  We study a class of graph analytics SQL queries, which we call relationship
queries. Relationship queries are a wide superset of fixed-length graph
reachability queries and of tree pattern queries. Intuitively, it discovers
target entities that are reachable from source entities specified by the query.
It usually also finds aggregated scores, which correspond to the target
entities and are calculated by applying aggregation functions on measure
attributes, which are found on the target entities, the source entities and the
paths from the sources to the targets. We present real-world OLAP scenarios,
where efficient relationship queries are needed. However, row stores, column
stores and graph databases are unacceptably slow in such OLAP scenarios. We
briefly comment on the straightforward extension of relationship queries that
allows accessing arbitrary schemas.
  The GQ-Fast in-memory analytics engine utilizes a bottom-up fully pipelined
query execution model running on a novel data organization that combines
salient features of column-based organization, indexing and compression.
Furthermore, GQ-Fast compiles its query plans into executable C++ source codes.
Besides achieving runtime efficiency, GQ-Fast also reduces main memory
requirements because, unlike column databases, GQ-Fast selectively allows more
dense forms of compression including heavy-weighted compressions, which do not
support random access.
  We used GQ-Fast to accelerate queries for two OLAP dashboards in the
biomedical field. It outperforms Postgres by 2-4 orders of magnitude and
outperforms MonetDB and Neo4j by 1-3 orders of magnitude when all of them are
running on RAM. In addition, it generally saves space due to the appropriate
use of compression methods.
