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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2021/11/05 13:36:00 UTC
[jira] [Commented] (SPARK-36182) Support TimestampNTZ type in
Parquet file source
[ https://issues.apache.org/jira/browse/SPARK-36182?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17439266#comment-17439266 ]
Apache Spark commented on SPARK-36182:
--------------------------------------
User 'gengliangwang' has created a pull request for this issue:
https://github.com/apache/spark/pull/34495
> 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.
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