From b4805faa084a2d10424883534631ad3ba6959ccf Mon Sep 17 00:00:00 2001 From: Paulo Rijnberg <55184544+paulorijnberg@users.noreply.github.com> Date: Tue, 20 Feb 2024 12:48:46 +0100 Subject: [PATCH] Feature/4 parse datetime in model (#8) * Added additional datetime columns to test csv's * Updated sample data and added first iteration of datetime parsing detection * Added print statement to detect date format * Added additional code to detect date format for date strings that are none. * Added additional regex date foramt * Added additional docstring. * Added additional regex date formats. * Add new columns to the model.json file * Added some failing pytest to the date time parser. * Added docstring to get_frame and changed an argument * Added a sentence to the get_dataframe method docstring * Added missing parameter alter_schema to entity get_dataframe * Added forked cdm repo. --- poetry.lock | 736 +++++++++--------- pyproject.toml | 9 +- pyspark_cdm/catalog.py | 27 + 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4012730..1434a95 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,11 +4,11 @@ version = "0.1.0" description = "Use pyspark to read CDM entities." authors = ["JulesHuisman "] readme = "README.md" -packages = [{include = "pyspark_cdm"}] +packages = [{ include = "pyspark_cdm" }] [tool.poetry.dependencies] python = "<4.0,>=3.8" -commondatamodel-objectmodel = "^1.7.3" +commondatamodel-objectmodel = { git = "https://github.com/quantile-development/CDM.git", branch = "master", subdirectory = "objectModel/Python" } nest-asyncio = "^1.5.6" tenacity = "^8.2.3" @@ -24,7 +24,4 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] -filterwarnings = [ - "error", - "ignore::DeprecationWarning", -] \ No newline at end of file +filterwarnings = ["error", "ignore::DeprecationWarning"] diff --git a/pyspark_cdm/catalog.py b/pyspark_cdm/catalog.py index 5ebe353..df10917 100644 --- a/pyspark_cdm/catalog.py +++ b/pyspark_cdm/catalog.py @@ -16,6 +16,7 @@ DecimalType, ) +TIMESTAMP_TYPES = [TimestampType(), DateType()] def catalog_factory(entity: "Entity") -> "Catalog": if entity.is_model: @@ -36,6 +37,32 @@ def type_mapping(self) -> dict: def schema(self) -> StructType: pass + @property + def timestamp_columns(self) -> list[str]: + """ + Extract the columns that are parsed as timestamp or datetimes data types. + """ + return [ + entity.name + for entity + in self.schema + if entity.dataType in TIMESTAMP_TYPES + ] + + def overwrite_timestamp_types(self, schema: StructType) -> StructType: + """ + Overwrite in the provided schema the timestamp date types to strings. + """ + overwritten_date_types = list() + + for entity in schema: + if entity.dataType in TIMESTAMP_TYPES: + entity.dataType = StringType() + + overwritten_date_types.append(entity) + + return StructType(overwritten_date_types) + class ModelCatalog(Catalog): @property diff --git a/pyspark_cdm/datetime_parser.py b/pyspark_cdm/datetime_parser.py new file mode 100644 index 0000000..71859b0 --- /dev/null +++ b/pyspark_cdm/datetime_parser.py @@ -0,0 +1,85 @@ +import re +from pyspark.sql import DataFrame +from pyspark.sql.functions import to_timestamp, col +from pyspark_cdm.catalog import Catalog +from typing import Optional +from .utils import first_non_empty_values + +DATE_FORMATS = { + None: r"\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d{1,}Z", # yyyy-MM-dd'T'HH:mm:ss.SSSZ doesn't work, pyspark format should be empty + "M/d/yyyy h:mm:ss a": r"\d{1,2}/\d{1,2}/\d{4} \d{1,2}:\d{2}:\d{2} [AP]M", + "yyyy-MM-dd'T'HH:mm:ss.SSS": r"\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}\.\d{3}", + "yyyy-MM-dd'T'HH:mm:ss'Z'": r"\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}Z", + "yyyy-MM-dd'T'HH:mm:ss": r"\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}", + "dd-MM-yyyy HH:mm:ss": r"\d{2}-(0[1-9]|1[0-2])-\d{4} \d{2}:\d{2}:\d{2}", + "dd-MM-yyyy": r"\d{2}-(0[1-9]|1[0-2])-\d{4}", + "MM-dd-yyyy HH:mm:ss": r"(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])-\d{4} \d{2}:\d{2}:\d{2}", + "MM-dd-yyyy": r"(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])-\d{4}", +} + +class DatetimeParser: + def __init__(self, df: DataFrame, catalog: Catalog) -> None: + self.df = df + self.catalog = catalog + + def detect_date_format(self, date_string: str) -> Optional[str]: + """ + Tries to find a matching regex pattern on the provided date_string. If it + matches it returns the corresponding pyspark_format. Otherwise, it raises + an exception. + """ + for pyspark_format, regex in DATE_FORMATS.items(): + # The regex match fails if the provided date_string is a none value, therefore, + # we return none once we stumble upon a none value. + if date_string == None: + return None + + if re.match(regex, date_string): + return pyspark_format + + raise Exception(f"Cant find a matching datetime pattern for {date_string}") + + def try_parsing_datetime_column( + self, + df: DataFrame, + column_name: str, + datetime_format: str, + ) -> DataFrame: + """ + Convert the a single datetime column using the to_timestamp method + with the required datetime format (e.g. "M/d/yyyy hh:mm:ss a"). + """ + try: + df = df.withColumn(column_name, to_timestamp(col(column_name), datetime_format)) + return df + + except: + print(f'Failed parsing {column_name} with {datetime_format}') + return df + + def convert_datetime_columns( + self, + ) -> DataFrame: + """ + Loops over all the timestamp related columns and transforms these from strings + into datetime objects. + """ + # Get for all the timestamp columns the first non empty value + sampled_values = first_non_empty_values( + self.df, + self.catalog.timestamp_columns, + ) + + # Loop over all the timestamp columns and convert it into a datetime column + df_parsed = self.df + + for column_name in self.catalog.timestamp_columns: + pyspark_format = self.detect_date_format(sampled_values[column_name]) + + df_parsed = self.try_parsing_datetime_column( + df_parsed, + column_name, + pyspark_format + ) + + return df_parsed \ No newline at end of file diff --git a/pyspark_cdm/entity.py b/pyspark_cdm/entity.py index 0f69cf3..9bd41c5 100644 --- a/pyspark_cdm/entity.py +++ b/pyspark_cdm/entity.py @@ -21,8 +21,9 @@ from cdm.utilities.copy_options import CopyOptions from cdm.utilities.resolve_options import ResolveOptions from cdm.persistence.modeljson import LocalEntityDeclarationPersistence -from pyspark.sql.types import StructField, StructType +from pyspark.sql.types import StructType from pyspark.sql import DataFrame +from pyspark_cdm.datetime_parser import DatetimeParser from tenacity import retry, stop_after_attempt, wait_random_exponential @@ -131,7 +132,7 @@ def file_paths(self) -> Generator[str, None, None]: yield remove_root_from_path(path, "/dbfs") @property - def schema(self) -> StructType: + def catalog(self) -> StructType: """ The schema of the entity. @@ -139,23 +140,59 @@ def schema(self) -> StructType: str: The schema of the entity. """ catalog = catalog_factory(self) - return catalog.schema + return catalog + @retry( stop=stop_after_attempt(2), wait=wait_random_exponential(multiplier=3, max=60), after=log_attempt_number, ) def get_dataframe( - self, + self, spark, + infer_timestamp_formats: bool = False, alter_schema=lambda schema: schema, ) -> DataFrame: - return spark.read.csv( - list(self.file_paths), - header=False, - schema=alter_schema(self.schema), - inferSchema=False, - multiLine=True, - escape='"', - ) + """ + Loads the data using Spark. + + Args: + spark: spark session. + infer_timestamp_formats (bool, optional): Whether we should infer the timestamp + formats using regex. Defaults to False. + alter_schema: Alter the schema. + + Returns: + DataFrame: Spark dataframe with the loaded data. + """ + + + if infer_timestamp_formats: + spark.sql("set spark.sql.legacy.timeParserPolicy=LEGACY") + schema_with_replaced_timestamp_types = self.catalog.overwrite_timestamp_types(self.catalog.schema) + + df = spark.read.csv( + list(self.file_paths), + header=False, + schema=alter_schema(schema_with_replaced_timestamp_types), + inferSchema=False, + multiLine=True, + escape='"', + ) + + datetime_parser = DatetimeParser(df, self.catalog) + parsed_df = datetime_parser.convert_datetime_columns() + + return parsed_df + + else: + + return spark.read.csv( + list(self.file_paths), + header=False, + schema=alter_schema(self.catalog.schema), + inferSchema=False, + multiLine=True, + escape='"', + ) diff --git a/pyspark_cdm/utils.py b/pyspark_cdm/utils.py index 11ab5b9..606552d 100644 --- a/pyspark_cdm/utils.py +++ b/pyspark_cdm/utils.py @@ -1,10 +1,12 @@ import asyncio -from typing import Optional +from typing import Optional, List from cdm.enums import CdmStatusLevel, CdmDataFormat from cdm.objectmodel import ( CdmCorpusDefinition, CdmObject, ) +from pyspark.sql import DataFrame, Row +import pyspark.sql.functions as F from pyspark_cdm.exceptions import DocumentLoadingException @@ -73,3 +75,22 @@ def remove_root_from_path(path: str, root: str) -> str: str: Path without the root. """ return f"/{path.lstrip(root)}" + +def first_non_empty_values( + df: DataFrame, + selected_columns: List[str] +) -> Row: + """ + Returns for each given column the first non empty value. Be aware that + once a column is full with nulls, it returns a None for that given + column. + """ + row = df.select( + [ + F.first(column, ignorenulls=True).alias(column) + for column + in selected_columns + ] + ).first() + + return row \ No newline at end of file diff --git a/tests/conftest.py b/tests/conftest.py index 56b5ee1..10d9f21 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3,6 +3,7 @@ from tests.fixtures.manifest import manifest from tests.fixtures.corpus import manifest_corpus, model_corpus from tests.fixtures.entity import entity +from tests.fixtures.datetime_parser import datetime_parser warnings.filterwarnings("ignore", module="cdm") warnings.simplefilter("ignore", category=DeprecationWarning) diff --git a/tests/fixtures/datetime_parser.py b/tests/fixtures/datetime_parser.py new file mode 100644 index 0000000..55e7b63 --- /dev/null +++ b/tests/fixtures/datetime_parser.py @@ -0,0 +1,16 @@ +import pytest +from pyspark.sql import SparkSession +from pyspark_cdm.datetime_parser import DatetimeParser +from pyspark_cdm.entity import Entity + +@pytest.fixture +def datetime_parser(spark: SparkSession, entity: Entity): + df = entity.get_dataframe(spark, True) + catalog = entity.catalog + + datetime_parser = DatetimeParser( + df=df, + catalog=catalog, + ) + + return datetime_parser diff --git a/tests/samples/model/example/1.csv b/tests/samples/model/example/1.csv deleted file mode 100644 index d864cf7..0000000 --- a/tests/samples/model/example/1.csv +++ /dev/null @@ -1,3 +0,0 @@ -7282fed7-c82c-eb11-a813-000d3a3a75c1,"6/12/2023 12:03:02 PM","6/12/2023 12:03:02 PM",6b82fed7-c82c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,7282fed7-c82c-eb11-a813-000d3a3a75c1,56635,False,"0001-01-01T00:00:00.0000000" -32b07058-d02c-eb11-a813-000d3a3a75c1,"6/12/2023 12:03:02 PM","6/12/2023 12:03:02 PM",2ab07058-d02c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,32b07058-d02c-eb11-a813-000d3a3a75c1,393639,False,"0001-01-01T00:00:00.0000000" -fd11d7fa-d42c-eb11-a813-000d3a3a75c1,"6/12/2023 12:03:02 PM","6/12/2023 12:03:02 PM",f511d7fa-d42c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,fd11d7fa-d42c-eb11-a813-000d3a3a75c1,542839,False,"0001-01-01T00:00:00.0000000" \ No newline at end of file diff --git a/tests/samples/model/example/Snapshot/1_1690183552.csv b/tests/samples/model/example/Snapshot/1_1690183552.csv index d864cf7..1edb923 100644 --- a/tests/samples/model/example/Snapshot/1_1690183552.csv +++ b/tests/samples/model/example/Snapshot/1_1690183552.csv @@ -1,3 +1,3 @@ -7282fed7-c82c-eb11-a813-000d3a3a75c1,"6/12/2023 12:03:02 PM","6/12/2023 12:03:02 PM",6b82fed7-c82c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,7282fed7-c82c-eb11-a813-000d3a3a75c1,56635,False,"0001-01-01T00:00:00.0000000" -32b07058-d02c-eb11-a813-000d3a3a75c1,"6/12/2023 12:03:02 PM","6/12/2023 12:03:02 PM",2ab07058-d02c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,32b07058-d02c-eb11-a813-000d3a3a75c1,393639,False,"0001-01-01T00:00:00.0000000" -fd11d7fa-d42c-eb11-a813-000d3a3a75c1,"6/12/2023 12:03:02 PM","6/12/2023 12:03:02 PM",f511d7fa-d42c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,fd11d7fa-d42c-eb11-a813-000d3a3a75c1,542839,False,"0001-01-01T00:00:00.0000000" \ No newline at end of file +7282fed7-c82c-eb11-a813-000d3a3a75c1,"6/15/2023 12:03:02 PM","6/12/2023 12:03:02 PM",6b82fed7-c82c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,7282fed7-c82c-eb11-a813-000d3a3a75c1,56635,False,"0001-01-01T00:00:00.0000000","2023-12-01T08:25:39.0000000+00:00","2024-01-30T13:24:06Z","20-12-2023","20-12-2023 15:45:03","2022-09-30T07:34:52.5348484Z","2022-03-22T13:40:11.0000000","20-12-2023","2022-05-05T14:38:48.8547924Z","2022-03-12T15:50:11.0000000" +32b07058-d02c-eb11-a813-000d3a3a75c1,"6/15/2023 12:03:02 PM","6/12/2023 12:03:02 PM",2ab07058-d02c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,32b07058-d02c-eb11-a813-000d3a3a75c1,393639,False,"0001-01-01T00:00:00.0000000","2023-12-01T08:25:39.0000000+00:00","2024-01-30T13:24:06Z","20-12-2023","20-12-2023 15:45:03","2022-09-30T07:34:52.5348484Z","2022-03-22T13:40:11.0000000","20-12-2023","2022-05-05T14:38:48.8547924Z","2022-03-12T15:50:11.0000000" +fd11d7fa-d42c-eb11-a813-000d3a3a75c1,"6/15/2023 12:03:02 PM","6/12/2023 12:03:02 PM",f511d7fa-d42c-eb11-a813-000d3a3a75c1,7a7bfed7-c82c-eb11-a813-000d3a3a75c1,fd11d7fa-d42c-eb11-a813-000d3a3a75c1,542839,False,"0001-01-01T00:00:00.0000000","2023-12-01T08:25:39.0000000+00:00","2024-01-30T13:24:06Z","20-12-2023","20-12-2023 15:45:03","2022-09-30T07:34:52.5348484Z","2022-03-22T13:40:11.0000000","20-12-2023","2022-05-05T14:38:48.8547924Z","2022-03-12T15:50:11.0000000" \ No newline at end of file diff --git a/tests/samples/model/model.json b/tests/samples/model/model.json index ef89b4e..a171e4e 100644 --- a/tests/samples/model/model.json +++ b/tests/samples/model/model.json @@ -66,6 +66,51 @@ "name": "CreatedOn", "dataType": "dateTimeOffset", "maxLength": -1 + }, + { + "name": "RandomDateTime", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime2", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime3", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime4", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime5", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime6", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime7", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime8", + "dataType": "dateTime", + "maxLength": -1 + }, + { + "name": "RandomDateTime9", + "dataType": "dateTime", + "maxLength": -1 } ], "partitions": [ diff --git a/tests/test_pyspark_cdm/test_catalog.py b/tests/test_pyspark_cdm/test_catalog.py new file mode 100644 index 0000000..95dd22c --- /dev/null +++ b/tests/test_pyspark_cdm/test_catalog.py @@ -0,0 +1,29 @@ +from pyspark_cdm import Entity +from pyspark_cdm.catalog import Catalog +from pyspark.sql.types import ( + StructType, + StructField, + StringType, + TimestampType, + DateType, +) +from pyspark_cdm.catalog import catalog_factory + + +def test_entity_schema_overwrite_occured(entity: Entity): + """ + When we use detection and "manually" detect the timestamp format we overwrite + the original schema. We expect that the timestamp typed columns are changed to + string types. + """ + schema = StructType([ + StructField("foo", TimestampType(), True), + StructField("foo", StringType(), True), + StructField("foo", DateType(), True), + ]) + + assert entity.catalog.overwrite_timestamp_types(schema) == StructType([ + StructField("foo", StringType(), True), + StructField("foo", StringType(), True), + StructField("foo", StringType(), True), + ]) \ No newline at end of file diff --git a/tests/test_pyspark_cdm/test_datetime_parser.py b/tests/test_pyspark_cdm/test_datetime_parser.py new file mode 100644 index 0000000..7a60829 --- /dev/null +++ b/tests/test_pyspark_cdm/test_datetime_parser.py @@ -0,0 +1,21 @@ +from pyspark_cdm.datetime_parser import DatetimeParser +import pytest + +@pytest.mark.filterwarnings("ignore::pytest.PytestUnraisableExceptionWarning") +def test_datetime_parser_datetime_regex(datetime_parser: DatetimeParser): + """ + Ensure that the datetime parser correctly find the regex pattern and returns + the correct pyspark datetime pattern. + """ + assert datetime_parser.detect_date_format('1/1/1999 01:12:12 PM') == "M/d/yyyy h:mm:ss a" + assert datetime_parser.detect_date_format('2022-03-22T13:40:11.000') == "yyyy-MM-dd'T'HH:mm:ss.SSS" + assert datetime_parser.detect_date_format('2024-01-30T13:24:06Z') == "yyyy-MM-dd'T'HH:mm:ss'Z'" + assert datetime_parser.detect_date_format('20-12-2023') == "dd-MM-yyyy" + assert datetime_parser.detect_date_format('20-12-2023 15:45:03') == "dd-MM-yyyy HH:mm:ss" + + with pytest.raises(Exception): + datetime_parser.detect_date_format('20-12ABC-2023') + + with pytest.raises(Exception): + datetime_parser.detect_date_format('1/1/1999 01:12:12 PAM') + \ No newline at end of file diff --git a/tests/test_pyspark_cdm/test_entity.py b/tests/test_pyspark_cdm/test_entity.py index e67bccf..dc51871 100644 --- a/tests/test_pyspark_cdm/test_entity.py +++ b/tests/test_pyspark_cdm/test_entity.py @@ -1,10 +1,11 @@ from cdm.objectmodel import CdmEntityDefinition from cdm.persistence.modeljson.types.local_entity import LocalEntity -import pytest from pyspark_cdm import Entity from tests.consts import MANIFEST_SAMPLE_PATH import pyspark.sql.types as T from pyspark.sql import DataFrame +import pyspark.sql.functions as F +import pytest def test_entity_name(entity: Entity): @@ -65,7 +66,7 @@ def test_entity_schema(entity: Entity): """ Make sure that the schema property correctly returns a StructType. """ - assert type(entity.schema) == T.StructType + assert type(entity.catalog.schema) == T.StructType def test_entity_dataframe(entity: Entity, spark): @@ -77,6 +78,27 @@ def test_entity_dataframe(entity: Entity, spark): assert df.count() == 3 +@pytest.mark.filterwarnings("ignore::pytest.PytestUnraisableExceptionWarning") +def test_entity_with_timestamp_parsing(entity: Entity, spark): + """ + Ensure that the timestamp parsing goes correctly. + """ + df_parsed = entity.get_dataframe(spark=spark, infer_timestamp_formats=True) + + if entity.is_model: + assert df_parsed.filter(F.col("SinkCreatedOn").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("SinkModifiedOn").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("CreatedOn").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("RandomDateTime3").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("RandomDateTime4").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("RandomDateTime5").isNotNull()).count() != 0 + + else: + assert df_parsed.filter(F.col("MODIFIEDDATETIME").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("CREATEDDATETIME").isNotNull()).count() != 0 + assert df_parsed.filter(F.col("CREDMANELIGIBLECREDITLIMITDATE").isNotNull()).count() != 0 + + def test_entity_alter_schema(entity: Entity, spark): """ Make sure that the alter_schema parameter correctly alters the schema of the dataframe. @@ -86,4 +108,4 @@ def alter_schema(schema): return T.StructType([T.StructField("_id", T.StringType()), *schema[1:]]) df = entity.get_dataframe(spark=spark, alter_schema=alter_schema) - assert df.columns[0] == "_id" + assert df.columns[0] == "_id" \ No newline at end of file diff --git a/tests/test_pyspark_cdm/test_utils.py b/tests/test_pyspark_cdm/test_utils.py index e715a44..7c1732a 100644 --- a/tests/test_pyspark_cdm/test_utils.py +++ b/tests/test_pyspark_cdm/test_utils.py @@ -1,5 +1,11 @@ -from pyspark_cdm.utils import remove_root_from_path - +from pyspark_cdm.utils import remove_root_from_path, first_non_empty_values +from pyspark.sql import Row +import pytest +from pyspark.sql.types import ( + StructType, + StructField, + StringType, +) def test_remove_root_from_path(): """ @@ -7,3 +13,46 @@ def test_remove_root_from_path(): """ assert remove_root_from_path("/dbfs/mnt/test/", "/dbfs") == "/mnt/test/" assert remove_root_from_path("/dbfs/mnt/test/", "/something") == "/dbfs/mnt/test/" + +@pytest.mark.filterwarnings("ignore::pytest.PytestUnraisableExceptionWarning") +def test_first_non_empty_values(spark): + """ + Checks if the util function correctly returns the first non empty value + for each selected column. + """ + # Create a spark dataframe with none values + df_with_empty_values = spark.createDataFrame( + data = [ + ("1", None, "0001-01-01T00:00:00.0000000"), + ("2", "12/9/2023 9:44:23 PM", "1234"), + ("3", "apple", None), + ("4", None, None), + ], + schema=["id","timestamp_1","timestamp_2"] + ) + + first_non_empty_value_per_column = first_non_empty_values(df_with_empty_values, df_with_empty_values.columns) + assert first_non_empty_value_per_column == Row(id='1', timestamp_1='12/9/2023 9:44:23 PM', timestamp_2='0001-01-01T00:00:00.0000000') + + +@pytest.mark.filterwarnings("ignore::pytest.PytestUnraisableExceptionWarning") +def test_extracting_non_empty_values_with_empty_columns(spark): + """ + Checks if the util function functions with a column with None values. + """ + df_with_empty_values = spark.createDataFrame( + data = [ + ("1", None, "0001-01-01T00:00:00.0000000"), + ("2", None, "1234"), + ("3", None, None), + ("4", None, None), + ], + schema=StructType([ + StructField("id", StringType(), True), + StructField("timestamp_1", StringType(), True), + StructField("timestamp_2", StringType(), True), + ]) + ) + + first_non_empty_value_per_column = first_non_empty_values(df_with_empty_values, df_with_empty_values.columns) + assert first_non_empty_value_per_column == Row(id='1', timestamp_1=None, timestamp_2='0001-01-01T00:00:00.0000000') \ No newline at end of file