def test_auto_mapper_amount(spark_session: SparkSession) -> None:
# Arrange
spark_session.createDataFrame(
[
(1, "Qureshi", "Imran", "54.45"),
(2, "Vidal", "Michael", "67.67"),
(3, "Alex", "Hearn", "1286782.17"),
],
["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")),
null_col=A.amount(AutoMapperDataTypeLiteral(None)),
)
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_compare_expressions(
sql_expressions["age"], col("b.my_age").cast("double").alias("age")
)
assert_compare_expressions(
sql_expressions["null_col"], lit(None).cast("double").alias("null_col")
)
result_df: DataFrame = mapper.transform(df=df)
# Assert
result_df.printSchema()
result_df.show()
assert approx(
result_df.where("member_id == 1").select("age", "null_col").collect()[0][:]
) == (approx(54.45), None)
assert approx(
result_df.where("member_id == 2").select("age", "null_col").collect()[0][:]
) == (approx(67.67), None)
# Ensuring exact match in situations in which float arithmetic errors might occur
assert (
str(result_df.where("member_id == 3").select("age").collect()[0][0])
== "1286782.17"
)
assert dict(result_df.dtypes)["age"] == "double"
assert dict(result_df.dtypes)["null_col"] == "double"
def test_automapper_filter(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").columns(
age=A.filter(column=A.column("identifier"),
func=lambda x: x["use"] == lit("usual")))
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("b.identifier",
lambda x: x["use"] == lit("usual")).alias("age"),
)
result_df: DataFrame = mapper.transform(df=source_df)
result_df.show(truncate=False)
def test_automapper_map(spark_session: SparkSession) -> None:
# Arrange
spark_session.createDataFrame(
[
(1, "Qureshi", "Imran", "Y"),
(2, "Vidal", "Michael", "N"),
(3, "Vidal", "Michael", "f"),
(4, "Qureshi", "Imran", None),
],
["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"),
{
None: "Unspecified",
"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_compare_expressions(
sql_expressions["has_kids"],
when(col("b.has_kids").eqNullSafe(lit(None)), lit("Unspecified")).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")
assert (result_df.where("member_id == 4").select("has_kids").collect()[0]
[0] == "Unspecified")
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_compare_expressions(
sql_expressions["my_column"],
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_number(spark_session: SparkSession) -> None:
# Arrange
spark_session.createDataFrame(
[
(1, "Qureshi", "Imran", "54"),
(2, "Vidal", "Michael", "67"),
(3, "Old", "Methusela", "131026061001"),
],
["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(
age=A.number(A.column("my_age")),
null_field=A.number(AutoMapperDataTypeLiteral(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_compare_expressions(
sql_expressions["age"],
col("b.my_age").cast("long").alias("age"),
)
assert_compare_expressions(
sql_expressions["null_field"], lit(None).cast("long").alias("null_field")
)
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] == 67
assert (
result_df.where("member_id == 3").select("age").collect()[0][0] == 131026061001
)
assert (
result_df.where("member_id == 1").select("null_field").collect()[0][0] is None
)
assert dict(result_df.dtypes)["age"] in ("int", "long", "bigint")
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")),
literal_val=A.unix_timestamp("1609390500"),
)
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["literal_val"],
to_timestamp(from_unixtime("1609390500", "yyyy-MM-dd HH:mm:ss"),
"yyyy-MM-dd HH:mm:ss").alias("literal_val"),
)
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 result_df.where("member_id == 1").select(
"literal_val").collect()[0][0] == datetime(2020, 12, 31, 4, 55, 0)
assert result_df.where("member_id == 2").select(
"literal_val").collect()[0][0] == datetime(2020, 12, 31, 4, 55, 0)
assert dict(result_df.dtypes)["timestamp"] == "timestamp"
assert dict(result_df.dtypes)["literal_val"] == "timestamp"
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_compare_expressions(sql_expressions["name"],
col("b.last_name").cast("string").alias("name"))
assert_compare_expressions(sql_expressions["age"],
col("b.my_age").cast("long").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"] in ("int", "long", "bigint")
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_compare_expressions(
sql_expressions["dst2"],
when(
array(lit("address1"), lit("address2"), lit(None)).isNotNull(),
filter(
coalesce(array(lit("address1"), lit("address2"), lit(None)),
array()),
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_regex_replace_unicode(
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")
not_normal_characters: str = r"[^\w\r\n\t _.,!\"'/$-]"
# source_df.select(regexp_extract('last_name', not_normal_characters, 1).alias('d')).show()
# Act
mapper = AutoMapper(
view="members", source_view="patients",
keys=["member_id"]).columns(my_column=A.column(
"last_name").regex_replace(not_normal_characters, "."))
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["my_column"],
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_if_not_null_or_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.if_not_null_or_empty(A.column("my_age"), A.column(
"my_age"), 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_compare_expressions(
sql_expressions["age"],
when(
col("b.my_age").isNull() | col("b.my_age").eqNullSafe(""),
lit("100").cast(StringType()),
).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] == "100"
assert result_df.where("member_id == 3").select(
"age").collect()[0][0] == "100"
assert dict(result_df.dtypes)["age"] == "string"
def test_auto_mapper_decimal(spark_session: SparkSession) -> None:
# Arrange
spark_session.createDataFrame(
[
(1, "Qureshi", "Imran", "54.45"),
(2, "Vidal", "Michael", "123467.678"),
(3, "Paul", "Kyle", "13"),
],
["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(age=A.decimal(A.column("my_age"), 10, 2))
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_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_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_compare_expressions(
sql_expressions["age"],
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_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_compare_expressions(
sql_expressions["openingTime"],
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_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_compare_expressions(
sql_expressions["age"],
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_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_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_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_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_compare_expressions(
sql_expressions["birthDate"],
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_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_compare_expressions(sql_expressions["birthDate"],
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_amount_typed(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")
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"
]).columns(age=A.amount(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}")
assert_compare_expressions(sql_expressions["age"],
col("b.my_age").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"] == "double"
def test_auto_mapper_split_by_delimiter(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"
]).columns(my_column=A.split_by_delimiter(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_compare_expressions(
sql_expressions["my_column"],
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] == ["Qureshi"]
assert result_df.where("member_id == 2").select(
"my_column").collect()[0][0] == [
"Vidal",
"Bates",
]
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_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_compare_expressions(
sql_expressions["my_column"],
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_auto_mapper_schema_pruning_with_defined_class(
spark_session: SparkSession, ) -> None:
# Arrange
clean_spark_session(spark_session)
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")
# Act
mapper = AutoMapper(
view="members",
source_view="patients",
).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=source_df)
# Assert
assert_compare_expressions(sql_expressions["name"],
col("b.last_name").cast("string").alias("name"))
assert_compare_expressions(sql_expressions["age"],
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_automapper_select_one(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").columns(age=A.column("identifier").filter(
lambda x: x["system"] == "http://hl7.org/fhir/sid/us-npi").
select_one(A.field("_.value")))
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"],
transform(
filter(
"b.identifier",
lambda x: x["system"] == lit("http://hl7.org/fhir/sid/us-npi"),
),
lambda x: x["value"],
)[0].alias("age"),
)
result_df: DataFrame = mapper.transform(df=source_df)
result_df.show(truncate=False)
assert result_df.select("age").collect()[0][0] == "1730325416"
assert result_df.select("age").collect()[1][0] == "1467734301"
def test_auto_mapper_with_filter(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"],
filter_by="left(last_name,2) == 'Vi'",
).columns(lname=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_compare_expressions(
sql_expressions["lname"], col("b.last_name").alias("lname")
)
result_df: DataFrame = mapper.transform(df=df)
# Assert
result_df.printSchema()
result_df.show()
assert result_df.count() == 1
assert result_df.where("member_id == 2").select("lname").collect()[0][0] == "Vidal"
def test_automapper_transform(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").complex(
MyObject(age=A.transform(
A.column("identifier"),
A.complex(bar=A.field("value"), bar2=A.field("system")),
)))
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"],
transform(
"b.identifier",
lambda x: struct(
col("x[value]").alias("bar"),
col("x[system]").alias("bar2")),
).alias("age"),
)
result_df: DataFrame = mapper.transform(df=source_df)
result_df.show(truncate=False)
assert result_df.select("age").collect()[0][0][0][0] == "123"
def test_auto_mapper_regex_replace(spark_session: SparkSession) -> None:
# Arrange
spark_session.createDataFrame(
[
(1, "Qureshi", "Imran", "1970-01-01"),
(2, "Vidal", "Michael", "1980-02-02"),
],
["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.regex_extract(A.column("date_of_birth"), r"^(\d{4}).*", 1))
# Assert
assert isinstance(mapper, AutoMapper)
sql_expressions: Dict[str, Column] = mapper.get_column_specs(source_df=source_df)
assert_compare_expressions(
sql_expressions["my_column"],
regexp_extract(col("b.date_of_birth"), r"^(\d{4}).*", 1).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] == "1970"
)
# noinspection SpellCheckingInspection
assert (
result_df.where("member_id == 2").select("my_column").collect()[0][0] == "1980"
)
def test_auto_mapper_join_using_delimiter(spark_session: SparkSession) -> None:
# Arrange
spark_session.createDataFrame(
[
(123456789, "Gagan", "Chawla", ["MD", "PhD"]),
],
["npi", "first_name", "last_name", "suffix"],
).createOrReplaceTempView("practitioners")
source_df: DataFrame = spark_session.table("practitioners")
df = source_df.select("npi")
df.createOrReplaceTempView("physicians")
# Act
mapper = AutoMapper(
view="physicians", source_view="practitioners", keys=[
"npi"
]).columns(my_column=A.join_using_delimiter(A.column("suffix"), ", "))
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["my_column"],
array_join(col("b.suffix"), ", ").alias("my_column"),
)
result_df: DataFrame = mapper.transform(df=df)
# Assert
result_df.printSchema()
result_df.show()
assert (result_df.where("npi == 123456789").select("my_column").collect()
[0][0] == "MD, PhD")
def test_auto_mapper_columns(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",
dst2=AutoMapperList(["address1"]),
dst3=AutoMapperList(["address1", "address2"]),
dst4=AutoMapperList(
[A.complex(use="usual", family=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
assert len(sql_expressions) == 4
assert_compare_expressions(sql_expressions["dst1"],
lit("src1").alias("dst1"))
assert_compare_expressions(
sql_expressions["dst2"],
when(
array(lit("address1")).isNotNull(),
filter(coalesce(array(lit("address1")), array()),
lambda x: x.isNotNull()),
).alias("dst2"),
)
assert_compare_expressions(
sql_expressions["dst3"],
when(
array(lit("address1"), lit("address2")).isNotNull(),
filter(
coalesce(array(lit("address1"), lit("address2")), array()),
lambda x: x.isNotNull(),
),
).alias("dst3"),
)
assert_compare_expressions(
sql_expressions["dst4"],
when(
array(
struct(
lit("usual").alias("use"),
col("b.last_name").alias("family"))).isNotNull(),
filter(
coalesce(
array(
struct(
lit("usual").alias("use"),
col("b.last_name").alias("family"),
)),
array(),
),
lambda x: x.isNotNull(),
),
).alias("dst4"),
)
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")
