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Today's large-scale services (e.g., video streaming platforms, data centers,sensor grids) need diverse real-time summary statistics across multiplesubpopulations of multidimensional datasets. However, state-of-the-artframeworks do not offer general and accurate analytics in real time atreasonable costs. The root cause is the combinatorial explosion of datasubpopulations and the diversity of summary statistics we need to monitorsimultaneously. We present Hydra, an efficient framework for multidimensionalanalytics that presents a novel combination of using a ``sketch of sketches''to avoid the overhead of monitoring exponentially-many subpopulations anduniversal sketching to ensure accurate estimates for multiple statistics. Webuild Hydra as an Apache Spark plugin and address practical system challengesto minimize overheads at scale. Across multiple real-world and syntheticmultidimensional datasets, we show that Hydra can achieve robust error boundsand is an order of magnitude more efficient in terms of operational cost andmemory footprint than existing frameworks (e.g., Spark, Druid) while ensuringinteractive estimation times.

Enabling Efficient and General Subpopulation Analytics in Multidimensional Data Streams