidimensional telemetry, which are not available in typical commodity NICs. Also, virtual switch might update some packet header fields (e.g., Network Address Translation).…”