From 03787e3aabc5bc516bb7bfcc3831a74681932c36 Mon Sep 17 00:00:00 2001 From: HastarTara Date: Wed, 28 Aug 2024 09:48:07 +0100 Subject: moved extract_l & dataframes into own directory in src --- src/dataframes.py | 228 ------------------------------- src/transform_lambda.py | 217 ----------------------------- src/transform_lambda/dataframes.py | 228 +++++++++++++++++++++++++++++++ src/transform_lambda/transform_lambda.py | 217 +++++++++++++++++++++++++++++ 4 files changed, 445 insertions(+), 445 deletions(-) delete mode 100644 src/dataframes.py delete mode 100644 src/transform_lambda.py create mode 100644 src/transform_lambda/dataframes.py create mode 100644 src/transform_lambda/transform_lambda.py diff --git a/src/dataframes.py b/src/dataframes.py deleted file mode 100644 index f122368..0000000 --- a/src/dataframes.py +++ /dev/null @@ -1,228 +0,0 @@ -import pandas as pd -from bs4 import BeautifulSoup -import requests - -# Table names: -# fact_sales_order -# fact_purchase_orders -# fact_payment -# dim_transaction -# dim_staff -# dim_payment_type -# dim_location -# dim_design -# dim_date -# dim_currency -# dim_counterparty - - -# no test, same as fact_payment -def create_fact_sales_order(dict_of_df): - df_sales = dict_of_df["sales_order"] - df_sales.index.name = "sales_record_id" - 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 - - -# no test, same as fact_payment - - -def create_fact_purchase_orders(dict_of_df): - df_po = dict_of_df["purchase_order"] - df_po.index.name = "purchase_record_id" - df_po["created_date"] = df_po["created_at"].astype("datetime64[ns]").dt.date - df_po["created_time"] = ( - df_po["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time - ) - df_po["last_updated_date"] = df_po["last_updated"].astype("datetime64[ns]").dt.date - df_po["last_updated_time"] = ( - df_po["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time - ) - df_po["agreed_delivery_date"] = pd.to_datetime( - df_po["agreed_delivery_date"], format="%Y-%m-%d" - ) - 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 - - -# test passed - - -def create_fact_payment(dict_of_df): - df_payment = dict_of_df["payment"] - df_payment.index.name = "payment_record_id" - df_payment["created_date"] = ( - df_payment["created_at"].astype("datetime64[ns]").dt.date - ) - df_payment["created_time"] = ( - df_payment["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time - ) - df_payment["last_updated_date"] = ( - df_payment["last_updated"].astype("datetime64[ns]").dt.date - ) - df_payment["last_updated_time"] = ( - df_payment["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time - ) - 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 - - -# 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 - - -# test passed - - -def create_dim_location(dict_of_df): - df_loc = ( - dict_of_df["address"] - .drop(labels=["created_at", "last_updated"], axis=1) - .rename(columns={"address_id": "location_id"}) - ) - return df_loc - - -def create_dim_counterparty(dict_of_df): - df_prefixed_address = dict_of_df["address"].add_prefix( - "counterparty_legal_", axis=1 - ) - df_cp = pd.merge( - dict_of_df["counterparty"], - df_prefixed_address, - left_on="legal_address_id", - right_on="counterparty_legal_address_id", - how="outer", - ) - df_cp.drop( - columns=["legal_address_id", "counterparty_legal_address_id"], inplace=True - ) - return df_cp - - -# test passed - - -def create_dim_date(dict_of_df): - fact_dfs = [ - create_fact_payment(dict_of_df), - create_fact_purchase_orders(dict_of_df), - create_fact_sales_order(dict_of_df), - ] - list_of_date_columns = [] - for df in fact_dfs: - date_col_names = [ - col_name for col_name in list(df.columns) if "_date" in col_name - ] - for col in date_col_names: - list_of_date_columns.append(df[col]) - 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["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 - df_date["day_of_week"] = df_date["date_id"].dt.dayofweek - df_date["day_name"] = df_date["date_id"].dt.day_name() - df_date["month_name"] = df_date["date_id"].dt.month_name() - df_date["quarter"] = df_date["date_id"].dt.quarter - return df_date - - -# tests passed - - -def scrape_currency_names(): - response = requests.get("https://www.xe.com/currency/").content - soup = BeautifulSoup(response, "html.parser") - currency = [ - item.text for item in soup.findAll("a", attrs={"class": "sc-299dec64-6 fZPTSw"}) - ] - sr = pd.Series(currency) - df_cur = sr.str.split(pat=" - ", expand=True).rename( - {0: "currency_code", 1: "currency_name"}, axis=1 - ) - return df_cur - - -# tests passed - - -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" - ) - return dim_cur - - -# tests passed - - -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"]] - return dim_payment_type - - -# tests passed - - -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"] - ] - return dim_design - - -# tests passed - - -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[ - :, - [ - "staff_id", - "first_name", - "last_name", - "department_name", - "location", - "email_address", - ], - ] - return dim_staff diff --git a/src/transform_lambda.py b/src/transform_lambda.py deleted file mode 100644 index 93b2284..0000000 --- a/src/transform_lambda.py +++ /dev/null @@ -1,217 +0,0 @@ -import json -import boto3 -import re -import logging -import pandas as pd -import pyarrow as pa -import pyarrow.parquet as pq -from dataframes import * -from botocore.exceptions import ClientError -from pg8000.native import Connection, InterfaceError -from datetime import datetime - - -class DBConnectionException(Exception): - """Wraps pg8000.native Error or DatabaseError.""" - - def __init__(self, e): - """Initialise with provided error message.""" - self.message = str(e) - super().__init__(self.message) - - -logger = logging.getLogger(__name__) - -logging.basicConfig( - format="{asctime} - {levelname} - {message}", - style="{", - datefmt="%Y-%m-%d %H:%M", - level=logging.DEBUG, -) - -logging.getLogger("botocore").setLevel(logging.WARNING) - -TABLES = [ - "sales_order", - "transaction", - "payment", - "counterparty", - "address", - "staff", - "purchase_order", - "department", - "currency", - "design", - "payment_type", -] - - -def lambda_handler(event, context): - db = None - - try: - db = connect_to_database() - bucket = bucket_name("transform") - - existing_s3_files = list_existing_s3_files(bucket) - - dict_of_df = read_from_s3_subfolder_to_df( - TABLES, bucket_name("extract"), client=boto3.client("s3") - ) - - immutable_df_dict = { - "dim_counterparty": create_dim_counterparty(dict_of_df), - "dim_date": create_dim_date(dict_of_df), - "dim_location": create_dim_location(dict_of_df), - "dim_staff": create_dim_staff(dict_of_df), - "dim_design": create_dim_design(dict_of_df), - } - - mutable_df_dict = { - "fact_sales_order": create_fact_sales_order(dict_of_df), - "fact_purchase_order": create_fact_purchase_orders(dict_of_df), - "fact_payment": create_fact_payment(dict_of_df), - "dim_currency": create_dim_currency(dict_of_df), - } - - status = process_to_parquet_and_upload_to_s3( - existing_s3_files, immutable_df_dict, mutable_df_dict, bucket - ) - - if not status["uploaded"]: - logger.info("No dataframes written to the bucket.") - return { - "statusCode": 204, - "body": json.dumps("No files where uploaded."), - } - - return { - "statusCode": 200, - "body": json.dumps( - f"""Parquet files processed for {', '.join(status['uploaded'])} and uploaded successfully.{ - 'The following tables were not uploaded: '+', '.join([status['not_uploaded']]) if status['not_uploaded'] else ''}""" - ), - } - - except Exception as e: - logger.error(f"Error: {e}", exc_info=True) - return {"statusCode": 500, "body": json.dumps("Internal server error.")} - finally: - if db: - db.close() - - -def process_to_parquet_and_upload_to_s3( - existing_s3_files, - immutable_df_dict, - mutable_df_dict, - bucket, - client=boto3.client("s3"), -): - status = {"uploaded": [], "not_uploaded": []} - - for table_name, df in immutable_df_dict.items(): - if table_name in existing_s3_files: - status["not_uploaded"].append(table_name) - else: - parquet_file = df.to_parquet( - f"{table_name}.parquet", engine="pyarrow" - ) # or fastparquet - # changed parquet_file variable to the file name - client.upload_file(f"{table_name}.parquet", bucket, f"{table_name}.parquet") - status["uploaded"].append(table_name) - - for table_name, df in mutable_df_dict.items(): - s3_key = datetime.strftime( - datetime.today(), f"{table_name}/%Y/%m/%d/{table_name}_%H:%M:%S.parquet" - ) - parquet_file = df.to_parquet( - f"{table_name}.parquet", engine="pyarrow" - ) # or fastparquet - client.upload_file(f"{table_name}.parquet", bucket, s3_key) - status["uploaded"].append(table_name) - - return status - - -def retrieve_secrets(): - secret_name = "bentley-secrets" - region_name = "eu-west-2" - - # Create a Secrets Manager client - session = boto3.session.Session() - client = session.client(service_name="secretsmanager", region_name=region_name) - - 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)}") - raise e - except KeyError: - logger.error(f"Secret {secret_name} does not contain a SecretString") - raise ValueError(f"Secret {secret_name} does not contain a SecretString") - - return get_secret_value_response["SecretString"] - - -def connect_to_database() -> Connection: - try: - secrets = json.loads(retrieve_secrets()) - host = secrets["host"] - port = secrets["port"] - user = secrets["user"] - password = secrets["password"] - database = secrets["database"] - - return Connection( - database=database, user=user, password=password, host=host, port=port - ) - except InterfaceError as i: - logger.error(f"Interface error: {i}") - raise DBConnectionException("Failed to connect to database") - - -def read_from_s3_subfolder_to_df(tables, bucket, client=boto3.client("s3")): - table_dfs = {} - for table in tables: - response = client.list_objects_v2(Bucket=bucket, Prefix=table) - list_of_keys = [ - "s3://" + bucket + "/" + object["Key"] for object in response["Contents"] - ] - list_of_df = [pd.read_csv(key) for key in list_of_keys] - table_dfs[table] = pd.concat(list_of_df) - return table_dfs - - -def bucket_name(bucket_prefix, client=boto3.client("s3")): - response = client.list_buckets() - bucket_filter = [ - bucket["Name"] - for bucket in response["Buckets"] - if bucket_prefix in bucket["Name"] - ] - - return bucket_filter[0] - - -def list_existing_s3_files(bucket_name, client=boto3.client("s3")): - logging.info("Listing existing S3 files") - - try: - response = client.list_objects_v2(Bucket=bucket_name) - - if "Contents" in response: - existing_files = [obj["Key"] for obj in response["Contents"]] - else: - logger.error("The bucket is empty") - return [] # changed from None to [] so it is an iterable - - except ClientError as e: - logger.error(f"Error listing S3 objects: {e}") - raise e - - return existing_files - - -if __name__ == "__main__": - lambda_handler({}, "") diff --git a/src/transform_lambda/dataframes.py b/src/transform_lambda/dataframes.py new file mode 100644 index 0000000..f122368 --- /dev/null +++ b/src/transform_lambda/dataframes.py @@ -0,0 +1,228 @@ +import pandas as pd +from bs4 import BeautifulSoup +import requests + +# Table names: +# fact_sales_order +# fact_purchase_orders +# fact_payment +# dim_transaction +# dim_staff +# dim_payment_type +# dim_location +# dim_design +# dim_date +# dim_currency +# dim_counterparty + + +# no test, same as fact_payment +def create_fact_sales_order(dict_of_df): + df_sales = dict_of_df["sales_order"] + df_sales.index.name = "sales_record_id" + 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 + + +# no test, same as fact_payment + + +def create_fact_purchase_orders(dict_of_df): + df_po = dict_of_df["purchase_order"] + df_po.index.name = "purchase_record_id" + df_po["created_date"] = df_po["created_at"].astype("datetime64[ns]").dt.date + df_po["created_time"] = ( + df_po["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_po["last_updated_date"] = df_po["last_updated"].astype("datetime64[ns]").dt.date + df_po["last_updated_time"] = ( + df_po["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_po["agreed_delivery_date"] = pd.to_datetime( + df_po["agreed_delivery_date"], format="%Y-%m-%d" + ) + 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 + + +# test passed + + +def create_fact_payment(dict_of_df): + df_payment = dict_of_df["payment"] + df_payment.index.name = "payment_record_id" + df_payment["created_date"] = ( + df_payment["created_at"].astype("datetime64[ns]").dt.date + ) + df_payment["created_time"] = ( + df_payment["created_at"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + df_payment["last_updated_date"] = ( + df_payment["last_updated"].astype("datetime64[ns]").dt.date + ) + df_payment["last_updated_time"] = ( + df_payment["last_updated"].astype("datetime64[ns]").dt.floor("s").dt.time + ) + 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 + + +# 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 + + +# test passed + + +def create_dim_location(dict_of_df): + df_loc = ( + dict_of_df["address"] + .drop(labels=["created_at", "last_updated"], axis=1) + .rename(columns={"address_id": "location_id"}) + ) + return df_loc + + +def create_dim_counterparty(dict_of_df): + df_prefixed_address = dict_of_df["address"].add_prefix( + "counterparty_legal_", axis=1 + ) + df_cp = pd.merge( + dict_of_df["counterparty"], + df_prefixed_address, + left_on="legal_address_id", + right_on="counterparty_legal_address_id", + how="outer", + ) + df_cp.drop( + columns=["legal_address_id", "counterparty_legal_address_id"], inplace=True + ) + return df_cp + + +# test passed + + +def create_dim_date(dict_of_df): + fact_dfs = [ + create_fact_payment(dict_of_df), + create_fact_purchase_orders(dict_of_df), + create_fact_sales_order(dict_of_df), + ] + list_of_date_columns = [] + for df in fact_dfs: + date_col_names = [ + col_name for col_name in list(df.columns) if "_date" in col_name + ] + for col in date_col_names: + list_of_date_columns.append(df[col]) + 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["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 + df_date["day_of_week"] = df_date["date_id"].dt.dayofweek + df_date["day_name"] = df_date["date_id"].dt.day_name() + df_date["month_name"] = df_date["date_id"].dt.month_name() + df_date["quarter"] = df_date["date_id"].dt.quarter + return df_date + + +# tests passed + + +def scrape_currency_names(): + response = requests.get("https://www.xe.com/currency/").content + soup = BeautifulSoup(response, "html.parser") + currency = [ + item.text for item in soup.findAll("a", attrs={"class": "sc-299dec64-6 fZPTSw"}) + ] + sr = pd.Series(currency) + df_cur = sr.str.split(pat=" - ", expand=True).rename( + {0: "currency_code", 1: "currency_name"}, axis=1 + ) + return df_cur + + +# tests passed + + +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" + ) + return dim_cur + + +# tests passed + + +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"]] + return dim_payment_type + + +# tests passed + + +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"] + ] + return dim_design + + +# tests passed + + +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[ + :, + [ + "staff_id", + "first_name", + "last_name", + "department_name", + "location", + "email_address", + ], + ] + return dim_staff diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py new file mode 100644 index 0000000..93b2284 --- /dev/null +++ b/src/transform_lambda/transform_lambda.py @@ -0,0 +1,217 @@ +import json +import boto3 +import re +import logging +import pandas as pd +import pyarrow as pa +import pyarrow.parquet as pq +from dataframes import * +from botocore.exceptions import ClientError +from pg8000.native import Connection, InterfaceError +from datetime import datetime + + +class DBConnectionException(Exception): + """Wraps pg8000.native Error or DatabaseError.""" + + def __init__(self, e): + """Initialise with provided error message.""" + self.message = str(e) + super().__init__(self.message) + + +logger = logging.getLogger(__name__) + +logging.basicConfig( + format="{asctime} - {levelname} - {message}", + style="{", + datefmt="%Y-%m-%d %H:%M", + level=logging.DEBUG, +) + +logging.getLogger("botocore").setLevel(logging.WARNING) + +TABLES = [ + "sales_order", + "transaction", + "payment", + "counterparty", + "address", + "staff", + "purchase_order", + "department", + "currency", + "design", + "payment_type", +] + + +def lambda_handler(event, context): + db = None + + try: + db = connect_to_database() + bucket = bucket_name("transform") + + existing_s3_files = list_existing_s3_files(bucket) + + dict_of_df = read_from_s3_subfolder_to_df( + TABLES, bucket_name("extract"), client=boto3.client("s3") + ) + + immutable_df_dict = { + "dim_counterparty": create_dim_counterparty(dict_of_df), + "dim_date": create_dim_date(dict_of_df), + "dim_location": create_dim_location(dict_of_df), + "dim_staff": create_dim_staff(dict_of_df), + "dim_design": create_dim_design(dict_of_df), + } + + mutable_df_dict = { + "fact_sales_order": create_fact_sales_order(dict_of_df), + "fact_purchase_order": create_fact_purchase_orders(dict_of_df), + "fact_payment": create_fact_payment(dict_of_df), + "dim_currency": create_dim_currency(dict_of_df), + } + + status = process_to_parquet_and_upload_to_s3( + existing_s3_files, immutable_df_dict, mutable_df_dict, bucket + ) + + if not status["uploaded"]: + logger.info("No dataframes written to the bucket.") + return { + "statusCode": 204, + "body": json.dumps("No files where uploaded."), + } + + return { + "statusCode": 200, + "body": json.dumps( + f"""Parquet files processed for {', '.join(status['uploaded'])} and uploaded successfully.{ + 'The following tables were not uploaded: '+', '.join([status['not_uploaded']]) if status['not_uploaded'] else ''}""" + ), + } + + except Exception as e: + logger.error(f"Error: {e}", exc_info=True) + return {"statusCode": 500, "body": json.dumps("Internal server error.")} + finally: + if db: + db.close() + + +def process_to_parquet_and_upload_to_s3( + existing_s3_files, + immutable_df_dict, + mutable_df_dict, + bucket, + client=boto3.client("s3"), +): + status = {"uploaded": [], "not_uploaded": []} + + for table_name, df in immutable_df_dict.items(): + if table_name in existing_s3_files: + status["not_uploaded"].append(table_name) + else: + parquet_file = df.to_parquet( + f"{table_name}.parquet", engine="pyarrow" + ) # or fastparquet + # changed parquet_file variable to the file name + client.upload_file(f"{table_name}.parquet", bucket, f"{table_name}.parquet") + status["uploaded"].append(table_name) + + for table_name, df in mutable_df_dict.items(): + s3_key = datetime.strftime( + datetime.today(), f"{table_name}/%Y/%m/%d/{table_name}_%H:%M:%S.parquet" + ) + parquet_file = df.to_parquet( + f"{table_name}.parquet", engine="pyarrow" + ) # or fastparquet + client.upload_file(f"{table_name}.parquet", bucket, s3_key) + status["uploaded"].append(table_name) + + return status + + +def retrieve_secrets(): + secret_name = "bentley-secrets" + region_name = "eu-west-2" + + # Create a Secrets Manager client + session = boto3.session.Session() + client = session.client(service_name="secretsmanager", region_name=region_name) + + 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)}") + raise e + except KeyError: + logger.error(f"Secret {secret_name} does not contain a SecretString") + raise ValueError(f"Secret {secret_name} does not contain a SecretString") + + return get_secret_value_response["SecretString"] + + +def connect_to_database() -> Connection: + try: + secrets = json.loads(retrieve_secrets()) + host = secrets["host"] + port = secrets["port"] + user = secrets["user"] + password = secrets["password"] + database = secrets["database"] + + return Connection( + database=database, user=user, password=password, host=host, port=port + ) + except InterfaceError as i: + logger.error(f"Interface error: {i}") + raise DBConnectionException("Failed to connect to database") + + +def read_from_s3_subfolder_to_df(tables, bucket, client=boto3.client("s3")): + table_dfs = {} + for table in tables: + response = client.list_objects_v2(Bucket=bucket, Prefix=table) + list_of_keys = [ + "s3://" + bucket + "/" + object["Key"] for object in response["Contents"] + ] + list_of_df = [pd.read_csv(key) for key in list_of_keys] + table_dfs[table] = pd.concat(list_of_df) + return table_dfs + + +def bucket_name(bucket_prefix, client=boto3.client("s3")): + response = client.list_buckets() + bucket_filter = [ + bucket["Name"] + for bucket in response["Buckets"] + if bucket_prefix in bucket["Name"] + ] + + return bucket_filter[0] + + +def list_existing_s3_files(bucket_name, client=boto3.client("s3")): + logging.info("Listing existing S3 files") + + try: + response = client.list_objects_v2(Bucket=bucket_name) + + if "Contents" in response: + existing_files = [obj["Key"] for obj in response["Contents"]] + else: + logger.error("The bucket is empty") + return [] # changed from None to [] so it is an iterable + + except ClientError as e: + logger.error(f"Error listing S3 objects: {e}") + raise e + + return existing_files + + +if __name__ == "__main__": + lambda_handler({}, "") -- cgit v1.2.3