You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Gengliang Wang (Jira)" <ji...@apache.org> on 2021/07/22 07:08:00 UTC
[jira] [Updated] (SPARK-36182) Support TimestampNTZ type in Parquet
file source
[ https://issues.apache.org/jira/browse/SPARK-36182?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Gengliang Wang updated SPARK-36182:
-----------------------------------
Fix Version/s: 3.3.0
Affects Version/s: (was: 3.2.0)
3.3.0
> Support TimestampNTZ type in Parquet file source
> ------------------------------------------------
>
> Key: SPARK-36182
> URL: https://issues.apache.org/jira/browse/SPARK-36182
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 3.3.0
> Reporter: Gengliang Wang
> Assignee: Gengliang Wang
> Priority: Major
> Fix For: 3.3.0
>
>
> As per https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#timestamp, Parquet supports both TIMESTAMP_NTZ and TIMESTAMP_LTZ (Spark's current default timestamp type):
> * A TIMESTAMP with isAdjustedToUTC=true => TIMESTAMP_LTZ
> * A TIMESTAMP with isAdjustedToUTC=false => TIMESTAMP_NTZ
> In Spark 3.1 or prior, the Parquet writer follows the definition and sets the field `isAdjustedToUTC` as `true`, while the Parquet reader doesn’t respect the `isAdjustedToUTC` flag and convert any Parquet Timestamp type as TIMESTAMP_LTZ.
> Since 3.2, with the support of timestamp without time zone type:
> * Parquet writer follows the definition and sets the field `isAdjustedToUTC` as `false` on writing TIMESTAMP_NTZ.
> * Parquet reader
> ** For schema inference, Spark converts the Parquet timestamp type to the corresponding catalyst timestamp type according to the timestamp annotation flag `isAdjustedToUTC`.
> ** If merge schema is enabled in schema inference and some of the files are inferred as TIMESTAMP_NTZ while the others are TIMESTAMP_LTZ, the result type is TIMESTAMP_LTZ which is considered as the “wider” type
> ** If a column of a user-provided schema is TIMESTAMP_LTZ and the column was written as TIMESTAMP_NTZ type, Spark allows the read operation.
> ** If a column of a user-provided schema is TIMESTAMP_NTZ and the column was written as TIMESTAMP_LTZ type, the read operation is not allowed since the TIMESTAMP_NTZ is considered as narrower than TIMESTAMP_LTZ.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org