gengliangwang opened a new pull request, #39777:
URL: https://github.com/apache/spark/pull/39777
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### What changes were proposed in this pull request?
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The TimestampNTZ schema inference over data sources is not consistent in the
current code (most of them are for the purpose of backward compatibility to
infer as Timestamp LTZ by default):
* CSV & JSON: depends on `spark.sql.timestampType` to determine the result
* ORC: depends on whether there is metadata written. If not, inferred as
Timestamp LTZ
* Parquet: infer timestamp column with annotation isAdjustedToUTC = false as
Timestamp NTZ. There is a configuration
`spark.sql.parquet.timestampNTZ.enabled` to determine whether to support NTZ.
When `spark.sql.parquet.timestampNTZ.enabled` is false, users can't write
Timestamp NTZ columns to parquet files.
* Avro: [Local
timestamp](https://avro.apache.org/docs/1.10.2/spec.html#Local+timestamp+%28microsecond+precision%29)
type is a new logical type so there is no backward compatibility issue and
there is no configuration to control the inference.
Since we are going to release Timestamp NTZ in Spark 3.4.0, I propose using
a new configuration `spark.sql.inferTimestampNTZInDataSources.enabled` for
TimestampNTZ schema inference. The flag is false by default for backward
compatibility. When true, if a column can be either TimestampNTZ or
TimestampLTZ, the infer result will be TimestampNTZ. This PR converts JSON/CSV
data sources. If the proposal is fine to others, I will continue on the other
data sources.
### Why are the changes needed?
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* The TimestampNTZ schema inference over data sources is not consistent in
the current code
* The configuration `spark.sql.timestampType` is heavy. It changes the
DDL/SQL functions's default timestamp type. If a user only wants to read back
the newly written TimestampNTZ data without breaking the existing workloads,
having a lightweight flag is a good idea.
### Does this PR introduce _any_ user-facing change?
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No, TimestampNTZ is not released yet.
### How was this patch tested?
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UTs
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