Improving Response Time for Cassandra with Query Scheduling

A management of large-scale data becomes more important, along with the spread of cloud service and the speed-up of networks. Since data management on a single machine can cause performance and scalability problems, data management across multiple machines has been proposed. Distributed Key Value Store(KVS) is a data store which manages data across multiple machines. Since distributed KVSs manage data which consists of simple key-value pair, they can achieve scalability easily. Distributed KVSs are widely used in many services managing large-scale data, such as Facebook and Twitter. Distributed KVSs provide interfaces to access key-value pair by simply specifying the key. In this paper, we refer to a query which only obtains a value from a key as a single query. Some distributed KVSs support a range query which obtains multiple values from a key range. However, under mixed query workloads that consist of single and range queries, single queries(which can be executed faster) are forced to wait until preceding range queries are finished. And this results in the increase of average response time. We propose an approach to reduce the average response time by query scheduling. We implemented our method on Cassandra, and evaluation results showed a reduction of the average response time.