def test_build_with_incremental_strategy(
self, incremental_source_df, spark_client, spark_session
):
# arrange
readers = [
# directly from column
FileReader(
id="test_1", path="path/to/file", format="format"
).with_incremental_strategy(
incremental_strategy=IncrementalStrategy(column="date")
),
# from milliseconds
FileReader(
id="test_2", path="path/to/file", format="format"
).with_incremental_strategy(
incremental_strategy=IncrementalStrategy().from_milliseconds(
column_name="milliseconds"
)
),
# from str
FileReader(
id="test_3", path="path/to/file", format="format"
).with_incremental_strategy(
incremental_strategy=IncrementalStrategy().from_string(
column_name="date_str", mask="dd/MM/yyyy"
)
),
# from year, month, day partitions
FileReader(
id="test_4", path="path/to/file", format="format"
).with_incremental_strategy(
incremental_strategy=(
IncrementalStrategy().from_year_month_day_partitions()
)
),
]
spark_client.read.return_value = incremental_source_df
target_df = incremental_source_df.where(
"date >= date('2020-07-29') and date <= date('2020-07-31')"
)
# act
for reader in readers:
reader.build(
client=spark_client, start_date="2020-07-29", end_date="2020-07-31"
)
output_dfs = [
spark_session.table(f"test_{i + 1}") for i, _ in enumerate(readers)
]
# assert
for output_df in output_dfs:
assert_dataframe_equality(output_df=output_df, target_df=target_df)
def test_consume_with_stream_without_schema(self, spark_client, target_df):
# arrange
path = "path/to/file.json"
format = "json"
schema = None
format_options = None
stream = True
options = dict({"path": path})
spark_client.read.return_value = target_df
file_reader = FileReader(
"test", path, format, schema, format_options, stream=stream
)
# act
output_df = file_reader.consume(spark_client)
# assert
# assert call for schema infer
spark_client.read.assert_any_call(format=format, options=options)
# assert call for stream read
# stream
spark_client.read.assert_called_with(
format=format, options=options, schema=output_df.schema, stream=stream
)
assert target_df.collect() == output_df.collect()
def test_csv_file_with_schema_and_header(self):
# given
spark_client = SparkClient()
schema_csv = StructType(
[
StructField("A", LongType()),
StructField("B", DoubleType()),
StructField("C", StringType()),
]
)
file = "tests/unit/butterfree/extract/readers/file-reader-test.csv"
# when
file_reader = FileReader(
id="id",
path=file,
format="csv",
schema=schema_csv,
format_options={"header": True},
)
df = file_reader.consume(spark_client)
# assert
assert schema_csv == df.schema
assert df.columns == ["A", "B", "C"]
for value in range(3):
assert df.first()[value] != ["A", "B", "C"][value]
def test_filter(self, feature_set_dataframe, spark_context, spark_session):
# given
file_reader = FileReader("test", "path/to/file", "format")
file_reader.with_(
transformer=filter,
condition="test not in ('fail') and feature in (110, 120)",
)
# when
result_df = file_reader._apply_transformations(feature_set_dataframe)
target_data = [
{
"id": 1,
TIMESTAMP_COLUMN: 1,
"feature": 110,
"test": "pass"
},
{
"id": 1,
TIMESTAMP_COLUMN: 2,
"feature": 120,
"test": "pass"
},
]
target_df = spark_session.read.json(
spark_context.parallelize(target_data, 1))
# then
assert result_df.collect() == target_df.collect()
def __init__(self):
super(UserChargebacksPipeline, self).__init__(
source=Source(
readers=[
FileReader(
id="chargeback_events",
path="data/order_events/input.csv",
format="csv",
format_options={"header": True},
)
],
query=("""
select
cpf,
timestamp(chargeback_timestamp) as timestamp,
order_id
from
chargeback_events
where
chargeback_timestamp is not null
"""),
),
feature_set=AggregatedFeatureSet(
name="user_chargebacks",
entity="user",
description="Aggregates the total of chargebacks from users in "
"different time windows.",
keys=[
KeyFeature(
name="cpf",
description="User unique identifier, entity key.",
dtype=DataType.STRING,
)
],
timestamp=TimestampFeature(),
features=[
Feature(
name="cpf_chargebacks",
description=
"Total of chargebacks registered on user's CPF",
transformation=AggregatedTransform(functions=[
Function(functions.count, DataType.INTEGER)
]),
from_column="order_id",
),
],
).with_windows(
definitions=["3 days", "7 days", "30 days"]).add_post_hook(
ZeroFillHook()),
sink=Sink(writers=[
LocalHistoricalFSWriter(),
OnlineFeatureStoreWriter(
interval_mode=True,
check_schema_hook=NotCheckSchemaHook(),
debug_mode=True,
),
]),
)
def test_filter_with_invalidations(self, feature_set_dataframe, condition,
spark_context, spark_session):
# given
file_reader = FileReader("test", "path/to/file", "format")
file_reader.with_(transformer=filter, condition=condition)
# then
with pytest.raises(TypeError):
file_reader._apply_transformations(feature_set_dataframe)
def test_build(self, target_df, spark_client, spark_session):
# arrange
file_reader = FileReader("test", "path/to/file", "format")
spark_client.read.return_value = target_df
# act
file_reader.build(spark_client)
result_df = spark_session.sql("select * from test")
# assert
assert target_df.collect() == result_df.collect()
def test_source(
self,
target_df_source,
target_df_table_reader,
spark_session,
):
# given
spark_client = SparkClient()
table_reader_id = "a_test_source"
table_reader_db = "db"
table_reader_table = "table_test_source"
create_temp_view(dataframe=target_df_table_reader,
name=table_reader_id)
create_db_and_table(
spark=spark_session,
table_reader_id=table_reader_id,
table_reader_db=table_reader_db,
table_reader_table=table_reader_table,
)
file_reader_id = "b_test_source"
data_sample_path = INPUT_PATH + "/data.json"
# when
source = Source(
readers=[
TableReader(
id=table_reader_id,
database=table_reader_db,
table=table_reader_table,
),
FileReader(id=file_reader_id,
path=data_sample_path,
format="json"),
],
query=f"select a.*, b.feature2 " # noqa
f"from {table_reader_id} a " # noqa
f"inner join {file_reader_id} b on a.id = b.id ", # noqa
)
result_df = source.construct(client=spark_client)
target_df = target_df_source
# then
assert (compare_dataframes(
actual_df=result_df,
expected_df=target_df,
columns_sort=result_df.columns,
) is True)
def test_with_(self, transformations, spark_client):
# arrange
file_reader = FileReader("test", "path/to/file", "format")
# act
for transformation in transformations:
file_reader.with_(
transformation["transformer"],
*transformation["args"],
**transformation["kwargs"],
)
# assert
assert file_reader.transformations == transformations
def test_build_with_columns(self, target_df, column_target_df,
spark_client, spark_session):
# arrange
file_reader = FileReader("test", "path/to/file", "format")
spark_client.read.return_value = target_df
# act
file_reader.build(
client=spark_client,
columns=[("col1", "new_col1"), ("col2", "new_col2")],
)
result_df = spark_session.sql("select * from test")
# assert
assert column_target_df.collect() == result_df.collect()
def test_consume(
self, path, format, schema, format_options, spark_client, target_df
):
# arrange
spark_client.read.return_value = target_df
file_reader = FileReader("test", path, format, schema, format_options)
# act
output_df = file_reader.consume(spark_client)
options = dict({"path": path}, **format_options if format_options else {})
# assert
spark_client.read.assert_called_once_with(
format=format, options=options, schema=schema, stream=False
)
assert target_df.collect() == output_df.collect()
def test_apply_pivot_transformation(self, input_df, pivot_df):
# arrange
file_reader = FileReader("test", "path/to/file", "format")
file_reader.with_(
transformer=pivot,
group_by_columns=["id", "ts"],
pivot_column="pivot_column",
agg_column="has_feature",
aggregation=first,
)
# act
result_df = file_reader._apply_transformations(input_df)
# assert
assert compare_dataframes(
actual_df=result_df,
expected_df=pivot_df,
)
def test_json_file_with_schema(self):
# given
spark_client = SparkClient()
schema_json = StructType(
[
StructField("A", StringType()),
StructField("B", DoubleType()),
StructField("C", StringType()),
]
)
file = "tests/unit/butterfree/extract/readers/file-reader-test.json"
# when
file_reader = FileReader(id="id", path=file, format="json", schema=schema_json)
df = file_reader.consume(spark_client)
# assert
assert schema_json == df.schema
def test__apply_transformations(
self,
input_data,
transformations,
transformed_data,
spark_context,
spark_session,
spark_client,
):
# arrange
file_reader = FileReader("test", "path/to/file", "format")
file_reader.transformations = transformations
input_df = spark_session.read.json(
spark_context.parallelize(input_data, 1))
target_df = spark_session.read.json(
spark_context.parallelize(transformed_data, 1))
# act
result_df = file_reader._apply_transformations(input_df)
# assert
assert target_df.collect() == result_df.collect()
def test_feature_set_args(self):
# arrange and act
out_columns = [
"user_id",
"timestamp",
"listing_page_viewed__rent_per_month__avg_over_7_days_fixed_windows",
"listing_page_viewed__rent_per_month__avg_over_2_weeks_fixed_windows",
"listing_page_viewed__rent_per_month__stddev_pop_over_7_days_fixed_windows",
"listing_page_viewed__rent_per_month__"
"stddev_pop_over_2_weeks_fixed_windows",
# noqa
]
pipeline = FeatureSetPipeline(
source=Source(
readers=[
TableReader(
id="source_a",
database="db",
table="table",
),
FileReader(
id="source_b",
path="path",
format="parquet",
),
],
query="select a.*, b.specific_feature "
"from source_a left join source_b on a.id=b.id",
),
feature_set=FeatureSet(
name="feature_set",
entity="entity",
description="description",
keys=[
KeyFeature(
name="user_id",
description="The user's Main ID or device ID",
dtype=DataType.INTEGER,
)
],
timestamp=TimestampFeature(from_column="ts"),
features=[
Feature(
name="listing_page_viewed__rent_per_month",
description="Average of something.",
transformation=SparkFunctionTransform(functions=[
Function(functions.avg, DataType.FLOAT),
Function(functions.stddev_pop, DataType.FLOAT),
], ).with_window(
partition_by="user_id",
order_by=TIMESTAMP_COLUMN,
window_definition=["7 days", "2 weeks"],
mode="fixed_windows",
),
),
],
),
sink=Sink(writers=[
HistoricalFeatureStoreWriter(db_config=None),
OnlineFeatureStoreWriter(db_config=None),
], ),
)
assert isinstance(pipeline.spark_client, SparkClient)
assert len(pipeline.source.readers) == 2
assert all(
isinstance(reader, Reader) for reader in pipeline.source.readers)
assert isinstance(pipeline.source.query, str)
assert pipeline.feature_set.name == "feature_set"
assert pipeline.feature_set.entity == "entity"
assert pipeline.feature_set.description == "description"
assert isinstance(pipeline.feature_set.timestamp, TimestampFeature)
assert len(pipeline.feature_set.keys) == 1
assert all(
isinstance(k, KeyFeature) for k in pipeline.feature_set.keys)
assert len(pipeline.feature_set.features) == 1
assert all(
isinstance(feature, Feature)
for feature in pipeline.feature_set.features)
assert pipeline.feature_set.columns == out_columns
assert len(pipeline.sink.writers) == 2
assert all(
isinstance(writer, Writer) for writer in pipeline.sink.writers)
def test_init_invalid_params(self, path, format):
# act and assert
with pytest.raises(ValueError):
FileReader("id", path, format)
def __init__(self):
super(AwesomeDatasetPipeline, self).__init__(
source=Source(
readers=[
FileReader(
id="order_events",
path="data/order_events/input.csv",
format="csv",
format_options={"header": True},
),
FileReader(
id="user_chargebacks",
path="data/feature_store/historical/user/user_chargebacks",
format="parquet",
),
FileReader(
id="user_orders",
path="data/feature_store/historical/user/user_orders",
format="parquet",
),
],
query="""
with feature_sets_merge as(
select
user_orders.cpf,
user_orders.timestamp,
user_chargebacks.timestamp as chargeback_timestamp,
cpf_orders__count_over_3_days_rolling_windows,
cpf_orders__count_over_7_days_rolling_windows,
cpf_orders__count_over_30_days_rolling_windows,
cpf_chargebacks__count_over_3_days_rolling_windows,
cpf_chargebacks__count_over_7_days_rolling_windows,
cpf_chargebacks__count_over_30_days_rolling_windows,
row_number() over (
partition by (user_orders.cpf, user_orders.timestamp)
order by user_chargebacks.timestamp desc
) as rn
from
user_orders
left join user_chargebacks
on user_orders.cpf = user_chargebacks.cpf
and user_orders.timestamp >= user_chargebacks.timestamp
),
feature_sets_rn_filter as(
select
*
from
feature_sets_merge
where
rn = 1
),
orders_with_feature_sets as(
select
order_events.order_id,
timestamp(order_events.order_timestamp) as timestamp,
timestamp(order_events.chargeback_timestamp) as chargeback_timestamp,
order_events.cpf,
feature_sets_rn_filter.cpf_orders__count_over_3_days_rolling_windows,
feature_sets_rn_filter.cpf_orders__count_over_7_days_rolling_windows,
feature_sets_rn_filter.cpf_orders__count_over_30_days_rolling_windows,
feature_sets_rn_filter.cpf_chargebacks__count_over_3_days_rolling_windows,
feature_sets_rn_filter.cpf_chargebacks__count_over_7_days_rolling_windows,
feature_sets_rn_filter.cpf_chargebacks__count_over_30_days_rolling_windows,
row_number() over (
partition by (order_events.cpf, order_events.order_timestamp)
order by feature_sets_rn_filter.timestamp desc
) as rn
from
order_events
join feature_sets_rn_filter
on order_events.cpf = feature_sets_rn_filter.cpf
and timestamp(order_events.order_timestamp) >=
feature_sets_rn_filter.timestamp
)
select
order_id,
timestamp,
chargeback_timestamp,
cpf,
cpf_orders__count_over_3_days_rolling_windows,
cpf_orders__count_over_7_days_rolling_windows,
cpf_orders__count_over_30_days_rolling_windows,
coalesce(
cpf_chargebacks__count_over_3_days_rolling_windows,
0) as cpf_chargebacks__count_over_3_days_rolling_windows,
coalesce(
cpf_chargebacks__count_over_7_days_rolling_windows,
0) as cpf_chargebacks__count_over_7_days_rolling_windows,
coalesce(
cpf_chargebacks__count_over_30_days_rolling_windows,
0) as cpf_chargebacks__count_over_30_days_rolling_windows
from
orders_with_feature_sets
where
rn = 1
""",
),
feature_set=FeatureSet(
name="awesome_dataset",
entity="user",
description="Dataset enriching orders events with aggregated features "
"on total of orders and chargebacks by user.",
keys=[
KeyFeature(
name="order_id",
description="Orders unique identifier.",
dtype=DataType.STRING,
)
],
timestamp=TimestampFeature(),
features=[
Feature(
name="chargeback_timestamp",
description="Timestamp for the order creation.",
dtype=DataType.TIMESTAMP,
),
Feature(
name="cpf",
description="User unique identifier, user entity key.",
dtype=DataType.STRING,
),
Feature(
name="cpf_orders__count_over_3_days_rolling_windows",
description="Count of orders over 3 days rolling windows group "
"by user (identified by CPF)",
dtype=DataType.INTEGER,
),
Feature(
name="cpf_orders__count_over_7_days_rolling_windows",
description="Count of orders over 7 days rolling windows group "
"by user (identified by CPF)",
dtype=DataType.INTEGER,
),
Feature(
name="cpf_orders__count_over_30_days_rolling_windows",
description="Count of orders over 30 days rolling windows group"
" by user (identified by CPF)",
dtype=DataType.INTEGER,
),
Feature(
name="cpf_chargebacks__count_over_3_days_rolling_windows",
description="Count of chargebacks over 3 days rolling windows "
"group by user (identified by CPF)",
dtype=DataType.INTEGER,
),
Feature(
name="cpf_chargebacks__count_over_7_days_rolling_windows",
description="Count of chargebacks over 7 days rolling windows "
"group by user (identified by CPF)",
dtype=DataType.INTEGER,
),
Feature(
name="cpf_chargebacks__count_over_30_days_rolling_windows",
description="Count of chargebacks over 30 days rolling windows "
"group by user (identified by CPF)",
dtype=DataType.INTEGER,
),
],
),
sink=Sink(writers=[DatasetWriter()]),
)