[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
Github user dilipbiswal commented on the issue: https://github.com/apache/spark/pull/22470 @cloud-fan Could you please check CSVinferSchema::tryParseDecimal() ? There is a condition to check negative scale. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
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[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22470 @cloud-fan I think the main problem about this (and it is the reason why I haven't proposed it) is that the range of operations supported would be smaller, so we may forbid operations which now can happen. For instance, the following code has been always working on Spark: `lit(BigDecimal(1e36)) * lit(BigDecimal(1))`. Indeed now this would become a `decimal(6, -36)`. With your change, this is going to be a `decimal(42, 0)` which is out of the range of supported values (ie. an overflow would occur). I am not sure if any user has something like this, but it is possible and I think we cannot exclude it. We may, though, restrict again the condition when it happens, ie. in case we are just parsing from a literal we can avoid returning a negative scale. But the other fix would be needed anyway in this case, as we could still have to deal with negative scales, so IMO this would be quite useless. I'd agree though about forbidding negative scale in 3.0. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
Github user dilipbiswal commented on the issue: https://github.com/apache/spark/pull/22470 @mgaido91 @cloud-fan On the other hand .. some use cases may work better :-) , for example Before ``` scala> spark.sql("create table dec as select (1e36 * 1) as col1") org.apache.spark.SparkException: Cannot recognize hive type string: decimal(3,-36) at org.apache.spark.sql.hive.client.HiveClientImpl$.org$apache$spark$sql$hive$client$HiveClientImpl$$getSparkSQLDataType(HiveClientImpl.scala:883) at org.apache.spark.sql.hive.client.HiveClientImpl$$anonfun$org$apache$spark$sql$hive$client$HiveClientImpl$$verifyColumnDataType$1.apply(HiveClientImpl.scala:905) with this pr ``` scala> spark.sql("create table dec as select (1e36 * 1) as col1") 18/09/19 14:29:29 WARN HiveMetaStore: Location: file:/user/hive/warehouse/dec specified for non-external table:dec 18/09/19 14:29:30 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException res0: org.apache.spark.sql.DataFrame = [] scala> spark.sql("describe table dec").show ++-+---+ |col_name|data_type|comment| ++-+---+ |col1|decimal(38,0)| null| ++-+---+ ``` Perhaps we may have issues writing out data frames containing decimal with negative scale to file based datasources as well. I have not verified though. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22470 @dilipbiswal yes, I definitely think that in general we should get to forbid negative scales. I only thought that this should be done in 3.0 rather than now. And for now the safest option to me was to update properly the only rule which was not working correctly in that case. I also agree in avoiding negative scales in places where they were not possible before 2.3, ie. when parsing from literals. But as there may be other cases where this happens, this solves only part of the problem. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
Github user dilipbiswal commented on the issue: https://github.com/apache/spark/pull/22470 @mgaido91 makes sense. Actually @cloud-fan had asked me to write some test cases for decimal values with -ve scale in another PR. While i was playing around, i found this issue. It seemed to me that the whole -ve scale thingy is not thought through properly. Thats the reason i wanted to share my findings here while you guys are discussing this :-). --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22470: [SPARK-25454][SQL] should not generate negative scale as...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22470 @mgaido91 you are right, this still has behavior changes if the intermedia result exceed the max precision. Since most of the storages don't support negative scale(hive, parquet, etc.), I think we should eventually forbid it too. Let's move the discussion to #22450 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org