[SPARK-42442][SQL] Use spark.sql.timestampType for data source inference #40022
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Sorry about getting back and forth about the schema inference. I am trying to tell a good story about the timestamp without time zone. This should be the final version before 3.4.0 release. |
HyukjinKwon
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Feb 15, 2023
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Merged to master and branch-3.4. |
HyukjinKwon
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Feb 15, 2023
With the configuration `spark.sql.timestampType`, TIMESTAMP in Spark is a user-specified alias associated with one of the TIMESTAMP_LTZ and TIMESTAMP_NTZ variations. This is quite complicated to Spark users. There is another option `spark.sql.sources.timestampNTZTypeInference.enabled` for schema inference. I would like to introduce it in #40005 but having two flags seems too much. After thoughts, I decide to merge `spark.sql.sources.timestampNTZTypeInference.enabled` into `spark.sql.timestampType` and let `spark.sql.timestampType` control the schema inference behavior. We can have followups to add data source options "inferTimestampNTZType" for CSV/JSON/partiton column like JDBC data source did. Make the new feature simpler. No, the feature is not released yet. Existing UT I also tried ``` git grep spark.sql.sources.timestampNTZTypeInference.enabled git grep INFER_TIMESTAMP_NTZ_IN_DATA_SOURCES ``` to make sure the flag INFER_TIMESTAMP_NTZ_IN_DATA_SOURCES is removed. Closes #40022 from gengliangwang/unifyInference. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org> (cherry picked from commit 46226c2) Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
snmvaughan
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Jun 20, 2023
With the configuration `spark.sql.timestampType`, TIMESTAMP in Spark is a user-specified alias associated with one of the TIMESTAMP_LTZ and TIMESTAMP_NTZ variations. This is quite complicated to Spark users. There is another option `spark.sql.sources.timestampNTZTypeInference.enabled` for schema inference. I would like to introduce it in apache#40005 but having two flags seems too much. After thoughts, I decide to merge `spark.sql.sources.timestampNTZTypeInference.enabled` into `spark.sql.timestampType` and let `spark.sql.timestampType` control the schema inference behavior. We can have followups to add data source options "inferTimestampNTZType" for CSV/JSON/partiton column like JDBC data source did. Make the new feature simpler. No, the feature is not released yet. Existing UT I also tried ``` git grep spark.sql.sources.timestampNTZTypeInference.enabled git grep INFER_TIMESTAMP_NTZ_IN_DATA_SOURCES ``` to make sure the flag INFER_TIMESTAMP_NTZ_IN_DATA_SOURCES is removed. Closes apache#40022 from gengliangwang/unifyInference. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org> (cherry picked from commit 46226c2) Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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What changes were proposed in this pull request?
With the configuration
spark.sql.timestampType, TIMESTAMP in Spark is a user-specified alias associated with one of the TIMESTAMP_LTZ and TIMESTAMP_NTZ variations. This is quite complicated to Spark users.There is another option
spark.sql.sources.timestampNTZTypeInference.enabledfor schema inference. I would like to introduce it in #40005 but having two flags seems too much. After thoughts, I decide to mergespark.sql.sources.timestampNTZTypeInference.enabledintospark.sql.timestampTypeand letspark.sql.timestampTypecontrol the schema inference behavior.We can have followups to add data source options "inferTimestampNTZType" for CSV/JSON/partiton column like JDBC data source did.
Why are the changes needed?
Make the new feature simpler.
Does this PR introduce any user-facing change?
No, the feature is not released yet.
How was this patch tested?
Existing UT
I also tried
to make sure the flag INFER_TIMESTAMP_NTZ_IN_DATA_SOURCES is removed.