  Time-evolving stream datasets exist ubiquitously in many real-world
applications where their inherent hot keys often evolve over times.
Nevertheless, few existing solutions can provide efficient load balance on
these time-evolving datasets while preserving low memory overhead. In this
paper, we present a novel grouping approach (named FISH), which can provide the
efficient time-evolving stream processing at scale. The key insight of this
work is that the keys of time-evolving stream data can have a skewed
distribution within any bounded distance of time interval. This enables to
accurately identify the recent hot keys for the real-time load balance within a
bounded scope. We therefore propose an epoch-based recent hot key
identification with specialized intra-epoch frequency counting (for maintaining
low memory overhead) and inter-epoch hotness decaying (for suppressing
superfluous computation). We also propose to heuristically infer the accurate
information of remote workers through computation rather than communication for
cost-efficient worker assignment. We have integrated our approach into Apache
Storm. Our results on a cluster of 128 nodes for both synthetic and real-world
stream datasets show that FISH significantly outperforms state-of-the-art with
the average and the 99th percentile latency reduction by 87.12% and 76.34% (vs.
W-Choices), and memory overhead reduction by 99.96% (vs. Shuffle Grouping).
