2021/02/23 by Cong Ding, Dixin Tang, Ding, Cong +7
Computer Science · Decision Sciences · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Databases (cs.DB) #FOS: Computer and information sciences #Scientific Computing and Data Management #cs.DB
paper · pdf · doi:10.48550/arxiv.2102.11793
arxiv created 2021/02/23 · openalex publication_date 2021/02/23 · arxiv updated 2021/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data loading has been one of the most common performance bottlenecks for many big data applications, especially when they are running on inefficient human-readable formats, such as JSON or CSV. Parsing, validating, integrity checking and data structure maintenance are all computationally expensive steps in loading these formats. Regardless of these costs, many records may be filtered later during query evaluation due to highly selective predicates -- resulting in wasted computation. Meanwhile, the computing power of client ends is typically not exploited. Here, we explore investing limited cycles of clients on prefiltering to accelerate data loading and enable data skipping for query execution. In this paper, we present CIAO, a tunable system to enable client cooperation with the server to enable efficient partial loading and data skipping for a given workload. We proposed an efficient algorithm that would select a near-optimal predicate set to push down within a given budget. Moreover, CIAO will address the trade-off between client cost and server savings by setting different budgets for different clients. We implemented CIAO and evaluated its performance on three real-world datasets. Our experimental results show that the system substantially accelerates data loading by up to 21x and query execution by up to 23x and improves end-to-end performance by up to 19x within a budget of 1.0 microseconds latency per record on clients.