Fast OLAP Query Execution in Main Memory on Large Data in a Cluster

Main memory column-stores have proven to be efficient for processing analytical queries. Still, there has been little 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 in a 128 node cluster running TPC-H queries with 30 000 gigabyte of uncompressed data. Currently, there are no official cluster results for more than 10 000 gigabyte of data, where we achieve up to one to two orders of magnitudes better performance than the current record holder.