CODEWITHSUNDEEP
pythonpandassqlalchemy
Peque
Peque

Reputation: 14831

Clean-up database connection with SQLAlchemy in Pandas

With Pandas, I can very easily read data from a database into a dataframe:

from sqlalchemy import create_engine
import pandas


query = 'SELECT * FROM Table_Name;'
engine = create_engine('...')

df = pandas.read_sql_query(query, engine)

print(df.head())

I would like to make sure that no connection is kept open after executing .read_sql_query(), no matters if the query succeeded or if it raised an exception.

I am currently:

Like so:

from sqlalchemy import create_engine
from sqlalchemy.pool import NullPool
import pandas


def get_data():
    query = 'SELECT * FROM Table_Name;'
    try:
        engine = create_engine('...', poolclass=NullPool)
        df = pandas.read_sql_query(query, engine)
    finally:
        engine.dispose()
    return df


print(get_data().head())

Is there a better way?

Upvotes: 24

Views: 20869

Answers (2)

Marco Graziano
Marco Graziano

Reputation: 476

I have tested and even after the connection is closed (connection.close()), it is still present on the table sys.sysprocesses (of the database) throughout the execution of the script. Thus, if the script (after the connection) lasts another 10 minutes, the connection remains present on the sys.sysprocesses table for 10 minutes.

I think it is significant to draw attention to this fact: connection closed YES, process in the database closed NO.

Here are some scripts I used for testing:

sql = "select * from tbltest"
s_con = '...' #connection information

con = URL.create("mssql+pyodbc", query={"odbc_connect": s_con})
engine = create_engine(con)

with engine.connect() as con:
    frame = pd.read_sql(sql=sql, con=con)
    print(con.closed) # False

print(con.closed) # True
engine.dispose()

from time import sleep
sleep(20) # Pause for 20 seconds to launch the query with SSMS

Use of SSMS

Query for check connection
SELECT * FROM sys.sysprocesses

Upvotes: 2

YaOzI
YaOzI

Reputation: 17538

Backgrounds:

When using sqlalchemy with pandas read_sql_query(query, con) method, it will create a SQLDatabase object with an attribute connectable to self.connectable.execute(query). And the SQLDatabase.connectable is initialized as con as long as it is an instance of sqlalchemy.engine.Connectable (i.e. Engine and Connection).

Case I: when passing Engine object as con

Just as example code in your question:

from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('...')
df = pd.read_sql_query(query, con=engine)

Internally, pandas just use result = engine.execute(query), which means:

Where above, the execute() method acquires a new Connection on its own, executes the statement with that object, and returns the ResultProxy. In this case, the ResultProxy contains a special flag known as close_with_result, which indicates that when its underlying DBAPI cursor is closed, the Connection object itself is also closed, which again returns the DBAPI connection to the connection pool, releasing transactional resources.

In this case, you don't have to worry about the Connection itself, which is closed automatically, but it will keep the connection pool of engine.

So you can either disable pooling by using:

engine = create_engine('...', poolclass=NullPool)

or dispose the engine entirely with engine.dispose() at the end.

But following the Engine Disposal doc (the last paragraph), these two are alternative, you don't have to use them at the same time. So in this case, for simple one-time usage of read_sql_query and clean-up, I think this should be enough:

# Clean up entirely after every query.
engine = create_engine('...')
df = pd.read_sql_query(query, con=engine)
engine.dispose()

Case II: when passing Connection object as con:

connection = engine.connect()
print(connection.closed) # False
df = pd.read_sql_query(query, con=connection)
print(connection.closed) # False again
# do_something_else(connection)
connection.close()
print(connection.closed) # True
engine.dispose()

You should do this whenever you want greater control over attributes of the connection, when it gets closed, etc. For example, a very import example of this is a Transaction, which lets you decide when to commit your changes to the database. (from this answer)

But with pandas, we have no control inside the read_sql_query, the only usefulness of connection is that it allows you to do more useful things before we explicitly close it.

So generally speaking:

I think I would like to use following pattern, which gives me more control of connections and leaves the future extensibility:

engine = create_engine('...')
# Context manager makes sure the `Connection` is closed safely and implicitly
with engine.connect() as conn:
    df = pd.read_sql_query(query, conn)
    print(conn.in_transaction()) # False
    # do_something_with(conn)
    trans = conn.begin()
    print(conn.in_transaction()) # True
    # do_whatever_with(trans)
    print(conn.closed) # False
print('Is Connection with-OUT closed?', conn.closed) # True
engine.dispose()

But for simple usage cases such as your example code, I think both ways are equally clean and simple for clean-up DB IO resources.

Upvotes: 47

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