  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.
