CIAO: An Optimization Framework for Client-Assisted Data Loading
Cong Ding, Dixin Tang, Xi Liang, Aaron J. Elmore, Sanjay Krishnan · 2021
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. Our experiments show that CIAO can significantly accelerate the data loading processing and query execution.