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减少 Databricks 中的事务冲突——Asana 的基本原理和应用.pdf

上传人: Fl****zo 编号:718809 2025-06-22 27页 2.03MB

1、Forward-looking StatementThis presentation has been prepared for informational purposes only.The information set forth herein does not purport to be complete or contain all relevant information.Statements contained herein are made as of the date of this presentation unless stated otherwise.This pres

2、entation and the accompanying oral commentary may contain forward-looking statements.In some cases,forward-looking statements can be identified by terms such as“may”,“will”,“should”,“expects”,“plans”,“anticipates”,“could”,“intends”,“projects”,“believes”,“estimates”,“predicts”,or“continue”,or the neg

3、ative of these words or other similar terms or expressions that concern Databricks expectations,strategy,plans,or intentions.Forward-looking statements are based on information available at the time those statements are made and are inherently subject to risks and uncertainties that could cause actu

4、al results to differ materially from those expressed in or suggested by the forward-looking statements.Forward-looking statements should not be read as a guarantee of future performance or outcomes.Except as required by law,Databricks does not undertake any obligation to publicly update or revise an

5、y forward-looking statement,whether as a result of new information,future developments or otherwise.2Reducing Transaction Conflicts in DatabricksFundamentals andApplications at AsanaDima KamalovDATE TBDWhats the Problem We Ran Into?Updating 1 table based on 700 upstream tablesWhen upstream changes,p

6、rocess kicks off to update downstreamMERGE INTO all_objects USING individual_object_tableUPDATE SET update_time=all_objects.update_timeWHEN NOT MATCHED INSERT*Many concurrency conflicts:(4How do transactions in Databricks workWhat Are Transactionsand Concurrency?TransactionPerform this set of action

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本文主要讨论了Databricks在处理大量数据表更新时遇到的并发冲突问题,并提出了一种解决方案。 - **关键数据**:涉及700个上游表对一个表的更新。 - **核心问题**:在更新下游表时,由于多并发操作导致冲突。 - **技术背景**:介绍了事务和并发控制的概念,以及Databricks使用的乐观并发控制。 - **解决方案**:通过引入中间的“盲追加”阶段,将700个`MERGE INTO`操作转换为700个`INSERT`操作和一个`MERGE INTO`操作,从而减少了并发冲突。 - **实施效果**:通过改变操作方式,提高了处理大量并发表更新的效率。 总结:文章围绕Databricks在处理并发更新操作中的问题,提出了有效的优化策略,显著降低了冲突并提高了处理能力。
"700表更新困境怎么破?" "如何玩转Databricks事务?" "Asana如何处理并发更新?"
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