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Merged
sbyrne-sf merged 4 commits into from
Jun 3, 2025
Merged

Various QoL Updates #22

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06e71b3
@W-18661856 added 1000 limit for dlo/dmo read, dataframe and output d…
chandresh-patelsf May 29, 2025
4287a56
Fixed some lint and test problems.
sbyrne-sf May 30, 2025
a853005
Updated tests.
sbyrne-sf Jun 2, 2025
5d69f32
Refactored for DI and testing.
sbyrne-sf Jun 2, 2025
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23 changes: 17 additions & 6 deletions src/datacustomcode/io/reader/query_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@
logger = logging.getLogger(__name__)


SQL_QUERY_TEMPLATE: Final = "SELECT * FROM {}"
SQL_QUERY_TEMPLATE: Final = "SELECT * FROM {} LIMIT {}"
PANDAS_TYPE_MAPPING = {
"object": StringType(),
"int64": LongType(),
Expand Down Expand Up @@ -85,29 +85,40 @@ def __init__(self, spark: SparkSession) -> None:
)

def read_dlo(
self, name: str, schema: Union[AtomicType, StructType, str, None] = None
self,
name: str,
schema: Union[AtomicType, StructType, str, None] = None,
row_limit: int = 1000,
) -> PySparkDataFrame:
"""
Read a Data Lake Object (DLO) from the Data Cloud.
Read a Data Lake Object (DLO) from the Data Cloud, limited to a number of rows.

Args:
name (str): The name of the DLO.
schema (Optional[Union[AtomicType, StructType, str]]): Schema of the DLO.
row_limit (int): Maximum number of rows to fetch.

Returns:
PySparkDataFrame: The PySpark DataFrame.
"""
pandas_df = self._conn.get_pandas_dataframe(SQL_QUERY_TEMPLATE.format(name))
pandas_df = self._conn.get_pandas_dataframe(
SQL_QUERY_TEMPLATE.format(name, row_limit)
)
if not schema:
# auto infer schema
schema = _pandas_to_spark_schema(pandas_df)
spark_dataframe = self.spark.createDataFrame(pandas_df, schema)
return spark_dataframe

def read_dmo(
self, name: str, schema: Union[AtomicType, StructType, str, None] = None
self,
name: str,
schema: Union[AtomicType, StructType, str, None] = None,
row_limit: int = 1000,
) -> PySparkDataFrame:
pandas_df = self._conn.get_pandas_dataframe(SQL_QUERY_TEMPLATE.format(name))
pandas_df = self._conn.get_pandas_dataframe(
SQL_QUERY_TEMPLATE.format(name, row_limit)
)
if not schema:
# auto infer schema
schema = _pandas_to_spark_schema(pandas_df)
Expand Down
49 changes: 49 additions & 0 deletions src/datacustomcode/io/writer/print.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,18 +16,67 @@

from pyspark.sql import DataFrame as PySparkDataFrame

from datacustomcode.io.reader.query_api import QueryAPIDataCloudReader
from datacustomcode.io.writer.base import BaseDataCloudWriter, WriteMode


class PrintDataCloudWriter(BaseDataCloudWriter):
CONFIG_NAME = "PrintDataCloudWriter"

def validate_dataframe_columns_against_dlo(
self,
dataframe: PySparkDataFrame,
dlo_name: str,
reader: QueryAPIDataCloudReader,
) -> None:
"""
Validates that all columns in the given dataframe exist in the DLO schema.

Args:
dataframe (PySparkDataFrame): The DataFrame to validate.
dlo_name (str): The name of the DLO to check against.
reader (QueryAPIDataCloudReader): The reader to use for schema retrieval.

Raises:
ValueError: If any columns in the dataframe are not present in the DLO
schema.
"""
# Get DLO schema (no data, just schema)
dlo_df = reader.read_dlo(dlo_name, row_limit=0)
dlo_columns = set(dlo_df.columns)
df_columns = set(dataframe.columns)

# Find columns in dataframe not present in DLO
extra_columns = df_columns - dlo_columns
if extra_columns:
raise ValueError(
"The following columns are not present in the \n"
f"DLO '{dlo_name}': {sorted(extra_columns)}.\n"
"To fix this error, you can either:\n"
" - Drop these columns from your DataFrame before writing, e.g.,\n"
" dataframe = dataframe.drop({cols})\n"
" - Or, add these columns to the DLO schema in Data Cloud.".format(
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@markdlv-sf markdlv-sf Jun 2, 2025

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This is going to be very helpful!

cols=sorted(extra_columns)
)
)

def write_to_dlo(
self, name: str, dataframe: PySparkDataFrame, write_mode: WriteMode
) -> None:

# Instantiate the reader
reader = QueryAPIDataCloudReader(self.spark)

# Validate columns before proceeding
self.validate_dataframe_columns_against_dlo(dataframe, name, reader)

dataframe.show()

def write_to_dmo(
self, name: str, dataframe: PySparkDataFrame, write_mode: WriteMode
) -> None:
# The way its validating for DLO and dataframes columns,
# its not going to work for DMO because DMO may not exists,
# so just show the dataframe.

dataframe.show()
55 changes: 5 additions & 50 deletions src/datacustomcode/scan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@

import ast
import os
import sys
from typing import (
Any,
ClassVar,
Expand All @@ -40,6 +41,8 @@
},
}

STANDARD_LIBS = set(sys.stdlib_module_names)


class DataAccessLayerCalls(pydantic.BaseModel):
read_dlo: frozenset[str]
Expand Down Expand Up @@ -137,54 +140,6 @@ def found(self) -> DataAccessLayerCalls:
class ImportVisitor(ast.NodeVisitor):
"""AST Visitor that extracts external package imports from Python code."""

# Standard library modules that should be excluded from requirements
STANDARD_LIBS: ClassVar[set[str]] = {
"abc",
"argparse",
"ast",
"asyncio",
"base64",
"collections",
"configparser",
"contextlib",
"copy",
"csv",
"datetime",
"enum",
"functools",
"glob",
"hashlib",
"http",
"importlib",
"inspect",
"io",
"itertools",
"json",
"logging",
"math",
"os",
"pathlib",
"pickle",
"random",
"re",
"shutil",
"site",
"socket",
"sqlite3",
"string",
"subprocess",
"sys",
"tempfile",
"threading",
"time",
"traceback",
"typing",
"uuid",
"warnings",
"xml",
"zipfile",
}

# Additional packages to exclude from requirements.txt
EXCLUDED_PACKAGES: ClassVar[set[str]] = {
"datacustomcode", # Internal package
Expand All @@ -200,7 +155,7 @@ def visit_Import(self, node: ast.Import) -> None:
# Get the top-level package name
package = name.name.split(".")[0]
if (
package not in self.STANDARD_LIBS
package not in STANDARD_LIBS
and package not in self.EXCLUDED_PACKAGES
and not package.startswith("_")
):
Expand All @@ -213,7 +168,7 @@ def visit_ImportFrom(self, node: ast.ImportFrom) -> None:
# Get the top-level package
package = node.module.split(".")[0]
if (
package not in self.STANDARD_LIBS
package not in STANDARD_LIBS
and package not in self.EXCLUDED_PACKAGES
and not package.startswith("_")
):
Expand Down
8 changes: 4 additions & 4 deletions tests/io/reader/test_query_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,7 +143,7 @@ def test_read_dlo(

# Verify get_pandas_dataframe was called with the right SQL
mock_connection.get_pandas_dataframe.assert_called_once_with(
SQL_QUERY_TEMPLATE.format("test_dlo")
SQL_QUERY_TEMPLATE.format("test_dlo", 1000)
)

# Verify DataFrame was created with auto-inferred schema
Expand Down Expand Up @@ -172,7 +172,7 @@ def test_read_dlo_with_schema(

# Verify get_pandas_dataframe was called with the right SQL
mock_connection.get_pandas_dataframe.assert_called_once_with(
SQL_QUERY_TEMPLATE.format("test_dlo")
SQL_QUERY_TEMPLATE.format("test_dlo", 1000)
)

# Verify DataFrame was created with provided schema
Expand All @@ -192,7 +192,7 @@ def test_read_dmo(

# Verify get_pandas_dataframe was called with the right SQL
mock_connection.get_pandas_dataframe.assert_called_once_with(
SQL_QUERY_TEMPLATE.format("test_dmo")
SQL_QUERY_TEMPLATE.format("test_dmo", 1000)
)

# Verify DataFrame was created
Expand Down Expand Up @@ -220,7 +220,7 @@ def test_read_dmo_with_schema(

# Verify get_pandas_dataframe was called with the right SQL
mock_connection.get_pandas_dataframe.assert_called_once_with(
SQL_QUERY_TEMPLATE.format("test_dmo")
SQL_QUERY_TEMPLATE.format("test_dmo", 1000)
)

# Verify DataFrame was created with provided schema
Expand Down
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