  Translating users' natural language questions into SQL queries (i.e., NL2SQL)
significantly lowers the barriers to accessing relational databases. The
emergence of Large Language Models has introduced a novel paradigm in NL2SQL
tasks, enhancing capabilities dramatically. However, this raises a critical
question: Are we fully prepared to deploy NL2SQL models in production?
  To address the posed questions, we present a multi-angle NL2SQL evaluation
framework, NL2SQL360, to facilitate the design and test of new NL2SQL methods
for researchers. Through NL2SQL360, we conduct a detailed comparison of leading
NL2SQL methods across a range of application scenarios, such as different data
domains and SQL characteristics, offering valuable insights for selecting the
most appropriate NL2SQL methods for specific needs. Moreover, we explore the
NL2SQL design space, leveraging NL2SQL360 to automate the identification of an
optimal NL2SQL solution tailored to user-specific needs. Specifically,
NL2SQL360 identifies an effective NL2SQL method, SuperSQL, distinguished under
the Spdier dataset using the execution accuracy metric. Remarkably, SuperSQL
achieves competitive performance with execution accuracy of 87% and 62.66% on
the Spider and BIRD test sets, respectively.
