  The use of deep learning models for forecasting the resource consumption
patterns of SQL queries have recently been a popular area of study. With many
companies using cloud platforms to power their data lakes for large scale
analytic demands, these models form a critical part of the pipeline in managing
cloud resource provisioning. While these models have demonstrated promising
accuracy, training them over large scale industry workloads are expensive.
Space inefficiencies of encoding techniques over large numbers of queries and
excessive padding used to enforce shape consistency across diverse query plans
implies 1) longer model training time and 2) the need for expensive, scaled up
infrastructure to support batched training. In turn, we developed Prestroid, a
tree convolution based data science pipeline that accurately predicts resource
consumption patterns of query traces, but at a much lower cost.
  We evaluated our pipeline over 19K Presto OLAP queries from Grab, on a data
lake of more than 20PB of data. Experimental results imply that our pipeline
outperforms benchmarks on predictive accuracy, contributing to more precise
resource prediction for large-scale workloads, yet also reduces per-batch
memory footprint by 13.5x and per-epoch training time by 3.45x. We demonstrate
direct cost savings of up to 13.2x for large batched model training over
Microsoft Azure VMs.
