[jira] [Comment Edited] (SPARK-12297) Add work-around for Parquet/Hive int96 timestamp bug.

["Reynold Xin (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-12297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16001214#comment-16001214 ] 

Reynold Xin edited comment on SPARK-12297 at 5/8/17 7:41 PM:
-------------------------------------------------------------

Sorry I'm going to revert this. I think this requires further discussions since it completely changes the behavior of one of the most important data types.

It'd be great to consider this more holistically and think about alternatives in fixing them (the current fix might end up being the best but we should at least explore other ones).

One of the fundamental problem is that Spark treats timestamp as timestamp with timezone, whereas both impala and hive treats timestamp as timestamp without timezone. The parquet storage is only a small piece here. Other statements such as cast would return different results for these types. Just fixing these piecemeal is a really bad idea when it comes to data type semantics.




was (Author: rxin):
Sorry I'm going to revert this. I think this requires further discussions since it completely changes the behavior of one of the most important data types.


> Add work-around for Parquet/Hive int96 timestamp bug.
> -----------------------------------------------------
>
>                 Key: SPARK-12297
>                 URL: https://issues.apache.org/jira/browse/SPARK-12297
>             Project: Spark
>          Issue Type: Task
>          Components: Spark Core
>            Reporter: Ryan Blue
>            Assignee: Imran Rashid
>             Fix For: 2.3.0
>
>
> Spark copied Hive's behavior for parquet, but this was inconsistent with other file formats, and inconsistent with Impala (which is the original source of putting a timestamp as an int96 in parquet, I believe).  This made timestamps in parquet act more like timestamps with timezones, while in other file formats, timestamps have no time zone, they are a "floating time".
> The easiest way to see this issue is to write out a table with timestamps in multiple different formats from one timezone, then try to read them back in another timezone.  Eg., here I write out a few timestamps to parquet and textfile hive tables, and also just as a json file, all in the "America/Los_Angeles" timezone:
> {code}
> import org.apache.spark.sql.Row
> import org.apache.spark.sql.types._
> val tblPrefix = args(0)
> val schema = new StructType().add("ts", TimestampType)
> val rows = sc.parallelize(Seq(
>   "2015-12-31 23:50:59.123",
>   "2015-12-31 22:49:59.123",
>   "2016-01-01 00:39:59.123",
>   "2016-01-01 01:29:59.123"
> ).map { x => Row(java.sql.Timestamp.valueOf(x)) })
> val rawData = spark.createDataFrame(rows, schema).toDF()
> rawData.show()
> Seq("parquet", "textfile").foreach { format =>
>   val tblName = s"${tblPrefix}_$format"
>   spark.sql(s"DROP TABLE IF EXISTS $tblName")
>   spark.sql(
>     raw"""CREATE TABLE $tblName (
>           |  ts timestamp
>           | )
>           | STORED AS $format
>      """.stripMargin)
>   rawData.write.insertInto(tblName)
> }
> rawData.write.json(s"${tblPrefix}_json")
> {code}
> Then I start a spark-shell in "America/New_York" timezone, and read the data back from each table:
> {code}
> scala> spark.sql("select * from la_parquet").collect().foreach{println}
> [2016-01-01 02:50:59.123]
> [2016-01-01 01:49:59.123]
> [2016-01-01 03:39:59.123]
> [2016-01-01 04:29:59.123]
> scala> spark.sql("select * from la_textfile").collect().foreach{println}
> [2015-12-31 23:50:59.123]
> [2015-12-31 22:49:59.123]
> [2016-01-01 00:39:59.123]
> [2016-01-01 01:29:59.123]
> scala> spark.read.json("la_json").collect().foreach{println}
> [2015-12-31 23:50:59.123]
> [2015-12-31 22:49:59.123]
> [2016-01-01 00:39:59.123]
> [2016-01-01 01:29:59.123]
> scala> spark.read.json("la_json").join(spark.sql("select * from la_textfile"), "ts").show()
> +--------------------+
> |                  ts|
> +--------------------+
> |2015-12-31 23:50:...|
> |2015-12-31 22:49:...|
> |2016-01-01 00:39:...|
> |2016-01-01 01:29:...|
> +--------------------+
> scala> spark.read.json("la_json").join(spark.sql("select * from la_parquet"), "ts").show()
> +---+
> | ts|
> +---+
> +---+
> {code}
> The textfile and json based data shows the same times, and can be joined against each other, while the times from the parquet data have changed (and obviously joins fail).
> This is a big problem for any organization that may try to read the same data (say in S3) with clusters in multiple timezones.  It can also be a nasty surprise as an organization tries to migrate file formats.  Finally, its a source of incompatibility between Hive, Impala, and Spark.
> HIVE-12767 aims to fix this by introducing a table property which indicates the "storage timezone" for the table.  Spark should add the same to ensure consistency between file formats, and with Hive & Impala.



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