Snowflake Plugin for Flyte
Run Snowflake SQL queries as Flyte tasks with parameterized inputs, key-pair authentication, batch inserts, and DataFrame support.
Installation
pip install flyteplugins-snowflake
Quick start
from flyteplugins.snowflake import Snowflake, SnowflakeConfig
import flyte
config = SnowflakeConfig(
account="myorg-myaccount",
user="flyte_user",
database="ANALYTICS",
schema="PUBLIC",
warehouse="COMPUTE_WH",
)
query = Snowflake(
name="count_users",
query_template="SELECT COUNT(*) FROM users",
plugin_config=config,
snowflake_private_key="snowflake-pk",
)
Authentication
The plugin supports Snowflake key-pair authentication. Pass secret keys via snowflake_private_key (and optionally snowflake_private_key_passphrase).
task = Snowflake(
name="my_task",
query_template="SELECT 1",
plugin_config=config,
snowflake_private_key="private-key",
snowflake_private_key_passphrase="passphrase",
# Generates env vars: PRIVATE_KEY, PASSPHRASE
)
For other auth methods (password, OAuth, etc.), pass them via connection_kwargs:
config = SnowflakeConfig(
account="myorg-myaccount",
user="flyte_user",
database="ANALYTICS",
schema="PUBLIC",
warehouse="COMPUTE_WH",
connection_kwargs={"password": "...", "role": "ADMIN"},
)
Parameterized queries
Use %(name)s placeholders and typed inputs:
lookup = Snowflake(
name="lookup_user",
query_template="SELECT * FROM users WHERE id = %(user_id)s",
plugin_config=config,
inputs={"user_id": int},
output_dataframe_type=pd.DataFrame,
snowflake_private_key="snowflake-pk",
)
Batch inserts
Set batch=True to expand list inputs into multi-row VALUES clauses:
insert_rows = Snowflake(
name="insert_users",
query_template="INSERT INTO users (id, name, age) VALUES (%(id)s, %(name)s, %(age)s)",
plugin_config=config,
inputs={"id": list[int], "name": list[str], "age": list[int]},
snowflake_private_key="snowflake-pk",
batch=True,
)
# Calling with id=[1,2], name=["Alice","Bob"], age=[30,25] expands to:
# INSERT INTO users (id, name, age) VALUES (%(id_0)s, %(name_0)s, %(age_0)s), (%(id_1)s, %(name_1)s, %(age_1)s)
Reading results as DataFrames
Set output_dataframe_type to get query results as a pandas DataFrame:
import pandas as pd
select_task = Snowflake(
name="get_users",
query_template="SELECT * FROM users",
plugin_config=config,
output_dataframe_type=pd.DataFrame,
snowflake_private_key="snowflake-pk",
)
Full example
import pandas as pd
from flyteplugins.snowflake import Snowflake, SnowflakeConfig
import flyte
config = SnowflakeConfig(
user="KEVIN",
account="PWGJLTH-XKB21544",
database="FLYTE",
schema="PUBLIC",
warehouse="COMPUTE_WH",
)
insert_task = Snowflake(
name="insert_rows",
inputs={"id": list[int], "name": list[str], "age": list[int]},
plugin_config=config,
query_template="INSERT INTO FLYTE.PUBLIC.TEST (ID, NAME, AGE) VALUES (%(id)s, %(name)s, %(age)s)",
snowflake_private_key="snowflake",
batch=True,
)
select_task = Snowflake(
name="select_all",
output_dataframe_type=pd.DataFrame,
plugin_config=config,
query_template="SELECT * FROM FLYTE.PUBLIC.TEST",
snowflake_private_key="snowflake",
)
snowflake_env = flyte.TaskEnvironment.from_task("snowflake_env", insert_task, select_task)
env = flyte.TaskEnvironment(
name="example_env",
image=flyte.Image.from_debian_base().with_pip_packages("flyteplugins-snowflake"),
secrets=[flyte.Secret(key="snowflake", as_env_var="SNOWFLAKE_PRIVATE_KEY")],
depends_on=[snowflake_env],
)
@env.task
async def main(ids: list[int], names: list[str], ages: list[int]) -> float:
await insert_task(id=ids, name=names, age=ages)
df = await select_task()
return df["AGE"].mean().item()
if __name__ == "__main__":
flyte.init_from_config()
run = flyte.with_runcontext(mode="remote").run(
main, ids=[123, 456], names=["Kevin", "Alice"], ages=[30, 25],
)
print(run.url)
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