Visual Place Recognition (VPR) is critical in intelligent robotics and computer vision. It involves retrieving similar database images based on a query photo from an extensive collection of known images. In real-world applications, this task encounters challenges when dealing with extreme illumination changes caused by nighttime query images. However, a large-scale training set with day-night correspondence for VPR remains absent. To address this challenge, we propose a novel pipeline that divides the general VPR into distinct domains of day and night, subsequently conquering Nocturnal Place Recognition (NPR). Specifically, we first establish a day-night street scene dataset named NightStreet and use it to train an unpaired image-to-image translation model. Then, we utilize this model to process existing large-scale VPR datasets, generate the night version of VPR datasets, and demonstrate how to combine them with two popular VPR pipelines. Finally, we introduce a divide-and-conquer VPR framework designed to solve the degradation of NPR during daytime conditions. We provide comprehensive explanations at theoretical, experimental, and application levels. Under our framework, the performance of previous methods can be significantly improved on two public datasets, including the top-ranked method. Datasets, code, and trained models are available for research at https://github.com/BinuxLiu/npr.