Cost models are an essential part of database systems, as they are the basis of query performance optimization. Disk based systems are well understood and sophisticated models exist to compare various data structures and to estimate query costs based on disk IO operations. Cost models for in-memory databases shift the focus from disk IOs to main memory accesses and CPU costs. However, modeling memory accesses is fundamentally different and common models do not apply anymore. In this work, we examine the plan operations scan with equality selection, scan with range selection, positional lookup and insert in in-memory column stores regarding different physical column organizations. We consider uncompressed columns, bit compressed and dictionary encoded columns with sorted and unsorted dictionaries. Furthermore, we discuss tree indices on columns and dictionaries and present a detailed parameter evaluation, considering the number of distinct values, value skewness and value disorder. Finally, we present and evaluate a cost model based on cache misses for estimating the runtime of the discussed plan operations.
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