load_s3_logs.py

from datetime import datetime
from io import StringIO

import boto3
import pandas as pd

from tools.config import (
    AWS_ACCESS_KEY,
    AWS_REGION,
    AWS_SECRET_KEY,
    DOCUMENT_REDACTION_BUCKET,
    OUTPUT_FOLDER,
)

# Combine together log files that can be then used for e.g. dashboarding and financial tracking.

# S3 setup. Try to use provided keys (needs S3 permissions), otherwise assume AWS SSO connection
if AWS_ACCESS_KEY and AWS_SECRET_KEY and AWS_REGION:
    s3 = boto3.client(
        "s3",
        aws_access_key_id=AWS_ACCESS_KEY,
        aws_secret_access_key=AWS_SECRET_KEY,
        region_name=AWS_REGION,
    )
else:
    s3 = boto3.client("s3")

bucket_name = DOCUMENT_REDACTION_BUCKET
prefix = "usage/"  # 'feedback/' # 'logs/' # Change as needed - top-level folder where logs are stored
earliest_date = "20250409"  # Earliest date of logs folder retrieved
latest_date = "20250423"  # Latest date of logs folder retrieved


# Function to list all files in a folder
def list_files_in_s3(bucket, prefix):
    response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)
    if "Contents" in response:
        return [content["Key"] for content in response["Contents"]]
    return []


# Function to filter date range
def is_within_date_range(date_str, start_date, end_date):
    date_obj = datetime.strptime(date_str, "%Y%m%d")
    return start_date <= date_obj <= end_date


# Define the date range
start_date = datetime.strptime(earliest_date, "%Y%m%d")  # Replace with your start date
end_date = datetime.strptime(latest_date, "%Y%m%d")  # Replace with your end date

# List all subfolders under 'usage/'
all_files = list_files_in_s3(bucket_name, prefix)

# Filter based on date range
log_files = []
for file in all_files:
    parts = file.split("/")
    if len(parts) >= 3:
        date_str = parts[1]
        if (
            is_within_date_range(date_str, start_date, end_date)
            and parts[-1] == "log.csv"
        ):
            log_files.append(file)

# Download, read and concatenate CSV files into a pandas DataFrame
df_list = []
for log_file in log_files:
    # Download the file
    obj = s3.get_object(Bucket=bucket_name, Key=log_file)
    try:
        csv_content = obj["Body"].read().decode("utf-8")
    except Exception as e:
        print("Could not load in log file:", log_file, "due to:", e)
        csv_content = obj["Body"].read().decode("latin-1")

    # Read CSV content into pandas DataFrame
    try:
        df = pd.read_csv(StringIO(csv_content))
    except Exception as e:
        print("Could not load in log file:", log_file, "due to:", e)
        continue

    df_list.append(df)

# Concatenate all DataFrames
if df_list:
    concatenated_df = pd.concat(df_list, ignore_index=True)

    # Save the concatenated DataFrame to a CSV file
    concatenated_df.to_csv(OUTPUT_FOLDER + "consolidated_s3_logs.csv", index=False)
    print("Consolidated CSV saved as 'consolidated_s3_logs.csv'")
else:
    print("No log files found in the given date range.")