DITIR

Abstract

The prosperity of mobile social network and location-based services, e.g., Uber, is backing the explosive growth of spatial temporal streams on the Internet. It raises new challenges to the underlying data store system, which is supposed to support extremely high-throughput trajectory insertion and low-latency querying with spatial and temporal constraints. State-of-the-art solutions, e.g., HBase, do not render satisfactory performance, due to the high overhead on index update. In this demonstration, we present DITIR, our new system prototype tailored to efficiently processing temporal and spacial queries over historical data as well as latest updates. Our system provides better performance guarantee, by physically partitioning the incoming data tuples on their arrivals and exploiting a template-based insertion schema, to reach the desired ingestion throughput. Load balancing mechanism is also introduced to DITIR, by using which the system is capable of achieving reliable performance against workload dynamics. Our demonstration shows that DITIR supports over 1 million tuple insertions in a second, when running on a 10-node cluster. It also significantly outperforms HBase by 7 times on ingestion throughput and 5 times faster on query latency.