Merge branch 'development' into test/tests_transform_lambda

3 files changed, 225 insertions, 105 deletions

diff --git a/src/load_lambda.py b/src/load_lambda.py
index 7339ab9..86189dc 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}}
 
 
@@ -56,12 +57,15 @@ 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)}")
+        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 +90,7 @@ 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 +101,7 @@ 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 +111,7 @@ 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]
@@ -118,7 +122,26 @@ def get_transform_bucket(client=None):
 # 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")
@@ -128,27 +151,53 @@ def convert_parquet_files_to_dfs(bucket_name=None, client=None):
 
         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))
+            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()
-                    dfs[file_key] = df
+                    # >> 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}")
+                    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
-
     return dfs
 
 
@@ -162,47 +211,65 @@ 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",
     ]
     mutable_df_dict = [
+        "dim_currency",
         "fact_sales_order",
         "fact_purchase_order",
         "fact_payment",
-        "dim_currency",
     ]
-
-    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,
-                    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,
+    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, "<<<IMMUTABLE_DF_DICT")
+            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, "<<<<<<<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)
+                except Exception as e:
+                    logger.error(
+                        f"Error uploading dataframe {file_name} to database: {e}",
+                        exc_info=True,
+                    )
+                    raise
+            else:
+                upload_status["not_uploaded"].append(file_name)
+                logger.error(
+                    f"{file_name} does not correspond with table in database",
+                    exc_info=True,
                 )
-                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")
+            print(upload_status)
     db_engine.dispose()
     return upload_status
 
diff --git a/src/transform_lambda/dataframes.py b/src/transform_lambda/dataframes.py
index 2a46bd6..6de58e7 100644
--- a/src/transform_lambda/dataframes.py
+++ b/src/transform_lambda/dataframes.py
@@ -18,8 +18,7 @@ import requests
 
 # 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 = dict_of_df["sales_order"].rename(columns={"staff_id": "sales_staff_id"})
 
     df_sales["created_date"] = df_sales["created_at"].astype("datetime64[ns]").dt.date
     df_sales["created_time"] = (
@@ -37,30 +36,31 @@ 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
-
-    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
+    fact_sales = df_sales.loc[
+        :,
+        [
+            "sales_order_id",
+            "created_date",
+            "created_time",
+            "last_updated_date",
+            "last_updated_time",
+            "sales_staff_id",
+            "counterparty_id",
+            "units_sold",
+            "unit_price",
+            "currency_id",
+            "design_id",
+            "agreed_payment_date",
+            "agreed_delivery_date",
+            "agreed_delivery_location_id",
+        ],
+    ]
+    fact_sales.convert_dtypes()
+    fact_sales.index = pd.RangeIndex(1, len(fact_sales.index) + 1)
+    fact_sales.index.name = "sales_record_id"
+    fact_sales.reset_index(inplace=True)
+    fact_sales.dropna(inplace=True)
+    return fact_sales
 
 
 # no test, same as fact_payment
@@ -68,7 +68,6 @@ def create_fact_sales_order(dict_of_df):
 
 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
@@ -83,9 +82,31 @@ 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.convert_dtypes()
+    fact_purchase_order.index = pd.RangeIndex(1, len(fact_purchase_order.index) + 1)
+    fact_purchase_order.index.name = "purchase_record_id"
+    fact_purchase_order.reset_index(inplace=True)
+    fact_purchase_order.dropna(inplace=True)
+    return fact_purchase_order
 
 
 # test passed
@@ -93,7 +114,6 @@ def create_fact_purchase_orders(dict_of_df):
 
 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
     )
@@ -109,38 +129,60 @@ 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.convert_dtypes()
+    fact_payment.index = pd.RangeIndex(1, len(fact_payment.index) + 1)
+    fact_payment.index.name = "payment_record_id"
+    fact_payment.reset_index(inplace=True)
+    fact_payment.dropna(inplace=True)
+    fact_payment = fact_payment.astype({"currency_id": "int", "payment_id": "int"})
+    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"]
+    ]
+    # dim_transaction = dim_transaction.astype({"sales_order_id":"Int64","purchase_order_id":"Int64"})
+    return dim_transaction
 
 
 # test passed
 
 
 def create_dim_location(dict_of_df):
-    df_loc = (
+    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 +191,19 @@ 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",
+            "created_at",
+            "last_updated",
+            "commercial_contact",
+            "delivery_contact",
         ],
-        inplace=True,
+        axis=1,
     )
-    return df_cp
+    return dim_counterparty
 
 
 # test passed
@@ -179,6 +225,7 @@ 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 +257,13 @@ 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)
+    dim_currency.astype({"currency_name": "string", "currency_code": "string"})
+    print(dim_currency.dtypes, "<<<<<<<<<Dtype")
+    return dim_currency
 
 
 # tests passed
diff --git a/src/transform_lambda/transform_lambda.py b/src/transform_lambda/transform_lambda.py
index 478b257..54d7d48 100644
--- a/src/transform_lambda/transform_lambda.py
+++ b/src/transform_lambda/transform_lambda.py
@@ -65,6 +65,8 @@ def lambda_handler(event, context):
             "dim_location": create_dim_location(dict_of_df),
             "dim_staff": create_dim_staff(dict_of_df),
             "dim_design": create_dim_design(dict_of_df),
+            "dim_transaction": create_dim_transaction(dict_of_df),
+            "dim_payment_type": create_dim_payment_type(dict_of_df),
         }
 
         mutable_df_dict = {
@@ -73,7 +75,8 @@ 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
         )