My Weak Consistency is Strong: When Bad Things Do Not Come in Threes
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AbstractIt is expensive to maintain strong data consistency during concurrent execution. However, weak consistency levels,
which are considered harmful, have been widely applied in analytical jobs. Their success challenges our belief: data
consistency, which is believed to be an essential to precise computing, does not always need to be preserved. In this
paper, we tackle one of the core questions related to the application of weak consistency: When does weak consistency work well? We propose an eective explanation for the success of weak consistency. We name it bad things do not come in threes, or BN3. It is based on the observation that the volume of data is far larger than the number of workers. If all workers are operating concurrently, the probability that two workers access the same data at the same time is relatively low. Although it is not small enough to be neglected, the chance that three or more workers access the same data at the same time is even lower. Based on the BN3 conjecture, we analyze dierent consistency levels. We show that a weak consistency level in transaction processing is equivalent to snapshot isolation (SI) under reasonable assumptions. Although the BN3 is an oversimpli cation of real scenarios, it explains why weak consistency often achieves results that are accurate enough. It also serves as a quality promise for the future wide application of weak consistency in analytical tasks. We verify our results in experimental studies.
All Author(s) ListZechao Shang, Jeffrey Xu Yu
Name of ConferenceConference on Innovation Data Systems Research (CIDR 2017)
Start Date of Conference08/01/2017
End Date of Conference11/01/2017
Place of ConferenceChaminade, California
Country/Region of ConferenceUnited States of America
Year2017
LanguagesEnglish-United States

Last updated on 2018-18-01 at 03:04