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This paper presents an in-depth measurement study of in-memory key-value systems. We examine in-memory data placement and processing techniques, including data structures, caching, performance of read/write operations, effects of different in-memory data structures on throughput performance of big data workloads. Based on the analysis of our measurement results, we attempt to answer a number of challenging and yet most frequently asked questions regarding in-memory key-value systems, such as how do in-memory key-value systems respond to the big data workloads, which exceeds the capacity of physical memory or the pre-configured size of in-memory data structures? How do in-memory key value systems maintain persistency and manage the overhead of supporting persistency? why do different in-memory key-value systems show different throughput performance? and what types of overheads …