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from src.transform_lambda import (
read_from_s3_subfolder_to_df,
list_existing_s3_files,
bucket_name,
process_to_parquet_and_upload_to_s3,
)
from moto import mock_aws
import pytest
import pandas as pd
import os
import boto3
from botocore.exceptions import ClientError
import numpy as np
# import caplog
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
@pytest.fixture(scope="class")
def aws_credentials():
os.environ["AWS_ACCESS_KEY_ID"] = "testing"
os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
os.environ["AWS_SECURIT_TOKEN"] = "testing"
os.environ["AWS_SESSION_TOKEN"] = "testing"
os.environ["AWS_DEFAULT_REGION"] = "eu-west-2"
@pytest.fixture(scope="class")
def s3_client(aws_credentials):
with mock_aws():
yield boto3.client("s3")
@pytest.fixture(scope="class")
def mock_extract_bucket(s3_client):
mock_extract_bucket = s3_client.create_bucket(
Bucket="dummy_extract_buc",
CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
)
return mock_extract_bucket
@pytest.fixture(scope="class")
def mock_transform_bucket(s3_client):
mock_transform_bucket = s3_client.create_bucket(
Bucket="dummy_transform_buc",
CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
)
return mock_transform_bucket
class TestReadFromS3:
# @pytest.mark.skip(reason="The test is broken!")
def test_returns_dictionary_with_correct_value_pair(
self, s3_client, mock_extract_bucket
):
s3_client.upload_file(
"tests/dummy_identical.csv",
"dummy_extract_buc",
"Foods/2024/08/21/Foods_12:03:10.csv",
)
tables = ["Foods"]
result = read_from_s3_subfolder_to_df(
tables, bucket="dummy_extract_buc", client=s3_client
)
print(result)
expected_df = pd.DataFrame(
np.array(
[
["Vegetable", "Sour", "Green", "2022-11-03 14:20:49.962"],
["Berry", "Sweet", "Red", "2022-11-03 14:20:49.962"],
]
),
columns=["Food_type", "Flavour", "Colour", "last_updated"],
)
assert isinstance(result, dict)
assert list(result.keys())[0] == "Foods"
assert isinstance(result["Foods"], pd.DataFrame)
assert result["Foods"].eq(expected_df, axis="columns").all(axis=None)
# @pytest.mark.skip(reason="The test is broken!")
def test_returns_dictionary_of_dataframes_for_multiple_tables(
self, s3_client, mock_extract_bucket
):
s3_client.upload_file(
"tests/dummy_2.csv",
"dummy_extract_buc",
"Cars/2024/08/21/Cars_14:03:56.csv",
)
tables = ["Foods", "Cars"]
result = read_from_s3_subfolder_to_df(
tables, bucket="dummy_extract_buc", client=s3_client
)
expected_foods_df = pd.DataFrame(
np.array(
[
["Vegetable", "Sour", "Green", "2022-11-03 14:20:49.962"],
["Berry", "Sweet", "Red", "2022-11-03 14:20:49.962"],
]
),
columns=["Food_type", "Flavour", "Colour", "last_updated"],
)
expected_cars_df = pd.DataFrame(
np.array(
[
["Truck", "Chevrolet", "Grey"],
["Convertible", "Mercedes", "Red"],
["Van", "Volkswagen", "Blue"],
]
),
columns=["Car_type", "Brand", "Colour"],
)
assert list(result.keys()) == tables
assert result["Foods"].eq(expected_foods_df, axis="columns").all(axis=None)
# assert result["Cars"].eq(expected_cars_df, axis="columns").all(axis=None)
class TestListExistingFiles:
def test_functions_receives_error_if_no_bucket(self, s3_client, caplog):
caplog.set_level(logging.INFO)
with pytest.raises(ClientError):
list_existing_s3_files("rando_bucket", client=s3_client)
assert (
"Error listing S3 objects: An error occurred (NoSuchBucket) when calling the ListObjectsV2 operation: The specified bucket does not exist"
in caplog.text
)
def test_recieves_logger_error_if_no_files_listed(self, s3_client, caplog):
caplog.set_level(logging.INFO)
s3_client.create_bucket(
Bucket="mock_bucket",
CreateBucketConfiguration={"LocationConstraint": "eu-west-2"},
)
response = list_existing_s3_files("mock_bucket", client=s3_client)
assert "The bucket is empty" in caplog.text
def test_retrieves_existing_files(self, s3_client, caplog):
caplog.set_level(logging.INFO)
s3_client.upload_file("tests/dummy.txt", "mock_bucket", "dummy.txt")
result = list_existing_s3_files("mock_bucket", client=s3_client)
assert result == ["dummy.txt"]
class TestBucketName:
def test_functions_retrieves__extractbucket(
self, mock_extract_bucket, mock_transform_bucket, s3_client
):
bucket = bucket_name("dummy_extract_buc", s3_client)
assert bucket == "dummy_extract_buc"
def test_transform_bucket_name(
self, mock_extract_bucket, mock_transform_bucket, s3_client
):
bucket2 = bucket_name("dummy_transform_buc", s3_client)
assert bucket2 == "dummy_transform_buc"
def test_recieves_error_when_bucket_doesnt_exist(
self, mock_extract_bucket, s3_client
):
s3_client.delete_bucket(Bucket="dummy_extract_buc")
with pytest.raises(ValueError):
bucket_name("dummy_extract_buc", s3_client)
class TestProcessToParquetUploadS3:
def test_func_uploads_to_s3(self, mock_transform_bucket, s3_client):
expected_cars_df = pd.DataFrame(
np.array(
[
["Truck", "Chevrolet", "Grey"],
["Convertible", "Mercedes", "Red"],
["Van", "Volkswagen", "Blue"],
]
),
columns=["Car_type", "Brand", "Colour"],
)
mock_dim_dict = {"car_data": expected_cars_df}
response = process_to_parquet_and_upload_to_s3(
[], mock_dim_dict, {}, mock_transform_bucket, s3_client
)
assert response == {"uploaded": ["car_data"], "not_uploaded": []}
|
