For several decades, online transaction processing has been one of the main applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Despite the novel approaches from industry and various research proposals from academia, recent studies emphasize that OLTP workloads still cannot exploit the full capability of modern processors. To better integrate OLTP and hardware in future systems, we perform a detailed analysis of instruction and data misses, the main causes of memory stalls. We demonstrate which operations and components of a typical storage manager cause the majority of different types of misses in each level of the memory hierarchy on a configuration that closely represents modern commodity hardware. We also observe the impact of data working set size on these misses. According to our experimental results, L1 instruction misses are an extensive cause of the overall stall time for OLTP even for data working set sizes as large as 100GB as long as the data fits in memory. Capacity misses coming from the index probe operation are the dominant cause of the instruction and data stalls when running typical OLTP workloads. During index probe (one of the most common operations in OLTP), the B-tree, lock, and buffer management components of a storage manager are responsible for more than half of the total misses.