Relational database management systems are usually deployed on single-node machines and have strict limitations in terms of data structure. This means they do not work well with big data, and NoSQL has been proposed as a solution. To make data querying more efficient, indexes and memory cache techniques are used in NoSQL databases. In this paper, we propose a hierarchical indexing mechanism and a prototype distributed data-storage system, called HMIBase, which has hierarchical indexes for non-primary keys in tables and makes data querying more efficient. HMIBase uses HBase as the lower data storage and creates a memory cache for more efficient data transmission. HMIBase supports coprocessor-to-process update requests. It also provides a client with query and update APIs and a server to support RPCs from the client and finish jobs. To improve the cache hit ratio, we propose a memory cache replacement strategy, called Hot Score algorithm, in HMIBase. The experimental results show that Hot Score algorithm is better than other cache-replacement strategies.