  Some mission critical systems, such as fraud detection, require accurate,
real-time metrics over long time windows on applications that demand high
throughputs and low latencies. As these applications need to run "forever",
cope with large and spiky data loads, they further require to be run in a
distributed setting. Unsurprisingly, we are unaware of any distributed
streaming system that provides all those properties. Instead, existing systems
take large simplifications, such as implementing sliding windows as a fixed set
of partially overlapping windows, jeopardizing metric accuracy (violating
financial regulator rules) or latency (breaching service agreements).
  In this paper, we propose Railgun, a fault-tolerant, elastic, and distributed
streaming system supporting real-time sliding windows for scenarios requiring
high loads and millisecond-level latencies. We benchmarked an initial prototype
of Railgun using real data, showing significant lower latency than Flink, and
low memory usage, independent of window size.
