Query Prediction in Large Scale Data Intensive Event Stream Analysis Systems

Hot-spot events accessing has recently received considerable attentions in the event stream historical analysis systems. Noting that predicates in SQL (Structured Query Language) requests usually have similarity features in a short time in event stream systems, that means events frequently queried recently might be queried again in the near future. This paper proposes a prediction model to forecast query predicates and then to choose them for speculative execution. We propose an adaptive two-level scoring (TLS) prediction algorithm, which can adjust parameters according to the system resource usage conditions. We introduce two metrics accuracy rate and efficiency rate, for query prediction evaluation, and make a detailed analysis of system costs. Our experimental results in DBroker system demonstrate the TLS algorithm and local speculative execution method can significantly reduce query response time.