Snowflake connection helper functions
Project description
sfconn
Snowflake connection helper functions
A Python library to simplify connecting to Snowflake databases
Notes
- This is a major version upgrade and breaks compatibility with the previous versions (<
0.3.0).sfconnnow relies onsnowflake-python-connectorfor accessing named connections (connections.toml). sfconnoptionally modifies the wayprivate_key_fileconnection option is evaluated. When--keyfile-pfx-mapoption is specified, or if$SFCONN_KEYFILE_PFX_MAPis set, (option value must be a pair of source and target paths separated by:).private_key_fileoption, if present and begins with the source path, it is temporarily modified as if it begins with the target path. Primary use-case is to be able to maintain one copy ofconnections.tomlfile across different execution environments, such as within containers.
Installation
Use Python's standard pip utility for installation:
pip install --upgrade sfconn
Usage
getconn() and getsess()
getconn and getsess are wrapper functions over native Snowflake functions with added functionality (mainly mapping private_key_file value as described above).
Note: getsess() function will be available only if snowflake-snowpark-python package is available at run-time.
Usage:
def getconn(connection_name: str | None, **overrides: dict[str, Any]) -> Connection:
def getsess(connection_name: str | None, **overrides: dict[str, Any]) -> Session:
getconn and getsess accept a connection name and return a connection or session object respectively with modified behavior as noted above.
Examples:
from sfconn import getconn
# assuming 'dev' is a named connection defined in connections.toml
with getconn('dev', schema='PUBLIC') as cnx:
with cnx.cursor() as csr:
csr.execute('SELECT CURRENT_USER()')
print("Hello " + csr.fetchone()[0])
from sfconn import getsess
# assuming 'dev' is a named connection defined in connections.toml
with getsess('dev', schema='PUBLIC') as session:
df = sess.sql("select current_user() as curr_user, current_role() as curr_role")
print(df.collect())
run_query*()
Cursor objects add a family of few convenience methods that return an Iterable of values instead of generic tuple or dict.
<cursor>.run_query(<callable>|<class>, <sql> [,<params>]): Returns an Iterable of values.<callable>is a mapping function that shall accept all column values of a row as individual arguments, in order, and returns a value that will be used forIterable.<class>is any Python class whose attribute names, after upper-casing, are treated as column names from the result set.<class>can include only a subset of a all available column from the query result as attributes and in a different order than the query.
<cursor>.run_query1(<callable>|<class>, <sql> [,<params>]): Similar torun_query, except returns a single value. Note, if at least one value is not available, raisesProgrammingErrorexception.<cursor>.run_query1_opt(<callable>|<class>, <sql> [,<params>]): Similar torun_query1, except instead of raising an exception, the method returnsNone.
Examples:
import datetime as dt
from collections import namedtuple
Result = namedtuple("Result", ["user", "date"])
def mkResult(x: str, y: dt.date) -> Result:
return Result(x, y)
with getconn() as cnx, cnx.cursor() as csr:
result = csr.run_query1(mkResult, "select current_user() as user, current_date() as date")
import datetime as dt
from dataclasses import dataclass
@dataclass
class Result:
date: dt.date
user: str
with getconn() as cnx, cnx.cursor() as csr:
result = csr.run_query1(
Result,
"select current_user() as user, current_date() as date, current_warehouse() as wh_name"
)
Decorator Functions
Python command-line scripts that use argparse library, can use decorator functions to further reduce boilerplate code needed for setting up a Snowflake connection and error checking
def with_connection_args(doc: str | None) -> Callable[[argparse.ArgumentParser], None]:
def with_connection(logger = None) -> Callable[[Connection, ...], None]:
def with_session(logger = None) -> Callable[[Session, ...], None]:
with_connection_args() decorator function:
- builds an
ArgumentParserobject - adds common Snowflake connection options as arguments including overriding role, database, schema and warehouse
- calls the decorated function with the parser object to allow adding any script specific options
with_connection() decorator function:
- consumes standard Snowflake connection options (specified with
with_connection_args()) - creates a connection object
- calls the decorated function with a connection object as first parameter and any other script specific options that were specified on command line
with_session() decorator function:
- Similar to
with_connection()but creates asnowflake.snowpark.Sessionobject instead of a connection object - Note: this decorator will be available only if
snowflake-snowpark-pythonpackage is available at run-time.
Note: Decorator function parenthesis cannot be omitted even if no arguments are supplied to the decorator functions
Example:
from sfconn import with_connection_args, with_connection
@with_connection()
def main(con, show_account: bool):
with con.cursor() as csr:
csr.execute('SELECT CURRENT_USER()')
print("Hello " + csr.fetchone()[0])
if show_account:
csr.execute("SELECT CURRENT_ACCOUNT()")
print("You are connected to account: " + csr.fetchone()[0])
@with_connection_args("Sample application that greets the current Snowflake user")
def getargs(parser):
parser.add_argument("-a", "--show-account", action='store_true', help="show snowflake account name")
if __name__ == '__main__':
main(**vars(getargs()))
get_token()
Function sfconn.get_token() returns a JWT token for connections that use private_key_path option. An optional lifetime value can be specified (default 54 minutes)
Example:
from sfconn import get_token
jwt_token = get_token(None, 120) # get token using default (None) connection, and valid for 120 minutes
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