  Join queries involving many relations pose a severe challenge to today's
query optimisation techniques. To some extent, this is due to the fact that
these techniques do not pay sufficient attention to structural properties of
the query. In stark contrast, the Database Theory community has intensively
studied structural properties of queries (such as acyclicity and various
notions of width) and proposed efficient query evaluation techniques through
variants of Yannakakis' algorithm. However, although most queries in practice
actually are acyclic or have low width, structure-guided query evaluation
techniques based on Yannakakis' algorithm have not found their way into
mainstream database technology yet. The goal of this work is to address this
gap between theory and practice and to demonstrate that the consideration of
query structure can improve query evaluation performance on modern DBMSs
significantly in cases that have been traditionally challenging. In particular,
we study the performance of structure-guided query evaluation in three
architecturally distinct DBMSs by rewriting SQL queries into a sequence of SQL
statements that express an execution of Yannakakis' algorithm. Moreover, we
identify a class of queries that is particularly well suited for our approach
and allows query answering in a variety of common scenarios without
materializing any join. Through empirical evaluation we show that
structure-guided query evaluation can make the evaluation of many difficult
join queries feasible whereas their evaluation requires a prohibitive amount of
time and memory on current DBMSs.
