I successfully tested a python azure function locally that once triggered in blob storage, converts any .xlsb files to .xlsx files. On a <100KB .xlsb file this took about a minute, and a ~70,000KB .xlsb file took 15 minutes (that feels very long! Also looking for performance enhancement here if you have!). The function code is below.

import logging
import pandas as pd
from io import BytesIO
import azure.functions as func
from azure.storage.blob import BlobServiceClient
 
app = func.FunctionApp()
 
@app.blob_trigger(arg_name="myblob", path="raw/Input/{name}.xlsb",
                               connection="")
def blob_trigger(myblob: func.InputStream):
    logging.info(f"Python blob trigger function processed blob"
                f"Name: {myblob.name}"
                f"Blob Size: {myblob.length} bytes")
   
    accountName = ""
    accountKey = ""
    connectionString = f"DefaultEndpointsProtocol=https;AccountName={accountName};AccountKey={accountKey};EndpointSuffix=core.windows.net"
   
    inputBlobname = myblob.name.replace("raw/Input/", "")
    containerName = "raw/Output"
    outputBlobname = inputBlobname.replace(".xlsb", ".xlsx")
 
    blob_service_client = BlobServiceClient.from_connection_string(connectionString)
    container_client = blob_service_client.get_container_client(containerName)
 
    input_data = myblob.read()
    df = pd.read_excel(BytesIO(input_data), engine="pyxlsb")
 
    output_data = BytesIO()
    df.to_excel(output_data, index=False)
    output_data.seek(0)
 
    bob_client = container_client.get_blob_client(outputBlobname)
    bob_client.upload_blob(output_data.getvalue(), overwrite=True)

I have deployed the function successfully to Azure from VS code and tested it successfully with the smaller .xlsb file. However when I try with the larger file, nothing happens, not even a failed invocation. I assume it times out, but I never see a failed invocation, can anyone explain why?