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https://issues.apache.org/jira/browse/SPARK-28975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ruiliang updated SPARK-28975:
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Attachment: QQ图片20190905011256.png
> How do I overwrite a piece of data and recalculate it?
> ------------------------------------------------------
>
> Key: SPARK-28975
> URL: https://issues.apache.org/jira/browse/SPARK-28975
> Project: Spark
> Issue Type: Question
> Components: Spark Core
> Affects Versions: 2.3.0
> Reporter: ruiliang
> Priority: Blocker
> Labels: spark
> Attachments: QQ图片20190905011256.png
>
> Original Estimate: 12h
> Remaining Estimate: 12h
>
> 翻译:
> I have a requirement to make real-time statistics of the total amount and
> quantity of today's orders, but there will be repeated order ids in the
> pushed data, so we need to repeat according to the order ID and order time,
> take the latest order at the order time, and overwrite the previous order
> data.And then recalculate.
> I see the documentation has this function,
>
> / / Without watermark using guid column
> streamingDf. DropDuplicates (" guid)"
>
> But this one doesn't add duplicate data, but it doesn't overwrite the old
> data, so I want the new data to overwrite the old data, and then I'm going to
> recalculate sum and things like that, but I don't find an interface for this
> function, right?
> I also thought StructuredSessionization this case, this is to maintain the
> state of a single id, in this case could you calculate all total online
> time?So if I want state: GroupState[SessionInfo]) ->sum(durationMs), is there
> any other solution?thank you
>
> 原：
> 我有一个需求，时实统计今日订单的总金额和总数量,但推送的数据会有订单ID重复,需要根据订单ID和订单时间来去重,取订单时间最新的一条订单，覆盖掉之前的订单数据。然后重新计算。
> 我看到文档有这个功能，
> {{// Without watermark using guid columnstreamingDf.dropDuplicates("guid")}}
> 但这个是不增加重复数据，但不会去覆盖旧数据，我想让新数据覆盖掉旧数据，然后在重新计算 sum(money) 类似这样，但是没有找到这个功能的接口可以用？
> 我也想到过 StructuredSessionization 这个例子，这个是维护单个id的状态，在这个例子中能否算出全部总在线时长呢？类似我要
> state: GroupState[SessionInfo]) ->sum(durationMs) 这样的计算，有没有其它解决方案呢？谢谢
> !image-2019-09-05-01-04-31-308.png!{{}}
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