  Formulating efficient SQL queries requires several cycles of tuning and
execution, particularly for inexperienced users. We examine methods that can
accelerate and improve this interaction by providing insights about SQL queries
prior to execution. We achieve this by predicting properties such as the query
answer size, its run-time, and error class. Unlike existing approaches, our
approach does not rely on any statistics from the database instance or query
execution plans. This is particularly important in settings with limited access
to the database instance. Our approach is based on using data-driven machine
learning techniques that rely on large query workloads to model SQL queries and
their properties. We evaluate the utility of neural network models and
traditional machine learning models. We use two real-world query workloads: the
Sloan Digital Sky Survey (SDSS) and the SQLShare query workload. Empirical
results show that the neural network models are more accurate in predicting the
query error class, achieving a higher F-measure on classes with fewer samples
as well as performing better on other problems such as run-time and answer size
prediction. These results are encouraging and confirm that SQL query workloads
and data-driven machine learning methods can be leveraged to facilitate query
composition and analysis.
