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from src.dataframes import *
import json
import boto3
import re
import logging
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
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
client.upload_file(parquet_file, 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(parquet_file, 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()
# for bucket in response["Buckets"]:
# if bucket_prefix in bucket["Name"]:
# return bucket["Name"]
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 None
except ClientError as e:
logger.error(f"Error listing S3 objects: {e}")
raise e
return existing_files
if __name__ == "__main__":
lambda_handler({}, "")
|
