Pipeline Test Issue: Fetching SFTP Files, Converting to JSON, and Updating Airtable Fails

Other Questions Airtable
1

Encountering a 500 error while running my cloud pipeline in GCP. It downloads Excel files from an SFTP server, converts them to JSON, and updates Airtable. Code snippet:

import os
import paramiko
import pandas as pd
import requests
import json


def get_excel_files(directory, file_names):
    ftp_host = os.environ.get('FTP_HOST')
    ftp_user = os.environ.get('FTP_USER')
    ftp_pass = os.environ.get('FTP_PASS')
    ftp_port = int(os.environ.get('FTP_PORT'))
    transport = paramiko.Transport((ftp_host, ftp_port))
    transport.connect(username=ftp_user, password=ftp_pass)
    sftp_client = paramiko.SFTPClient.from_transport(transport)
    frames = []
    for name in file_names:
        with sftp_client.open(os.path.join(directory, name), 'rb') as f:
            df = pd.read_excel(f)
            frames.append(df)
    sftp_client.close()
    transport.close()
    return frames


def send_updates(data_records):
    url = os.environ.get('AIRTABLE_ENDPOINT')
    token = os.environ.get('AIRTABLE_API_TOKEN')
    headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
    payload = {"records": data_records}
    response = requests.patch(url + '/records', headers=headers, data=json.dumps(payload))
    return response.status_code


def main_handler(request):
    files = get_excel_files('remote_folder/', ['data_one.xlsx', 'data_two.xlsx'])
    # Data processing logic here
    return 'Pipeline executed successfully.'
2

hey, try adding error logging so you can catch any auth or sftp file open probs. sometimes a wrong file path or token issue causes a 500 error. debugging each step might show the root cause.

3

After reviewing the pipeline flow, one observation is that potential issues with data type conversions could be causing the failure in the later stages of the process. For instance, if the Excel file content is not cleanly formatted or contains unexpected data types, the conversion to JSON may yield malformed records. I have experienced similar issues when input data was inconsistent, and ensuring data quality through pre-validation helped in isolating the problem. Additionally, verifying that the Airtable endpoint and its accepted HTTP methods align with the request made can further diagnose the root cause.

4

In my experience, similar issues were often rooted in data formatting and connection configurations. I once encountered a situation where the data conversion failed because the Excel files contained cells with inconsistent data types. This resulted in generating malformed JSON, which then failed the update to Airtable. I resolved this by validating each row of data before converting it. Additionally, double-checking the SFTP connection parameters and ensuring that the API endpoint was correctly set up helped prevent timeout errors and miscommunication between services. This approach significantly reduced the 500 error occurrences.

5

hey, i’ve seen issues with airtable patch requests. try using post or splitting the request so you don’t hit rate limits. a small payload sometimes avoids errors and makes the update smoother in your pipline