  Corporations today collect data at an unprecedented and accelerating scale,
making the need to run queries on large datasets increasingly important.
Technologies such as columnar block-based data organization and compression
have become standard practice in most commercial database systems. However, the
problem of best assigning records to data blocks on storage is still open. For
example, today's systems usually partition data by arrival time into row
groups, or range/hash partition the data based on selected fields. For a given
workload, however, such techniques are unable to optimize for the important
metric of the number of blocks accessed by a query. This metric directly
relates to the I/O cost, and therefore performance, of most analytical queries.
Further, they are unable to exploit additional available storage to drive this
metric down further.
  In this paper, we propose a new framework called a query-data routing tree,
or qd-tree, to address this problem, and propose two algorithms for their
construction based on greedy and deep reinforcement learning techniques.
Experiments over benchmark and real workloads show that a qd-tree can provide
physical speedups of more than an order of magnitude compared to current
blocking schemes, and can reach within 2X of the lower bound for data skipping
based on selectivity, while providing complete semantic descriptions of created
blocks.
