From 03a5959df25f74d52ed5393c2a5af6b1b9eb34c9 Mon Sep 17 00:00:00 2001 From: T-Aji Date: Wed, 28 Aug 2024 12:48:13 +0100 Subject: refactored functs to include columns instead of drop columns --- src/load_lambda.py | 5 +- src/transform_lambda/dataframes.py | 157 ++++++++++++++++++++----------- src/transform_lambda/transform_lambda.py | 5 +- tests/test_dataframes.py | 2 +- 4 files changed, 111 insertions(+), 58 deletions(-) diff --git a/src/load_lambda.py b/src/load_lambda.py index 7339ab9..926b4db 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -134,6 +134,9 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None): file_obj = client.get_object(Bucket=bucket_name, Key=file_key) parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read())) df = parquet_file.read().to_pandas() + print("df", df) + print("type", type(df)) + print(df.columns) dfs[file_key] = df except ClientError as e: logger.error(f"Unable to retrieve S3 object {file_key}: {e}") @@ -148,7 +151,7 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None): except ClientError as client_error: logger.error(f"Unable to list objects: {client_error}") raise - + print() return dfs diff --git a/src/transform_lambda/dataframes.py b/src/transform_lambda/dataframes.py index 2a46bd6..bf0556b 100644 --- a/src/transform_lambda/dataframes.py +++ b/src/transform_lambda/dataframes.py @@ -37,30 +37,28 @@ def create_fact_sales_order(dict_of_df): df_sales["agreed_payment_date"] = pd.to_datetime( df_sales["agreed_payment_date"], format="%Y-%m-%d" ) - df_sales = df_sales.drop(labels=["created_at", "last_updated"], axis=1) - - df_sales.reset_index(inplace=True) - return df_sales + fact_sales = df_sales.loc[:, + [ + "sales_order_id", + "created_date", + "created_time", + "last_updated_date", + "last_updated_time", + "staff_id", + "counterparty_id", + "units_sold", + "unit_price", + "currency_id", + "design_id", + "agreed_payment_date", + "agreed_delivery_date", + "agreed_delivery_location_id" + ], + ] + fact_sales.rename(columns={"staff_id": "sales_staff_id"}).reset_index(inplace=True) + - df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date - df_sales["created_time"] = ( - df_sales["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time - ) - df_sales["last_updated_date"] = ( - df_sales["last_updated"].astype("datetime64[ns]").dt.date - ) - df_sales["last_updated_time"] = ( - df_sales["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time - ) - df_sales["agreed_delivery_date"] = pd.to_datetime( - df_sales["agreed_delivery_date"], format="%Y-%m-%d" - ) - df_sales["agreed_payment_date"] = pd.to_datetime( - df_sales["agreed_payment_date"], format="%Y-%m-%d" - ) - df_sales = df_sales.drop(labels=["created_at", "last_updated"], axis=1) - df_sales.reset_index(inplace=True) - return df_sales + return fact_sales # no test, same as fact_payment @@ -83,9 +81,27 @@ def create_fact_purchase_orders(dict_of_df): df_po["agreed_payment_date"] = pd.to_datetime( df_po["agreed_payment_date"], format="%Y-%m-%d" ) - df_po = df_po.drop(labels=["created_at", "last_updated"], axis=1) - df_po.reset_index(inplace=True) - return df_po + fact_purchase_order = df_po.loc[:, + [ + "purchase_order_id", + "created_date", + "created_time", + "last_updated_date", + "last_updated_time", + "staff_id", + "counterparty_id", + "item_code", + "item_quantity", + "item_unit_price", + "currency_id", + "agreed_delivery_date", + "agreed_payment_date", + "agreed_delivery_location_id" + ] + + ] + fact_purchase_order.reset_index(inplace=True) + return fact_purchase_order # test passed @@ -109,38 +125,57 @@ def create_fact_payment(dict_of_df): df_payment["payment_date"] = pd.to_datetime( df_payment["payment_date"], format="%Y-%m-%d" ) - df_payment = df_payment.drop(labels=["created_at", "last_updated"], axis=1) - - df_payment.reset_index(inplace=True) - return df_payment + fact_payment = df_payment.loc[:, + [ + "payment_id", + "created_date", + "created_time", + "last_updated_date", + "last_updated_time", + "transaction_id", + "counterparty_id", + "payment_amount", + "currency_id", + "payment_type_id", + "paid", + "payment_date" + ] + ] + fact_payment.reset_index(inplace=True) + return fact_payment # test passed def create_dim_transaction(dict_of_df): - df_transaction = dict_of_df["transaction"].drop( - labels=["created_at", "last_updated"], axis=1 - ) - return df_transaction + dim_transaction = dict_of_df["transaction"].loc[:, + [ + "transaction_id", + "transaction_type", + "sales_order_id", + "purchase_order_id" + ] + ] + return dim_transaction # test passed def create_dim_location(dict_of_df): - df_loc = ( - dict_of_df["address"] - .drop(labels=["created_at", "last_updated"], axis=1) + dim_location = ( + dict_of_df["address"].drop(labels=["created_at", "last_updated"], axis=1) .rename(columns={"address_id": "location_id"}) ) - return df_loc + return dim_location def create_dim_counterparty(dict_of_df): df_prefixed_address = ( dict_of_df["address"] .drop(labels=["created_at", "last_updated"], axis=1) + .rename(columns={"phone": "phone_number"}) .add_prefix("counterparty_legal_", axis=1) ) df_cp = pd.merge( @@ -149,15 +184,18 @@ def create_dim_counterparty(dict_of_df): left_on="legal_address_id", right_on="counterparty_legal_address_id", how="inner", - ) - df_cp.drop( - columns=[ + )#.dropna(inplace=True) + dim_counterparty = df_cp.drop( + labels=[ "legal_address_id", "counterparty_legal_address_id", - ], - inplace=True, + "created_at", + "last_updated", + "commercial_contact", + "delivery_contact" + ], axis=1 ) - return df_cp + return dim_counterparty # test passed @@ -179,6 +217,7 @@ def create_dim_date(dict_of_df): sr_date = pd.array(pd.concat(list_of_date_columns), dtype="datetime64[ns]") df_date = pd.DataFrame(data=sr_date, columns=["date_id"]) df_date.drop_duplicates(inplace=True) + # df_date.dropna(inplace=True) df_date["year"] = df_date["date_id"].dt.year df_date["month"] = df_date["date_id"].dt.month df_date["day"] = df_date["date_id"].dt.day @@ -210,10 +249,11 @@ def scrape_currency_names(): def create_dim_currency(dict_of_df, names=scrape_currency_names()): df_cur = dict_of_df["currency"].drop(labels=["created_at", "last_updated"], axis=1) - dim_cur = pd.merge( - df_cur, names, left_on="currency_code", right_on="currency_code", how="inner" + dim_currency = pd.merge( + df_cur, names, left_on="currency_code", right_on="currency_code", how="left" ) - return dim_cur + dim_currency.drop_duplicates(inplace=True) + return dim_currency # tests passed @@ -221,7 +261,12 @@ def create_dim_currency(dict_of_df, names=scrape_currency_names()): def create_dim_payment_type(dict_of_df): df_payment_type = dict_of_df["payment_type"] - dim_payment_type = df_payment_type.loc[:, ["payment_type_id", "payment_type_name"]] + dim_payment_type = df_payment_type.loc[:, + [ + "payment_type_id", + "payment_type_name" + ] + ] return dim_payment_type @@ -230,8 +275,13 @@ def create_dim_payment_type(dict_of_df): def create_dim_design(dict_of_df): df_design = dict_of_df["design"] - dim_design = df_design.loc[ - :, ["design_id", "design_name", "file_name", "file_location"] + dim_design = df_design.loc[:, + [ + "design_id", + "design_name", + "file_name", + "file_location" + ] ] return dim_design @@ -243,15 +293,14 @@ def create_dim_staff(dict_of_df): staff_department = pd.merge( dict_of_df["staff"], dict_of_df["department"], on="department_id", how="left" ) - dim_staff = staff_department.loc[ - :, + dim_staff = staff_department.loc[:, [ "staff_id", "first_name", "last_name", "department_name", "location", - "email_address", - ], + "email_address" + ] ] return dim_staff diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py index 93b2284..1453c6c 100644 --- a/src/transform_lambda/transform_lambda.py +++ b/src/transform_lambda/transform_lambda.py @@ -42,7 +42,7 @@ TABLES = [ "department", "currency", "design", - "payment_type", + "payment_type" ] @@ -73,7 +73,8 @@ def lambda_handler(event, context): "fact_payment": create_fact_payment(dict_of_df), "dim_currency": create_dim_currency(dict_of_df), } - + print(immutable_df_dict.values()) + print(mutable_df_dict.values()) status = process_to_parquet_and_upload_to_s3( existing_s3_files, immutable_df_dict, mutable_df_dict, bucket ) diff --git a/tests/test_dataframes.py b/tests/test_dataframes.py index ea7bad1..7dd592a 100644 --- a/tests/test_dataframes.py +++ b/tests/test_dataframes.py @@ -1,4 +1,4 @@ -from src.dataframes import * +from src.transform_lambda.dataframes import * import pandas as pd from unittest.mock import patch from datetime import datetime as dt -- cgit v1.2.3 From d064b2ec2c7393f8de50560a7edfe100851bfea3 Mon Sep 17 00:00:00 2001 From: T-Aji Date: Wed, 28 Aug 2024 14:39:13 +0100 Subject: debugging load_lambda --- src/load_lambda.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/load_lambda.py b/src/load_lambda.py index 926b4db..272cb8c 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -164,13 +164,13 @@ def upload_dfs_to_database(): "dim_date.parquet", # this needs to be mutable "dim_location.parquet", "dim_staff.parquet", - "dim_design.parquet", + "dim_design.parquet" ] mutable_df_dict = [ "fact_sales_order", "fact_purchase_order", "fact_payment", - "dim_currency", + "dim_currency" ] for file_name, df in dict_of_dfs.items(): @@ -182,6 +182,7 @@ def upload_dfs_to_database(): df.to_sql( table_name, con=db_engine, + schema="project_team_2", if_exists="append", index=False, ) -- cgit v1.2.3 From 6235a2bb04b60d57a41196b07bbf0296920c6980 Mon Sep 17 00:00:00 2001 From: T-Aji Date: Wed, 28 Aug 2024 17:52:45 +0100 Subject: wip commit --- src/load_lambda.py | 174 +++++++++++++++++++------------ src/transform_lambda/dataframes.py | 8 +- src/transform_lambda/transform_lambda.py | 2 +- tests/test_transform_lambda.py | 2 +- 4 files changed, 115 insertions(+), 71 deletions(-) diff --git a/src/load_lambda.py b/src/load_lambda.py index 272cb8c..cdcf105 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -7,7 +7,8 @@ import logging import json import traceback from sqlalchemy import create_engine - +from datetime import datetime as dt +import re logger = logging.getLogger(__name__) @@ -15,10 +16,10 @@ logging.basicConfig( format="{asctime} - {levelname} - {message}", style="{", datefmt="%Y-%m-%d %H:%M", - level=logging.DEBUG, + level=logging.INFO, ) - -logging.getLogger("botocore").setLevel(logging.INFO) +# logging.getLogger("botocore").setLevel(logging.INFO) +# logging.getLogger('sqlalchemy.engine').setLevel(logging.DEBUG) def lambda_handler(event, context): @@ -38,10 +39,10 @@ def lambda_handler(event, context): ), } else: - logger.error(f"error") + logger.error(f"error", exc_info=True) return {"error"} except Exception as e: - logger.error({e}) + logger.error({e}, exc_info=True) return {"statusCode": 500, "body": {e}} @@ -58,10 +59,10 @@ def retrieve_secrets(client=None, secret_name=None): get_secret_value_response = client.get_secret_value(SecretId=secret_name) print(get_secret_value_response) except ClientError as e: - logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}") + logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True) raise e except KeyError: - logger.error(f"Secret {secret_name} does not contain a SecretString") + logger.error(f"Secret {secret_name} does not contain a SecretString", exc_info=True) raise ValueError(f"Secret {secret_name} does not contain a SecretString") return get_secret_value_response["SecretString"] @@ -86,7 +87,7 @@ def connect_to_db_and_return_engine(sm_secret=None): engine = create_engine(conn_str) return engine except Exception as e: - logger.error(f"Interface error: {e}") + logger.error(f"Interface error: {e}", exc_info=True) raise RuntimeError("Failed to create database engine") @@ -97,7 +98,7 @@ def get_transform_bucket(client=None): try: response = client.list_buckets() except ClientError as e: - logger.error(f"Error listing S3 buckets: {e}") + logger.error(f"Error listing S3 buckets: {e}", exc_info=True) raise RuntimeError("Error listing S3 buckets") transform_bucket_filter = [ @@ -107,7 +108,7 @@ def get_transform_bucket(client=None): ] if not transform_bucket_filter: - logger.error("No transform bucket found") + logger.error("No transform bucket found", exc_info=True) raise ValueError("No transform bucket found") return transform_bucket_filter[0] @@ -117,41 +118,78 @@ def get_transform_bucket(client=None): # convert parquet files into dataframes # return a dictionary of dataframes with name as key, and dataframe object as value +def get_latest_timestamp(existing_files): + if existing_files: + all_datetimes = [] + for file_name in existing_files: + match = re.search(r"\/(.+/).+_(.+)\.parquet", file_name) + if match: + datetime_str = "".join(match.group(1, 2)) + all_datetimes.append( + dt.strptime(datetime_str, "%Y/%m/%d/%H:%M:%S") + ) + return max(all_datetimes) if all_datetimes else dt.min + return existing_files def convert_parquet_files_to_dfs(bucket_name=None, client=None): + mutable_df_dict = [ + "dim_currency", + "fact_sales_order", + "fact_purchase_order", + "fact_payment" + + ] + try: if client is None: client = boto3.client("s3") if bucket_name is None: bucket_name = get_transform_bucket() files = client.list_objects_v2(Bucket=bucket_name) - + dfs = {} if "Contents" in files: - for file in files["Contents"]: - file_key = file["Key"] + s3_key_list = [file["Key"]for file in files["Contents"]] + immutables_l = [] + mutables_d = {prefix:[] for prefix in mutable_df_dict} + for tab, s3_key in mutables_d.items(): + for file in s3_key_list: + if tab in file: + s3_key.append(file) + elif "2024" not in file: + immutables_l.append(file) + else: + continue + immutables_l = list(set(immutables_l)) + print(mutables_d,'mutables_d') + latest_s3_keys = [] + for k,v in mutables_d.items(): + latest_s3_keys.append(dt.strftime(get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet")) + print(latest_s3_keys,'latest') + print(immutables_l,'immutables_l') + for file_key in latest_s3_keys+immutables_l: try: file_obj = client.get_object(Bucket=bucket_name, Key=file_key) parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read())) df = parquet_file.read().to_pandas() - print("df", df) - print("type", type(df)) - print(df.columns) - dfs[file_key] = df + df_without_nulls = df.dropna() + #print("df_without_nulls", df_without_nulls) + #print("type", type(df_without_nulls)) + #print(df_without_nulls.columns) + dfs[file_key] = df_without_nulls except ClientError as e: - logger.error(f"Unable to retrieve S3 object {file_key}: {e}") + logger.error(f"Unable to retrieve S3 object {file_key}: {e}", exc_info=True) except Exception as e: - logger.error(f"Unable to process file {file_key}: {e}") + logger.error(f"Unable to process file {file_key}: {e}", exc_info=True) else: - logger.error(f"No files found in {bucket_name}.") + logger.error(f"No files found in {bucket_name}.", exc_info=True) return {} except ValueError as value_error: - logger.error(f"Unable to list objects: {value_error}") + logger.error(f"Unable to list objects: {value_error}", exc_info=True) raise except ClientError as client_error: - logger.error(f"Unable to list objects: {client_error}") + logger.error(f"Unable to list objects: {client_error}", exc_info=True) raise - print() return dfs @@ -160,53 +198,57 @@ def upload_dfs_to_database(): dict_of_dfs = convert_parquet_files_to_dfs() db_engine = connect_to_db_and_return_engine() immutable_df_dict = [ - "dim_counterparty.parquet", - "dim_date.parquet", # this needs to be mutable - "dim_location.parquet", - "dim_staff.parquet", - "dim_design.parquet" + # #"dim_counterparty.parquet", + # "dim_date.parquet", # this needs to be mutable + # "dim_location.parquet", + # "dim_staff.parquet", + # "dim_design.parquet" ] mutable_df_dict = [ + "dim_currency", "fact_sales_order", "fact_purchase_order", - "fact_payment", - "dim_currency" + "fact_payment" + ] - - for file_name, df in dict_of_dfs.items(): - print(df) - if file_name in immutable_df_dict: - table_name = file_name.split(".")[0] - print(table_name, "<<<<<") - try: - df.to_sql( - table_name, - con=db_engine, - schema="project_team_2", - if_exists="append", - index=False, - ) - upload_status["uploaded"].append(table_name) - except Exception as e: - logger.error(f"Error uploading dataframe {file_name} to database: {e}") - raise - elif file_name.rsplit("_", 1)[0] in mutable_df_dict: - table_name = file_name.rsplit("_", 1)[0] - try: - df.to_sql( - table_name, - con=db_engine, - schema="project_team_2", - if_exists="append", - index=False, - ) - upload_status["uploaded"].append(table_name) - except Exception as e: - logger.error(f"Error uploading dataframe {file_name} to database: {e}") - raise - else: - upload_status["not_uploaded"].append(file_name) - logger.error(f"{file_name} does not correspond with table in database") + with db_engine.begin() as connection: + for file_name, df in dict_of_dfs.items(): + print(df.dtypes, "dtypes") + print(df.head()) + if file_name in immutable_df_dict: + table_name = file_name.split(".")[0] + print(table_name, "<<<<<") + try: + df.to_sql( + table_name, + con=connection, + schema="project_team_2", + if_exists="append", + index=False, + ) + upload_status["uploaded"].append(table_name) + print(upload_status) + except Exception as e: + logger.error(f"Error uploading dataframe {file_name} to database: {e}", exc_info=True) + raise + elif file_name.split("/")[0] in mutable_df_dict: + table_name = file_name.split("/")[0] + print(table_name, "<<<<<< Date: Wed, 28 Aug 2024 22:46:00 +0100 Subject: fix: adds missing dataframes and resolves tables upload to end data warehouse in case the table is empty --- .gitignore | 6 +++++- src/load_lambda.py | 24 +++++++++++++----------- src/transform_lambda/dataframes.py | 19 ++++++++++++++----- src/transform_lambda/transform_lambda.py | 4 +++- 4 files changed, 35 insertions(+), 18 deletions(-) diff --git a/.gitignore b/.gitignore index 6aa03fc..480ae4b 100644 --- a/.gitignore +++ b/.gitignore @@ -14,4 +14,8 @@ __pycache__/ # OS-Related Files .DS_Store -venv \ No newline at end of file +venv + +#files +/dim_* +/fact_* \ No newline at end of file diff --git a/src/load_lambda.py b/src/load_lambda.py index cdcf105..8f921b8 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -161,18 +161,15 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None): else: continue immutables_l = list(set(immutables_l)) - print(mutables_d,'mutables_d') latest_s3_keys = [] for k,v in mutables_d.items(): latest_s3_keys.append(dt.strftime(get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet")) - print(latest_s3_keys,'latest') - print(immutables_l,'immutables_l') - for file_key in latest_s3_keys+immutables_l: + for file_key in immutables_l+latest_s3_keys: try: file_obj = client.get_object(Bucket=bucket_name, Key=file_key) parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read())) df = parquet_file.read().to_pandas() - df_without_nulls = df.dropna() + df_without_nulls = df.dropna(how='all') #>> can't do 'any' (default) because we lose rows in dim_location #print("df_without_nulls", df_without_nulls) #print("type", type(df_without_nulls)) #print(df_without_nulls.columns) @@ -202,12 +199,14 @@ def upload_dfs_to_database(): # "dim_date.parquet", # this needs to be mutable # "dim_location.parquet", # "dim_staff.parquet", - # "dim_design.parquet" + # "dim_design.parquet", + # 'dim_transaction.parquet' #This one was missing, + 'dim_payment_type.parquet' ] mutable_df_dict = [ - "dim_currency", - "fact_sales_order", - "fact_purchase_order", + # "dim_currency", + # "fact_sales_order", + # "fact_purchase_order", "fact_payment" ] @@ -215,7 +214,9 @@ def upload_dfs_to_database(): for file_name, df in dict_of_dfs.items(): print(df.dtypes, "dtypes") print(df.head()) - if file_name in immutable_df_dict: + print(file_name,"<<< FILE NAME") + print(immutable_df_dict,"<< Date: Thu, 29 Aug 2024 09:47:58 +0100 Subject: fix: added comma. Code complete and uploads all tables in one go if no data exists per each table --- src/load_lambda.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/src/load_lambda.py b/src/load_lambda.py index 8f921b8..941ae97 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -57,7 +57,6 @@ def retrieve_secrets(client=None, secret_name=None): try: get_secret_value_response = client.get_secret_value(SecretId=secret_name) - print(get_secret_value_response) except ClientError as e: logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True) raise e @@ -195,18 +194,18 @@ def upload_dfs_to_database(): dict_of_dfs = convert_parquet_files_to_dfs() db_engine = connect_to_db_and_return_engine() immutable_df_dict = [ - # #"dim_counterparty.parquet", - # "dim_date.parquet", # this needs to be mutable - # "dim_location.parquet", - # "dim_staff.parquet", - # "dim_design.parquet", - # 'dim_transaction.parquet' #This one was missing, + "dim_counterparty.parquet", + "dim_date.parquet", # this needs to be mutable + "dim_location.parquet", + "dim_staff.parquet", + "dim_design.parquet", + 'dim_transaction.parquet', #This one was missing, 'dim_payment_type.parquet' ] mutable_df_dict = [ - # "dim_currency", - # "fact_sales_order", - # "fact_purchase_order", + "dim_currency", + "fact_sales_order", + "fact_purchase_order", "fact_payment" ] -- cgit v1.2.3 From 42ad135b25044bb1c7ab8a553f038c8da9de0f75 Mon Sep 17 00:00:00 2001 From: "deepsource-autofix[bot]" <62050782+deepsource-autofix[bot]@users.noreply.github.com> Date: Thu, 29 Aug 2024 08:57:48 +0000 Subject: style: format code with Autopep8, Black and Ruff Formatter This commit fixes the style issues introduced in 48e7dae according to the output from Autopep8, Black and Ruff Formatter. Details: https://github.com/ajschofield/de-project-bentley/pull/107 --- src/load_lambda.py | 78 +++++++++++++-------- src/transform_lambda/dataframes.py | 116 ++++++++++++++----------------- src/transform_lambda/transform_lambda.py | 6 +- 3 files changed, 105 insertions(+), 95 deletions(-) diff --git a/src/load_lambda.py b/src/load_lambda.py index 941ae97..86189dc 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -58,10 +58,14 @@ def retrieve_secrets(client=None, secret_name=None): try: get_secret_value_response = client.get_secret_value(SecretId=secret_name) except ClientError as e: - logger.error(f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True) + logger.error( + f"Failed to retrieve secret {secret_name}: {str(e)}", exc_info=True + ) raise e except KeyError: - logger.error(f"Secret {secret_name} does not contain a SecretString", exc_info=True) + logger.error( + f"Secret {secret_name} does not contain a SecretString", exc_info=True + ) raise ValueError(f"Secret {secret_name} does not contain a SecretString") return get_secret_value_response["SecretString"] @@ -117,6 +121,7 @@ def get_transform_bucket(client=None): # convert parquet files into dataframes # return a dictionary of dataframes with name as key, and dataframe object as value + def get_latest_timestamp(existing_files): if existing_files: all_datetimes = [] @@ -124,19 +129,17 @@ def get_latest_timestamp(existing_files): match = re.search(r"\/(.+/).+_(.+)\.parquet", file_name) if match: datetime_str = "".join(match.group(1, 2)) - all_datetimes.append( - dt.strptime(datetime_str, "%Y/%m/%d/%H:%M:%S") - ) + all_datetimes.append(dt.strptime(datetime_str, "%Y/%m/%d/%H:%M:%S")) return max(all_datetimes) if all_datetimes else dt.min return existing_files + def convert_parquet_files_to_dfs(bucket_name=None, client=None): mutable_df_dict = [ "dim_currency", "fact_sales_order", "fact_purchase_order", - "fact_payment" - + "fact_payment", ] try: @@ -145,12 +148,12 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None): if bucket_name is None: bucket_name = get_transform_bucket() files = client.list_objects_v2(Bucket=bucket_name) - + dfs = {} if "Contents" in files: - s3_key_list = [file["Key"]for file in files["Contents"]] + s3_key_list = [file["Key"] for file in files["Contents"]] immutables_l = [] - mutables_d = {prefix:[] for prefix in mutable_df_dict} + mutables_d = {prefix: [] for prefix in mutable_df_dict} for tab, s3_key in mutables_d.items(): for file in s3_key_list: if tab in file: @@ -161,22 +164,31 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None): continue immutables_l = list(set(immutables_l)) latest_s3_keys = [] - for k,v in mutables_d.items(): - latest_s3_keys.append(dt.strftime(get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet")) - for file_key in immutables_l+latest_s3_keys: + for k, v in mutables_d.items(): + latest_s3_keys.append( + dt.strftime( + get_latest_timestamp(v), f"{k}/%Y/%m/%d/{k}_%H:%M:%S.parquet" + ) + ) + for file_key in immutables_l + latest_s3_keys: try: file_obj = client.get_object(Bucket=bucket_name, Key=file_key) parquet_file = pq.ParquetFile(BytesIO(file_obj["Body"].read())) df = parquet_file.read().to_pandas() - df_without_nulls = df.dropna(how='all') #>> can't do 'any' (default) because we lose rows in dim_location - #print("df_without_nulls", df_without_nulls) - #print("type", type(df_without_nulls)) - #print(df_without_nulls.columns) + # >> can't do 'any' (default) because we lose rows in dim_location + df_without_nulls = df.dropna(how="all") + # print("df_without_nulls", df_without_nulls) + # print("type", type(df_without_nulls)) + # print(df_without_nulls.columns) dfs[file_key] = df_without_nulls except ClientError as e: - logger.error(f"Unable to retrieve S3 object {file_key}: {e}", exc_info=True) + logger.error( + f"Unable to retrieve S3 object {file_key}: {e}", exc_info=True + ) except Exception as e: - logger.error(f"Unable to process file {file_key}: {e}", exc_info=True) + logger.error( + f"Unable to process file {file_key}: {e}", exc_info=True + ) else: logger.error(f"No files found in {bucket_name}.", exc_info=True) return {} @@ -199,23 +211,22 @@ def upload_dfs_to_database(): "dim_location.parquet", "dim_staff.parquet", "dim_design.parquet", - 'dim_transaction.parquet', #This one was missing, - 'dim_payment_type.parquet' + "dim_transaction.parquet", # This one was missing, + "dim_payment_type.parquet", ] mutable_df_dict = [ "dim_currency", "fact_sales_order", "fact_purchase_order", - "fact_payment" - + "fact_payment", ] with db_engine.begin() as connection: for file_name, df in dict_of_dfs.items(): print(df.dtypes, "dtypes") print(df.head()) - print(file_name,"<<< FILE NAME") - print(immutable_df_dict,"<<