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dianfu pushed a commit to branch release-1.13
in repository https://gitbox.apache.org/repos/asf/flink.git
The following commit(s) were added to refs/heads/release-1.13 by this push:
new 70f7e2d [hotfix][docs] Improve the documentation about the data types
supported in Python DataStream API
70f7e2d is described below
commit 70f7e2d0a5b31ad60fdb49dc023333330192e0d6
Author: Dian Fu <dia...@apache.org>
AuthorDate: Wed Jun 23 19:21:03 2021 +0800
[hotfix][docs] Improve the documentation about the data types supported in
Python DataStream API
---
.../docs/dev/python/datastream/data_types.md | 62 ++++++++++++++++------
.../docs/dev/python/datastream/data_types.md | 62 ++++++++++++++++------
2 files changed, 90 insertions(+), 34 deletions(-)
diff --git a/docs/content.zh/docs/dev/python/datastream/data_types.md
b/docs/content.zh/docs/dev/python/datastream/data_types.md
index 54109e6..8adfd40 100644
--- a/docs/content.zh/docs/dev/python/datastream/data_types.md
+++ b/docs/content.zh/docs/dev/python/datastream/data_types.md
@@ -94,22 +94,50 @@ Explicit types allow PyFlink to use efficient serializers
when moving records th
## Supported Data Types
-You can use `pyflink.common.typeinfo.Types` to specify types in Python
DataStream API.
-The table below shows the type supported now and how to define them:
+You can use `pyflink.common.typeinfo.Types` to define types in Python
DataStream API.
+The table below shows the types supported now and how to define them:
-| PyFlink Type | Usage | Corresponding Python Type |
+| PyFlink Type | Python Type | Java Type |
|:-----------------|:-----------------------|:-----------------------|
-| `BOOLEAN` | `Types.BOOLEAN()` | `bool` |
-| `SHORT` | `Types.SHORT()` | `int` |
-| `INT` | `Types.INT()` | `int` |
-| `LONG` | `Types.LONG()` | `int` |
-| `FLOAT` | `Types.FLOAT()` | `float` |
-| `DOUBLE` | `Types.DOUBLE()` | `float` |
-| `CHAR` | `Types.CHAR()` | `str` |
-| `BIG_INT` | `Types.BIG_INT()` | `bytes` |
-| `BIG_DEC` | `Types.BIG_DEC()` | `decimal.Decimal` |
-| `STRING` | `Types.STRING()` | `str` |
-| `BYTE` | `Types.BYTE()` | `int` |
-| `TUPLE` | `Types.TUPLE()` | `tuple` |
-| `PRIMITIVE_ARRAY` | `Types.PRIMITIVE_ARRAY()` | `list` |
-| `ROW` | `Types.ROW()` | `dict` |
+|`Types.BOOLEAN()` | `bool` | `java.lang.Boolean` |
+|`Types.BYTE()` | `int` | `java.lang.Byte` |
+|`Types.SHORT()` | `int` | `java.lang.Short` |
+|`Types.INT()` | `int` | `java.lang.Integer` |
+|`Types.LONG()` | `int` | `java.lang.Long` |
+|`Types.FLOAT()` | `float` | `java.lang.Float` |
+|`Types.DOUBLE()` | `float` | `java.lang.Double` |
+|`Types.CHAR()` | `str` | `java.lang.Character` |
+|`Types.STRING()` | `str` | `java.lang.String` |
+|`Types.BIG_INT()` | `int` | `java.math.BigInteger` |
+|`Types.BIG_DEC()` | `decimal.Decimal` | `java.math.BigDecimal` |
+|`Types.TUPLE()` | `tuple` | `org.apache.flink.api.java.tuple.Tuple0` ~
`org.apache.flink.api.java.tuple.Tuple25` |
+|`Types.ROW()` | `pyflink.common.Row` | `org.apache.flink.types.Row` |
+|`Types.ROW_NAMED()` | `pyflink.common.Row` | `org.apache.flink.types.Row` |
+|`Types.MAP()` | `dict` | `java.util.Map` |
+|`Types.PICKLED_BYTE_ARRAY()` | `The actual unpickled Python object` |
`byte[]` |
+|`Types.SQL_DATE()` | `datetime.date` | `java.sql.Date` |
+|`Types.SQL_TIME()` | `datetime.time` | `java.sql.Time` |
+|`Types.SQL_TIMESTAMP()` | `datetime.datetime` | `java.sql.Timestamp` |
+|`Types.LIST()` | `list of Python object` | `java.util.List` |
+
+The table below shows the array types supported:
+
+| PyFlink Array Type | Python Type | Java Type |
+|:-----------------|:-----------------------|:-----------------------|
+|`Types.PRIMITIVE_ARRAY(Types.BYTE())` | `bytes` | `byte[]` |
+|`Types.PRIMITIVE_ARRAY(Types.BOOLEAN())` | `list of bool` | `boolean[]` |
+|`Types.PRIMITIVE_ARRAY(Types.SHORT())` | `list of int` | `short[]` |
+|`Types.PRIMITIVE_ARRAY(Types.INT())` | `list of int` | `int[]` |
+|`Types.PRIMITIVE_ARRAY(Types.LONG())` | `list of int` | `long[]` |
+|`Types.PRIMITIVE_ARRAY(Types.FLOAT())` | `list of float` | `float[]` |
+|`Types.PRIMITIVE_ARRAY(Types.DOUBLE())` | `list of float` | `double[]` |
+|`Types.PRIMITIVE_ARRAY(Types.CHAR())` | `list of str` | `char[]` |
+|`Types.BASIC_ARRAY(Types.BYTE())` | `list of int` | `java.lang.Byte[]` |
+|`Types.BASIC_ARRAY(Types.BOOLEAN())` | `list of bool` | `java.lang.Boolean[]`
|
+|`Types.BASIC_ARRAY(Types.SHORT())` | `list of int` | `java.lang.Short[]` |
+|`Types.BASIC_ARRAY(Types.INT())` | `list of int` | `java.lang.Integer[]` |
+|`Types.BASIC_ARRAY(Types.LONG())` | `list of int` | `java.lang.Long[]` |
+|`Types.BASIC_ARRAY(Types.FLOAT())` | `list of float` | `java.lang.Float[]` |
+|`Types.BASIC_ARRAY(Types.DOUBLE())` | `list of float` | `java.lang.Double[]` |
+|`Types.BASIC_ARRAY(Types.CHAR())` | `list of str` | `java.lang.Character[]` |
+|`Types.BASIC_ARRAY(Types.STRING())` | `list of str` | `java.lang.String[]` |
diff --git a/docs/content/docs/dev/python/datastream/data_types.md
b/docs/content/docs/dev/python/datastream/data_types.md
index be0b683..35d0340 100644
--- a/docs/content/docs/dev/python/datastream/data_types.md
+++ b/docs/content/docs/dev/python/datastream/data_types.md
@@ -94,22 +94,50 @@ Explicit types allow PyFlink to use efficient serializers
when moving records th
## Supported Data Types
-You can use `pyflink.common.typeinfo.Types` to specify types in Python
DataStream API.
-The table below shows the type supported now and how to define them:
+You can use `pyflink.common.typeinfo.Types` to define types in Python
DataStream API.
+The table below shows the types supported now and how to define them:
-| PyFlink Type | Usage | Corresponding Python Type |
+| PyFlink Type | Python Type | Java Type |
|:-----------------|:-----------------------|:-----------------------|
-| `BOOLEAN` | `Types.BOOLEAN()` | `bool` |
-| `SHORT` | `Types.SHORT()` | `int` |
-| `INT` | `Types.INT()` | `int` |
-| `LONG` | `Types.LONG()` | `int` |
-| `FLOAT` | `Types.FLOAT()` | `float` |
-| `DOUBLE` | `Types.DOUBLE()` | `float` |
-| `CHAR` | `Types.CHAR()` | `str` |
-| `BIG_INT` | `Types.BIG_INT()` | `bytes` |
-| `BIG_DEC` | `Types.BIG_DEC()` | `decimal.Decimal` |
-| `STRING` | `Types.STRING()` | `str` |
-| `BYTE` | `Types.BYTE()` | `int` |
-| `TUPLE` | `Types.TUPLE()` | `tuple` |
-| `PRIMITIVE_ARRAY` | `Types.PRIMITIVE_ARRAY()` | `list` |
-| `ROW` | `Types.ROW()` | `dict` |
+|`Types.BOOLEAN()` | `bool` | `java.lang.Boolean` |
+|`Types.BYTE()` | `int` | `java.lang.Byte` |
+|`Types.SHORT()` | `int` | `java.lang.Short` |
+|`Types.INT()` | `int` | `java.lang.Integer` |
+|`Types.LONG()` | `int` | `java.lang.Long` |
+|`Types.FLOAT()` | `float` | `java.lang.Float` |
+|`Types.DOUBLE()` | `float` | `java.lang.Double` |
+|`Types.CHAR()` | `str` | `java.lang.Character` |
+|`Types.STRING()` | `str` | `java.lang.String` |
+|`Types.BIG_INT()` | `int` | `java.math.BigInteger` |
+|`Types.BIG_DEC()` | `decimal.Decimal` | `java.math.BigDecimal` |
+|`Types.TUPLE()` | `tuple` | `org.apache.flink.api.java.tuple.Tuple0` ~
`org.apache.flink.api.java.tuple.Tuple25` |
+|`Types.ROW()` | `pyflink.common.Row` | `org.apache.flink.types.Row` |
+|`Types.ROW_NAMED()` | `pyflink.common.Row` | `org.apache.flink.types.Row` |
+|`Types.MAP()` | `dict` | `java.util.Map` |
+|`Types.PICKLED_BYTE_ARRAY()` | `The actual unpickled Python object` |
`byte[]` |
+|`Types.SQL_DATE()` | `datetime.date` | `java.sql.Date` |
+|`Types.SQL_TIME()` | `datetime.time` | `java.sql.Time` |
+|`Types.SQL_TIMESTAMP()` | `datetime.datetime` | `java.sql.Timestamp` |
+|`Types.LIST()` | `list of Python object` | `java.util.List` |
+
+The table below shows the array types supported:
+
+| PyFlink Array Type | Python Type | Java Type |
+|:-----------------|:-----------------------|:-----------------------|
+|`Types.PRIMITIVE_ARRAY(Types.BYTE())` | `bytes` | `byte[]` |
+|`Types.PRIMITIVE_ARRAY(Types.BOOLEAN())` | `list of bool` | `boolean[]` |
+|`Types.PRIMITIVE_ARRAY(Types.SHORT())` | `list of int` | `short[]` |
+|`Types.PRIMITIVE_ARRAY(Types.INT())` | `list of int` | `int[]` |
+|`Types.PRIMITIVE_ARRAY(Types.LONG())` | `list of int` | `long[]` |
+|`Types.PRIMITIVE_ARRAY(Types.FLOAT())` | `list of float` | `float[]` |
+|`Types.PRIMITIVE_ARRAY(Types.DOUBLE())` | `list of float` | `double[]` |
+|`Types.PRIMITIVE_ARRAY(Types.CHAR())` | `list of str` | `char[]` |
+|`Types.BASIC_ARRAY(Types.BYTE())` | `list of int` | `java.lang.Byte[]` |
+|`Types.BASIC_ARRAY(Types.BOOLEAN())` | `list of bool` | `java.lang.Boolean[]`
|
+|`Types.BASIC_ARRAY(Types.SHORT())` | `list of int` | `java.lang.Short[]` |
+|`Types.BASIC_ARRAY(Types.INT())` | `list of int` | `java.lang.Integer[]` |
+|`Types.BASIC_ARRAY(Types.LONG())` | `list of int` | `java.lang.Long[]` |
+|`Types.BASIC_ARRAY(Types.FLOAT())` | `list of float` | `java.lang.Float[]` |
+|`Types.BASIC_ARRAY(Types.DOUBLE())` | `list of float` | `java.lang.Double[]` |
+|`Types.BASIC_ARRAY(Types.CHAR())` | `list of str` | `java.lang.Character[]` |
+|`Types.BASIC_ARRAY(Types.STRING())` | `list of str` | `java.lang.String[]` |
