[SPARK-31710][SQL]Add compatibility flag to cast long to timestamp #28570
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As we know,long datatype is interpreted as milliseconds when conversion to timestamp in hive, while long is interpreted as seconds when conversion to timestamp in spark, we have many sqls runing in product, so we need a compatibility flag to make them migrating smoothly ,meanwhile do not change the user behavior in spark.
As we know,long datatype is interpreted as milliseconds when conversion to timestamp in hive, while long is interpreted as seconds when conversion to timestamp in spark, we have many sqls runing in product, so we need a compatibility flag to make them migrating smoothly ,meanwhile do not change the user behavior in spark.
As we know,long datatype is interpreted as milliseconds when conversion to timestamp in hive, while long is interpreted as seconds when conversion to timestamp in spark, we have many sqls runing in product, so we need a compatibility flag to make them migrating smoothly ,meanwhile do not change the user behavior in spark.
…710-3 [SPARK-31710][SQL]Add compatibility flag to cast long to timestamp
…710-2 [SPARK-31710][SQL]Add compatibility flag to cast long to timestamp
…710-1 [SPARK-31710][SQL]Add compatibility flag to cast long to timestamp
As we know,long datatype is interpreted as milliseconds when conversion to timestamp in hive, while long is interpreted as seconds when conversion to timestamp in spark, we have been facing error data during migrating hive sql to spark sql. with compatibility flag we can fix this error,
GuoPhilipse
changed the title
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Can one of the admins verify this patch? |
MaxGekk
requested changes
May 18, 2020
| private[this] def longToTimestamp(t: Long): Long = SECONDS.toMicros(t) | ||
| // SPARK-31710 converting seconds to us,Add compatibility flag | ||
| private[this] def longToTimestamp(t: Long): Long = { | ||
| if ( SQLConf.get.getConf( SQLConf.LONG_TIMESTAMP_CONVERSION_IN_SECONDS )) t * 1000000L |
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I guess checking the flag per each value brings significant performance drop. Could you show by a benchmark that performance is not sacrificed by your changes.
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Hi MaxGekk
i will add new benchmark later
| // SPARK-31710 converting seconds to us,Add compatibility flag | ||
| private[this] def longToTimestamp(t: Long): Long = { | ||
| if ( SQLConf.get.getConf( SQLConf.LONG_TIMESTAMP_CONVERSION_IN_SECONDS )) t * 1000000L | ||
| else t * 1000L |
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There are constants in DateTimeConstants. Please, use them.
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Nice suggestion, i will recheck my code
| val negativeTs = Timestamp.valueOf("1900-05-05 18:34:56.1") | ||
| assert(negativeTs.getTime < 0) | ||
| val expectedSecs = Math.floorDiv(negativeTs.getTime, MILLIS_PER_SECOND) | ||
| checkExceptionInExpression[ArithmeticException](cast(negativeTs, ByteType), errMsg("byte")) |
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No errors while casting to byte anymore in ANSI mode?
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sorry ,it is a mistake, i will revert this commit.
| @@ -1353,17 +1353,33 @@ class AnsiCastSuite extends CastSuiteBase { | |||
| cast("abc.com", dataType), "invalid input") | |||
| } | |||
| } | |||
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| test("cast a timestamp before the epoch 1970-01-01 00:00:00Z") { | |||
| } | ||
| } | ||
| } | ||
| } |
| test("SPARK-31710:Add compatibility flag to cast long to timestamp") { | ||
| withSQLConf( | ||
| SQLConf.LONG_TIMESTAMP_CONVERSION_IN_SECONDS.key -> "false") { | ||
| for (tz <- ALL_TIMEZONES) { |
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What is the reason to test all time zones?
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There have now been 4 PRs with the same name and description. @GuoPhilipse is there something wrong with your workflow? I'm losing track of all the different PRs, and the comments are spread out across them. |
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What changes were proposed in this pull request?
As we know,long datatype is interpreted as milliseconds when conversion to timestamp in hive, while long is interpreted as seconds when conversion to timestamp in spark, we have been facing error data during migrating hive sql to spark sql. with compatibility flag we can fix this error,
Why are the changes needed?
we have many sqls runing in product, so we need a compatibility flag to make them migrating smoothly ,meanwhile do not change the user behavior in spark.
Does this PR introduce any user-facing change?
if user use this patch ,then user should set this paramter ,
if not, user do not need to do anything.
How was this patch tested?
unit test added