Wireless channel charting consists in the application of dimensionality reduction methods to the channel state information (CSI) collected during the operation of wireless communication systems. Due to hardware limitations, it is desirable to limit the amount of information stored for that purpose; hence, this work considers online sample selection to perform channel charting from streaming CSI data. We specifically focus on the case where dimensionality reduction is performed via contrastive learning using sample triplets, and study several suitable sample selection strategies. The proposed approaches are numerically evaluated and compared using measured data.