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"
