Data is replicated and stored redundantly over multiple servers for availability in distributed databases. We focus on databases with frequent reads and writes, where both read and write latencies are important. This is in contrast to databases designed primarily for either read or write applications. Redundancy has contrasting effects on read and write latency. Read latency can be reduced by potential parallel access from multiple servers, whereas write latency increases as a larger number of replicas have to be updated. We quantify this tradeoff between read and write latency as a function of redundancy, and provide a closed-form approximation when the request arrival is Poisson and the service is memoryless. We empirically show that this approximation is tight across all ranges of system parameters. Thus, we provide guidelines for redundancy selection in distributed databases.