  Interactive time responses are a crucial requirement for users analyzing
large amounts of data. Such analytical queries are typically run in a
distributed setting, with data being sharded across thousands of nodes for high
throughput. However, providing real-time analytics is still a very big
challenge; with data distributed across thousands of nodes, the probability
that some of the required nodes are unavailable or very slow during query
execution is very high and unavailability may result in slow execution or even
failures. The sheer magnitude of data and users increase resource contention
and this exacerbates the phenomenon of stragglers and node failures during
execution. In this paper, we propose a novel solution to alleviate the
straggler/failure problem that exploits existing efficient partitioning
properties of the data, particularly, co-hash partitioned data, and provides
approximate answers along with confidence bounds to queries affected by
failed/straggler nodes. We consider aggregate queries that involve joins, group
bys, having clauses and a subclass of nested subqueries. Finally, we validate
our approach through extensive experiments on the TPC-H dataset.
