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[SPARK-42430][SQL][DOC] Add documentation for TimestampNTZ type #40005

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[SPARK-42430][SQL][DOC] Add documentation for TimestampNTZ type #40005

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16 changes: 12 additions & 4 deletions docs/sql-ref-datatypes.md
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Original file line number Diff line number Diff line change
Expand Up @@ -44,11 +44,15 @@ Spark SQL and DataFrames support the following data types:
* Boolean type
- `BooleanType`: Represents boolean values.
* Datetime type
- `TimestampType`: Represents values comprising values of fields year, month, day,
hour, minute, and second, with the session local time-zone. The timestamp value represents an
absolute point in time.
- `DateType`: Represents values comprising values of fields year, month and day, without a
time-zone.
- `TimestampType`: Timestamp with local time zone(TIMESTAMP_LTZ). It represents values comprising values of fields year, month, day,
hour, minute, and second, with the session local time-zone. The timestamp value represents an
absolute point in time.
- `TimestampNTZType`: Timestamp without time zone(TIMESTAMP_NTZ). It represents values comprising values of fields year, month, day,
hour, minute, and second. All operations are performed without taking any time zone into account.
- Note: TIMESTAMP in Spark is a user-specified alias associated with one of the TIMESTAMP_LTZ and TIMESTAMP_NTZ variations. Users can set the default timestamp type as `TIMESTAMP_LTZ`(default value) or `TIMESTAMP_NTZ` via the configuration `spark.sql.timestampType`.

* Interval types
- `YearMonthIntervalType(startField, endField)`: Represents a year-month interval which is made up of a contiguous subset of the following fields:
- MONTH, months within years `[0..11]`,
Expand Down Expand Up @@ -124,6 +128,7 @@ You can access them by doing
|**BinaryType**|Array[Byte]|BinaryType|
|**BooleanType**|Boolean|BooleanType|
|**TimestampType**|java.sql.Timestamp|TimestampType|
|**TimestampNTZType**|java.time.LocalDateTime| TimestampNTZType|
|**DateType**|java.sql.Date|DateType|
|**YearMonthIntervalType**|java.time.Period|YearMonthIntervalType|
|**DayTimeIntervalType**|java.time.Duration|DayTimeIntervalType|
Expand Down Expand Up @@ -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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@cloud-fan cloud-fan Feb 15, 2023

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nit: seems all others are prefixed by DataTypes.

|**DateType**|java.sql.Date|DataTypes.DateType|
|**YearMonthIntervalType**|java.time.Period|YearMonthIntervalType|
|**DayTimeIntervalType**|java.time.Duration|DayTimeIntervalType|
Expand Down Expand Up @@ -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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@gengliangwang gengliangwang Feb 14, 2023

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cc @HyukjinKwon on this one.

|**DateType**|datetime.date|DateType()|
|**DayTimeIntervalType**|datetime.timedelta|DayTimeIntervalType()|
|**ArrayType**|list, tuple, or array|ArrayType(*elementType*, [*containsNull*])<br/>**Note:**The default value of *containsNull* is True.|
Expand Down Expand Up @@ -231,7 +238,8 @@ The following table shows the type names as well as aliases used in Spark SQL pa
|**FloatType**|FLOAT, REAL|
|**DoubleType**|DOUBLE|
|**DateType**|DATE|
|**TimestampType**|TIMESTAMP|
|**TimestampType**|TIMESTAMP, TIMESTAMP_LTZ|
|**TimestampNTZType**|TIMESTAMP_NTZ|
|**StringType**|STRING|
|**BinaryType**|BINARY|
|**DecimalType**|DECIMAL, DEC, NUMERIC|
Expand Down