In this article, we explore the possibility of finding flow from fine-grained process data extracted from programming environments. We propose measuring affective factors non-intrusively based on log data, as presently, affective factors such as flow are still often measured intrusively. This, in the case of flow, naturally may break the flow. We evaluate this approach in a pilot study, where we use log data and survey data collected from an introductory programming course. The log data is fine-grained, containing timestamped keystrokes from the process of solving programming assignments, while the survey data has been collected at the end of every completed assignment. The survey data in the pilot study comprises of Likert-like items measuring perceived educational value, perceived difficulty, and students’ self-reported focus when solving the assignments. In the pilot study, we study raw and derived log data metrics, by looking for relationships between the metrics and the survey data. We discuss the results of the pilot study and provide suggestions for future work related to non-intrusive measures of programmer affect.