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Posted to reviews@spark.apache.org by "gengliangwang (via GitHub)" <gi...@apache.org> on 2024/03/12 21:42:04 UTC
[PR] [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files [spark]
gengliangwang opened a new pull request, #45482:
URL: https://github.com/apache/spark/pull/45482
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Add migration doc: TimestampNTZ type inference on Parquet files
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Update docs. The behavior change was not mentioned in the SQL migration guide
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It's just doc change
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Re: [PR] [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files [spark]
Posted by "gengliangwang (via GitHub)" <gi...@apache.org>.
gengliangwang closed pull request #45482: [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files
URL: https://github.com/apache/spark/pull/45482
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Re: [PR] [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45482:
URL: https://github.com/apache/spark/pull/45482#discussion_r1522163241
##########
docs/sql-migration-guide.md:
##########
@@ -67,6 +67,7 @@ license: |
- Since Spark 3.4, vectorized readers are enabled by default for the nested data types (array, map and struct). To restore the legacy behavior, set `spark.sql.orc.enableNestedColumnVectorizedReader` and `spark.sql.parquet.enableNestedColumnVectorizedReader` to `false`.
- Since Spark 3.4, `BinaryType` is not supported in CSV datasource. In Spark 3.3 or earlier, users can write binary columns in CSV datasource, but the output content in CSV files is `Object.toString()` which is meaningless; meanwhile, if users read CSV tables with binary columns, Spark will throw an `Unsupported type: binary` exception.
- Since Spark 3.4, bloom filter joins are enabled by default. To restore the legacy behavior, set `spark.sql.optimizer.runtime.bloomFilter.enabled` to `false`.
+ - Since Spark 3.4, when schema inference on external Parquet files, INT64 timestamps with annotation `isAdjustedToUTC=false` will be inferred as TimestampNTZ type instead of Timestamp type. To restore the legacy behavior, set `spark.sql.parquet.inferTimestampNTZ.enabled` to `false`.
Review Comment:
SPARK-38829 seems to add this at 3.3.0. When this behavior change happens, @gengliangwang ?
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Re: [PR] [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files [spark]
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on code in PR #45482:
URL: https://github.com/apache/spark/pull/45482#discussion_r1522175787
##########
docs/sql-migration-guide.md:
##########
@@ -67,6 +67,7 @@ license: |
- Since Spark 3.4, vectorized readers are enabled by default for the nested data types (array, map and struct). To restore the legacy behavior, set `spark.sql.orc.enableNestedColumnVectorizedReader` and `spark.sql.parquet.enableNestedColumnVectorizedReader` to `false`.
- Since Spark 3.4, `BinaryType` is not supported in CSV datasource. In Spark 3.3 or earlier, users can write binary columns in CSV datasource, but the output content in CSV files is `Object.toString()` which is meaningless; meanwhile, if users read CSV tables with binary columns, Spark will throw an `Unsupported type: binary` exception.
- Since Spark 3.4, bloom filter joins are enabled by default. To restore the legacy behavior, set `spark.sql.optimizer.runtime.bloomFilter.enabled` to `false`.
+ - Since Spark 3.4, when schema inference on external Parquet files, INT64 timestamps with annotation `isAdjustedToUTC=false` will be inferred as TimestampNTZ type instead of Timestamp type. To restore the legacy behavior, set `spark.sql.parquet.inferTimestampNTZ.enabled` to `false`.
Review Comment:
Thank you for the confirmation.
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Re: [PR] [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files [spark]
Posted by "gengliangwang (via GitHub)" <gi...@apache.org>.
gengliangwang commented on PR #45482:
URL: https://github.com/apache/spark/pull/45482#issuecomment-1992668601
@dongjoon-hyun Thanks, merging to master/branch-3.5/branch-3.4
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Re: [PR] [SPARK-47370][Doc] Add migration doc: TimestampNTZ type inference on Parquet files [spark]
Posted by "gengliangwang (via GitHub)" <gi...@apache.org>.
gengliangwang commented on code in PR #45482:
URL: https://github.com/apache/spark/pull/45482#discussion_r1522165215
##########
docs/sql-migration-guide.md:
##########
@@ -67,6 +67,7 @@ license: |
- Since Spark 3.4, vectorized readers are enabled by default for the nested data types (array, map and struct). To restore the legacy behavior, set `spark.sql.orc.enableNestedColumnVectorizedReader` and `spark.sql.parquet.enableNestedColumnVectorizedReader` to `false`.
- Since Spark 3.4, `BinaryType` is not supported in CSV datasource. In Spark 3.3 or earlier, users can write binary columns in CSV datasource, but the output content in CSV files is `Object.toString()` which is meaningless; meanwhile, if users read CSV tables with binary columns, Spark will throw an `Unsupported type: binary` exception.
- Since Spark 3.4, bloom filter joins are enabled by default. To restore the legacy behavior, set `spark.sql.optimizer.runtime.bloomFilter.enabled` to `false`.
+ - Since Spark 3.4, when schema inference on external Parquet files, INT64 timestamps with annotation `isAdjustedToUTC=false` will be inferred as TimestampNTZ type instead of Timestamp type. To restore the legacy behavior, set `spark.sql.parquet.inferTimestampNTZ.enabled` to `false`.
Review Comment:
The TimestampNTZ feature was disabled in Spark 3.3 (https://issues.apache.org/jira/browse/SPARK-38813) and release in Spark 3.4
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