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Improve the error message when a column is missing from the schema #216

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Nov 7, 2023
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21 changes: 17 additions & 4 deletions typedspark/_core/validate_schema.py
Original file line number Diff line number Diff line change
@@ -1,8 +1,10 @@
"""Module containing functions that are related to validating schema's at runtime."""
from typing import Dict, Set
from typing import Dict

from pyspark.sql.types import ArrayType, DataType, MapType, StructField, StructType

from typedspark._utils.create_dataset_from_structtype import create_schema_from_structtype


def validate_schema(
structtype_expected: StructType, structtype_observed: StructType, schema_name: str
Expand All @@ -11,7 +13,7 @@ def validate_schema(
expected = unpack_schema(structtype_expected)
observed = unpack_schema(structtype_observed)

check_names(set(expected.keys()), set(observed.keys()), schema_name)
check_names(expected, observed, schema_name)
check_dtypes(expected, observed, schema_name)


Expand All @@ -31,15 +33,26 @@ def unpack_schema(schema: StructType) -> Dict[str, StructField]:
return res


def check_names(names_expected: Set[str], names_observed: Set[str], schema_name: str) -> None:
def check_names(
expected: Dict[str, StructField], observed: Dict[str, StructField], schema_name: str
) -> None:
"""Checks whether the observed and expected list of column names overlap.

Is order insensitive.
"""
names_observed = set(observed.keys())
names_expected = set(expected.keys())

diff = names_observed - names_expected
if diff:
diff_schema = create_schema_from_structtype(
StructType([observed[colname] for colname in diff]), schema_name
)
raise TypeError(
f"Data contains the following columns not present in schema {schema_name}: {diff}"
f"Data contains the following columns not present in schema {schema_name}: {diff}.\n\n"
"If you believe these columns should be part of the schema, consider adding the "
"following lines to it.\n\n"
f"{diff_schema.get_schema_definition_as_string(generate_imports=False)}"
)

diff = names_expected - names_observed
Expand Down
70 changes: 70 additions & 0 deletions typedspark/_utils/create_dataset_from_structtype.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,70 @@
"""Utility functions for creating a ``Schema`` from a ``StructType``"""
from typing import Dict, Literal, Optional, Type

from pyspark.sql.types import ArrayType as SparkArrayType
from pyspark.sql.types import DataType
from pyspark.sql.types import DayTimeIntervalType as SparkDayTimeIntervalType
from pyspark.sql.types import DecimalType as SparkDecimalType
from pyspark.sql.types import MapType as SparkMapType
from pyspark.sql.types import StructType as SparkStructType

from typedspark._core.column import Column
from typedspark._core.datatypes import (
ArrayType,
DayTimeIntervalType,
DecimalType,
MapType,
StructType,
)
from typedspark._schema.schema import MetaSchema, Schema
from typedspark._utils.camelcase import to_camel_case


def create_schema_from_structtype(
structtype: SparkStructType, schema_name: Optional[str] = None
) -> Type[Schema]:
"""Dynamically builds a ``Schema`` based on a ``DataFrame``'s ``StructType``"""
type_annotations = {}
attributes: Dict[str, None] = {}
for column in structtype:
name = column.name
data_type = _extract_data_type(column.dataType, name)
type_annotations[name] = Column[data_type] # type: ignore
attributes[name] = None

if not schema_name:
schema_name = "DynamicallyLoadedSchema"

schema = MetaSchema(schema_name, tuple([Schema]), attributes)
schema.__annotations__ = type_annotations

return schema # type: ignore


def _extract_data_type(dtype: DataType, name: str) -> Type[DataType]:
"""Given an instance of a ``DataType``, it extracts the corresponding ``DataType``
class, potentially including annotations (e.g. ``ArrayType[StringType]``)."""
if isinstance(dtype, SparkArrayType):
element_type = _extract_data_type(dtype.elementType, name)
return ArrayType[element_type] # type: ignore

if isinstance(dtype, SparkMapType):
key_type = _extract_data_type(dtype.keyType, name)
value_type = _extract_data_type(dtype.valueType, name)
return MapType[key_type, value_type] # type: ignore

if isinstance(dtype, SparkStructType):
subschema = create_schema_from_structtype(dtype, to_camel_case(name))
return StructType[subschema] # type: ignore

if isinstance(dtype, SparkDayTimeIntervalType):
start_field = dtype.startField
end_field = dtype.endField
return DayTimeIntervalType[Literal[start_field], Literal[end_field]] # type: ignore

if isinstance(dtype, SparkDecimalType):
precision = dtype.precision
scale = dtype.scale
return DecimalType[Literal[precision], Literal[scale]] # type: ignore

return type(dtype)
81 changes: 9 additions & 72 deletions typedspark/_utils/load_table.py
Original file line number Diff line number Diff line change
@@ -1,27 +1,13 @@
"""Functions for loading `DataSet` and `Schema` in notebooks."""

import re
from typing import Dict, Literal, Optional, Tuple, Type
from typing import Dict, Optional, Tuple, Type

from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.types import ArrayType as SparkArrayType
from pyspark.sql.types import DataType
from pyspark.sql.types import DayTimeIntervalType as SparkDayTimeIntervalType
from pyspark.sql.types import DecimalType as SparkDecimalType
from pyspark.sql.types import MapType as SparkMapType
from pyspark.sql.types import StructType as SparkStructType

from typedspark._core.column import Column

from typedspark._core.dataset import DataSet
from typedspark._core.datatypes import (
ArrayType,
DayTimeIntervalType,
DecimalType,
MapType,
StructType,
)
from typedspark._schema.schema import MetaSchema, Schema
from typedspark._utils.camelcase import to_camel_case
from typedspark._schema.schema import Schema
from typedspark._utils.create_dataset_from_structtype import create_schema_from_structtype
from typedspark._utils.register_schema_to_dataset import register_schema_to_dataset


Expand Down Expand Up @@ -64,60 +50,11 @@ def _replace_illegal_characters(column_name: str) -> str:
return re.sub("[^A-Za-z0-9]", "_", column_name)


def _create_schema(structtype: SparkStructType, schema_name: Optional[str] = None) -> Type[Schema]:
"""Dynamically builds a ``Schema`` based on a ``DataFrame``'s
``StructType``"""
type_annotations = {}
attributes: Dict[str, None] = {}
for column in structtype:
name = column.name
data_type = _extract_data_type(column.dataType, name)
type_annotations[name] = Column[data_type] # type: ignore
attributes[name] = None

if not schema_name:
schema_name = "DynamicallyLoadedSchema"

schema = MetaSchema(schema_name, tuple([Schema]), attributes)
schema.__annotations__ = type_annotations

return schema # type: ignore


def _extract_data_type(dtype: DataType, name: str) -> Type[DataType]:
"""Given an instance of a ``DataType``, it extracts the corresponding
``DataType`` class, potentially including annotations (e.g.
``ArrayType[StringType]``)."""
if isinstance(dtype, SparkArrayType):
element_type = _extract_data_type(dtype.elementType, name)
return ArrayType[element_type] # type: ignore

if isinstance(dtype, SparkMapType):
key_type = _extract_data_type(dtype.keyType, name)
value_type = _extract_data_type(dtype.valueType, name)
return MapType[key_type, value_type] # type: ignore

if isinstance(dtype, SparkStructType):
subschema = _create_schema(dtype, to_camel_case(name))
return StructType[subschema] # type: ignore

if isinstance(dtype, SparkDayTimeIntervalType):
start_field = dtype.startField
end_field = dtype.endField
return DayTimeIntervalType[Literal[start_field], Literal[end_field]] # type: ignore

if isinstance(dtype, SparkDecimalType):
precision = dtype.precision
scale = dtype.scale
return DecimalType[Literal[precision], Literal[scale]] # type: ignore

return type(dtype)


def create_schema(
dataframe: DataFrame, schema_name: Optional[str] = None
) -> Tuple[DataSet[Schema], Type[Schema]]:
"""This function inferres a ``Schema`` in a notebook based on a the provided ``DataFrame``.
"""This function inferres a ``Schema`` in a notebook based on a the provided
``DataFrame``.

This allows for autocompletion on column names, amongst other
things.
Expand All @@ -127,7 +64,7 @@ def create_schema(
df, Person = create_schema(df)
"""
dataframe = _replace_illegal_column_names(dataframe)
schema = _create_schema(dataframe.schema, schema_name)
schema = create_schema_from_structtype(dataframe.schema, schema_name)
dataset = DataSet[schema](dataframe) # type: ignore
schema = register_schema_to_dataset(dataset, schema)
return dataset, schema
Expand All @@ -136,8 +73,8 @@ def create_schema(
def load_table(
spark: SparkSession, table_name: str, schema_name: Optional[str] = None
) -> Tuple[DataSet[Schema], Type[Schema]]:
"""This function loads a ``DataSet``, along with its inferred ``Schema``,
in a notebook.
"""This function loads a ``DataSet``, along with its inferred ``Schema``, in a
notebook.

This allows for autocompletion on column names, amongst other
things.
Expand Down