[SPARK-42430][SQL][DOC] Add documentation for TimestampNTZ type #40005
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gengliangwang
commented
Feb 14, 2023
| @@ -185,6 +191,7 @@ from pyspark.sql.types import * | |||
| |**BinaryType**|bytearray|BinaryType()| | |||
| |**BooleanType**|bool|BooleanType()| | |||
| |**TimestampType**|datetime.datetime|TimestampType()| | |||
| |**TimestampNTZType**|datetime.datetime|TimestampNTZType()| | |||
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cc @HyukjinKwon on this one.
HyukjinKwon
approved these changes
Feb 14, 2023
|
Merging to master/3.4 |
gengliangwang
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Feb 14, 2023
### What changes were proposed in this pull request? Add documentation for TimestampNTZ type ### Why are the changes needed? Add documentation for the new data type TimestampNTZ. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Build docs and preview: <img width="782" alt="image" src="https://user-images.githubusercontent.com/1097932/218656254-096df429-851d-4046-8a6f-f368819c405b.png"> <img width="777" alt="image" src="https://user-images.githubusercontent.com/1097932/218656277-e8cfe747-2c45-476d-b70f-83c654e0b0f2.png"> Closes #40005 from gengliangwang/ntzDoc. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Gengliang Wang <gengliang@apache.org> (cherry picked from commit 46a2341) Signed-off-by: Gengliang Wang <gengliang@apache.org>
cloud-fan
reviewed
Feb 15, 2023
| @@ -154,6 +159,7 @@ please use factory methods provided in | |||
| |**BinaryType**|byte[]|DataTypes.BinaryType| | |||
| |**BooleanType**|boolean or Boolean|DataTypes.BooleanType| | |||
| |**TimestampType**|java.sql.Timestamp|DataTypes.TimestampType| | |||
| |**TimestampNTZType**|java.time.LocalDateTime| TimestampNTZType| | |||
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nit: seems all others are prefixed by DataTypes.
HyukjinKwon
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Feb 15, 2023
### 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.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. ### 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 ``` 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>
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>
MaxGekk
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Feb 18, 2023
…ANSI interval types ### What changes were proposed in this pull request? As #40005 (review) pointed out, the java doc for data type recommends using factory methods provided in org.apache.spark.sql.types.DataTypes. Since the ANSI interval types missed the `DataTypes` as well, this PR also revise their doc. ### Why are the changes needed? Unify the data type doc ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Local preview <img width="826" alt="image" src="https://user-images.githubusercontent.com/1097932/219821685-321c2fd1-6248-4930-9c61-eec68f0dcb50.png"> Closes #40074 from gengliangwang/reviseNTZDoc. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Max Gekk <max.gekk@gmail.com>
MaxGekk
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Feb 18, 2023
…ANSI interval types ### What changes were proposed in this pull request? As #40005 (review) pointed out, the java doc for data type recommends using factory methods provided in org.apache.spark.sql.types.DataTypes. Since the ANSI interval types missed the `DataTypes` as well, this PR also revise their doc. ### Why are the changes needed? Unify the data type doc ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Local preview <img width="826" alt="image" src="https://user-images.githubusercontent.com/1097932/219821685-321c2fd1-6248-4930-9c61-eec68f0dcb50.png"> Closes #40074 from gengliangwang/reviseNTZDoc. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Max Gekk <max.gekk@gmail.com> (cherry picked from commit 8cfd5bf) Signed-off-by: Max Gekk <max.gekk@gmail.com>
a0x8o
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Feb 18, 2023
…ANSI interval types ### What changes were proposed in this pull request? As apache/spark#40005 (review) pointed out, the java doc for data type recommends using factory methods provided in org.apache.spark.sql.types.DataTypes. Since the ANSI interval types missed the `DataTypes` as well, this PR also revise their doc. ### Why are the changes needed? Unify the data type doc ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Local preview <img width="826" alt="image" src="https://user-images.githubusercontent.com/1097932/219821685-321c2fd1-6248-4930-9c61-eec68f0dcb50.png"> Closes #40074 from gengliangwang/reviseNTZDoc. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Max Gekk <max.gekk@gmail.com>
snmvaughan
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Jun 20, 2023
### What changes were proposed in this pull request? Add documentation for TimestampNTZ type ### Why are the changes needed? Add documentation for the new data type TimestampNTZ. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Build docs and preview: <img width="782" alt="image" src="https://user-images.githubusercontent.com/1097932/218656254-096df429-851d-4046-8a6f-f368819c405b.png"> <img width="777" alt="image" src="https://user-images.githubusercontent.com/1097932/218656277-e8cfe747-2c45-476d-b70f-83c654e0b0f2.png"> Closes apache#40005 from gengliangwang/ntzDoc. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Gengliang Wang <gengliang@apache.org> (cherry picked from commit 46a2341) Signed-off-by: Gengliang Wang <gengliang@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>
snmvaughan
pushed a commit
to snmvaughan/spark
that referenced
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Jun 20, 2023
…ANSI interval types ### What changes were proposed in this pull request? As apache#40005 (review) pointed out, the java doc for data type recommends using factory methods provided in org.apache.spark.sql.types.DataTypes. Since the ANSI interval types missed the `DataTypes` as well, this PR also revise their doc. ### Why are the changes needed? Unify the data type doc ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Local preview <img width="826" alt="image" src="https://user-images.githubusercontent.com/1097932/219821685-321c2fd1-6248-4930-9c61-eec68f0dcb50.png"> Closes apache#40074 from gengliangwang/reviseNTZDoc. Authored-by: Gengliang Wang <gengliang@apache.org> Signed-off-by: Max Gekk <max.gekk@gmail.com> (cherry picked from commit 8cfd5bf) Signed-off-by: Max Gekk <max.gekk@gmail.com>
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What changes were proposed in this pull request?
Add documentation for TimestampNTZ type
Why are the changes needed?
Add documentation for the new data type TimestampNTZ.
Does this PR introduce any user-facing change?
No
How was this patch tested?
Build docs and preview:
