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cloud-fan commented on pull request #28962: URL: https://github.com/apache/spark/pull/28962#issuecomment-652375713 The test failure is legitimate, can you take a look? Seems we need to skip the `NormalizeFloatingNumbers` rule if the input expression contains `KnownFloatingPointNormalized`

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LantaoJin edited a comment on pull request #28963: URL: https://github.com/apache/spark/pull/28963#issuecomment-652396532 > Personally, I don't think we need an extra configuration for this, because we use HiveJDBC to operate with Spark thrift server. For the default Hive 2.3.7 we use,

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attilapiros commented on pull request #28940: URL: https://github.com/apache/spark/pull/28940#issuecomment-652331458 Regarding the changes below I will create a PR and open a new Jira ticket (so it will be taken care of). > We have to measure it for sure. > > Regarding

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yaooqinn commented on pull request #28912: URL: https://github.com/apache/spark/pull/28912#issuecomment-652392159 The test failures look like that we need to set up a dedicated JVM for this newly added test This is an

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LantaoJin commented on pull request #28963: URL: https://github.com/apache/spark/pull/28963#issuecomment-652399895 > I could not reproduce the case you provided in that PR, the query didn't fail. But to this question, the answer is obviously yes. You'd better to test it before

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[GitHub] [spark] yaooqinn edited a comment on pull request #28963: [SPARK-32145][SQL] ThriftCLIService.GetOperationStatus should include exception's stack trace to the error message

yaooqinn edited a comment on pull request #28963: URL: https://github.com/apache/spark/pull/28963#issuecomment-652390360 I guess the current approach is able to uncover the real reason for task failures via printing all suppressed exceptions if any. Too much important information is

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[GitHub] [spark] LantaoJin commented on a change in pull request #28947: [SPARK-32129][SQL] Support AQE skew join with Union

LantaoJin commented on a change in pull request #28947: URL: https://github.com/apache/spark/pull/28947#discussion_r448352093 ## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala ## @@ -286,6 +283,36 @@ case class

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[GitHub] [spark] MaxGekk opened a new pull request #28966: [SPARK-32130][SQL] Disable the JSON option `inferTimestamp` by default

MaxGekk opened a new pull request #28966: URL: https://github.com/apache/spark/pull/28966 ### What changes were proposed in this pull request? Set the JSON option `inferTimestamp` to `false` if an user don't pass it as datasource option. ### Why are the changes needed? To

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[GitHub] [spark] HyukjinKwon commented on pull request #28940: [SPARK-32121][SHUFFLE] Support Windows OS in ExecutorDiskUtils

HyukjinKwon commented on pull request #28940: URL: https://github.com/apache/spark/pull/28940#issuecomment-652371340 Sure, @attilapiros. Thanks you for following up and investigating deep. I will merge this one as soon as the tests pass.

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[GitHub] [spark] MaxGekk commented on pull request #28966: [SPARK-32130][SQL] Disable the JSON option `inferTimestamp` by default

MaxGekk commented on pull request #28966: URL: https://github.com/apache/spark/pull/28966#issuecomment-652371322 I am running JSONBenchmark now. If it will show significantly different numbers, I will push the changes to this PR.

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