  Sampling is often used to reduce query latency for interactive big data
analytics. The established parallel data processing paradigm relies on function
shipping, where a coordinator dispatches queries to worker nodes and then
collects the results. The commoditization of high-performance networking makes
data shipping possible, where the coordinator directly reads data in the
workers' memory using RDMA while workers process other queries. In this work,
we explore when to use function shipping or data shipping for interactive query
processing with sampling. Whether function shipping or data shipping should be
preferred depends on the amount of data transferred, the current CPU
utilization, the sampling method and the number of queries executed over the
data set. The results show that data shipping is up to 6.5x faster when
performing clustered sampling with heavily-utilized workers.
