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Posted to issues@spark.apache.org by "Imran Rashid (JIRA)" <ji...@apache.org> on 2017/03/15 15:53:41 UTC
[jira] [Comment Edited] (SPARK-12297) Add work-around for
Parquet/Hive int96 timestamp bug.
[ https://issues.apache.org/jira/browse/SPARK-12297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15926403#comment-15926403 ]
Imran Rashid edited comment on SPARK-12297 at 3/15/17 3:53 PM:
---------------------------------------------------------------
To expand on the original description-- Spark copied Hive's behavior for parquet, but this was inconsistent with Impala (which is the original source of putting a timestamp as an int96 in parquet, I believe), and inconsistent with other file formats. 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.
[~rdblue] unless you have any objections I'll update the bug description to reflect this.
was (Author: irashid):
To expand on the original description-- Spark copied Hive's behavior for parquet, but this was inconsistent with Impala (which is the original source of putting a timestamp as an int96 in parquet, I believe), and inconsistent with other file formats. 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}
{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.
[~rdblue] unless you have any objections I'll update the bug description to reflect this.
> 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
>
> Hive has a bug where timestamps in Parquet data are incorrectly adjusted as though they were in the SQL session time zone to UTC. This is incorrect behavior because timestamp values are SQL timestamp without time zone and should not be internally changed.
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