In this paper, we describe the solutions developed to address key technical challenges encountered while building a distributed database system that can smoothly handle demanding real-time workloads and provide a high level of fault tolerance. Specifically, we describe schemes for the efficient clustering and data partitioning for the automatic scale out of processing across multiple nodes and for optimizing the usage of CPUs, DRAM, SSDs and networks to efficiently scale up performance on one node. The techniques described here were used to develop Aerospike (formerly Citrusleaf), a high performance distributed database system built to handle the needs of today's interactive online services. Most real-time decision systems that use Aerospike require very high scale and need to make decisions within a strict SLA by reading from, and writing to, a database containing billions of data items at a rate of millions of operations per second with sub-millisecond latency. For over five years, Aerospike has been continuously used in over a hundred successful production deployments, as many enterprises have discovered that it can substantially enhance their user experience.