Fine-Grained Re-Execution for Efficient Batched Commit of Distributed Transactions

Zhiyuan Dong, Zhaoguo Wang, Xiaodong Zhang, Xian Xu, Changgeng Zhao, Haibo Chen, Aurojit Panda, Jinyang Li

Research output: Contribution to journalConference articlepeer-review


Distributed transaction systems incur extensive cross-node communication to execute and commit serializable OLTP transactions. As a result, their performance greatly suffers. Caching data at nodes that execute transactions can cut down remote reads. Batching transactions for validation and persistence can amortize the communication cost during committing. However, caching and batching can significantly increase the likelihood of conflicts, causing expensive aborts. In this paper, we develop Hackwrench to address the challenge of caching and batching. Instead of aborting conflicted transactions, Hackwrench tries to repair them using fine-grained re-execution by tracking the dependencies of operations among a batch of transactions. Tracked dependencies allowHackwrench to selectively invalidate and re-execute only those operations necessary to “fix” the conflict, which is cheaper than aborting and executing an entire batch of transactions. Evaluations using TPC-C and other micro-benchmarks show that Hackwrench can outperform existing commercial and research systems including FoundationDB, Calvin,COCO, and Sundial under comparable settings.

Original languageEnglish (US)
Pages (from-to)1930-1943
Number of pages14
JournalProceedings of the VLDB Endowment
Issue number8
StatePublished - 2023
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: Aug 28 2023Sep 1 2023

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


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