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 nontrivial and may result in imbalances. This study addresses this optimization challenge as the Restricted Assignment problem on Intervals (RAI). We propose an efficient (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.