  Main memory column-stores have proven to be efficient for processing
analytical queries. Still, there has been much less work in the context of
clusters. Using only a single machine poses several restrictions: Processing
power and data volume are bounded to the number of cores and main memory
fitting on one tightly coupled system. To enable the processing of larger data
sets, switching to a cluster becomes necessary. In this work, we explore
techniques for efficient execution of analytical SQL queries on large amounts
of data in a parallel database cluster while making maximal use of the
available hardware. This includes precompiled query plans for efficient CPU
utilization, full parallelization on single nodes and across the cluster, and
efficient inter-node communication. We implement all features in a prototype
for running a subset of TPC-H benchmark queries. We evaluate our implementation
using a 128 node cluster running TPC-H queries with 30 000 gigabyte of
uncompressed data.
