Lessons Learned #534: Azure SQL Database Connections with Managed Identity and Python ODBC
We worked on a service request that our customer trying to enable their Python application, hosted on Azure App Service, to connect securely to Azure SQL Database using a user-assigned managed identity. They attempted to use the Microsoft ODBC Driver for SQL Server with the managed identity for authentication. During our troubleshooting process we found several issues/error messages causing by an incorrect settings in the connection string: The initial connection string used the 'ActiveDirectoryInteractive' authentication method, which is not compatible with managed identities. The correct approach is to use 'ActiveDirectoryMsi' or 'ActiveDirectoryManagedIdentity' for system/user-assigned managed identities. Switching to 'ActiveDirectoryMsi' led to a pyodbc error: pyodbc.Error: (FA005, [FA005] [Microsoft][ODBC Driver 18 for SQL Server]Cannot use Access Token with any of the following options: Authentication, Integrated Security, User, Password. (0) (SQLDriverConnect)). The FA005 error message indicated a mismatch between the use of an access token and the connection string properties. Specifically, when passing an access token, the connection string must not include conflicting authentication parameters such as User, Password, or Integrated Security.Lesson Learned #522: Troubleshooting TLS and Cipher Suites with Python connecting to Azure SQL DB
A few days ago, we were working on a service request where our customer was experiencing several issues connecting to Azure SQL Database due to TLS version and cipher suite mismatches when using Python and ODBC Driver 18. Although we were able to get that information through a network trace, I would like to share things that I learned. Using the library SSL in Python allows to establish a TLS/SSL context where I can control the TLS version and specify or inspect the cipher suite. Here’s a small script that demonstrates how to connect to the Azure SQL Gateway over port 1433 and inspect the TLS configuration: import ssl import socket #ServerName to connect (Only Gateway) host = 'servername.database.windows.net' port = 1433 # TLS context context = ssl.create_default_context() print("Python uses:", ssl.OPENSSL_VERSION) context.minimum_version = ssl.TLSVersion.TLSv1_2 context.maximum_version = ssl.TLSVersion.TLSv1_2 context.check_hostname = True context.verify_mode = ssl.CERT_REQUIRED context.load_default_certs() # Testing the connection. with socket.create_connection((host, port)) as sock: with context.wrap_socket(sock, server_hostname=host) as ssock: print("TLS connection established.") print("TLS version:", ssock.version()) print("Cipher suite:", ssock.cipher()) # CN (Common Name) cert = ssock.getpeercert() try: cn = dict(x[0] for x in cert['subject'])['commonName'] print(f"\n Certificate CN: {cn}") except Exception as e: print(" Error extracting CN:", e) print("Valid from :", cert.get('notBefore')) print("Valid until:", cert.get('notAfter')) Using this script I was able to: Enforce a specific TLS version by setting minimum_version and maximum_version , for example, (1.2 or 1.3) Retrieve the cipher suite negotiated. Inspect the details of the certificate. Enjoy!Lesson Learned #513: Using SQL Copilot and Python Retry Logic to Resolve Deadlocks in Azure SQL DB
A few weeks ago, I worked with a service request where our customer has a Python application that was reporting a deadlock with the following error message: INFO:root:Connected to the Database in dbname - Thread id 21132 - 0.0454 seconds --- INFO:root:Connected to the Database in dbname - Thread id 39856 - 0.4066 seconds --- ERROR:root:Transaction 2 failed: ('40001', '[40001] [Microsoft][ODBC Driver 18 for SQL Server][SQL Server]Transaction (Process ID 84) was deadlocked on lock resources with another process and has been chosen as the deadlock victim. Rerun the transaction. (1205) (SQLExecDirectW)') INFO:root:Transaction 1 completed successfully. In many of our articles and public information like these ones Analyze and prevent deadlocks in Azure SQL Database or Lesson Learned #19: How to obtain the deadlocks of your Azure SQL Database or Managed Instance?, we review ways to detect and resolve deadlocks by analyzing their details. At this time, I would like to share my experience using Copilot in Azure SQL Database to pinpoint the deadlock. After the error received, we started using with several prompts like Give me the deadlock that occurred in my database recently or Provide detailed information about the last deadlock in my database, including involved tables and queries and we got the following information: Based on the internal Azure SQL telemetry, the following deadlock query hashes have been identified in your database within the past 2 hours: Deadlock Details Query Hash 1: 0xD57AFD9584A2C053 Query Text 1: (@2 tinyint,@1 varchar(8000))UPDATE [dbo].[MS_TableA_MS] SET [ValueA] = @1 WHERE [ID]=@2; Query Hash 2: 0xBEAB84B27CE1F534 Query Text 2: (@2 tinyint,@1 varchar(8000))UPDATE [dbo].[MS_TableB_MS] SET [ValueB] = @1 WHERE [ID]=@2; At this point we identified the queries involved on this deadlock and we executed a second prompt that we used was: Suggest options to prevent or mitigate the recent deadlock in my database and we got very useful information about multiple options to prevent or mitigate the deadlock, like, Index Optimization, Force a Query Plan, Rewriting Transactions, Retry Logic in Application or T-SQL Code, etc.. For application retry logic, we traced the source code that triggered the issue and added a retry mechanism to handle the deadlock. Here’s a simple example of what we implemented. Basically, this small code, creates two tables, runs two transactions in two different threads and depending on the value of retry parameter retry or not the deadlock operation. # Constants for table name used in the deadlock scenario TABLE_A_NAME = "dbo.MS_TableA_MS" TABLE_B_NAME = "dbo.MS_TableB_MS" TABLE_A_COLUMN = "ValueA" TABLE_B_COLUMN = "ValueB" ITEM_ID = 1 TABLE_A_INITIAL_VALUE = 'Value A1' TABLE_B_INITIAL_VALUE = 'Value B1' def simulate_deadlock(retry=False, retry_attempts=3): """ Simulates a deadlock by running two transactions in parallel that lock resources. If retry is enabled, the transactions will attempt to retry up to a specified number of attempts. """ setup_deadlock_tables() thread1 = threading.Thread(target=run_transaction_with_retry, args=(deadlock_transaction1, retry_attempts,retry)) thread2 = threading.Thread(target=run_transaction_with_retry, args=(deadlock_transaction2, retry_attempts,retry)) thread1.start() thread2.start() thread1.join() thread2.join() def run_transaction_with_retry(transaction_func, attempt_limit,retry): attempt = 0 while attempt < attempt_limit: try: transaction_func(retry) break except pyodbc.Error as e: if 'deadlock' in str(e).lower() and retry: attempt += 1 logging.warning(f"Deadlock detected. Retrying transaction... Attempt {attempt}/{attempt_limit}") time.sleep(1) else: logging.error(f"Transaction failed: {e}") break def setup_deadlock_tables(): """ Sets up the tables required for the deadlock simulation, using constants for table and column names. """ conn, dbNameReturn = ConnectToTheDB() if conn is None: logging.info('Error establishing connection to the database. Exiting setup.') return cursor = conn.cursor() try: cursor.execute(f""" IF OBJECT_ID('{TABLE_A_NAME}', 'U') IS NULL CREATE TABLE {TABLE_A_NAME} ( ID INT PRIMARY KEY, {TABLE_A_COLUMN} VARCHAR(100) ); """) cursor.execute(f""" IF OBJECT_ID('{TABLE_B_NAME}', 'U') IS NULL CREATE TABLE {TABLE_B_NAME} ( ID INT PRIMARY KEY, {TABLE_B_COLUMN} VARCHAR(100) ); """) cursor.execute(f"SELECT COUNT(*) FROM {TABLE_A_NAME}") if cursor.fetchone()[0] == 0: cursor.execute(f"INSERT INTO {TABLE_A_NAME} (ID, {TABLE_A_COLUMN}) VALUES ({ITEM_ID}, '{TABLE_A_INITIAL_VALUE}');") cursor.execute(f"SELECT COUNT(*) FROM {TABLE_B_NAME}") if cursor.fetchone()[0] == 0: cursor.execute(f"INSERT INTO {TABLE_B_NAME} (ID, {TABLE_B_COLUMN}) VALUES ({ITEM_ID}, '{TABLE_B_INITIAL_VALUE}');") conn.commit() logging.info("Tables prepared successfully.") except Exception as e: logging.error(f"An error occurred in setup_deadlock_tables: {e}") conn.rollback() finally: conn.close() def deadlock_transaction1(retry=False): conn, dbNameReturn = ConnectToTheDB() if conn is None: logging.info('Error establishing connection to the database. Exiting transaction 1.') return cursor = conn.cursor() try: cursor.execute("BEGIN TRANSACTION;") cursor.execute(f"UPDATE {TABLE_A_NAME} SET {TABLE_A_COLUMN} = 'Transaction 1' WHERE ID = {ITEM_ID};") time.sleep(2) cursor.execute(f"UPDATE {TABLE_B_NAME} SET {TABLE_B_COLUMN} = 'Transaction 1' WHERE ID = {ITEM_ID};") conn.commit() logging.info("Transaction 1 completed successfully.") except pyodbc.Error as e: logging.error(f"Transaction 1 failed: {e}") conn.rollback() if 'deadlock' in str(e).lower() and retry: raise e # Rethrow the exception to trigger retry in run_transaction_with_retry finally: conn.close() def deadlock_transaction2(retry=False): conn, dbNameReturn = ConnectToTheDB() if conn is None: logging.info('Error establishing connection to the database. Exiting transaction 2.') return cursor = conn.cursor() try: cursor.execute("BEGIN TRANSACTION;") cursor.execute(f"UPDATE {TABLE_B_NAME} SET {TABLE_B_COLUMN} = 'Transaction 2' WHERE ID = {ITEM_ID};") time.sleep(2) cursor.execute(f"UPDATE {TABLE_A_NAME} SET {TABLE_A_COLUMN} = 'Transaction 2' WHERE ID = {ITEM_ID};") conn.commit() logging.info("Transaction 2 completed successfully.") except pyodbc.Error as e: logging.error(f"Transaction 2 failed: {e}") conn.rollback() if 'deadlock' in str(e).lower() and retry: raise e # Rethrow the exception to trigger retry in run_transaction_with_retry, finally: conn.close()Lesson Learned #510: Using CProfiler to Analyze Python Call Performance in Support Scenarios
Last week, while working on a support case, our customer was facing performance issues in their Python application. After some investigation, I decided to suggest CProfiler to identify which function call was taking the most time and use that as a starting point for troubleshooting. So profiling the Python code became essential to pinpoint the bottleneck. I suggested using CProfiler, a built-in Python module, which helps you profile your code and identify performance issues in real time.LangChain integration with Azure Database for PostgreSQL (Part 1)
Use LangChain to split documents into smaller chunks, generate embeddings for each chunk using Azure OpenAI, and store them in a PostgreSQL database via the pgvector extension. Then, we’ll perform a vector similarity search on the embedded documents.Lesson Learned #269: Unable to connect - Is unavailable or does not exist - Connection Time out
Today, I worked on a service request that your customer is facing the following error message: During handling of the above exception, another exception occurred: Traceback (most recent call last): File "src/pymssql/_pymssql.pyx", line 653, in pymssql._pymssql.connect pymssql._pymssql.OperationalError: (20009, b'DB-Lib error message 20009, severity 9:\nUnable to connect: Adaptive Server is unavailable or does not exist (servername.database.windows.net)\nNet-Lib error during Connection timed out (110)\nDB-Lib error message 20009, severity 9:\nUnable to connect: Adaptive Server is unavailable or does not exist (servername.database.windows.net)\nNet-Lib error during Connection timed out (110)\n')
