pythonsqlalchemypyodbcazure-synapsemsodbcsql17

SQLAlchemy error: An attempt to complete a transaction has failed. No corresponding transaction found

I have installed:

and I want to create just a proof of concept using SQLAlchemy with an Azure SQL Data Warehouse. However, when I try to run a query on Customer model which is mapped to the customers view table using the code:

import urllib

from sqlalchemy import create_engine
from sqlalchemy import Column, Integer
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

db_username = 'username'
db_password = 'password'
db_database = 'dbname'
db_hostname = 'dbhost'
db_driver = 'ODBC Driver 17 for SQL Server'
db_port = '1433'

db_connectionString = f"DRIVER={{{db_driver}}}; SERVER={{{db_hostname}}}; DATABASE={{{db_database}}}; UID={{{db_username}}}; PWD={{{db_password}}}; PORT={{{db_port}}};"

engine_params = urllib.parse.quote_plus(db_connectionString)

engine = create_engine(f"mssql+pyodbc:///?odbc_connect={engine_params}", echo=True)

Base = declarative_base()

class Customer(Base):
    __tablename__ = 'customers'

    id = Column('Customer_ID', Integer, primary_key=True)

Session = sessionmaker(bind=engine)
session = Session()

customers_count = session.query(Customer).count()

session.close()

the following exception is thrown:

ProgrammingError: (pyodbc.ProgrammingError) ('42000', '[42000] [Microsoft][ODBC Driver 17 for SQL Server][SQL Server]111214;An attempt to complete a transaction has failed. No corresponding transaction found. (111214) (SQLEndTran)

Please, keep in mind that I can use the SQLAlchemy's engine with pandas and run native SQL queries such:

data_frame = pandas.read_sql("SELECT COUNT(*) FROM customers", engine)

However, my need is to use the high-level query API of SQLAlchemy:

customers_count = session.query(Customer).count()

Any help would be really appreciated.

Solution

  • The SQLAlchemy documentation for mssql+pyodbc://… has just been updated to include the following (for SQLA 1.4/2.0):

    Azure SQL Data Warehouse does not support transactions, and that can cause problems with SQLAlchemy's "autobegin" (and implicit commit/rollback) behavior. We can avoid these problems by enabling autocommit at both the pyodbc and engine levels:

    connection_url = sa.engine.URL.create(
    "mssql+pyodbc",
    username="scott",
    password="tiger",
    host="dw.azure.example.com",
    database="mydb",
    query={
        "driver": "ODBC Driver 17 for SQL Server",
        "autocommit": "True",
    },
)
engine = create_engine(connection_url).execution_options(
    isolation_level="AUTOCOMMIT"
)