Snowflake SQLAlchemy runs on the top of the Snowflake Connector for Python as a dialect to bridge a Snowflake database and SQLAlchemy applications.
The only requirement for Snowflake SQLAlchemy is the Snowflake Connector for Python; however, the connector does not need to be installed because installing Snowflake SQLAlchemy automatically installs the connector.
Snowflake SQLAlchemy can be used with Pandas, Jupyter and Pyramid, which provide higher levels of application frameworks for data analytics and web applications. However, building a working environment from scratch is not a trivial task, particularly for novice users. Installing the frameworks requires C compilers and tools, and choosing the right tools and versions is a hurdle that might deter users from using Python applications.
An easier way to build an environment is through Anaconda, which provides a complete, precompiled technology stack for all users, including non-Python experts such as data analysts and students. For Anaconda installation instructions, see the Anaconda install documentation. The Snowflake SQLAlchemy package can then be installed on top of Anaconda using pip.
The Snowflake SQLAlchemy package can be installed from the public PyPI repository using pip:
pip install --upgrade snowflake-sqlalchemypip automatically installs all required modules, including the Snowflake Connector for Python.
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Create a file (e.g.
validate.py) that contains the following Python sample code, which connects to Snowflake and displays the Snowflake version:#!/usr/bin/env python from sqlalchemy import create_engine engine = create_engine( 'snowflake://{user}:{password}@{account}/'.format( user='<your_user_login_name>', password='<your_password>', account='<your_account_name>', ) ) try: connection = engine.connect() results = connection.execute('select current_version()').fetchone() print(results[0]) finally: connection.close() engine.dispose()
-
Replace
<your_user_login_name>,<your_password>, and<your_account_name>with the appropriate values for your Snowflake account and user.For more details, see Connection Parameters.
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Execute the sample code. For example, if you created a file named
validate.py:python validate.py
The Snowflake version (e.g.
1.48.0) should be displayed.
As much as possible, Snowflake SQLAlchemy provides compatible functionality for SQLAlchemy applications. For information on using SQLAlchemy, see the SQLAlchemy documentation.
However, Snowflake SQLAlchemy also provides Snowflake-specific parameters and behavior, which are described in the following sections.
Snowflake SQLAlchemy uses the following syntax for the connection string used to connect to Snowflake and initiate a session:
'snowflake://<user_login_name>:<password>@<account_name>'Where:
<user_login_name>is the login name for your Snowflake user.<password>is the password for your Snowflake user.<account_name>is the name of your Snowflake account.
Include the region in the <account_name> if applicable, more info is available here.
You can optionally specify the initial database and schema for the Snowflake session by including them at the end of the connection string, separated by /. You can also specify the initial warehouse and role for the session as a parameter string at the end of the connection string:
'snowflake://<user_login_name>:<password>@<account_name>/<database_name>/<schema_name>?warehouse=<warehouse_name>&role=<role_name>'Note: After login, the initial database, schema, warehouse and role specified in the connection string can always be changed for the session.
The following example calls the create_engine method with the user name testuser1, password 0123456, account name abc123, database testdb, schema public, warehouse testwh, and role myrole:
from sqlalchemy import create_engine
engine = create_engine(
'snowflake://testuser1:0123456@abc123/testdb/public?warehouse=testwh&role=myrole'
)Other parameters, such as timezone, can also be specified as a URI parameter or in connect_args parameters. For example:
from sqlalchemy import create_engine
engine = create_engine(
'snowflake://testuser1:0123456@abc123/testdb/public?warehouse=testwh&role=myrole',
connect_args={
'timezone': 'America/Los_Angeles',
}
)For convenience, you can use the snowflake.sqlalchemy.URL method to construct the connection string and connect to the database. The following example constructs the same connection string from the previous example:
from snowflake.sqlalchemy import URL
from sqlalchemy import create_engine
engine = create_engine(URL(
account = 'abc123',
user = 'testuser1',
password = '0123456',
database = 'testdb',
schema = 'public',
warehouse = 'testwh',
role='myrole',
timezone = 'America/Los_Angeles',
))Use the supported environment variables, HTTPS_PROXY, HTTP_PROXY and NO_PROXY to configure a proxy server.
Open a connection by executing engine.connect(); avoid using engine.execute(). Make certain to close the connection by executing connection.close() before
engine.dispose(); otherwise, the Python Garbage collector removes the resources required to communicate with Snowflake, preventing the Python connector from closing the session properly.
# Avoid this.
engine = create_engine(...)
engine.execute(<SQL>)
engine.dispose()
# Do this.
engine = create_engine(...)
connection = engine.connect()
try:
connection.execute(<SQL>)
finally:
connection.close()
engine.dispose()Auto-incrementing a value requires the Sequence object. Include the Sequence object in the primary key column to automatically increment the value as each new record is inserted. For example:
t = Table('mytable', metadata,
Column('id', Integer, Sequence('id_seq'), primary_key=True),
Column(...), ...
)Snowflake stores all case-insensitive object names in uppercase text. In contrast, SQLAlchemy considers all lowercase object names to be case-insensitive. Snowflake SQLAlchemy converts the object name case during schema-level communication, i.e. during table and index reflection. If you use uppercase object names, SQLAlchemy assumes they are case-sensitive and encloses the names with quotes. This behavior will cause mismatches agaisnt data dictionary data received from Snowflake, so unless identifier names have been truly created as case sensitive using quotes, e.g., "TestDb", all lowercase names should be used on the SQLAlchemy side.
Snowflake does not utilize indexes, so neither does Snowflake SQLAlchemy.
Snowflake SQLAlchemy supports binding and fetching NumPy data types. Binding is always supported. To enable fetching NumPy data types, add numpy=True to the connection parameters.
The following example shows the round trip of numpy.datetime64 data:
import numpy as np
import pandas as pd
engine = create_engine(URL(
account = 'abc123',
user = 'testuser1',
password = 'pass',
database = 'db',
schema = 'public',
warehouse = 'testwh',
role='myrole',
numpy=True,
))
specific_date = np.datetime64('2016-03-04T12:03:05.123456789Z')
connection = engine.connect()
connection.execute(
"CREATE OR REPLACE TABLE ts_tbl(c1 TIMESTAMP_NTZ)")
connection.execute(
"INSERT INTO ts_tbl(c1) values(%s)", (specific_date,)
)
df = pd.read_sql_query("SELECT * FROM ts_tbl", engine)
assert df.c1.values[0] == specific_dateThe following NumPy data types are supported:
- numpy.int64
- numpy.float64
- numpy.datatime64
SQLAlchemy provides the runtime inspection API to get the runtime information about the various objects. One of the common use case is get all tables and their column metadata in a schema in order to construct a schema catalog. For example, alembic on top of SQLAlchemy manages database schema migrations. A pseudo code flow is as follows:
inspector = inspect(engine)
schema = inspector.default_schema_name
for table_name in inspector.get_table_names(schema):
column_metadata = inspector.get_columns(table_name, schema)
primary_keys = inspector.get_pk_constraint(table_name, schema)
foreign_keys = inspector.get_foreign_keys(table_name, schema)
...In this flow, a potential problem is it may take quite a while as queries run on each table. The results are cached but getting column metadata is expensive.
To mitigate the problem, Snowflake SQLAlchemy takes a flag cache_column_metadata=True such that all of column metadata for all tables are cached when get_table_names is called and the rest of get_columns, get_primary_keys and get_foreign_keys can take advantage of the cache.
engine = create_engine(URL(
account = 'abc123',
user = 'testuser1',
password = 'pass',
database = 'db',
schema = 'public',
warehouse = 'testwh',
role='myrole',
cache_column_metadata=True,
))Note that this flag has been deprecated, as our caching now uses the built-in SQLAlchemy reflection cache, the flag has been removed, but caching has been improved and if possible extra data will be fetched and cached.
Snowflake SQLAlchemy supports fetching VARIANT, ARRAY and OBJECT data types. All types are converted into str in Python so that you can convert them to native data types using json.loads.
This example shows how to create a table including VARIANT, ARRAY, and OBJECT data type columns.
from snowflake.sqlalchemy import (VARIANT, ARRAY, OBJECT)
t = Table('my_semi_strucutred_datatype_table', metadata,
Column('va', VARIANT),
Column('ob', OBJECT),
Column('ar', ARRAY))
metdata.create_all(engine)In order to retrieve VARIANT, ARRAY, and OBJECT data type columns and convert them to the native Python data types, fetch data and call the json.loads method as follows:
import json
connection = engine.connect()
results = connection.execute(select([t])
row = results.fetchone()
data_variant = json.loads(row[0])
data_object = json.loads(row[1])
data_array = json.loads(row[2])Snowflake SQLAchemy supports the CLUSTER BY parameter for tables. For information about the parameter, see :doc:/sql-reference/sql/create-table.
This example shows how to create a table with two columns, id and name, as the clustering keys:
t = Table('myuser', metadata,
Column('id', Integer, primary_key=True),
Column('name', String),
snowflake_clusterby=['id', 'name'], ...
)
metadata.create_all(engine)Alembic is a database migration tool on top of SQLAlchemy. Snowflake SQLAlchemy works by adding the following code to alembic/env.py so that Alembic can recognize Snowflake SQLAlchemy.
from alembic.ddl.impl import DefaultImpl
class SnowflakeImpl(DefaultImpl):
__dialect__ = 'snowflake'See Alembic Documentation for general usage.
Snowflake SQLAlchemy supports key pair authentication by leveraging its Snowflake Connector for Python underpinnings. See Using Key Pair Authentication for steps to create the private and public keys.
The private key parameter is passed through connect_args as follows:
...
from snowflake.sqlalchemy import URL
from sqlalchemy import create_engine
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives.asymmetric import dsa
from cryptography.hazmat.primitives import serialization
with open("rsa_key.p8", "rb") as key:
p_key= serialization.load_pem_private_key(
key.read(),
password=os.environ['PRIVATE_KEY_PASSPHRASE'].encode(),
backend=default_backend()
)
pkb = p_key.private_bytes(
encoding=serialization.Encoding.DER,
format=serialization.PrivateFormat.PKCS8,
encryption_algorithm=serialization.NoEncryption())
engine = create_engine(URL(
account='abc123',
user='testuser1',
),
connect_args={
'private_key': pkb,
},
)Where PRIVATE_KEY_PASSPHRASE is a passphrase to decrypt the private key file, rsa_key.p8.
Currently a private key parameter is not accepted by the snowflake.sqlalchemy.URL method.
Snowflake SQLAlchemy supports upserting with its MergeInto custom expression.
See Merge for full documentation.
Use it as follows:
from sqlalchemy.orm import sessionmaker
from sqlalchemy import MetaData, create_engine
from snowflake.sqlalchemy import MergeInto
engine = create_engine(db.url, echo=False)
session = sessionmaker(bind=engine)()
connection = engine.connect()
meta = MetaData()
meta.reflect(bind=session.bind)
t1 = meta.tables['t1']
t2 = meta.tables['t2']
merge = MergeInto(target=t1, source=t2, on=t1.c.t1key == t2.c.t2key)
merge.when_matched_then_delete().where(t2.c.marked == 1)
merge.when_matched_then_update().where(t2.c.isnewstatus == 1).values(val = t2.c.newval, status=t2.c.newstatus)
merge.when_matched_then_update().values(val=t2.c.newval)
merge.when_not_matched_then_insert().values(val=t2.c.newval, status=t2.c.newstatus)
connection.execute(merge)Snowflake SQLAlchemy supports saving tables/query results into different stages, as well as into Azure Containers and
AWS buckets with its custom CopyIntoStorage expression. See Copy into
for full documentation.
Use it as follows:
from sqlalchemy.orm import sessionmaker
from sqlalchemy import MetaData, create_engine
from snowflake.sqlalchemy import CopyIntoStorage, AWSBucket, CSVFormatter
engine = create_engine(db.url, echo=False)
session = sessionmaker(bind=engine)()
connection = engine.connect()
meta = MetaData()
meta.reflect(bind=session.bind)
users = meta.tables['users']
copy_into = CopyIntoStorage(from_=users,
into=AWSBucket.from_uri('s3://my_private_backup').encryption_aws_sse_kms('1234abcd-12ab-34cd-56ef-1234567890ab'),
formatter=CSVFormatter().null_if(['null', 'Null']))
connection.execute(copy_into)Feel free to file an issue or submit a PR here for general cases. For official support, contact Snowflake support at: https://community.snowflake.com/s/article/How-To-Submit-a-Support-Case-in-Snowflake-Lodge