mirror of
https://github.com/Hestia-Homes/Model.git
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407 lines
15 KiB
Python
407 lines
15 KiB
Python
import os
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import sys
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import json
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import pandas as pd
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import requests
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import boto3
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from uuid import UUID, uuid4
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from utils.s3 import read_csv_from_s3 as read_csv_from_s3_dict, save_csv_to_s3, parse_s3_uri
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from utils.logger import setup_logger
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from tqdm import tqdm
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from backend.app.db.functions.tasks.Tasks import SubTaskInterface
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from datetime import datetime
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logger = setup_logger()
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def upload_batch_to_s3(
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batch_df: pd.DataFrame, task_id: str, sub_task_id: str, bucket_name: str = None
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) -> str:
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"""
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Upload batch DataFrame to S3 as CSV.
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Args:
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batch_df: The DataFrame containing batch data
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task_id: The parent task ID (used for file path)
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sub_task_id: The subtask ID (used for file path)
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bucket_name: The S3 bucket name (defaults to env variable)
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Returns:
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S3 URI (s3://bucket/key) of the uploaded file
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"""
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if bucket_name is None:
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bucket_name = os.getenv("S3_BUCKET_NAME")
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if not bucket_name:
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logger.error(
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"S3 bucket name not provided and S3_BUCKET_NAME environment variable not set"
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)
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raise ValueError("S3_BUCKET_NAME not configured")
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try:
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file_name = f"{datetime.now().isoformat()}_{str(uuid4())[:8]}"
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file_key = (
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f"ara_postcode_splitter_batches/{task_id}/{sub_task_id}/{file_name}.csv"
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)
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success = save_csv_to_s3(batch_df, bucket_name, file_key)
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if success:
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s3_uri = f"s3://{bucket_name}/{file_key}"
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logger.info(f"Successfully uploaded batch to {s3_uri}")
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return s3_uri
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else:
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logger.error(f"Failed to upload batch to S3")
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raise ValueError("Failed to save CSV to S3")
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except Exception as e:
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logger.error(f"Error uploading batch to S3: {str(e)}")
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raise
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def send_to_address2uprn_queue(task_id: str, sub_task_id: str, s3_uri: str) -> str:
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"""
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Send a batch to the address2UPRN SQS queue with S3 reference.
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Args:
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task_id: The parent task ID
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sub_task_id: The new subtask ID for this batch
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s3_uri: S3 URI pointing to the batch CSV file
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Returns:
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Message ID from SQS
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"""
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sqs_client = boto3.client("sqs")
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queue_url = os.getenv("ADDRESS2UPRN_QUEUE_URL")
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if not queue_url:
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raise ValueError("ADDRESS2UPRN_QUEUE_URL environment variable not set")
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message_body = {
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"task_id": task_id,
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"sub_task_id": sub_task_id,
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"s3_uri": s3_uri,
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}
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response = sqs_client.send_message(
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QueueUrl=queue_url,
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MessageBody=json.dumps(message_body),
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)
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logger.info(
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f"Sent message to address2UPRN queue. "
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f"Task: {task_id}, SubTask: {sub_task_id}, MessageId: {response['MessageId']}"
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)
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return response["MessageId"]
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def create_batch_and_send_to_address2uprn(
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batch_rows: list,
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task_id: str,
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subtask_interface: SubTaskInterface,
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bucket_name: str,
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) -> str:
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"""
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Create a batch DataFrame, upload to S3, create subtask, and send to address2UPRN queue.
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Args:
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batch_rows: List of row dictionaries for this batch
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task_id: The parent task ID
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subtask_interface: SubTaskInterface instance
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bucket_name: S3 bucket name
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Returns:
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The created batch subtask ID
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"""
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# Generate unique batch subtask ID
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batch_sub_task_id = str(uuid4())
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# Upload batch to S3
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batch_df = pd.DataFrame(batch_rows)
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s3_uri = upload_batch_to_s3(batch_df, str(task_id), batch_sub_task_id, bucket_name)
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# Create a new subtask for this batch with all inputs
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created_batch_sub_task_id = subtask_interface.create_subtask(
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task_id=task_id,
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inputs={
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"task_id": str(task_id),
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"sub_task_id": batch_sub_task_id,
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"batch_size": len(batch_rows),
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"s3_uri": s3_uri,
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},
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)
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logger.info(f"Created batch subtask {created_batch_sub_task_id}")
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# Send message with S3 reference
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send_to_address2uprn_queue(
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task_id=str(task_id),
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sub_task_id=batch_sub_task_id,
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s3_uri=s3_uri,
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)
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return created_batch_sub_task_id
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def handler(event, context):
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print(f"Function: {context.function_name}")
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print(f"Request ID: {context.aws_request_id}")
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# Example SQS message for testing (copy and paste into SQS):
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# {
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# "task_id":"e31f2f21-175b-4a91-a3ec-a6baa325e917",
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# "s3_uri":"s3://retrofit-data-dev/ara_raw_inputs/peabody/2025_11_11 - Peabody - Data Extracts for Domna_transformed.csv"
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# }
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# Handle both single event and batch events (SQS, etc.)
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records = event.get("Records", [event])
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results = []
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errors = []
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subtask_interface = SubTaskInterface()
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bucket_name = os.getenv("S3_BUCKET_NAME")
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for record in records:
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task_id = None
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subtask_id = None
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try:
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# Parse body (inputs)
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if isinstance(record.get("body"), str):
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body = json.loads(record["body"])
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else:
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body = record.get("body", {})
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# Validate required fields
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task_id = body.get("task_id")
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s3_uri = body.get("s3_uri")
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if not task_id:
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errors.append({"error": "Missing required field: task_id"})
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continue
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if not s3_uri:
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errors.append({"error": "Missing required field: s3_uri"})
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continue
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# Convert task_id to UUID
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try:
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task_id = UUID(task_id) if isinstance(task_id, str) else task_id
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except ValueError as e:
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errors.append({"error": f"Invalid UUID format for task_id: {str(e)}"})
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continue
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# Create a new subtask for this postcode splitter invocation
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subtask_id = subtask_interface.create_subtask(
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task_id=task_id, inputs={"s3_uri": s3_uri}
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)
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logger.info(f"Created subtask {subtask_id} for task {task_id}")
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# Mark subtask as in progress
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subtask_interface.update_subtask_status(subtask_id, "in progress")
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logger.info(f"Marked subtask {subtask_id} as in progress")
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# Read CSV from S3
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logger.info(f"Processing S3 URI: {s3_uri}")
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bucket, key = parse_s3_uri(s3_uri)
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logger.info(f"S3 Bucket: {bucket}, Key: {key}")
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csv_data = read_csv_from_s3_dict(bucket, key)
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df = pd.DataFrame(csv_data)
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df = df.head(1983)
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logger.info(f"CSV loaded: {len(df)} rows, {len(df.columns)} columns")
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# Sanitise postcodes
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df["postcode_clean"] = df["postcode"].str.upper().str.replace(" ", "")
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clean_df = df.dropna(subset=["postcode_clean"])
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postcode_to_addresses = {
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postcode: group.to_dict(orient="records")
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for postcode, group in clean_df.groupby("postcode_clean", sort=False)
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}
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logger.info(f"Total postcodes: {len(postcode_to_addresses)}")
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# Calculate total rows to send
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total_rows = sum(len(rows) for rows in postcode_to_addresses.values())
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logger.info(f"Total rows to send: {total_rows}")
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batch_size = 500
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# If all rows fit in one batch, just send them all at once
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if total_rows <= batch_size:
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all_rows = []
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for postcode, rows in postcode_to_addresses.items():
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all_rows.extend(rows)
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try:
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create_batch_and_send_to_address2uprn(
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batch_rows=all_rows,
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task_id=task_id,
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subtask_interface=subtask_interface,
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bucket_name=bucket_name,
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)
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logger.info(
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f"Sent all {len(all_rows)} rows in single batch to address2UPRN queue"
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)
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except Exception as e:
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logger.error(
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f"Failed to send all rows to address2UPRN queue: {e}",
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exc_info=True,
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)
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errors.append(
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{
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"error": "Failed to send to address2UPRN queue",
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"details": str(e),
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}
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)
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else:
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# Multi-batch processing for large datasets
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batch_rows = []
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total_sent = 0
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for postcode, rows in postcode_to_addresses.items():
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logger.info(f"Processing postcode {postcode} with {len(rows)} rows")
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# If postcode itself is larger than batch_size, send it individually
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if len(rows) > batch_size:
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# First, send the current batch if it has data
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if batch_rows:
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try:
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create_batch_and_send_to_address2uprn(
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batch_rows=batch_rows,
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task_id=task_id,
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subtask_interface=subtask_interface,
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bucket_name=bucket_name,
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)
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logger.info(
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f"Sent batch of {len(batch_rows)} rows to address2UPRN queue"
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)
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batch_rows = []
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except Exception as e:
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logger.error(
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f"Failed to send batch to address2UPRN queue: {e}",
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exc_info=True,
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)
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errors.append(
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{
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"error": "Failed to send to address2UPRN queue",
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"details": str(e),
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}
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)
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# Send the large postcode on its own
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try:
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create_batch_and_send_to_address2uprn(
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batch_rows=rows,
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task_id=task_id,
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subtask_interface=subtask_interface,
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bucket_name=bucket_name,
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)
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logger.info(
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f"Sent large postcode {postcode} ({len(rows)} rows) to address2UPRN queue"
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)
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except Exception as e:
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logger.error(
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f"Failed to send large postcode to address2UPRN queue: {e}",
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exc_info=True,
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)
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errors.append(
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{
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"error": "Failed to send to address2UPRN queue",
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"details": str(e),
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}
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)
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continue
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# If adding this postcode's rows would exceed batch_size, send current batch
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current_batch_size = len(batch_rows) + len(rows)
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if batch_rows and current_batch_size > batch_size:
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logger.info(
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f"Batch threshold reached: current {len(batch_rows)} + next postcode {len(rows)} = {current_batch_size} > {batch_size}"
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)
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try:
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create_batch_and_send_to_address2uprn(
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batch_rows=batch_rows,
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task_id=task_id,
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subtask_interface=subtask_interface,
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bucket_name=bucket_name,
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)
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logger.info(
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f"Sent batch of {len(batch_rows)} rows to address2UPRN queue (total sent: {total_sent})"
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)
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total_sent += len(batch_rows)
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batch_rows = []
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except Exception as e:
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logger.error(
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f"Failed to send batch to address2UPRN queue: {e}",
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exc_info=True,
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)
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errors.append(
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{
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"error": "Failed to send to address2UPRN queue",
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"details": str(e),
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}
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)
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# Add current postcode's rows to batch
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batch_rows.extend(rows)
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# Send remaining batch
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if batch_rows:
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try:
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create_batch_and_send_to_address2uprn(
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batch_rows=batch_rows,
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task_id=task_id,
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subtask_interface=subtask_interface,
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bucket_name=bucket_name,
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)
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total_sent += len(batch_rows)
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logger.info(
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f"Sent final batch of {len(batch_rows)} rows to address2UPRN queue (total sent: {total_sent})"
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)
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batch_rows = []
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except Exception as e:
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logger.error(
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f"Failed to send final batch to address2UPRN queue: {e}",
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exc_info=True,
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)
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errors.append(
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{
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"error": "Failed to send to address2UPRN queue",
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"details": str(e),
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}
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)
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except json.JSONDecodeError as e:
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logger.error(f"Invalid JSON in request body: {e}")
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errors.append({"error": "Invalid JSON in request body", "details": str(e)})
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# Mark subtask as failed if we have one
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if subtask_id:
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try:
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subtask_interface.update_subtask_status(
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subtask_id, "failed", outputs={"error": str(e)}
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)
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except Exception as db_error:
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logger.error(f"Failed to update subtask status: {db_error}")
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except Exception as e:
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logger.error(f"Unexpected error processing record: {e}", exc_info=True)
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errors.append({"error": "Unexpected error", "details": str(e)})
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# Mark subtask as failed if we have one
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if subtask_id:
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try:
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subtask_interface.update_subtask_status(
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subtask_id, "failed", outputs={"error": str(e)}
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)
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except Exception as db_error:
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logger.error(f"Failed to update subtask status: {db_error}")
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# Return error if all records failed
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if errors and not results:
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return {"statusCode": 500, "body": json.dumps({"errors": errors})}
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return {
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"statusCode": 200,
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"body": json.dumps(
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{"processed": results, "errors": errors if errors else None}
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),
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}
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