Modern distributed key-value stores, such as Apache Cassandra, enhance performance through multi-get requests, minimizing network round-trips between the client and the database. However, partitioning these requests for appropriate storage server distribution is non-trivial and may result in imbalances. This study addresses this optimization challenge as the Restricted Assignment problem on Intervals (RAI). We propose an effcient (2−1/m)-approximation algorithm, where m is the number of machines. Then, we generalize the problem to the Restricted Assignment problem on Circular Intervals (RACI), matching key-value store implementations, and we present an optimal O(n log n) algorithm for RACI with fixed machines and unitary jobs. Additionally, we obtain a (4−2/m)-approximation for arbitrary jobs and introduce new heuristics, whose solutions are very close to the optimal in practice. Finally, we show that optimizing multi-get requests individually also leads to global improvements, increasing achieved throughput by 27%-34% in realistic cases compared to state-of-the-art strategy.