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Schema

toolbox_pyspark.schema 🔗

Summary

The schema module is used for checking, validating, and viewing any schema differences between two different tables, either from in-memory variables, or pointing to locations on disk.

check_schemas_match 🔗

check_schemas_match(
    method: str = "by_table_and_table",
    left_table: Optional[psDataFrame] = None,
    right_table: Optional[psDataFrame] = None,
    left_table_path: Optional[str] = None,
    left_table_name: Optional[str] = None,
    right_table_path: Optional[str] = None,
    right_table_name: Optional[str] = None,
    spark_session: Optional[SparkSession] = None,
    left_table_format: str = "delta",
    right_table_format: str = "delta",
    include_change_field: bool = True,
    include_add_field: bool = True,
    include_remove_field: bool = True,
    include_change_nullable: bool = False,
    return_object: Literal["results", "check"] = "check",
) -> Union[list[tuple[str, dict[str, StructField]]], bool]

Summary

Check the schemas between two different tables.

Details

This function is heavily inspired by other packages which check and validate schema differences for pyspark tables. This function just streamlines it a bit, and adds additional functionality for whether or not table on either left or right side is already in-memory or sitting on a directory somewhere else.

Parameters:

Name Type Description Default
method str

The method to use for the comparison. That is, is either side a table in memory or is it a table sitting on a path?. Check the Notes section for all options available for this parameter.
Defaults to "by_table_and_table".

 'by_table_and_table'
spark_session Optional[SparkSession]

The SparkSession to use if either the left or right tables are sitting on a path somewhere.
Defaults to None.

 None
left_table Optional[DataFrame]

If method defines the left table as a table, then this parameter is the actual dataframe to do the checking against.
Defaults to None.

 None
left_table_path Optional[str]

If method defines the left table as a path, then this parameter is the actual path location where the table can be found.
Defaults to None.

 None
left_table_name Optional[str]

If method defines the left table as a path, then this parameter is the name of the table found at the given left_table_path location.
Defaults to None.

 None
left_table_format str

If method defines the left table as a path, then this parameter is the format of the table found at the given left_table_path location.
Defaults to "delta".

 'delta'
right_table Optional[DataFrame]

If method defines the right table as a table, then this parameter is the actual dataframe to do the checking against.
Defaults to None.

 None
right_table_path Optional[str]

If method defines the right table as a path, then this parameter is the actual path location where the table can be found.
Defaults to None.

 None
right_table_name Optional[str]

If method defines the right table as a path, then this parameter is the name of the table found at the given right_table_path location.
Defaults to None.

 None
right_table_format str

If method defines the right table as a path, then this parameter is the format of the table found at the given right_table_path location.
Defaults to "delta".

 'delta'
include_change_field bool

When doing the schema validations, do you want to include any fields where the data-type on the right-hand side is different from the left-hand side?
This can be read as: "What fields have had their data type changed between the left-hand side and the right-hand side?"
Defaults to True.

 True
include_add_field bool

When doing the schema validations, do you want to include any fields that have had any additional fields added to the left-hand side, when compared to the right-hand side?
This can be read as: "What fields have been added to the left-hand side?"
Defaults to True.

 True
include_remove_field bool

When doing the schema validations, do you want to include any fields which are missing from the left-hand side and only existing on the right-hand side?
This can be read as: "What fields been removed from the left-hand side?"
Defaults to True.

 True
include_change_nullable bool

When doing the schema validations, do you want to include any fields which have had their nullability metadata changed on the right-hand side, when compared to the left-hand side?.
This can be read as: "What fields had their nullability changed between the left-hand side and the right-hand side?"
Defaults to False.

 False
return_object Literal['results', 'check']

After having checked the schema, how do you want the results to be returned? If "check", then will only return a bool value: True if the schemas actually match, False if there are any differences. If "results", then the actual schema differences will be returned. Check the Notes section for more information on the structure of this object.
Defaults to "check".

 'check'

Raises:

Type Description
TypeError

If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
AttributeError

If the value parse'd to method is not a valid option.

Returns:

Type Description
Union[list[tuple[str, dict[str, StructField]]], bool]

If return_object is "results", then this will be a list of tuple's of dict's containing the details of the schema differences. If return_object is "check", then it will only be a bool object about whether the schemas match or not.
Examples

Set up
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>>> # Imports
>>> from pprint import pprint
>>> import pandas as pd
>>> from pyspark.sql import SparkSession, functions as F
>>> from toolbox_pyspark.schema import check_schemas_match
>>> from toolbox_pyspark.io import write_to_path
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df1 = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [0, 1, 2, 3],
...             "b": ["a", "b", "c", "d"],
...             "c": ["1", "1", "1", "1"],
...             "d": ["2", "2", "2", "2"],
...             "e": ["3", "3", "3", "3"],
...             "f": ["4", "4", "4", "4"],
...         }
...     )
... )
>>> df2 = (
...     df1.withColumn("c", F.col("c").cast("int"))
...     .withColumn("g", F.lit("a"))
...     .withColumn("d", F.lit("null"))
...     .drop("e")
... )
>>> write_to_path(
...     table=df1,
...     name="left",
...     path="./test",
...     data_format="parquet",
...     mode="overwrite",
...     write_options={"overwriteSchema": "true"},
... )
>>> write_to_path(
...     table=df2,
...     name="right",
...     path="./test",
...     data_format="parquet",
...     mode="overwrite",
...     write_options={"overwriteSchema": "true"},
... )
>>>
>>> # Check
>>> pprint(df1.dtypes)
>>> print(df1.show())
>>> print(table_exists("left", "./test", "parquet", spark))
>>> pprint(df2.dtypes)
>>> print(df2.show())
>>> print(table_exists("right", "./test", "parquet", spark))
Terminal
[
    ("a", "bigint"),
    ("b", "string"),
    ("c", "string"),
    ("d", "string"),
    ("e", "string"),
    ("f", "string"),
]
Terminal
+---+---+---+---+---+---+
| a | b | c | d | e | f |
+---+---+---+---+---+---+
| 0 | a | 1 | 2 | 3 | 4 |
| 1 | b | 1 | 2 | 3 | 4 |
| 2 | c | 1 | 2 | 3 | 4 |
| 3 | d | 1 | 2 | 3 | 4 |
+---+---+---+---+---+---+
Terminal
True
Terminal
[
    ("a", "bigint"),
    ("b", "string"),
    ("c", "int"),
    ("d", "string"),
    ("f", "string"),
    ("g", "string"),
]
Terminal
+---+---+---+------+---+---+
| a | b | c |    d | f | g |
+---+---+---+------+---+---+
| 0 | a | 1 | null | 4 | 2 |
| 1 | b | 1 | null | 4 | 2 |
| 2 | c | 1 | null | 4 | 2 |
| 3 | d | 1 | null | 4 | 2 |
+---+---+---+------+---+---+
Terminal
True

Example 1: Check matching
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>>> diff = check_schemas_match(
...     method="table_table",
...     left_table=df1,
...     right_table=df1,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     return_object="check",
... )
>>> print(diff)
Terminal
True

Conclusion: Schemas match.

Example 2: Check not matching
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>>> diff = check_schemas_match(
...     method="table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     return_object="check",
... )
>>> print(diff)
Terminal
False

Conclusion: Schemas do not match.

Example 3: Show only `add`
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>>> diff = check_schemas_match(
...     method="table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=False,
...     include_remove_field=False,
...     include_change_nullable=False,
...     return_object="results",
... )
>>> print(diff)
Terminal
[
    (
        "add",
        {"left": T.StructField("e", T.StringType(), False)},
    ),
]

Conclusion: Schemas do not match because the e field was added.

Example 4: Show `add` and `remove`
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>>> diff = check_schemas_match(
...     method="table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=False,
...     include_remove_field=True,
...     include_change_nullable=False,
...     return_object="results",
... )
>>> print(diff)
Terminal
[
    (
        "add",
        {"left": T.StructField("e", T.StringType(), False)},
    ),
    (
        "remove",
        {"right": T.StructField("g", T.StringType(), False)},
    ),
]

Conclusion: Schemas do not match because the e field was added and the g field was removed.

Example 5: Show all changes
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>>> diff = check_schemas_match(
...     method="table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     return_object="results",
... )
>>> print(diff)
Terminal
[
    (
        "add",
        {"left": T.StructField("e", T.StringType(), False)},
    ),
    (
        "remove",
        {"right": T.StructField("g", T.StringType(), False)},
    ),
    (
        "change_type",
        {
            "left": T.StructField("c", T.StringType(), False),
            "right": T.StructField("c", T.IntegerType(), True),
        },
    ),
    (
        "change_nullable",
        {
            "left": T.StructField("c", T.StringType(), False),
            "right": T.StructField("c", T.IntegerType(), True),
        },
    ),
]

Conclusion: Schemas do not match because the e field was added, the g field was removed, the c field had its data type changed, and the c field had its nullability changed.

Example 6: Check where right-hand side is a `path`
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>>> diff = check_schemas_match(
...     method="path_table",
...     spark_session=spark,
...     left_table=df1,
...     right_table_path="./test",
...     right_table_name="right",
...     right_table_format="parquet",
...     include_add_field=True,
...     include_change_field=False,
...     include_remove_field=False,
...     include_change_nullable=False,
...     return_object="results",
... )
>>> print(diff)
Terminal
[
    (
        "add",
        {"left": T.StructField("e", T.StringType(), False)},
    ),
]

Conclusion: Schemas do not match because the e field was added.

Example 7: Check where both sides are a `path`
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>>> diff = check_schemas_match(
...     method="path_path",
...     spark_session=spark,
...     left_table_path="./test",
...     left_table_name="left",
...     left_table_format="parquet",
...     right_table_path="./test",
...     right_table_name="right",
...     right_table_format="parquet",
...     include_add_field=False,
...     include_change_field=True,
...     include_remove_field=False,
...     include_change_nullable=False,
...     return_object="results",
... )
>>> print(diff)
Terminal
[
    (
        "remove",
        {"right": T.StructField("g", T.StringType(), True)},
    ),
]

Conclusion: Schemas do not match because the g field was removed.

Example 8: Invalid `method` parameter
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>>> diff = check_schemas_match(
...     method="invalid",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     return_object="check",
... )

AttributeError: Invalid value for `method`: 'invalid'
Please use one of the following options:
- For `by_table_and_table`, use one of the following values: ['table', 'table_table', 'tables', 'by_table', 'by_table_and_table', 'table_and_table']
- For `by_table_and_path`, use one of the following values: ['table_and_path', 'table_path', 'by_table_and_path']
- For `by_path_and_table`, use one of the following values: ['path_and_table', 'path_table', 'by_path_and_table']
- For `by_path_and_path`, use one of the following values: ['path_and_path', 'path_path', 'by_path_and_path', 'path', 'paths']

Conclusion: Invalid method parameter.

Notes
Options available in the method parameter

The options available in the method parameter include:

  • If the objects on both the left-hand side and the right-hand side are both dataframes already loaded to memory, use one of the following values:
    • "table"
    • "table_table"
    • "tables"
    • "by_table"
    • "by_table_and_table"
    • "table_and_table"
  • If the object on the left-hand side is a dataframe already loaded to memory, but the object on the right-hand side is a table sitting on a path somewhere, use one of the following values:
    • "table_and_path"
    • "table_path"
    • "by_table_and_path"
  • If the object on the left-hand side is a table sitting on a path somewhere, but the object on the right-hand side is a dataframe already loaded to memory, use one of the following values:
    • "path_and_table"
    • "path_table"
    • "by_path_and_table"
  • If the objects on both the left-hand side and the right-hand side are both tables sitting on a path somewhere, then use one of the following values:
    • "path_and_path"
    • "path_path"
    • "by_path_and_path"
    • "path"
    • "paths"
    • Details about the return object when we set the parameter return_object="results"
    • When we set the parameter return_object="results", then we will get an object returned from this function.
    • That object will be a list of tuple's, each tuple is only two-elements long, where the first element is a str object, and the second is a dict where the keys are str and the values are a StructField object.
    • For each of the tuple elements, the first element (the str object) describes what the tuple is there for. It will be one of four words: "add", "remove", "change_type", or "change_nullable".
    • You can change whether these options are included in the schema check by changing the other parameters: include_change_field, include_add_field, include_remove_field, include_change_nullable.
    • The structure of the list will look like this:
    [
    (
        "add",  # (1)!
        {"left": T.StructField("e", T.StringType(), False)},  # (2)!
    ),
    (
        "add",  # (3)!
        {"left": T.StructField("h", T.StringType(), False)},
    ),
    (
        "remove",  # (4)!
        {"right": T.StructField("g", T.StringType(), False)},  # (5)!
    ),
    (
        "change_type",  # (6)!
        {
            "left": T.StructField("c", T.StringType(), False),  # (7)!
            "right": T.StructField("c", T.IntegerType(), True),
        },
    ),
    (
        "change_nullable",  # (8)!
        {
            "left": T.StructField("c", T.StringType(), False),  # (9)!
            "right": T.StructField("c", T.IntegerType(), True),
        },
    ),
]
    1. When include_add_field=True, then the add section will always appear first.
      If include_add_field=False, then this section is omitted.
    2. The second element of the tuple is a dict that has only one key-value pair.
      The key will always be the value "left", because these are fields which have been added to the table on the left-hand side and not found on the right-hand side.
    3. When there are multiple fields added to the table on the left-hand side, they will appear like this.
    4. When include_remove_field=True, then the remove section will always appear next.
      If include_remove_field=False, then this section is omitted.
    5. The second element of the tuple is a dict that has only one key-value pair.
      The key will always be the value "right", because these are fields which have been removed from the left-hand side and only visible on the right-hand side.
    6. When include_change_field=True, then the change_type section will always appear next.
      If include_change_field=False, then this section is omitted.
    7. The second element of the tuple is a dict that has two key-value pairs.
      The key's will always be the values "left" then "right", because these are fields where the data type has changed between the left-hand side and the right-hand side, and therefore you need to see both to see exactly what has changed.
    8. When include_change_nullable=True, then the change_nullable section will always appear next.
      If include_change_nullable=False, then this section is omitted.
    9. The sectond element of the tuple is a dict that has two key-value pairs.
      The key's will always be the values "left" then "right", because these are fields where the nullability of the firlds are changed between the left-hand side and the right-hand side, and therefore you need to see both to see exactly what has changed.
    Source code in src/toolbox_pyspark/schema.py 
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    @typechecked
def check_schemas_match(
    method: str = "by_table_and_table",
    left_table: Optional[psDataFrame] = None,
    right_table: Optional[psDataFrame] = None,
    left_table_path: Optional[str] = None,
    left_table_name: Optional[str] = None,
    right_table_path: Optional[str] = None,
    right_table_name: Optional[str] = None,
    spark_session: Optional[SparkSession] = None,
    left_table_format: str = "delta",
    right_table_format: str = "delta",
    include_change_field: bool = True,
    include_add_field: bool = True,
    include_remove_field: bool = True,
    include_change_nullable: bool = False,
    return_object: Literal["results", "check"] = "check",
) -> Union[list[tuple[str, dict[str, StructField]]], bool]:
    """
    !!! note "Summary"
        Check the schemas between two different tables.

    ???+ abstract "Details"
        This function is heavily inspired by other packages which check and validate schema differences for `pyspark` tables. This function just streamlines it a bit, and adds additional functionality for whether or not table on either `left` or `right` side is already in-memory or sitting on a directory somewhere else.

    Params:
        method (str, optional):
            The method to use for the comparison. That is, is either side a table in memory or is it a `table` sitting on a `path`?. Check the Notes section for all options available for this parameter.<br>
            Defaults to `#!py "by_table_and_table"`.
        spark_session (Optional[SparkSession], optional):
            The `SparkSession` to use if either the `left` or `right` tables are sitting on a `path` somewhere.<br>
            Defaults to `#!py None`.
        left_table (Optional[psDataFrame], optional):
            If `method` defines the `left` table as a `table`, then this parameter is the actual `dataframe` to do the checking against.<br>
            Defaults to `#!py None`.
        left_table_path (Optional[str], optional):
            If `method` defines the `left` table as a `path`, then this parameter is the actual path location where the table can be found.<br>
            Defaults to `#!py None`.
        left_table_name (Optional[str], optional):
            If `method` defines the `left` table as a `path`, then this parameter is the name of the table found at the given `left_table_path` location.<br>
            Defaults to `#!py None`.
        left_table_format (str, optional):
            If `method` defines the `left` table as a `path`, then this parameter is the format of the table found at the given `left_table_path` location.<br>
            Defaults to `#!py "delta"`.
        right_table (Optional[psDataFrame], optional):
            If `method` defines the `right` table as a `table`, then this parameter is the actual `dataframe` to do the checking against.<br>
            Defaults to `#!py None`.
        right_table_path (Optional[str], optional):
            If `method` defines the `right` table as a `path`, then this parameter is the actual path location where the table can be found.<br>
            Defaults to `#!py None`.
        right_table_name (Optional[str], optional):
            If `method` defines the `right` table as a `path`, then this parameter is the name of the table found at the given `right_table_path` location.<br>
            Defaults to `#!py None`.
        right_table_format (str, optional):
            If `method` defines the `right` table as a `path`, then this parameter is the format of the table found at the given `right_table_path` location.<br>
            Defaults to `#!py "delta"`.
        include_change_field (bool, optional):
            When doing the schema validations, do you want to include any fields where the data-type on the right-hand side is different from the left-hand side?<br>
            This can be read as: "What fields have had their data type _changed **between**_ the left-hand side and the right-hand side?"<br>
            Defaults to `#!py True`.
        include_add_field (bool, optional):
            When doing the schema validations, do you want to include any fields that have had any additional fields added to the left-hand side, when compared to the right-hand side?<br>
            This can be read as: "What fields have been _added **to**_ the left-hand side?"<br>
            Defaults to `#!py True`.
        include_remove_field (bool, optional):
            When doing the schema validations, do you want to include any fields which are missing from the left-hand side and only existing on the right-hand side?<br>
            This can be read as: "What fields been _removed **from**_ the left-hand side?"<br>
            Defaults to `#!py True`.
        include_change_nullable (bool, optional):
            When doing the schema validations, do you want to include any fields which have had their nullability metadata changed on the right-hand side, when compared to the left-hand side?.<br>
            This can be read as: "What fields had their nullability _changed **between**_ the left-hand side and the right-hand side?"<br>
            Defaults to `#!py False`.
        return_object (Literal["results", "check"], optional):
            After having checked the schema, how do you want the results to be returned? If `#!py "check"`, then will only return a `#!py bool` value: `#!py True` if the schemas actually match, `#!py False` if there are any differences. If `#!py "results"`, then the actual schema differences will be returned. Check the Notes section for more information on the structure of this object.<br>
            Defaults to `#!py "check"`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        AttributeError:
            If the value parse'd to `method` is not a valid option.

    Returns:
        (Union[list[tuple[str, dict[str, StructField]]], bool]):
            If `return_object` is `#!py "results"`, then this will be a `#!py list` of `#!py tuple`'s of `#!py dict`'s containing the details of the schema differences. If `return_object` is `#!py "check"`, then it will only be a `#!py bool` object about whether the schemas match or not.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> from pprint import pprint
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession, functions as F
        >>> from toolbox_pyspark.schema import check_schemas_match
        >>> from toolbox_pyspark.io import write_to_path
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df1 = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [0, 1, 2, 3],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": ["1", "1", "1", "1"],
        ...             "d": ["2", "2", "2", "2"],
        ...             "e": ["3", "3", "3", "3"],
        ...             "f": ["4", "4", "4", "4"],
        ...         }
        ...     )
        ... )
        >>> df2 = (
        ...     df1.withColumn("c", F.col("c").cast("int"))
        ...     .withColumn("g", F.lit("a"))
        ...     .withColumn("d", F.lit("null"))
        ...     .drop("e")
        ... )
        >>> write_to_path(
        ...     table=df1,
        ...     name="left",
        ...     path="./test",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ...     write_options={"overwriteSchema": "true"},
        ... )
        >>> write_to_path(
        ...     table=df2,
        ...     name="right",
        ...     path="./test",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ...     write_options={"overwriteSchema": "true"},
        ... )
        >>>
        >>> # Check
        >>> pprint(df1.dtypes)
        >>> print(df1.show())
        >>> print(table_exists("left", "./test", "parquet", spark))
        >>> pprint(df2.dtypes)
        >>> print(df2.show())
        >>> print(table_exists("right", "./test", "parquet", spark))
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            ("a", "bigint"),
            ("b", "string"),
            ("c", "string"),
            ("d", "string"),
            ("e", "string"),
            ("f", "string"),
        ]
        ```
        ```{.txt .text title="Terminal"}
        +---+---+---+---+---+---+
        | a | b | c | d | e | f |
        +---+---+---+---+---+---+
        | 0 | a | 1 | 2 | 3 | 4 |
        | 1 | b | 1 | 2 | 3 | 4 |
        | 2 | c | 1 | 2 | 3 | 4 |
        | 3 | d | 1 | 2 | 3 | 4 |
        +---+---+---+---+---+---+
        ```
        ```{.sh .shell title="Terminal"}
        True
        ```
        ```{.sh .shell title="Terminal"}
        [
            ("a", "bigint"),
            ("b", "string"),
            ("c", "int"),
            ("d", "string"),
            ("f", "string"),
            ("g", "string"),
        ]
        ```
        ```{.txt .text title="Terminal"}
        +---+---+---+------+---+---+
        | a | b | c |    d | f | g |
        +---+---+---+------+---+---+
        | 0 | a | 1 | null | 4 | 2 |
        | 1 | b | 1 | null | 4 | 2 |
        | 2 | c | 1 | null | 4 | 2 |
        | 3 | d | 1 | null | 4 | 2 |
        +---+---+---+------+---+---+
        ```
        ```{.sh .shell title="Terminal"}
        True
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Check matching"}
        >>> diff = check_schemas_match(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df1,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     return_object="check",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        True
        ```
        !!! success "Conclusion: Schemas match."
        </div>

        ```{.py .python linenums="1" title="Example 2: Check not matching"}
        >>> diff = check_schemas_match(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     return_object="check",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        False
        ```
        !!! failure "Conclusion: Schemas do not match."
        </div>

        ```{.py .python linenums="1" title="Example 3: Show only `add`"}
        >>> diff = check_schemas_match(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=False,
        ...     include_remove_field=False,
        ...     include_change_nullable=False,
        ...     return_object="results",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            (
                "add",
                {"left": T.StructField("e", T.StringType(), False)},
            ),
        ]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added."
        </div>

        ```{.py .python linenums="1" title="Example 4: Show `add` and `remove`"}
        >>> diff = check_schemas_match(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=False,
        ...     include_remove_field=True,
        ...     include_change_nullable=False,
        ...     return_object="results",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            (
                "add",
                {"left": T.StructField("e", T.StringType(), False)},
            ),
            (
                "remove",
                {"right": T.StructField("g", T.StringType(), False)},
            ),
        ]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added and the `g` field was removed."
        </div>

        ```{.py .python linenums="1" title="Example 5: Show all changes"}
        >>> diff = check_schemas_match(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     return_object="results",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            (
                "add",
                {"left": T.StructField("e", T.StringType(), False)},
            ),
            (
                "remove",
                {"right": T.StructField("g", T.StringType(), False)},
            ),
            (
                "change_type",
                {
                    "left": T.StructField("c", T.StringType(), False),
                    "right": T.StructField("c", T.IntegerType(), True),
                },
            ),
            (
                "change_nullable",
                {
                    "left": T.StructField("c", T.StringType(), False),
                    "right": T.StructField("c", T.IntegerType(), True),
                },
            ),
        ]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added, the `g` field was removed, the `c` field had its data type changed, and the `c` field had its nullability changed."
        </div>

        ```{.py .python linenums="1" title="Example 6: Check where right-hand side is a `path`"}
        >>> diff = check_schemas_match(
        ...     method="path_table",
        ...     spark_session=spark,
        ...     left_table=df1,
        ...     right_table_path="./test",
        ...     right_table_name="right",
        ...     right_table_format="parquet",
        ...     include_add_field=True,
        ...     include_change_field=False,
        ...     include_remove_field=False,
        ...     include_change_nullable=False,
        ...     return_object="results",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            (
                "add",
                {"left": T.StructField("e", T.StringType(), False)},
            ),
        ]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added."
        </div>

        ```{.py .python linenums="1" title="Example 7: Check where both sides are a `path`"}
        >>> diff = check_schemas_match(
        ...     method="path_path",
        ...     spark_session=spark,
        ...     left_table_path="./test",
        ...     left_table_name="left",
        ...     left_table_format="parquet",
        ...     right_table_path="./test",
        ...     right_table_name="right",
        ...     right_table_format="parquet",
        ...     include_add_field=False,
        ...     include_change_field=True,
        ...     include_remove_field=False,
        ...     include_change_nullable=False,
        ...     return_object="results",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            (
                "remove",
                {"right": T.StructField("g", T.StringType(), True)},
            ),
        ]
        ```
        !!! failure "Conclusion: Schemas do not match because the `g` field was removed."
        </div>

        ```{.py .python linenums="1" title="Example 8: Invalid `method` parameter"}
        >>> diff = check_schemas_match(
        ...     method="invalid",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     return_object="check",
        ... )
        ```
        <div class="result" markdown>
        ```{.py .python .title="Terminal"}
        AttributeError: Invalid value for `method`: 'invalid'
        Please use one of the following options:
        - For `by_table_and_table`, use one of the following values: ['table', 'table_table', 'tables', 'by_table', 'by_table_and_table', 'table_and_table']
        - For `by_table_and_path`, use one of the following values: ['table_and_path', 'table_path', 'by_table_and_path']
        - For `by_path_and_table`, use one of the following values: ['path_and_table', 'path_table', 'by_path_and_table']
        - For `by_path_and_path`, use one of the following values: ['path_and_path', 'path_path', 'by_path_and_path', 'path', 'paths']
        ```
        !!! failure "Conclusion: Invalid `method` parameter."
        </div>

    ???+ info "Notes"

        ???+ info "Options available in the `method` parameter"

            The options available in the `method` parameter include:

            - If the objects on both the left-hand side and the right-hand side are both `dataframes` already loaded to memory, use one of the following values:
                <div class="mdx-three-columns" markdown>
                - `#!py "table"`
                - `#!py "table_table"`
                - `#!py "tables"`
                - `#!py "by_table"`
                - `#!py "by_table_and_table"`
                - `#!py "table_and_table"`
                </div>
            - If the object on the left-hand side is a `dataframe` already loaded to memory, but the object on the right-hand side is a table sitting on a path somewhere, use one of the following values:
                <div class="mdx-three-columns" markdown>
                - `#!py "table_and_path"`
                - `#!py "table_path"`
                - `#!py "by_table_and_path"`
                </div>
            - If the object on the left-hand side is a table sitting on a path somewhere, but the object on the right-hand side is a `dataframe` already loaded to memory, use one of the following values:
                <div class="mdx-three-columns" markdown>
                - `#!py "path_and_table"`
                - `#!py "path_table"`
                - `#!py "by_path_and_table"`
                </div>
            - If the objects on both the left-hand side and the right-hand side are both tables sitting on a path somewhere, then use one of the following values:
                <div class="mdx-three-columns" markdown>
                - `#!py "path_and_path"`
                - `#!py "path_path"`
                - `#!py "by_path_and_path"`
                - `#!py "path"`
                - `#!py "paths"`
                </div>

        ???+ info "Details about the return object when we set the parameter `#!py return_object="results"`"

            - When we set the parameter `#!py return_object="results"`, then we will get an object returned from this function.
            - That object will be a `#!py list` of `#!py tuple`'s, each `#!py tuple` is only two-elements long, where the first element is a `#!py str` object, and the second is a `#!py dict` where the keys are `#!py str` and the values are a `#!py StructField` object.
            - For each of the `#!py tuple` elements, the first element (the `#!py str` object) describes what the `#!py tuple` is there for. It will be one of four words: `#!py "add"`, `#!py "remove"`, `#!py "change_type"`, or `#!py "change_nullable"`.
            - You can change whether these options are included in the schema check by changing the other parameters: `#!py include_change_field`, `#!py include_add_field`, `#!py include_remove_field`, `#!py include_change_nullable`.
            - The structure of the list will look like this:

            ```{.py .python .title="The structure of the returned object"}
            [
                (
                    "add",  # (1)!
                    {"left": T.StructField("e", T.StringType(), False)},  # (2)!
                ),
                (
                    "add",  # (3)!
                    {"left": T.StructField("h", T.StringType(), False)},
                ),
                (
                    "remove",  # (4)!
                    {"right": T.StructField("g", T.StringType(), False)},  # (5)!
                ),
                (
                    "change_type",  # (6)!
                    {
                        "left": T.StructField("c", T.StringType(), False),  # (7)!
                        "right": T.StructField("c", T.IntegerType(), True),
                    },
                ),
                (
                    "change_nullable",  # (8)!
                    {
                        "left": T.StructField("c", T.StringType(), False),  # (9)!
                        "right": T.StructField("c", T.IntegerType(), True),
                    },
                ),
            ]
            ```

            1. When `#!py include_add_field=True`, then the `add` section will always appear first.<br>
                If `#!py include_add_field=False`, then this section is omitted.
            2. The second element of the `#!py tuple` is a `#!py dict` that has only one `key`-`value` pair.<br>
                The `key` will _always_ be the value `#!py "left"`, because these are fields which have been added to the table on the left-hand side and not found on the right-hand side.
            3. When there are multiple fields added to the table on the left-hand side, they will appear like this.
            4. When `#!py include_remove_field=True`, then the `remove` section will always appear next.<br>
                If `#!py include_remove_field=False`, then this section is omitted.
            5. The second element of the `#!py tuple` is a `#!py dict` that has only one `key`-`value` pair.<br>
                The `key` will _always_ be the value `#!py "right"`, because these are fields which have been removed from the left-hand side and only visible on the right-hand side.
            6. When `#!py include_change_field=True`, then the `change_type` section will always appear next.<br>
                If `#!py include_change_field=False`, then this section is omitted.
            7. The second element of the `#!py tuple` is a `#!py dict` that has two `key`-`value` pairs.<br>
                The `key`'s will _always_ be the values `#!py "left"` then `#!py "right"`, because these are fields where the data type has changed between the left-hand side and the right-hand side, and therefore you need to see both to see exactly what has changed.
            8. When `#!py include_change_nullable=True`, then the `change_nullable` section will always appear next.<br>
                If `#!py include_change_nullable=False`, then this section is omitted.
            9. The sectond element of the `#!py tuple` is a `#!py dict` that has two `key`-`value` pairs.<br>
                The `key`'s will _always_ be the values `#!py "left"` then `#!py "right"`, because these are fields where the nullability of the firlds are changed between the left-hand side and the right-hand side, and therefore you need to see both to see exactly what has changed.
    """

    valid_methods = ValidMethods()
    msg: str = "If using the '{meth}' method, then '{name}' cannot be 'None'."

    if method in valid_methods.by_table_and_table:
        assert left_table is not None, msg.format(meth=method, name="left_table")
        assert right_table is not None, msg.format(meth=method, name="right_table")
        return _check_schemas_match_by_table_and_table(
            left_table=left_table,
            right_table=right_table,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            return_object=return_object,
        )
    elif method in valid_methods.by_table_and_path:
        assert left_table is not None, msg.format(meth=method, name="left_table")
        assert right_table_path is not None, msg.format(meth=method, name="right_table_path")
        assert right_table_name is not None, msg.format(meth=method, name="right_table_name")
        assert spark_session is not None, msg.format(meth=method, name="spark_session")
        return _check_schemas_match_by_table_and_path(
            left_table=left_table,
            right_table_path=right_table_path,
            right_table_name=right_table_name,
            right_table_format=right_table_format,
            spark_session=spark_session,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            return_object=return_object,
        )
    elif method in valid_methods.by_path_and_table:
        assert left_table_path is not None, msg.format(meth=method, name="left_table_path")
        assert left_table_name is not None, msg.format(meth=method, name="left_table_name")
        assert right_table is not None, msg.format(meth=method, name="right_table")
        assert spark_session is not None, msg.format(meth=method, name="spark_session")
        return _check_schemas_match_by_path_and_table(
            left_table_path=left_table_path,
            left_table_name=left_table_name,
            right_table=right_table,
            spark_session=spark_session,
            left_table_format=left_table_format,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            return_object=return_object,
        )
    elif method in valid_methods.by_path_and_path:
        assert left_table_path is not None, msg.format(meth=method, name="left_table_path")
        assert left_table_name is not None, msg.format(meth=method, name="left_table_name")
        assert right_table_path is not None, msg.format(meth=method, name="right_table_path")
        assert right_table_name is not None, msg.format(meth=method, name="right_table_name")
        assert spark_session is not None, msg.format(meth=method, name="spark_session")
        return _check_schemas_match_by_path_and_path(
            left_table_path=left_table_path,
            left_table_name=left_table_name,
            left_table_format=left_table_format,
            right_table_path=right_table_path,
            right_table_name=right_table_name,
            right_table_format=right_table_format,
            spark_session=spark_session,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            return_object=return_object,
        )
    else:
        raise AttributeError(
            f"Invalid value for `method`: '{method}'\n"
            f"Please use one of the following options:\n"
            f"- For `by_table_and_table`, use one of: {valid_methods.by_table_and_table}\n"
            f"- For `by_table_and_path`, use one of: {valid_methods.by_table_and_path}\n"
            f"- For `by_path_and_table`, use one of: {valid_methods.by_path_and_table}\n"
            f"- For `by_path_and_path`, use one of: {valid_methods.by_path_and_path}\n"
        )

    view_schema_differences 🔗

    view_schema_differences(
    method: str = "by_table_and_table",
    spark_session: Optional[SparkSession] = None,
    left_table: Optional[psDataFrame] = None,
    left_table_path: Optional[str] = None,
    left_table_name: Optional[str] = None,
    left_table_format: str = "delta",
    right_table: Optional[psDataFrame] = None,
    right_table_path: Optional[str] = None,
    right_table_name: Optional[str] = None,
    right_table_format: str = "delta",
    include_change_field: bool = True,
    include_add_field: bool = True,
    include_remove_field: bool = True,
    include_change_nullable: bool = False,
    view_type: Literal[
        "print", "pprint", "return"
    ] = "pprint",
) -> Optional[
    Union[list[tuple[str, dict[str, StructField]]], bool]
]

    Summary

    View the schemas between two different tables.

    Details

    The primary differences between check_schemas_match() and view_schema_differences() is that check_...() returns either a bool result, or the actual details of the schema differences, whilst view_...() may also return the actual details object, but it will also print the result to the terminal for you to review.
    For full details of all the parameters and all the options, including nuances and detailed explanations and thorough examples, please check the check_schemas_match() function.

    Parameters:

    Name Type Description Default
    method str

    The method to use for the comparison. That is, is either side a table in memory or is it a table sitting on a path?. Check the Notes section for all options available for this parameter.
    Defaults to "by_table_and_table".

    		 'by_table_and_table'
    spark_session Optional[SparkSession]

    The SparkSession to use if either the left or right tables are sitting on a path somewhere.
    Defaults to None.

    		 None
    left_table Optional[DataFrame]

    If method defines the left table as a table, then this parameter is the actual dataframe to do the checking against.
    Defaults to None.

    		 None
    left_table_path Optional[str]

    If method defines the left table as a path, then this parameter is the actual path location where the table can be found.
    Defaults to None.

    		 None
    left_table_name Optional[str]

    If method defines the left table as a path, then this parameter is the name of the table found at the given left_table_path location.
    Defaults to None.

    		 None
    left_table_format str

    If method defines the left table as a path, then this parameter is the format of the table found at the given left_table_path location.
    Defaults to "delta".

    		 'delta'
    right_table Optional[DataFrame]

    If method defines the right table as a table, then this parameter is the actual dataframe to do the checking against.
    Defaults to None.

    		 None
    right_table_path Optional[str]

    If method defines the right table as a path, then this parameter is the actual path location where the table can be found.
    Defaults to None.

    		 None
    right_table_name Optional[str]

    If method defines the right table as a path, then this parameter is the name of the table found at the given right_table_path location.
    Defaults to None.

    		 None
    right_table_format str

    If method defines the right table as a path, then this parameter is the format of the table found at the given right_table_path location.
    Defaults to "delta".

    		 'delta'
    include_change_field bool

    When doing the schema validations, do you want to include any fields where the data-type on the right-hand side is different from the left-hand side?
    This can be read as: "What fields have had their data type changed between the left-hand side and the right-hand side?"
    Defaults to True.

    		 True
    include_add_field bool

    When doing the schema validations, do you want to include any fields that have had any additional fields added to the left-hand side, when compared to the right-hand side?
    This can be read as: "What fields have been added to the left-hand side?"
    Defaults to True.

    		 True
    include_remove_field bool

    When doing the schema validations, do you want to include any fields which are missing from the left-hand side and only existing on the right-hand side?
    This can be read as: "What fields been removed from the left-hand side?"
    Defaults to True.

    		 True
    include_change_nullable bool

    When doing the schema validations, do you want to include any fields which have had their nullability metadata changed on the right-hand side, when compared to the left-hand side?.
    This can be read as: "What fields had their nullability changed between the left-hand side and the right-hand side?"
    Defaults to False.

    		 False
    view_type Literal['print', 'pprint', 'return']

    When returning the output from this function, how do you want it to be displayed? Must be one of ["print", "pprint", "return"].
    Logically, the difference is that "print" will display a text value to the terminal that is not formatted in any way; "pprint" will display a pretty-printed text value to the terminal; and "return" will return the schema differences which can then be assigned to another variable.
    Defaults to "pprint".

    		 'pprint'

    Raises:

    Type Description
    TypeError

    If any of the inputs parsed to the parameters of this function are not the correct type. Uses the @typeguard.typechecked decorator.
    AttributeError

    If the value parse'd to method is not a valid option.

    Returns:

    Type Description
    Optional[list[tuple[str, dict[str, StructField]]]]

    If view_type="return", then this will be a list of tuple's of dict's containing the details of the schema differences. If view_type!="return" (or if view_type="return", but there are actually no differences in the schema), then nothing is returned; only printed to terminal.
    Examples

    Set up
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    >>> # Imports
>>> from pprint import pprint
>>> import pandas as pd
>>> from pyspark.sql import SparkSession, functions as F
>>> from toolbox_pyspark.schema import view_schema_differences
>>> from toolbox_pyspark.io import write_to_path
>>> from toolbox_pyspark.checks import table_exists
>>>
>>> # Instantiate Spark
>>> spark = SparkSession.builder.getOrCreate()
>>>
>>> # Create data
>>> df1 = spark.createDataFrame(
...     pd.DataFrame(
...         {
...             "a": [0, 1, 2, 3],
...             "b": ["a", "b", "c", "d"],
...             "c": ["1", "1", "1", "1"],
...             "d": ["2", "2", "2", "2"],
...             "e": ["3", "3", "3", "3"],
...             "f": ["4", "4", "4", "4"],
...         }
...     )
... )
>>> df2 = (
...     df1.withColumn("c", F.col("c").cast("int"))
...     .withColumn("g", F.lit("a"))
...     .withColumn("d", F.lit("null"))
...     .drop("e")
... )
>>> write_to_path(
...     table=df1,
...     name="left",
...     path="./test",
...     data_format="parquet",
...     mode="overwrite",
...     write_options={"overwriteSchema": "true"},
... )
>>> write_to_path(
...     table=df2,
...     name="right",
...     path="./test",
...     data_format="parquet",
...     mode="overwrite",
...     write_options={"overwriteSchema": "true"},
... )
>>>
>>> # Check
>>> pprint(df1.dtypes)
>>> print(df1.show())
>>> print(table_exists("left", "./test", "parquet", spark))
>>> pprint(df2.dtypes)
>>> print(df2.show())
>>> print(table_exists("right", "./test", "parquet", spark))
    Terminal
    [
    ("a", "bigint"),
    ("b", "string"),
    ("c", "string"),
    ("d", "string"),
    ("e", "string"),
    ("f", "string"),
]
    Terminal
    +---+---+---+---+---+---+
| a | b | c | d | e | f |
+---+---+---+---+---+---+
| 0 | a | 1 | 2 | 3 | 4 |
| 1 | b | 1 | 2 | 3 | 4 |
| 2 | c | 1 | 2 | 3 | 4 |
| 3 | d | 1 | 2 | 3 | 4 |
+---+---+---+---+---+---+
    Terminal
    True
    Terminal
    [
    ("a", "bigint"),
    ("b", "string"),
    ("c", "int"),
    ("d", "string"),
    ("f", "string"),
    ("g", "string"),
]
    Terminal
    +---+---+---+------+---+---+
| a | b | c |    d | f | g |
+---+---+---+------+---+---+
| 0 | a | 1 | null | 4 | 2 |
| 1 | b | 1 | null | 4 | 2 |
| 2 | c | 1 | null | 4 | 2 |
| 3 | d | 1 | null | 4 | 2 |
+---+---+---+------+---+---+
    Terminal
    True

    Example 1: Check matching
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    >>> view_schema_differences(
...     method="table_table",
...     left_table=df1,
...     right_table=df1,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     view_type="return",
... )
>>> print(diff)
    Terminal
    None

    Conclusion: Schemas match.

    Example 2: Check print
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    >>> view_schema_differences(
...     method="table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=False,
...     include_remove_field=False,
...     include_change_nullable=False,
...     view_type="print",
... )
    Terminal
    [('add', {'left': StructField('e', StringType(), True)})]

    Conclusion: Schemas do not match because the e field was added.

    Example 3: Check pprint
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    >>> view_schema_differences(
...     method="table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     view_type="pprint",
... )
    Terminal
    [('add', {'left': StructField('e', StringType(), False)}),
 ('remove', {'right': StructField('g', StringType(), False)}),
 ('change_type',
  {'left': StructField('c', StringType(), False),
   'right': StructField('c', IntegerType(), True)}),
 ('change_nullable',
  {'left': StructField('c', StringType(), False),
   'right': StructField('c', IntegerType(), True)})]

    Conclusion: Schemas do not match because the e field was added, the g field was removed, the c field had its data type changed, and the c field had its nullability changed.

    Example 4: Check with right-hand side as a `path`
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    >>> view_schema_differences(
...     method="table_table",
...     spark_session=spark,
...     left_table=df1,
...     right_table_path="./test",
...     right_table_name="right",
...     right_table_format="parquet",
...     include_add_field=True,
...     include_change_field=False,
...     include_remove_field=False,
...     include_change_nullable=False,
...     view_type="pprint",
... )
    Terminal
    [('add', {'left': StructField('e', StringType(), True)})]

    Conclusion: Schemas do not match because the e field was added.

    Example 5: Check with both sides being a `path`
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    >>> view_schema_differences(
...     method="table_table",
...     spark_session=spark,
...     left_table_path="./test",
...     left_table_name="left",
...     left_table_format="parquet",
...     right_table_path="./test",
...     right_table_name="right",
...     right_table_format="parquet",
...     include_add_field=False,
...     include_change_field=False,
...     include_remove_field=True,
...     include_change_nullable=False,
...     view_type="pprint",
... )
    Terminal
    [('remove', {'right': StructField('g', StringType(), True)})]

    Conclusion: Schemas do not match because the g field was removed.

    Example 6: Invalid `method` parameter
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    >>> view_schema_differences(
...     method="table_table_table",
...     left_table=df1,
...     right_table=df2,
...     include_add_field=True,
...     include_change_field=True,
...     include_remove_field=True,
...     include_change_nullable=True,
...     view_type="return",
... )
    Terminal
    AttributeError: Invalid value for `method`: 'table_table_table'
Please use one of the following options:
- For `by_table_and_table`, use one of the following values: ['table', 'table_table', 'tables', 'by_table', 'by_table_and_table', 'table_and_table']
- For `by_table_and_path`, use one of the following values: ['table_and_path', 'table_path', 'by_table_and_path']
- For `by_path_and_table`, use one of the following values: ['path_and_table', 'path_table', 'by_path_and_table']
- For `by_path_and_path`, use one of the following values: ['path_and_path', 'path_path', 'by_path_and_path', 'path', 'paths']

    Conclusion: Invalid method parameter.

    See Also
    Source code in src/toolbox_pyspark/schema.py 
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    @typechecked
def view_schema_differences(
    method: str = "by_table_and_table",
    spark_session: Optional[SparkSession] = None,
    left_table: Optional[psDataFrame] = None,
    left_table_path: Optional[str] = None,
    left_table_name: Optional[str] = None,
    left_table_format: str = "delta",
    right_table: Optional[psDataFrame] = None,
    right_table_path: Optional[str] = None,
    right_table_name: Optional[str] = None,
    right_table_format: str = "delta",
    include_change_field: bool = True,
    include_add_field: bool = True,
    include_remove_field: bool = True,
    include_change_nullable: bool = False,
    view_type: Literal["print", "pprint", "return"] = "pprint",
) -> Optional[Union[list[tuple[str, dict[str, StructField]]], bool]]:
    """
    !!! note "Summary"
        View the schemas between two different tables.

    ???+ abstract "Details"
        The primary differences between [`check_schemas_match()`][toolbox_pyspark.schema.check_schemas_match] and [`view_schema_differences()`][toolbox_pyspark.schema.view_schema_differences] is that [`check_...()`][toolbox_pyspark.schema.check_schemas_match] returns either a `#!py bool` result, or the actual details of the schema differences, whilst [`view_...()`][toolbox_pyspark.schema.view_schema_differences] may also return the actual details object, but it will also print the result to the terminal for you to review.<br>
        For full details of all the parameters and all the options, including nuances and detailed explanations and thorough examples, please check the [`check_schemas_match()`][toolbox_pyspark.schema.check_schemas_match] function.

    Params:
        method (str, optional):
            The method to use for the comparison. That is, is either side a table in memory or is it a `table` sitting on a `path`?. Check the Notes section for all options available for this parameter.<br>
            Defaults to `#!py "by_table_and_table"`.
        spark_session (Optional[SparkSession], optional):
            The `SparkSession` to use if either the `left` or `right` tables are sitting on a `path` somewhere.<br>
            Defaults to `#!py None`.
        left_table (Optional[psDataFrame], optional):
            If `method` defines the `left` table as a `table`, then this parameter is the actual `dataframe` to do the checking against.<br>
            Defaults to `#!py None`.
        left_table_path (Optional[str], optional):
            If `method` defines the `left` table as a `path`, then this parameter is the actual path location where the table can be found.<br>
            Defaults to `#!py None`.
        left_table_name (Optional[str], optional):
            If `method` defines the `left` table as a `path`, then this parameter is the name of the table found at the given `left_table_path` location.<br>
            Defaults to `#!py None`.
        left_table_format (str, optional):
            If `method` defines the `left` table as a `path`, then this parameter is the format of the table found at the given `left_table_path` location.<br>
            Defaults to `#!py "delta"`.
        right_table (Optional[psDataFrame], optional):
            If `method` defines the `right` table as a `table`, then this parameter is the actual `dataframe` to do the checking against.<br>
            Defaults to `#!py None`.
        right_table_path (Optional[str], optional):
            If `method` defines the `right` table as a `path`, then this parameter is the actual path location where the table can be found.<br>
            Defaults to `#!py None`.
        right_table_name (Optional[str], optional):
            If `method` defines the `right` table as a `path`, then this parameter is the name of the table found at the given `right_table_path` location.<br>
            Defaults to `#!py None`.
        right_table_format (str, optional):
            If `method` defines the `right` table as a `path`, then this parameter is the format of the table found at the given `right_table_path` location.<br>
            Defaults to `#!py "delta"`.
        include_change_field (bool, optional):
            When doing the schema validations, do you want to include any fields where the data-type on the right-hand side is different from the left-hand side?<br>
            This can be read as: "What fields have had their data type _changed **between**_ the left-hand side and the right-hand side?"<br>
            Defaults to `#!py True`.
        include_add_field (bool, optional):
            When doing the schema validations, do you want to include any fields that have had any additional fields added to the left-hand side, when compared to the right-hand side?<br>
            This can be read as: "What fields have been _added **to**_ the left-hand side?"<br>
            Defaults to `#!py True`.
        include_remove_field (bool, optional):
            When doing the schema validations, do you want to include any fields which are missing from the left-hand side and only existing on the right-hand side?<br>
            This can be read as: "What fields been _removed **from**_ the left-hand side?"<br>
            Defaults to `#!py True`.
        include_change_nullable (bool, optional):
            When doing the schema validations, do you want to include any fields which have had their nullability metadata changed on the right-hand side, when compared to the left-hand side?.<br>
            This can be read as: "What fields had their nullability _changed **between**_ the left-hand side and the right-hand side?"<br>
            Defaults to `#!py False`.
        view_type (Literal["print", "pprint", "return"], optional):
            When returning the output from this function, how do you want it to be displayed? Must be one of `#!py ["print", "pprint", "return"]`.<br>
            Logically, the difference is that `#!py "print"` will display a text value to the terminal that is not formatted in any way; `#!py "pprint"` will display a pretty-printed text value to the terminal; and `#!py "return"` will return the schema differences which can then be assigned to another variable.<br>
            Defaults to `#!py "pprint"`.

    Raises:
        TypeError:
            If any of the inputs parsed to the parameters of this function are not the correct type. Uses the [`@typeguard.typechecked`](https://typeguard.readthedocs.io/en/stable/api.html#typeguard.typechecked) decorator.
        AttributeError:
            If the value parse'd to `method` is not a valid option.

    Returns:
        (Optional[list[tuple[str, dict[str, StructField]]]]):
            If `#!py view_type="return"`, then this will be a `#!py list` of `#!py tuple`'s of `#!py dict`'s containing the details of the schema differences. If `#!py view_type!="return"` (or if `#!py view_type="return"`, but there are actually no differences in the schema), then nothing is returned; only printed to terminal.

    ???+ example "Examples"

        ```{.py .python linenums="1" title="Set up"}
        >>> # Imports
        >>> from pprint import pprint
        >>> import pandas as pd
        >>> from pyspark.sql import SparkSession, functions as F
        >>> from toolbox_pyspark.schema import view_schema_differences
        >>> from toolbox_pyspark.io import write_to_path
        >>> from toolbox_pyspark.checks import table_exists
        >>>
        >>> # Instantiate Spark
        >>> spark = SparkSession.builder.getOrCreate()
        >>>
        >>> # Create data
        >>> df1 = spark.createDataFrame(
        ...     pd.DataFrame(
        ...         {
        ...             "a": [0, 1, 2, 3],
        ...             "b": ["a", "b", "c", "d"],
        ...             "c": ["1", "1", "1", "1"],
        ...             "d": ["2", "2", "2", "2"],
        ...             "e": ["3", "3", "3", "3"],
        ...             "f": ["4", "4", "4", "4"],
        ...         }
        ...     )
        ... )
        >>> df2 = (
        ...     df1.withColumn("c", F.col("c").cast("int"))
        ...     .withColumn("g", F.lit("a"))
        ...     .withColumn("d", F.lit("null"))
        ...     .drop("e")
        ... )
        >>> write_to_path(
        ...     table=df1,
        ...     name="left",
        ...     path="./test",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ...     write_options={"overwriteSchema": "true"},
        ... )
        >>> write_to_path(
        ...     table=df2,
        ...     name="right",
        ...     path="./test",
        ...     data_format="parquet",
        ...     mode="overwrite",
        ...     write_options={"overwriteSchema": "true"},
        ... )
        >>>
        >>> # Check
        >>> pprint(df1.dtypes)
        >>> print(df1.show())
        >>> print(table_exists("left", "./test", "parquet", spark))
        >>> pprint(df2.dtypes)
        >>> print(df2.show())
        >>> print(table_exists("right", "./test", "parquet", spark))
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [
            ("a", "bigint"),
            ("b", "string"),
            ("c", "string"),
            ("d", "string"),
            ("e", "string"),
            ("f", "string"),
        ]
        ```
        ```{.txt .text title="Terminal"}
        +---+---+---+---+---+---+
        | a | b | c | d | e | f |
        +---+---+---+---+---+---+
        | 0 | a | 1 | 2 | 3 | 4 |
        | 1 | b | 1 | 2 | 3 | 4 |
        | 2 | c | 1 | 2 | 3 | 4 |
        | 3 | d | 1 | 2 | 3 | 4 |
        +---+---+---+---+---+---+
        ```
        ```{.sh .shell title="Terminal"}
        True
        ```
        ```{.sh .shell title="Terminal"}
        [
            ("a", "bigint"),
            ("b", "string"),
            ("c", "int"),
            ("d", "string"),
            ("f", "string"),
            ("g", "string"),
        ]
        ```
        ```{.txt .text title="Terminal"}
        +---+---+---+------+---+---+
        | a | b | c |    d | f | g |
        +---+---+---+------+---+---+
        | 0 | a | 1 | null | 4 | 2 |
        | 1 | b | 1 | null | 4 | 2 |
        | 2 | c | 1 | null | 4 | 2 |
        | 3 | d | 1 | null | 4 | 2 |
        +---+---+---+------+---+---+
        ```
        ```{.sh .shell title="Terminal"}
        True
        ```
        </div>

        ```{.py .python linenums="1" title="Example 1: Check matching"}
        >>> view_schema_differences(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df1,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     view_type="return",
        ... )
        >>> print(diff)
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        None
        ```
        !!! success "Conclusion: Schemas match."
        </div>

        ```{.py .python linenums="1" title="Example 2: Check print"}
        >>> view_schema_differences(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=False,
        ...     include_remove_field=False,
        ...     include_change_nullable=False,
        ...     view_type="print",
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [('add', {'left': StructField('e', StringType(), True)})]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added."
        </div>

        ```{.py .python linenums="1" title="Example 3: Check pprint"}
        >>> view_schema_differences(
        ...     method="table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     view_type="pprint",
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [('add', {'left': StructField('e', StringType(), False)}),
         ('remove', {'right': StructField('g', StringType(), False)}),
         ('change_type',
          {'left': StructField('c', StringType(), False),
           'right': StructField('c', IntegerType(), True)}),
         ('change_nullable',
          {'left': StructField('c', StringType(), False),
           'right': StructField('c', IntegerType(), True)})]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added, the `g` field was removed, the `c` field had its data type changed, and the `c` field had its nullability changed."
        </div>

        ```{.py .python linenums="1" title="Example 4: Check with right-hand side as a `path`"}
        >>> view_schema_differences(
        ...     method="table_table",
        ...     spark_session=spark,
        ...     left_table=df1,
        ...     right_table_path="./test",
        ...     right_table_name="right",
        ...     right_table_format="parquet",
        ...     include_add_field=True,
        ...     include_change_field=False,
        ...     include_remove_field=False,
        ...     include_change_nullable=False,
        ...     view_type="pprint",
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [('add', {'left': StructField('e', StringType(), True)})]
        ```
        !!! failure "Conclusion: Schemas do not match because the `e` field was added."
        </div>

        ```{.py .python linenums="1" title="Example 5: Check with both sides being a `path`"}
        >>> view_schema_differences(
        ...     method="table_table",
        ...     spark_session=spark,
        ...     left_table_path="./test",
        ...     left_table_name="left",
        ...     left_table_format="parquet",
        ...     right_table_path="./test",
        ...     right_table_name="right",
        ...     right_table_format="parquet",
        ...     include_add_field=False,
        ...     include_change_field=False,
        ...     include_remove_field=True,
        ...     include_change_nullable=False,
        ...     view_type="pprint",
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell title="Terminal"}
        [('remove', {'right': StructField('g', StringType(), True)})]
        ```
        !!! failure "Conclusion: Schemas do not match because the `g` field was removed."
        </div>

        ```{.py .python linenums="1" title="Example 6: Invalid `method` parameter"}
        >>> view_schema_differences(
        ...     method="table_table_table",
        ...     left_table=df1,
        ...     right_table=df2,
        ...     include_add_field=True,
        ...     include_change_field=True,
        ...     include_remove_field=True,
        ...     include_change_nullable=True,
        ...     view_type="return",
        ... )
        ```
        <div class="result" markdown>
        ```{.sh .shell  title="Terminal"}
        AttributeError: Invalid value for `method`: 'table_table_table'
        Please use one of the following options:
        - For `by_table_and_table`, use one of the following values: ['table', 'table_table', 'tables', 'by_table', 'by_table_and_table', 'table_and_table']
        - For `by_table_and_path`, use one of the following values: ['table_and_path', 'table_path', 'by_table_and_path']
        - For `by_path_and_table`, use one of the following values: ['path_and_table', 'path_table', 'by_path_and_table']
        - For `by_path_and_path`, use one of the following values: ['path_and_path', 'path_path', 'by_path_and_path', 'path', 'paths']
        ```
        !!! failure "Conclusion: Invalid `method` parameter."
        </div>

    ??? tip "See Also"
        - [`check_schemas_match()`][toolbox_pyspark.schema.check_schemas_match]
    """

    valid_methods: ValidMethods = ValidMethods()
    msg: str = "If using the '{meth}' method, then '{name}' cannot be 'None'."

    if method in valid_methods.by_table_and_table:
        assert left_table is not None, msg.format(meth=method, name="left_table")
        assert right_table is not None, msg.format(meth=method, name="right_table")
        return _view_schema_differences_by_table_and_table(
            left_table=left_table,
            right_table=right_table,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            view_type=view_type,
        )
    elif method in valid_methods.by_table_and_path:
        assert left_table is not None, msg.format(meth=method, name="left_table")
        assert right_table_path is not None, msg.format(meth=method, name="right_table_path")
        assert right_table_name is not None, msg.format(meth=method, name="right_table_name")
        assert spark_session is not None, msg.format(meth=method, name="spark_session")
        return _view_schema_differences_by_table_and_path(
            left_table=left_table,
            right_table_path=right_table_path,
            right_table_name=right_table_name,
            right_table_format=right_table_format,
            spark_session=spark_session,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            view_type=view_type,
        )
    elif method in valid_methods.by_path_and_table:
        assert left_table_path is not None, msg.format(meth=method, name="left_table_path")
        assert left_table_name is not None, msg.format(meth=method, name="left_table_name")
        assert right_table is not None, msg.format(meth=method, name="right_table")
        assert spark_session is not None, msg.format(meth=method, name="spark_session")
        return _view_schema_differences_by_path_and_table(
            left_table_path=left_table_path,
            left_table_name=left_table_name,
            left_table_format=left_table_format,
            right_table=right_table,
            spark_session=spark_session,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            view_type=view_type,
        )
    elif method in valid_methods.by_path_and_path:
        assert left_table_path is not None, msg.format(meth=method, name="left_table_path")
        assert left_table_name is not None, msg.format(meth=method, name="left_table_name")
        assert right_table_path is not None, msg.format(meth=method, name="right_table_path")
        assert right_table_name is not None, msg.format(meth=method, name="right_table_name")
        assert spark_session is not None, msg.format(meth=method, name="spark_session")
        return _view_schema_differences_by_path_and_path(
            left_table_path=left_table_path,
            left_table_name=left_table_name,
            left_table_format=left_table_format,
            right_table_path=right_table_path,
            right_table_name=right_table_name,
            right_table_format=right_table_format,
            spark_session=spark_session,
            include_change_field=include_change_field,
            include_add_field=include_add_field,
            include_remove_field=include_remove_field,
            include_change_nullable=include_change_nullable,
            view_type=view_type,
        )
    else:
        raise AttributeError(
            f"Invalid value for `method`: '{method}'\n"
            f"Please use one of the following options:\n"
            f"- For `by_table_and_table`, use one of: {valid_methods.by_table_and_table}\n"
            f"- For `by_table_and_path`, use one of: {valid_methods.by_table_and_path}\n"
            f"- For `by_path_and_table`, use one of: {valid_methods.by_path_and_table}\n"
            f"- For `by_path_and_path`, use one of: {valid_methods.by_path_and_path}\n"
        )