  Smart databases are adopting artificial intelligence (AI) technologies to
achieve {\em instance optimality}, and in the future, databases will come with
prepackaged AI models within their core components. The reason is that every
database runs on different workloads, demands specific resources, and settings
to achieve optimal performance. It prompts the necessity to understand
workloads running in the system along with their features comprehensively,
which we dub as workload characterization.
  To address this workload characterization problem, we propose our query plan
encoders that learn essential features and their correlations from query plans.
Our pretrained encoders capture the {\em structural} and the {\em computational
performance} of queries independently. We show that our pretrained encoders are
adaptable to workloads that expedite the transfer learning process. We
performed independent assessments of structural encoder and performance
encoders with multiple downstream tasks. For the overall evaluation of our
query plan encoders, we architect two downstream tasks (i) query latency
prediction and (ii) query classification. These tasks show the importance of
feature-based workload characterization. We also performed extensive
experiments on individual encoders to verify the effectiveness of
representation learning and domain adaptability.
