Database can provide efficient storage and access for massive data. However, it is nontrivial for non-experts to command database query language like SQL, which is essential for querying databases. Hence, querying databases using natural language (i.e., text-to-SQL) has received extensive attention in recent years. This study provides a holistic view of text-to-SQL technologies and elaborates on current advancements. It first introduces the background of the research and describes the research problem. Then the study focuses on the current text-to-SQL technologies, including pipeline-based methods, statistical-learning-based methods, as well as techniques developed for multi-turn text-to-SQL task. The study goes further to discuss the field of semantic parsing to which text-to-SQL belongs. Afterward, it introduces the benchmarks and evaluation metrics that are widely used in the research field. Moreover, it compares and analyzes the state-of-the-art models from multiple perspectives. Finally, the study summarizes the potential challenges for text-to-SQL task, and gives some suggestions for future research.