Here’s the abstract:
Modern streaming systems rely on persistent KV stores to perform stateful processing on data streams. Although the choice of the state store is crucial for the system’s performance, there has been little research in designing state stores tailored for streaming workloads. Streaming systems use general-purpose KV stores, such as RocksDB, to manage state. Being oblivious to workload characteristics of stream-ing applications, such stores incur unnecessary overheads.Our methodology to tackle this challenge consists of the following steps. First, we have been conducting a thorough study of streaming state workloads to further the understanding of their characteristics and differences from traditional work-loads. Second, we are developing a new benchmark that can faithfully mimic streaming state workloads and enables re-searchers to easily evaluate alternative store designs. Our long-term goal is to design and develop workload-aware streaming state management to improve the latency and throughput of streaming analytics.
We are looking forward to the workshop!