  Sketches are a family of streaming algorithms widely used in the world of big
data to perform fast, real-time analytics. A popular sketch type is Quantiles,
which estimates the data distribution of a large input stream. We present
Quancurrent, a highly scalable concurrent Quantiles sketch. Quancurrent's
throughput increases linearly with the number of available threads, and with
$32$ threads, it reaches an update speedup of $12$x and a query speedup of
$30$x over a sequential sketch. Quancurrent allows queries to occur
concurrently with updates and achieves an order of magnitude better query
freshness than existing scalable solutions.
