def test_auto_mapper_decimal_typed(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", 54.45), (2, "Vidal", "Michael", 123467.678), (3, "Paul", "Kyle", 13.0), ], ["member_id", "last_name", "first_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") # source_df = source_df.withColumn("my_age", col("my_age").cast("float")) df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).columns(age=A.decimal(A.column("my_age"), 10, 2)) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert_compare_expressions( sql_expressions["age"], col("b.my_age").cast("decimal(10,2)").alias("age") ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select("age").collect()[0][0] == Decimal( "54.45" ) assert result_df.where("member_id == 2").select("age").collect()[0][0] == Decimal( "123467.68" ) assert result_df.where("member_id == 3").select("age").collect()[0][0] == Decimal( "13.00" ) assert dict(result_df.dtypes)["age"] == "decimal(10,2)"
def test_automapper_optional_ifexists(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "54"), (2, "Vidal", "Michael", None), ], ["member_id", "last_name", "first_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).columns( optional_age=AutoMapperIfColumnExistsType( column=A.column("my_age"), if_exists=A.number(A.column("my_age")), if_not_exists=A.text("no age"), ), optional_foo=AutoMapperIfColumnExistsType( column=A.column("foo"), if_exists=A.text("foo col is there"), if_not_exists=A.text("no foo"), ), ) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "optional_age", "optional_foo" ).collect()[0][:] == (54, "no foo") assert result_df.where("member_id == 2").select( "optional_age", "optional_foo" ).collect()[0][:] == (None, "no foo")
def test_auto_mapper_boolean(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "0"), (2, "Vidal", "Michael", "1"), ], ["member_id", "last_name", "first_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper(view="members", source_view="patients", keys=["member_id"]).columns( age=A.boolean(A.column("my_age")), is_active=A.boolean("False"), ) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert_compare_expressions(sql_expressions["age"], col("b.my_age").cast("boolean").alias("age")) assert_compare_expressions(sql_expressions["is_active"], lit("False").cast("boolean").alias("is_active")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "age", "is_active", ).collect()[0][:] == (False, False) assert result_df.where("member_id == 2").select( "age", "is_active", ).collect()[0][:] == (True, False) assert dict(result_df.dtypes)["age"] == "boolean" assert dict(result_df.dtypes)["is_active"] == "boolean"
def test_auto_mapper_hash(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "54"), (2, "Vidal", "67"), (3, "Vidal", None), (4, None, None), ], ["member_id", "last_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") source_df = source_df.withColumn("my_age", col("my_age").cast("int")) df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"] ).columns(age=A.hash(A.column("my_age"), A.column("last_name"))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["age"]) == str( hash(col("b.my_age"), col("b.last_name")).cast("string").alias("age") ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert ( result_df.where("member_id == 1").select("age").collect()[0][0] == "-543157534" ) assert ( result_df.where("member_id == 2").select("age").collect()[0][0] == "2048196121" ) assert ( result_df.where("member_id == 3").select("age").collect()[0][0] == "-80001407" ) assert result_df.where("member_id == 4").select("age").collect()[0][0] == "42" assert dict(result_df.dtypes)["age"] == "string"
def test_automapper_map_no_default(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "Y"), (2, "Vidal", "Michael", "N"), (3, "Vidal", "Michael", "f"), ], ["member_id", "last_name", "first_name", "has_kids"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], keep_null_rows=True).columns(has_kids=A.map(A.column("has_kids"), { "Y": "Yes", "N": "No" })) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert_compare_expressions( sql_expressions["has_kids"], when(col("b.has_kids").eqNullSafe(lit("Y")), lit("Yes")).when( col("b.has_kids").eqNullSafe(lit("N")), lit("No")).otherwise(lit(None)).alias("___has_kids"), ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "has_kids").collect()[0][0] == "Yes" assert result_df.where("member_id == 2").select( "has_kids").collect()[0][0] == "No" assert result_df.where("member_id == 3").select( "has_kids").collect()[0][0] is None
def test_auto_mapper_date_format(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "1970-01-01 12:30"), (2, "Vidal", "Michael", "1970-02-02 06:30"), ], ["member_id", "last_name", "first_name", "opening_time"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") source_df = source_df.withColumn( "opening_time", to_timestamp("opening_time", format="yyyy-MM-dd hh:mm")) assert dict(source_df.dtypes)["opening_time"] == "timestamp" df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"]).columns(openingTime=A.datetime( A.column("opening_time")).to_date_format("hh:mm:ss")) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["openingTime"]) == str( date_format(coalesce(to_timestamp(col("b.opening_time"))), "hh:mm:ss").alias("openingTime")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert (result_df.where("member_id == 1").select("openingTime").collect() [0][0] == "12:30:00") assert (result_df.where("member_id == 2").select("openingTime").collect() [0][0] == "06:30:00") # check type assert dict(result_df.dtypes)["openingTime"] == "string"
def test_automapper_nested_array_filter_simple_with_array( spark_session: SparkSession, ) -> None: clean_spark_session(spark_session) data_dir: Path = Path(__file__).parent.joinpath("./") environ["LOGLEVEL"] = "DEBUG" data_json_file: Path = data_dir.joinpath("data.json") source_df: DataFrame = spark_session.read.json(str(data_json_file), multiLine=True) source_df.createOrReplaceTempView("patients") source_df.show(truncate=False) # Act mapper = AutoMapper( view="members", source_view="patients").columns(age=A.nested_array_filter( array_field=A.column("array1"), inner_array_field=A.field("array2"), match_property="reference", match_value=A.text("bar"), )) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert_compare_expressions( sql_expressions["age"], filter( col("b.array1"), lambda y: exists( y["array2"], lambda x: x["reference"] == lit("bar").cast( "string")), ).alias("age"), ) result_df: DataFrame = mapper.transform(df=source_df) result_df.printSchema() result_df.show(truncate=False) assert result_df.count() == 2 assert result_df.select("age").collect()[0][0] == [] assert result_df.select( "age").collect()[1][0][0]["array2"][0]["reference"] == "bar"
def test_auto_mapper_sanitize(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ ( 1, "MedStar NRN PMR at Good Samaritan Hosp Good Health Center", "Imran", "1970-01-01", ), (2, "Vidal", "Michael", "1970-02-02"), ], ["member_id", "last_name", "first_name", "date_of_birth"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=[ "member_id" ]).columns(my_column=A.column("last_name").sanitize(replacement=".")) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") not_normal_characters: str = r"[^\w\r\n\t _.,!\"'/$-]" assert str(sql_expressions["my_column"]) == str( regexp_replace(col("b.last_name"), not_normal_characters, ".").alias("my_column")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show(truncate=False) # noinspection SpellCheckingInspection assert (result_df.where("member_id == 1").select("my_column").collect()[0] [0] == "MedStar NRN PMR at Good Samaritan Hosp.Good Health Center") # noinspection SpellCheckingInspection assert (result_df.where("member_id == 2").select("my_column").collect()[0] [0] == "Vidal")
def test_automapper_null_if_empty(spark_session: SparkSession) -> None: # Arrange clean_spark_session(session=spark_session) spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "54"), (2, "Vidal", "Michael", ""), (3, "Vidal3", "Michael", None), ], ["member_id", "last_name", "first_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") source_df.show() df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).columns(age=A.column("my_age").to_null_if_empty()) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["age"]) == str( when(col("b.my_age").eqNullSafe(""), lit(None)).otherwise(col("b.my_age")).alias("age")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "age").collect()[0][0] == "54" assert result_df.where("member_id == 2").select( "age").collect()[0][0] is None assert result_df.where("member_id == 3").select( "age").collect()[0][0] is None assert dict(result_df.dtypes)["age"] == "string"
def test_automapper_if_list(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "54"), (2, "Qureshi", "Imran", "59"), (3, "Vidal", "Michael", None), ], ["member_id", "last_name", "first_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper(view="members", source_view="patients", keys=["member_id"]).columns(age=A.if_( column=A.column("my_age"), check=["54", "59"], value=A.number(A.column("my_age")), else_=A.number(A.text("100")), )) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["age"]) == str( when(col("b.my_age").isin(["54", "59"]), col("b.my_age").cast("long")).otherwise( lit("100").cast(StringType()).cast(LongType())).alias("age")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "age").collect()[0][0] == 54 assert result_df.where("member_id == 2").select( "age").collect()[0][0] == 59 assert result_df.where("member_id == 3").select( "age").collect()[0][0] == 100 assert dict(result_df.dtypes)["age"] in ("int", "long", "bigint")
def test_auto_mapper_complex_with_extension( spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame([ (1, 'Qureshi', 'Imran', 45), (2, 'Vidal', 'Michael', 35), ], ['member_id', 'last_name', 'first_name', 'my_age' ]).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper(view="members", source_view="patients", keys=["member_id"], drop_key_columns=False).complex( MyClass( name=A.column("last_name"), age=A.number(A.column("my_age")), extension=AutoMapperList([ MyProcessingStatusExtension( processing_status=A.text("foo"), request_id=A.text("bar"), date_processed=A.date("2021-01-01")) ]))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert assert str(sql_expressions["name"]) == str( col("b.last_name").cast("string").alias("name")) assert str(sql_expressions["age"]) == str( col("b.my_age").cast("int").alias("age")) result_df.printSchema() result_df.show(truncate=False) assert result_df.where("member_id == 1").select( "name").collect()[0][0] == "Qureshi" assert dict(result_df.dtypes)["age"] == "int"
def test_auto_mapper_fhir_reference(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi"), (2, "Vidal"), ], ["member_id", "last_name"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"] ).columns( patient=Patient( id_=FhirId(A.column("last_name")), managingOrganization=Reference( reference=FhirReference("Organization", A.column("last_name")) ), ) ) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show(truncate=False) assert ( result_df.where("member_id == 1") .selectExpr("patient.managingOrganization.reference") .collect()[0][0] == "Organization/Qureshi" ) assert ( result_df.where("member_id == 2") .selectExpr("patient.managingOrganization.reference") .collect()[0][0] == "Organization/Vidal" )
def test_auto_mapper_struct(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran"), (2, "Vidal", "Michael"), ], ["member_id", "last_name", "first_name"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).columns(dst2=A.struct({ "use": "usual", "family": "imran" })) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert assert_compare_expressions( sql_expressions["dst2"], struct(lit("usual").alias("use"), lit("imran").alias("family")).alias("dst2"), ) result_df.printSchema() result_df.show() result_df.where("member_id == 1").select("dst2").show() result_df.where("member_id == 1").select("dst2").printSchema() result = result_df.where("member_id == 1").select("dst2").collect()[0][0] assert result[0] == "usual" assert result[1] == "imran"
def test_auto_mapper_multiple_columns_simpler_syntax( spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame([ (1, 'Qureshi', 'Imran'), (2, 'Vidal', 'Michael'), ], ['member_id', 'last_name', 'first_name' ]).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False).columns(dst1="src1").columns( dst2=["address1"]).columns(dst3=["address1", "address2"]).columns( dst4=[dict(use="usual", family="[last_name]")]) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert len(result_df.columns) == 5 assert result_df.where("member_id == 1").select( "dst1").collect()[0][0] == "src1" assert result_df.where("member_id == 1").select( "dst2").collect()[0][0][0] == "address1" assert result_df.where("member_id == 1").select( "dst3").collect()[0][0][0] == "address1" assert result_df.where("member_id == 1").select( "dst3").collect()[0][0][1] == "address2" assert result_df.where("member_id == 1").select( "dst4").collect()[0][0][0][0] == "usual" assert result_df.where("member_id == 1").select( "dst4").collect()[0][0][0][1] == "Qureshi"
def test_automapper_copy_unmapped_properties( spark_session: SparkSession) -> None: # Arrange clean_spark_session(session=spark_session) spark_session.createDataFrame( [ ("Qureshi", "Imran", "Iqbal"), ("Vidal", "Michael", "Lweis"), ], ["last_name", "first_name", "middle_name"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") # Act mapper = AutoMapper( view="members", source_view="patients", copy_all_unmapped_properties=True, copy_all_unmapped_properties_exclude=["first_name"], ).columns(last_name="last_name", ) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") mapper.transform(df=source_df) result_df: DataFrame = spark_session.table("members") # Assert result_df.printSchema() result_df.show() assert len(result_df.columns) == 2, list(result_df.columns) assert result_df.columns == ["last_name", "middle_name"]
def test_auto_mapper_date_column_typed(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "1970-01-01"), (2, "Vidal", "Michael", "1970-02-02"), ], ["member_id", "last_name", "first_name", "date_of_birth"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") source_df = source_df.withColumn( "date_of_birth", to_date("date_of_birth", format="yyyy-MM-dd") ) assert dict(source_df.dtypes)["date_of_birth"] == "date" df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"] ).columns(birthDate=A.date(A.column("date_of_birth"))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["birthDate"]) == str( col("b.date_of_birth").alias("birthDate") ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select("birthDate").collect()[0][ 0 ] == date(1970, 1, 1) assert result_df.where("member_id == 2").select("birthDate").collect()[0][ 0 ] == date(1970, 2, 2) assert dict(result_df.dtypes)["birthDate"] == "date"
def test_auto_mapper_handles_duplicates(spark_session: SparkSession) -> None: # Arrange clean_spark_session(session=spark_session) spark_session.createDataFrame( [ (1, "Qureshi", "Imran"), (2, "Qureshi", "Imran"), (3, "Qureshi", "Imran2"), (4, "Vidal", "Michael"), ], ["member_id", "last_name", "first_name"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") # Act mapper = AutoMapper(view="members", source_view="patients", keys=["member_id" ]).columns(dst1="src1", dst2=A.column("last_name"), dst3=A.column("first_name")) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") mapper.transform(df=source_df) result_df: DataFrame = spark_session.table("members") # Assert result_df.printSchema() result_df.show() assert result_df.count() == 3
def test_auto_mapper_full_no_views(spark_session: SparkSession) -> None: # Arrange source_df = spark_session.createDataFrame([ (1, 'Qureshi', 'Imran'), (2, 'Vidal', 'Michael'), ], ['member_id', 'last_name', 'first_name']) # example of a variable client_address_variable: str = "address1" # Act mapper = AutoMapper(keys=["member_id"], drop_key_columns=False).columns( dst1="src1", dst2=AutoMapperList([client_address_variable]), dst3=AutoMapperList([client_address_variable, "address2"])) company_name: str = "Microsoft" if company_name == "Microsoft": mapper = mapper.columns(dst4=AutoMapperList( [A.complex(use="usual", family=A.column("last_name"))])) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=source_df) # Assert result_df.printSchema() result_df.show() assert len(result_df.columns) == 5 assert result_df.where("member_id == 1").select( "dst1").collect()[0][0] == "src1" assert result_df.where("member_id == 1").select( "dst2").collect()[0][0][0] == "address1" assert result_df.where("member_id == 1").select( "dst3").collect()[0][0][0] == "address1" assert result_df.where("member_id == 1").select( "dst3").collect()[0][0][1] == "address2" assert result_df.where("member_id == 1").select( "dst4").collect()[0][0][0][0] == "usual" assert result_df.where("member_id == 1").select( "dst4").collect()[0][0][0][1] == "Qureshi"
def test_auto_mapper_date_literal(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran"), (2, "Vidal", "Michael"), ], ["member_id", "last_name", "first_name"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).columns(birthDate=A.date("1970-01-01")) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["birthDate"]) == str( coalesce( to_date(lit("1970-01-01"), format="y-M-d"), to_date(lit("1970-01-01"), format="yyyyMMdd"), to_date(lit("1970-01-01"), format="M/d/y"), ).alias("birthDate") ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select("birthDate").collect()[0][ 0 ] == date(1970, 1, 1) assert result_df.where("member_id == 2").select("birthDate").collect()[0][ 0 ] == date(1970, 1, 1)
def test_auto_mapper_array_multiple_items_with_null( spark_session: SparkSession, ) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran"), (2, "Vidal", "Michael"), ], ["member_id", "last_name", "first_name"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df: DataFrame = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).columns(dst2=AutoMapperList(["address1", "address2", None])) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["dst2"]) == str( filter(array(lit("address1"), lit("address2"), lit(None)), lambda x: x.isNotNull()).alias("dst2")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert (result_df.where("member_id == 1").select("dst2").collect()[0][0][0] == "address1") assert (result_df.where("member_id == 1").select("dst2").collect()[0][0][1] == "address2") assert (result_df.where("member_id == 2").select("dst2").collect()[0][0][0] == "address1") assert (result_df.where("member_id == 2").select("dst2").collect()[0][0][1] == "address2")
def test_auto_mapper_coalesce(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", None), (2, None, "Michael", "1970-02-02"), (3, None, "Michael", None), ], ["member_id", "last_name", "first_name", "date_of_birth"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=[ "member_id" ]).columns(my_column=A.coalesce(A.column( "last_name"), A.column("date_of_birth"), A.text("last_resort"))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["my_column"]) == str( coalesce( col("b.last_name"), col("b.date_of_birth"), lit("last_resort").cast(StringType()), ).alias("my_column")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert (result_df.where("member_id == 1").select("my_column").collect()[0] [0] == "Qureshi") assert (result_df.where("member_id == 2").select("my_column").collect()[0] [0] == "1970-02-02") assert (result_df.where("member_id == 3").select("my_column").collect()[0] [0] == "last_resort")
def test_auto_mapper_datetime_column_default( spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "18922"), (2, "Vidal", "Michael", "1609390500"), ], ["member_id", "last_name", "first_name", "ts"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"]).columns(timestamp=A.unix_timestamp(A.column("ts"))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert_compare_expressions( sql_expressions["timestamp"], to_timestamp(from_unixtime(col("b.ts"), "yyyy-MM-dd HH:mm:ss"), "yyyy-MM-dd HH:mm:ss").alias("timestamp"), ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.count() == 2 assert result_df.where("member_id == 1").select( "timestamp").collect()[0][0] == datetime(1970, 1, 1, 5, 15, 22) assert result_df.where("member_id == 2").select( "timestamp").collect()[0][0] == datetime(2020, 12, 31, 4, 55, 0) assert dict(result_df.dtypes)["timestamp"] == "timestamp"
def test_auto_mapper_split_by_delimiter_and_transform( spark_session: SparkSession, ) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", "1970-01-01"), (2, "Vidal|Bates", "Michael", "1970-02-02"), ], ["member_id", "last_name", "first_name", "date_of_birth"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"]).complex( MyObject(my_column=A.transform( A.split_by_delimiter(A.column("last_name"), "|"), A.complex(bar=A.field("_"), bar2=A.field("_")), ))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") # assert str(sql_expressions["my_column"]) == str( # split(col("b.last_name"), "[|]", -1).alias("my_column") # ) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert (result_df.where("member_id == 1").select("my_column").collect()[0] [0][0]["bar"] == "Qureshi") assert (result_df.where("member_id == 2").select("my_column").collect()[0] [0][0]["bar"] == "Vidal") assert (result_df.where("member_id == 2").select("my_column").collect()[0] [0][1]["bar"] == "Bates")
def test_auto_mapper_lpad(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "1234"), (2, "1234567"), (3, "123456789"), ], ["member_id", "empi"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"] ).columns(my_column=A.lpad(column=A.column("empi"), length=9, pad="0")) # Assert assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df) assert str(sql_expressions["my_column"]) == str( lpad(col=col("b.empi"), len=9, pad="0").alias("my_column") ) result_df: DataFrame = mapper.transform(df=df) # noinspection SpellCheckingInspection assert ( result_df.where("member_id == 1").select("my_column").collect()[0][0] == "000001234" ) # noinspection SpellCheckingInspection assert ( result_df.where("member_id == 2").select("my_column").collect()[0][0] == "001234567" ) # noinspection SpellCheckingInspection assert ( result_df.where("member_id == 3").select("my_column").collect()[0][0] == "123456789" )
def test_automapper_if_regex(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame([ (1, 'Qureshi', 'Imran', "54"), (2, 'Vidal', 'Michael', None), ], ['member_id', 'last_name', 'first_name', "my_age" ]).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper(view="members", source_view="patients", keys=["member_id"]).columns( age=A.if_regex(column=A.column("my_age"), check="5*", value=A.number(A.column("my_age")), else_=A.number(A.text("100")))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["age"]) == str( when(col("b.my_age").rlike("5*"), col("b.my_age").cast(IntegerType())).otherwise( lit("100").cast(StringType()).cast( IntegerType())).alias("age")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "age").collect()[0][0] == 54 assert result_df.where("member_id == 2").select( "age").collect()[0][0] == 100 assert dict(result_df.dtypes)["age"] == "int"
def test_auto_mapper_complex_with_defined_class( spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, "Qureshi", "Imran", 45), (2, "Vidal", "Michael", 35), ], ["member_id", "last_name", "first_name", "my_age"], ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False, ).complex( MyClass(name=A.column("last_name"), age=A.number(A.column("my_age")))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert assert str(sql_expressions["name"]) == str( col("b.last_name").cast("string").alias("name")) assert str(sql_expressions["age"]) == str( col("b.my_age").cast("long").alias("age")) result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "name").collect()[0][0] == "Qureshi" assert dict(result_df.dtypes)["age"] in ("int", "long", "bigint")
def test_auto_mapper_amount(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame([ (1, 'Qureshi', 'Imran', "54.45"), (2, 'Vidal', 'Michael', "67.67"), ], ['member_id', 'last_name', 'first_name', "my_age" ]).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper(view="members", source_view="patients", keys=["member_id" ]).columns(age=A.amount(A.column("my_age"))) debug_text: str = mapper.to_debug_string() print(debug_text) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["age"]) == str( col("b.my_age").cast("float").alias("age")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert approx( result_df.where("member_id == 1").select("age").collect()[0] [0]) == approx(54.45) assert approx( result_df.where("member_id == 2").select("age").collect()[0] [0]) == approx(67.67) assert dict(result_df.dtypes)["age"] == "float"
def test_automapper_concat_array(spark_session: SparkSession) -> None: clean_spark_session(spark_session) data_dir: Path = Path(__file__).parent.joinpath("./") data_json_file: Path = data_dir.joinpath("data.json") source_df: DataFrame = spark_session.read.json(str(data_json_file), multiLine=True) source_df.createOrReplaceTempView("patients") source_df.show(truncate=False) # Act mapper = AutoMapper( view="members", source_view="patients", drop_key_columns=False).columns( age=A.column("identifier").concat(A.text("foo").to_array())) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert_compare_expressions( sql_expressions["age"], concat(col("b.identifier"), array(lit("foo").cast("string"))).alias("age"), ) result_df: DataFrame = mapper.transform(df=source_df) result_df.show(truncate=False) assert result_df.where("id == 1730325416").select( "age").collect()[0][0] == [ "bar", "foo", ] assert result_df.where("id == 1467734301").select( "age").collect()[0][0] == [ "John", "foo", ]
def test_automapper_map(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame([ (1, 'Qureshi', 'Imran', "Y"), (2, 'Vidal', 'Michael', "N"), (3, 'Vidal', 'Michael', "f"), ], ['member_id', 'last_name', 'first_name', "has_kids" ]).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"]).columns(has_kids=A.map(A.column("has_kids"), { "Y": "Yes", "N": "No" }, "unknown")) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") assert str(sql_expressions["has_kids"]) == str( when(col("b.has_kids").eqNullSafe(lit("Y")), lit("Yes")).when( col("b.has_kids").eqNullSafe(lit("N")), lit("No")).otherwise(lit("unknown")).alias("___has_kids")) result_df: DataFrame = mapper.transform(df=df) # Assert result_df.printSchema() result_df.show() assert result_df.where("member_id == 1").select( "has_kids").collect()[0][0] == "Yes" assert result_df.where("member_id == 2").select( "has_kids").collect()[0][0] == "No" assert result_df.where("member_id == 3").select( "has_kids").collect()[0][0] == "unknown"
def test_auto_mapper_complex_with_mappers(spark_session: SparkSession) -> None: # Arrange spark_session.createDataFrame( [ (1, 'Qureshi', 'Imran'), (2, 'Vidal', 'Michael'), ], ['member_id', 'last_name', 'first_name'] ).createOrReplaceTempView("patients") source_df: DataFrame = spark_session.table("patients") df = source_df.select("member_id") df.createOrReplaceTempView("members") # Act mapper = AutoMapper( view="members", source_view="patients", keys=["member_id"], drop_key_columns=False ).columns(dst2=A.complex(use="usual", family=A.complex(given="foo"))) assert isinstance(mapper, AutoMapper) sql_expressions: Dict[str, Column] = mapper.get_column_specs( source_df=source_df ) for column_name, sql_expression in sql_expressions.items(): print(f"{column_name}: {sql_expression}") result_df: DataFrame = mapper.transform(df=df) # Assert assert str(sql_expressions["dst2"]) == str( struct( expr("usual").alias("use"), struct(expr("foo").alias("given")).alias("family") ).alias("dst2") ) result_df.printSchema() result_df.show() result = result_df.where("member_id == 1").select("dst2").collect()[0][0] assert result[0] == "usual" assert result[1][0] == "foo"