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from pg8000.native import Connection, DatabaseError, InterfaceError
from dotenv import dotenv_values
import boto3
import csv
from botocore.exceptions import ClientError
import logging
import json
from datetime import datetime
import re
logger = logging.getLogger()
logger.setLevel(logging.INFO)
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)
def lambda_handler(event, context):
"""This lambda function connects to the Totesys database, lists the contents of the ingestion bucket,
and converts all tables to CSV and if any of those tables do not exist in, or are different to the ones in s3, it uploads them
it uses 3 helper functions to achieve these 3 functionalities
"""
try:
db = connect_to_database()
existing_files = list_existing_s3_files()
any_changes = process_and_upload_tables(db, existing_files)
if not any_changes:
logger.info("No changes detected in the database.")
return {
'statusCode': 200,
'body': json.dumps('No changes detected, no CSV files were uploaded.')
}
else:
return {
'statusCode': 200,
'body': json.dumps('CSV files processed and uploaded successfully.')
}
except Exception as e:
logger.error(f'Error: {e}')
return {
'statusCode': 500,
'body': json.dumps('Internal server error.')
}
finally:
if db:
db.close()
def get_config(path: str = ".env") -> dict:
return dotenv_values(path)
def connect_to_database() -> Connection:
try:
config = get_config()
host = config["host"]
port = config["port"]
user = config["user"]
password = config["password"]
database = config["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 list_existing_s3_files(bucket_name='extract_bucket', client=boto3.client('s3')):
"""Creates a dictionary and populates it with the
results of listing the contents of the s3 bucket, then
returns the populated dictionary
"""
existing_files = {}
try:
response = client.list_objects_v2(Bucket='extract_bucket')
if 'Contents' in response:
for obj in response['Contents']:
s3_key = obj['Key']
try:
file_obj = client.get_object(Bucket=bucket_name, Key=s3_key)
file_content = file_obj['Body'].read().decode('utf-8')
existing_files[s3_key] = file_content
except ClientError as e:
logger.error(f'Error retrieving S3 object {s3_key}: {e}')
else:
logger.error('The bucket is empty')
except ClientError as e:
logger.error(f'Error listing S3 objects: {e}')
return existing_files
def process_and_upload_tables(db, existing_files, client=boto3.client('s3')):
"""Creates a list of the tables from a database query and
then selects everything from each table in individual queries
it then writes each table to CSV files and compares with the item
in the existing_files dictionary with the same name. If it finds any changes
to files, or new tables/files it uploads them to the s3 bucket
"""
## NEW CODE
all_datetimes = []
for file_names in existing_files.keys():
datetime_str_on_s3 = ''.join(re.search(r'\/(.+/).+_(.+)\.csv',file_names).group(1,2))
all_datetimes.append(datetime.strptime(datetime_str_on_s3, '%Y/%m/%d/%H:%M:%S'))
latest_timestamp = max(all_datetimes)
## END OF NEW CODE
tables = db.run("SELECT table_name FROM information_schema.tables WHERE table_schema='public' AND table_type='BASE TABLE';")
print(tables)
for table in tables:
table_name = table[0]
rows = db.run(f"SELECT * FROM {table_name};")
csv_file_path = f"/tmp/{table_name}.csv"
with open(csv_file_path, "w", newline='') as file:
writer = csv.writer(file)
#column_names = [desc["name"] for desc in db.columns(f"SELECT * FROM {table_name};")]
column_names = [col_name[0] for col_name in db.run(f"SELECT column_name FROM INFORMATION_SCHEMA.COLUMNS where table_name = '{table_name}';")]
writer.writerow(column_names)
writer.writerows(rows)
s3_key = datetime.strftime(datetime.today(),f'{table_name}/%Y/%m/%d/{table_name}_%H:%M:%S.csv')
new_csv_content = open(csv_file_path, "r").read()
## NEW CODE
latest_s3_object_key = datetime.strftime(latest_timestamp,f'{table_name}/%Y/%m/%d/{table_name}_%H:%M:%S.csv')
## END OF NEW CODE
if existing_files[latest_s3_object_key] != new_csv_content:
try:
client.upload_file(csv_file_path, 'extract_bucket', s3_key)
logger.info(f"Uploaded {s3_key} to S3.")
except ClientError as e:
logger.error(f'Error uploading to S3: {e}')
else:
logger.info(f"No new data.")
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