import configparser

import os

import random

import shutil

import string

from logging import getLogger

from pathlib import Path

from tempfile import TemporaryDirectory

from typing import Any, Dict, Iterator, List, Optional, Tuple, cast

import pandas as pd

import pyarrow

from cryptography.hazmat.backends import default_backend

from cryptography.hazmat.primitives import serialization

from tenacity import (

retry,

retry_if_exception_type,

stop_after_attempt,

wait_exponential,

)

import feast

from feast.errors import SnowflakeIncompleteConfig, SnowflakeQueryUnknownError

from feast.feature_view import FeatureView

from feast.repo_config import RepoConfig

try:

import snowflake.connector

from snowflake.connector import ProgrammingError, SnowflakeConnection

from snowflake.connector.cursor import SnowflakeCursor

except ImportError as e:

from feast.errors import FeastExtrasDependencyImportError

raise FeastExtrasDependencyImportError("snowflake", str(e))

getLogger("snowflake.connector.cursor").disabled = True

getLogger("snowflake.connector.connection").disabled = True

getLogger("snowflake.connector.network").disabled = True

logger = getLogger(__name__)

_cache = {}

class GetSnowflakeConnection:

def __init__(self, config: Any, autocommit=True):

self.config = config

self.autocommit = autocommit

def __enter__(self):

assert self.config.type in [

"snowflake.registry",

"snowflake.offline",

"snowflake.engine",

"snowflake.online",

]

if self.config.type not in _cache:

if self.config.type == "snowflake.registry":

config_header = "connections.feast_registry"

elif self.config.type == "snowflake.offline":

config_header = "connections.feast_offline_store"

if self.config.type == "snowflake.engine":

config_header = "connections.feast_batch_engine"

elif self.config.type == "snowflake.online":

config_header = "connections.feast_online_store"

config_dict = dict(self.config)

# read config file

config_reader = configparser.ConfigParser()

config_reader.read([config_dict["config_path"]])

kwargs: Dict[str, Any] = {}

if config_reader.has_section(config_header):

kwargs = dict(config_reader[config_header])

kwargs.update((k, v) for k, v in config_dict.items() if v is not None)

for k, v in kwargs.items():

if k in ["role", "warehouse", "database", "schema_"]:

kwargs[k] = f'"{v}"'

kwargs["schema"] = kwargs.pop("schema_")

# https://docs.snowflake.com/en/user-guide/python-connector-example.html#using-key-pair-authentication-key-pair-rotation

# https://docs.snowflake.com/en/user-guide/key-pair-auth.html#configuring-key-pair-authentication

if "private_key" in kwargs or "private_key_content" in kwargs:

kwargs["private_key"] = parse_private_key_path(

kwargs.get("private_key_passphrase"),

kwargs.get("private_key"),

kwargs.get("private_key_content"),

)

try:

_cache[self.config.type] = snowflake.connector.connect(

application="feast",

client_session_keep_alive=True,

autocommit=self.autocommit,

**kwargs,

)

_cache[self.config.type].cursor().execute(

"ALTER SESSION SET TIMEZONE = 'UTC'", _is_internal=True

)

except KeyError as e:

raise SnowflakeIncompleteConfig(e)

self.client = _cache[self.config.type]

return self.client

def __exit__(self, exc_type, exc_val, exc_tb):

pass

def assert_snowflake_feature_names(feature_view: FeatureView) -> None:

for feature in feature_view.features:

assert feature.name not in [

"entity_key",

"feature_name",

"feature_value",

], f"Feature Name: {feature.name} is a protected name to ensure query stability"

return None

def execute_snowflake_statement(conn: SnowflakeConnection, query) -> SnowflakeCursor:

cursor = conn.cursor().execute(query)

if cursor is None:

raise SnowflakeQueryUnknownError(query)

return cursor

def get_snowflake_online_store_path(

config: RepoConfig,

feature_view: FeatureView,

) -> str:

path_tag = "snowflake-online-store/online_path"

if path_tag in feature_view.tags:

online_path = feature_view.tags[path_tag]

else:

online_path = (

f'"{config.online_store.database}"."{config.online_store.schema_}"'

)

return online_path

def package_snowpark_zip(project_name) -> Tuple[str, str]:

path = os.path.dirname(feast.__file__)

copy_path = path + f"/snowflake_feast_{project_name}"

if os.path.exists(copy_path):

shutil.rmtree(copy_path)

copy_files = [

"/infra/utils/snowflake/snowpark/snowflake_udfs.py",

"/infra/key_encoding_utils.py",

"/type_map.py",

"/value_type.py",

"/protos/feast/types/Value_pb2.py",

"/protos/feast/types/EntityKey_pb2.py",

]

package_path = copy_path + "/feast"

for feast_file in copy_files:

idx = feast_file.rfind("/")

if idx > -1:

Path(package_path + feast_file[:idx]).mkdir(parents=True, exist_ok=True)

feast_file = shutil.copy(path + feast_file, package_path + feast_file[:idx])

else:

feast_file = shutil.copy(path + feast_file, package_path + feast_file)

zip_path = shutil.make_archive(package_path, "zip", copy_path)

return copy_path, zip_path

def _run_snowflake_field_mapping(snowflake_job_sql: str, field_mapping: dict) -> str:

snowflake_mapped_sql = snowflake_job_sql

for key in field_mapping.keys():

snowflake_mapped_sql = snowflake_mapped_sql.replace(

f'"{key}"', f'"{key}" AS "{field_mapping[key]}"', 1

)

return snowflake_mapped_sql

# TO DO -- sfc-gh-madkins

# Remove dependency on write_pandas function by falling back to native snowflake python connector

# Current issue is datetime[ns] types are read incorrectly in Snowflake, need to coerce to datetime[ns, UTC]

def write_pandas(

conn: SnowflakeConnection,

df: pd.DataFrame,

table_name: str,

database: Optional[str] = None,

schema: Optional[str] = None,

chunk_size: Optional[int] = None,

compression: str = "gzip",

on_error: str = "abort_statement",

parallel: int = 4,

quote_identifiers: bool = True,

auto_create_table: bool = False,

create_temp_table: bool = False,

):

"""Allows users to most efficiently write back a pandas DataFrame to Snowflake.

It works by dumping the DataFrame into Parquet files, uploading them and finally copying their data into the table.

Returns whether all files were ingested correctly, number of chunks uploaded, and number of rows ingested

with all of the COPY INTO command's output for debugging purposes.

Example usage:

import pandas

from snowflake.connector.pandas_tools import write_pandas

df = pandas.DataFrame([('Mark', 10), ('Luke', 20)], columns=['name', 'balance'])

success, nchunks, nrows, _ = write_pandas(cnx, df, 'customers')

Args:

conn: Connection to be used to communicate with Snowflake.

df: Dataframe we'd like to write back.

table_name: Table name where we want to insert into.

database: Database table is in, if not provided the connection one will be used.

schema: Schema table is in, if not provided the connection one will be used.

chunk_size: Number of elements to be inserted once, if not provided all elements will be dumped once

(Default value = None).

compression: The compression used on the Parquet files, can only be gzip, or snappy. Gzip gives supposedly a

better compression, while snappy is faster. Use whichever is more appropriate (Default value = 'gzip').

on_error: Action to take when COPY INTO statements fail, default follows documentation at:

https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html#copy-options-copyoptions

(Default value = 'abort_statement').

parallel: Number of threads to be used when uploading chunks, default follows documentation at:

https://docs.snowflake.com/en/sql-reference/sql/put.html#optional-parameters (Default value = 4).

quote_identifiers: By default, identifiers, specifically database, schema, table and column names

(from df.columns) will be quoted. If set to False, identifiers are passed on to Snowflake without quoting.

I.e. identifiers will be coerced to uppercase by Snowflake. (Default value = True)

auto_create_table: When true, will automatically create a table with corresponding columns for each column in

the passed in DataFrame. The table will not be created if it already exists

create_temp_table: Will make the auto-created table as a temporary table

"""

cursor: SnowflakeCursor = conn.cursor()

stage_name = create_temporary_sfc_stage(cursor)

upload_df(df, cursor, stage_name, chunk_size, parallel, compression)

copy_uploaded_data_to_table(

cursor,

stage_name,

list(df.columns),

table_name,

database,

schema,

compression,

on_error,

quote_identifiers,

auto_create_table,

create_temp_table,

)

def write_parquet(

conn: SnowflakeConnection,

path: Path,

dataset_schema: pyarrow.Schema,

table_name: str,

database: Optional[str] = None,

schema: Optional[str] = None,

compression: str = "gzip",

on_error: str = "abort_statement",

parallel: int = 4,

quote_identifiers: bool = True,

auto_create_table: bool = False,

create_temp_table: bool = False,

):

cursor: SnowflakeCursor = conn.cursor()

stage_name = create_temporary_sfc_stage(cursor)

columns = [field.name for field in dataset_schema]

upload_local_pq(path, cursor, stage_name, parallel)

copy_uploaded_data_to_table(

cursor,

stage_name,

columns,

table_name,

database,

schema,

compression,

on_error,

quote_identifiers,

auto_create_table,

create_temp_table,

)

def copy_uploaded_data_to_table(

cursor: SnowflakeCursor,

stage_name: str,

columns: List[str],

table_name: str,

database: Optional[str] = None,

schema: Optional[str] = None,

compression: str = "gzip",

on_error: str = "abort_statement",

quote_identifiers: bool = True,

auto_create_table: bool = False,

create_temp_table: bool = False,

):

if database is not None and schema is None:

raise ProgrammingError(

"Schema has to be provided to write_pandas when a database is provided"

)

# This dictionary maps the compression algorithm to Snowflake put copy into command type

# https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html#type-parquet

compression_map = {"gzip": "auto", "snappy": "snappy"}

if compression not in compression_map.keys():

raise ProgrammingError(

"Invalid compression '{}', only acceptable values are: {}".format(

compression, compression_map.keys()

)

)

if quote_identifiers:

location = (

(('"' + database + '".') if database else "")

+ (('"' + schema + '".') if schema else "")

+ ('"' + table_name + '"')

)

else:

location = (

(database + "." if database else "")

+ (schema + "." if schema else "")

+ (table_name)

)

if quote_identifiers:

quoted_columns = '"' + '","'.join(columns) + '"'

else:

quoted_columns = ",".join(columns)

if auto_create_table:

file_format_name = create_file_format(compression, compression_map, cursor)

infer_schema_sql = f"SELECT COLUMN_NAME, TYPE FROM table(infer_schema(location=>'@\"{stage_name}\"', file_format=>'{file_format_name}'))"

logger.debug(f"inferring schema with '{infer_schema_sql}'")

result_cursor = cursor.execute(infer_schema_sql, _is_internal=True)

if result_cursor is None:

raise SnowflakeQueryUnknownError(infer_schema_sql)

result = cast(List[Tuple[str, str]], result_cursor.fetchall())

column_type_mapping: Dict[str, str] = dict(result)

# Infer schema can return the columns out of order depending on the chunking we do when uploading

# so we have to iterate through the dataframe columns to make sure we create the table with its

# columns in order

quote = '"' if quote_identifiers else ""

create_table_columns = ", ".join(

[f"{quote}{c}{quote} {column_type_mapping[c]}" for c in columns]

)

create_table_sql = (

f"CREATE {'TEMP ' if create_temp_table else ''}TABLE IF NOT EXISTS {location} "

f"({create_table_columns})"

f" /* Python:snowflake.connector.pandas_tools.write_pandas() */ "

)

logger.debug(f"auto creating table with '{create_table_sql}'")

cursor.execute(create_table_sql, _is_internal=True)

drop_file_format_sql = f"DROP FILE FORMAT IF EXISTS {file_format_name}"

logger.debug(f"dropping file format with '{drop_file_format_sql}'")

cursor.execute(drop_file_format_sql, _is_internal=True)

# in Snowflake, all parquet data is stored in a single column, $1, so we must select columns explicitly

# see (https://docs.snowflake.com/en/user-guide/script-data-load-transform-parquet.html)

if quote_identifiers:

parquet_columns = "$1:" + ",$1:".join(f'"{c}"' for c in columns)

else:

parquet_columns = "$1:" + ",$1:".join(columns)

copy_into_sql = (

"COPY INTO {location} /* Python:snowflake.connector.pandas_tools.write_pandas() */ "

"({columns}) "

'FROM (SELECT {parquet_columns} FROM @"{stage_name}") '

"FILE_FORMAT=(TYPE=PARQUET COMPRESSION={compression}) "

"PURGE=TRUE ON_ERROR={on_error}"

).format(

location=location,

columns=quoted_columns,

parquet_columns=parquet_columns,

stage_name=stage_name,

compression=compression_map[compression],

on_error=on_error,

)

logger.debug("copying into with '{}'".format(copy_into_sql))

# Snowflake returns the original cursor if the query execution succeeded.

result_cursor = cursor.execute(copy_into_sql, _is_internal=True)

if result_cursor is None:

raise SnowflakeQueryUnknownError(copy_into_sql)

result_cursor.close()

def upload_df(

df: pd.DataFrame,

cursor: SnowflakeCursor,

stage_name: str,

chunk_size: Optional[int] = None,

parallel: int = 4,

compression: str = "gzip",

):

"""

Args:

df: Dataframe we'd like to write back.

cursor: cursor to be used to communicate with Snowflake.

stage_name: stage name in Snowflake connection.

chunk_size: Number of elements to be inserted once, if not provided all elements will be dumped once

(Default value = None).

parallel: Number of threads to be used when uploading chunks, default follows documentation at:

https://docs.snowflake.com/en/sql-reference/sql/put.html#optional-parameters (Default value = 4).

compression: The compression used on the Parquet files, can only be gzip, or snappy. Gzip gives supposedly a

better compression, while snappy is faster. Use whichever is more appropriate (Default value = 'gzip').

"""

if chunk_size is None:

chunk_size = len(df)

with TemporaryDirectory() as tmp_folder:

for i, chunk in chunk_helper(df, chunk_size):

chunk_path = os.path.join(tmp_folder, "file{}.txt".format(i))

# Dump chunk into parquet file

chunk.to_parquet(

chunk_path,

compression=compression,

use_deprecated_int96_timestamps=True,

)

# Upload parquet file

upload_sql = (

"PUT /* Python:feast.infra.utils.snowflake_utils.upload_df() */ "

"'file://{path}' @\"{stage_name}\" PARALLEL={parallel}"

).format(

path=chunk_path.replace("\\", "\\\\").replace("'", "\\'"),

stage_name=stage_name,

parallel=parallel,

)

logger.debug(f"uploading files with '{upload_sql}'")

cursor.execute(upload_sql, _is_internal=True)

# Remove chunk file

os.remove(chunk_path)

def upload_local_pq(

path: Path,

cursor: SnowflakeCursor,

stage_name: str,

parallel: int = 4,

):

"""

Args:

path: Path to parquet dataset on disk

cursor: cursor to be used to communicate with Snowflake.

stage_name: stage name in Snowflake connection.

parallel: Number of threads to be used when uploading chunks, default follows documentation at:

https://docs.snowflake.com/en/sql-reference/sql/put.html#optional-parameters (Default value = 4).

"""

for file in path.iterdir():

upload_sql = (

"PUT /* Python:feast.infra.utils.snowflake_utils.upload_local_pq() */ "

"'file://{path}' @\"{stage_name}\" PARALLEL={parallel}"

).format(

path=str(file).replace("\\", "\\\\").replace("'", "\\'"),

stage_name=stage_name,

parallel=parallel,

)

logger.debug(f"uploading files with '{upload_sql}'")

cursor.execute(upload_sql, _is_internal=True)

@retry(

wait=wait_exponential(multiplier=1, max=4),

retry=retry_if_exception_type(ProgrammingError),

stop=stop_after_attempt(5),

reraise=True,

)

def create_file_format(

compression: str, compression_map: Dict[str, str], cursor: SnowflakeCursor

) -> str:

file_format_name = (

'"' + "".join(random.choice(string.ascii_lowercase) for _ in range(5)) + '"'

)

file_format_sql = (

f"CREATE FILE FORMAT {file_format_name} "

f"/* Python:snowflake.connector.pandas_tools.write_pandas() */ "

f"TYPE=PARQUET COMPRESSION={compression_map[compression]}"

)

logger.debug(f"creating file format with '{file_format_sql}'")

cursor.execute(file_format_sql, _is_internal=True)

return file_format_name

@retry(

wait=wait_exponential(multiplier=1, max=4),

retry=retry_if_exception_type(ProgrammingError),

stop=stop_after_attempt(5),

reraise=True,

)

def create_temporary_sfc_stage(cursor: SnowflakeCursor) -> str:

stage_name = "".join(random.choice(string.ascii_lowercase) for _ in range(5))

create_stage_sql = (

"create temporary stage /* Python:snowflake.connector.pandas_tools.write_pandas() */ "

'"{stage_name}"'

).format(stage_name=stage_name)

logger.debug(f"creating stage with '{create_stage_sql}'")

result_cursor = cursor.execute(create_stage_sql, _is_internal=True)

if result_cursor is None:

raise SnowflakeQueryUnknownError(create_stage_sql)

result_cursor.fetchall()

return stage_name

def chunk_helper(lst: pd.DataFrame, n: int) -> Iterator[Tuple[int, pd.DataFrame]]:

"""Helper generator to chunk a sequence efficiently with current index like if enumerate was called on sequence."""

for i in range(0, len(lst), n):

yield int(i / n), lst[i : i + n]

def parse_private_key_path(

private_key_passphrase: str,

key_path: Optional[str] = None,

private_key_content: Optional[bytes] = None,

) -> bytes:

"""Returns snowflake pkb by parsing and reading either from key path or private_key_content as byte string."""

if private_key_content:

p_key = serialization.load_pem_private_key(

private_key_content,

password=private_key_passphrase.encode(),

backend=default_backend(),

)

elif key_path:

with open(key_path, "rb") as key:

p_key = serialization.load_pem_private_key(

key.read(),

password=private_key_passphrase.encode(),

backend=default_backend(),

)

else:

raise ValueError("Please provide key_path or private_key_content.")

pkb = p_key.private_bytes(

encoding=serialization.Encoding.DER,

format=serialization.PrivateFormat.PKCS8,

encryption_algorithm=serialization.NoEncryption(),

)

return pkb

def write_pandas_binary(

conn: SnowflakeConnection,

df: pd.DataFrame,

table_name: str,

database: Optional[str] = None,

schema: Optional[str] = None,

chunk_size: Optional[int] = None,

compression: str = "gzip",

on_error: str = "abort_statement",

parallel: int = 4,

quote_identifiers: bool = True,

auto_create_table: bool = False,

create_temp_table: bool = False,

):

"""Allows users to most efficiently write back a pandas DataFrame to Snowflake.

It works by dumping the DataFrame into Parquet files, uploading them and finally copying their data into the table.

Returns whether all files were ingested correctly, number of chunks uploaded, and number of rows ingested

with all of the COPY INTO command's output for debugging purposes.

Example usage:

import pandas

from snowflake.connector.pandas_tools import write_pandas

df = pandas.DataFrame([('Mark', 10), ('Luke', 20)], columns=['name', 'balance'])

success, nchunks, nrows, _ = write_pandas(cnx, df, 'customers')

Args:

conn: Connection to be used to communicate with Snowflake.

df: Dataframe we'd like to write back.

table_name: Table name where we want to insert into.

database: Database table is in, if not provided the connection one will be used.

schema: Schema table is in, if not provided the connection one will be used.

chunk_size: Number of elements to be inserted once, if not provided all elements will be dumped once

(Default value = None).

compression: The compression used on the Parquet files, can only be gzip, or snappy. Gzip gives supposedly a

better compression, while snappy is faster. Use whichever is more appropriate (Default value = 'gzip').

on_error: Action to take when COPY INTO statements fail, default follows documentation at:

https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html#copy-options-copyoptions

(Default value = 'abort_statement').

parallel: Number of threads to be used when uploading chunks, default follows documentation at:

https://docs.snowflake.com/en/sql-reference/sql/put.html#optional-parameters (Default value = 4).

quote_identifiers: By default, identifiers, specifically database, schema, table and column names

(from df.columns) will be quoted. If set to False, identifiers are passed on to Snowflake without quoting.

I.e. identifiers will be coerced to uppercase by Snowflake. (Default value = True)

auto_create_table: When true, will automatically create a table with corresponding columns for each column in

the passed in DataFrame. The table will not be created if it already exists

create_temp_table: Will make the auto-created table as a temporary table

"""

if database is not None and schema is None:

raise ProgrammingError(

"Schema has to be provided to write_pandas when a database is provided"

)

# This dictionary maps the compression algorithm to Snowflake put copy into command type

# https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html#type-parquet

compression_map = {"gzip": "auto", "snappy": "snappy"}

if compression not in compression_map.keys():

raise ProgrammingError(

"Invalid compression '{}', only acceptable values are: {}".format(

compression, compression_map.keys()

)

)

if quote_identifiers:

location = (

(('"' + database + '".') if database else "")

+ (('"' + schema + '".') if schema else "")

+ ('"' + table_name + '"')

)

else:

location = (

(database + "." if database else "")

+ (schema + "." if schema else "")

+ (table_name)

)

if chunk_size is None:

chunk_size = len(df)

cursor: SnowflakeCursor = conn.cursor()

stage_name = create_temporary_sfc_stage(cursor)

with TemporaryDirectory() as tmp_folder:

for i, chunk in chunk_helper(df, chunk_size):

chunk_path = os.path.join(tmp_folder, "file{}.txt".format(i))

# Dump chunk into parquet file

chunk.to_parquet(

chunk_path,

compression=compression,

use_deprecated_int96_timestamps=True,

)

# Upload parquet file

upload_sql = (

"PUT /* Python:snowflake.connector.pandas_tools.write_pandas() */ "

"'file://{path}' @\"{stage_name}\" PARALLEL={parallel}"

).format(

path=chunk_path.replace("\\", "\\\\").replace("'", "\\'"),

stage_name=stage_name,

parallel=parallel,

)

logger.debug(f"uploading files with '{upload_sql}'")

cursor.execute(upload_sql, _is_internal=True)

# Remove chunk file

os.remove(chunk_path)

if quote_identifiers:

columns = '"' + '","'.join(list(df.columns)) + '"'

else:

columns = ",".join(list(df.columns))

if auto_create_table:

file_format_name = create_file_format(compression, compression_map, cursor)

infer_schema_sql = f"SELECT COLUMN_NAME, TYPE FROM table(infer_schema(location=>'@\"{stage_name}\"', file_format=>'{file_format_name}'))"

logger.debug(f"inferring schema with '{infer_schema_sql}'")

result_cursor = cursor.execute(infer_schema_sql, _is_internal=True)

if result_cursor is None:

raise SnowflakeQueryUnknownError(infer_schema_sql)

result = cast(List[Tuple[str, str]], result_cursor.fetchall())

column_type_mapping: Dict[str, str] = dict(result)

# Infer schema can return the columns out of order depending on the chunking we do when uploading

# so we have to iterate through the dataframe columns to make sure we create the table with its

# columns in order

quote = '"' if quote_identifiers else ""

create_table_columns = ", ".join(

[f"{quote}{c}{quote} {column_type_mapping[c]}" for c in df.columns]

)

create_table_sql = (

f"CREATE {'TEMP ' if create_temp_table else ''}TABLE IF NOT EXISTS {location} "

f"({create_table_columns})"

f" /* Python:snowflake.connector.pandas_tools.write_pandas() */ "

)

logger.debug(f"auto creating table with '{create_table_sql}'")

cursor.execute(create_table_sql, _is_internal=True)

drop_file_format_sql = f"DROP FILE FORMAT IF EXISTS {file_format_name}"

logger.debug(f"dropping file format with '{drop_file_format_sql}'")

cursor.execute(drop_file_format_sql, _is_internal=True)

# in Snowflake, all parquet data is stored in a single column, $1, so we must select columns explicitly

# see (https://docs.snowflake.com/en/user-guide/script-data-load-transform-parquet.html)

if quote_identifiers:

parquet_columns = ",".join(

f'TO_BINARY($1:"{c}")'

if c in ["entity_feature_key", "entity_key", "value"]

else f'$1:"{c}"'

for c in df.columns

)

else:

parquet_columns = ",".join(

f"TO_BINARY($1:{c})"

if c in ["entity_feature_key", "entity_key", "value"]

else f"$1:{c}"

for c in df.columns

)

copy_into_sql = (

"COPY INTO {location} /* Python:snowflake.connector.pandas_tools.write_pandas() */ "

"({columns}) "

'FROM (SELECT {parquet_columns} FROM @"{stage_name}") '

"FILE_FORMAT=(TYPE=PARQUET COMPRESSION={compression} BINARY_AS_TEXT = FALSE) "

"PURGE=TRUE ON_ERROR={on_error}"

).format(

location=location,

columns=columns,

parquet_columns=parquet_columns,

stage_name=stage_name,

compression=compression_map[compression],

on_error=on_error,

)

logger.debug("copying into with '{}'".format(copy_into_sql))

# Snowflake returns the original cursor if the query execution succeeded.

result_cursor = cursor.execute(copy_into_sql, _is_internal=True)

if result_cursor is None:

raise SnowflakeQueryUnknownError(copy_into_sql)

result_cursor.close()