Local testing framework¶

Prerequisites¶

To use the local testing framework:

• You must use version 1.18.0 or later of the Snowpark Python library with the optional dependency localtest.

• The supported versions of Python are:

  • 3.9

  • 3.10

  • 3.11

  • 3.12

Create a session and enable local testing¶

1. Create a Snowpark Session and set the local testing configuration to True:

   from snowflake.snowpark import Session
   
   session = Session.builder.config('local_testing', True).create()
   

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2. Use the session to create and operate on DataFrames:

   df = session.create_dataframe([[1,2],[3,4]],['a','b'])
   df.with_column('c', df['a']+df['b']).show()
   

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Loading data¶

You can create Snowpark DataFrames from Python primitives, files, and pandas DataFrames. This is useful for specifying the input and expected output of test cases. With this method, the data is in source control, which makes it easier to keep the test data in sync with the test cases.

col1,col2,col3,col4
1,a,true,1.23
2,b,false,4.56

from snowflake.snowpark.types import StructType, StructField, IntegerType, BooleanType, StringType, DoubleType


# Put file onto stage
session.file.put("data.csv", "@mystage", auto_compress=False)
schema = StructType(
    [
        StructField("col1", IntegerType()),
        StructField("col2", StringType()),
        StructField("col3", BooleanType()),
        StructField("col4", DoubleType()),
    ]
)

# with option SKIP_HEADER set to 1, the header will be skipped when the csv file is loaded
dataframe = session.read.schema(schema).option("SKIP_HEADER", 1).csv("@mystage/data.csv")
dataframe.show()

-------------------------------------
|"COL1"  |"COL2"  |"COL3"  |"COL4"  |
-------------------------------------
|1       |a       |True    |1.23    |
|2       |b       |False   |4.56    |
-------------------------------------

import pandas as pd

pandas_df = pd.DataFrame(
    data={
        "col1": pd.Series(["value1", "value2"]),
        "col2": pd.Series([1.23, 4.56]),
        "col3": pd.Series([123, 456]),
        "col4": pd.Series([True, False]),
    }
)

dataframe = session.create_dataframe(data=pandas_df)
dataframe.show()

-------------------------------------
|"col1"  |"col2"  |"col3"  |"col4"  |
-------------------------------------
|value1  |1.23    |123     |True    |
|value2  |4.56    |456     |False   |
-------------------------------------

from snowflake.snowpark.types import StructType, StructField, StringType, DoubleType, LongType, BooleanType

dataframe = session.create_dataframe(
    data=[
        ["value1", 1.23, 123, True],
        ["value2", 4.56, 456, False],
    ],
    schema=StructType([
        StructField("col1", StringType()),
        StructField("col2", DoubleType()),
        StructField("col3", LongType()),
        StructField("col4", BooleanType()),
    ])
)

pandas_dataframe = dataframe.to_pandas()
print(pandas_dataframe.to_string())

    COL1  COL2  COL3   COL4
0  value1  1.23   123   True
1  value2  4.56   456  False

Create a PyTest Fixture for a session¶

PyTest fixtures are functions that are executed before a test (or module of tests), typically to provide data or connections to tests. In this procedure, you create a fixture that returns a Snowpark Session object.

1. If you do not already have a test directory, create one.

2. In the test directory, create a file named conftest.py with the following contents, where connection_parameters is a dictionary with your Snowflake account credentials:

   # test/conftest.py
   import pytest
   from snowflake.snowpark.session import Session
   
   def pytest_addoption(parser):
       parser.addoption("--snowflake-session", action="store", default="live")
   
   @pytest.fixture(scope='module')
   def session(request) -> Session:
       if request.config.getoption('--snowflake-session') == 'local':
           return Session.builder.config('local_testing', True).create()
       else:
           return Session.builder.configs(CONNECTION_PARAMETERS).create()
   

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For more information about the dictionary format, see Creating a Session.

The call to pytest_addoption adds a command line option named snowflake-session to the pytest command. The Session fixture checks this command line option and creates a local or live Session, depending on its value. This lets you easily switch between local and live modes for testing, as shown in the following command line examples:

# Using local mode:
pytest --snowflake-session local

# Using live mode
pytest

SQL operations¶

Session.sql(...) is not supported in the local testing framework. Use Snowpark’s DataFrame APIs whenever possible, and in cases where you must use Session.sql(...), you can mock the tabular return value by using Python’s unittest.mock.patch to patch the expected response from a given Session.sql() call.

In the following example, mock_sql() maps the SQL query text to the desired DataFrame response. The conditional statement checks whether the current session is using local testing, and if so, applies the patch to the Session.sql() method.

from unittest import mock
from functools import partial

def test_something(pytestconfig, session):

    def mock_sql(session, sql_string):  # patch for SQL operations
        if sql_string == "select 1,2,3":
            return session.create_dataframe([[1,2,3]])
        else:
            raise RuntimeError(f"Unexpected query execution: {sql_string}")

    if pytestconfig.getoption('--snowflake-session') == 'local':
        with mock.patch.object(session, 'sql', wraps=partial(mock_sql, session)): # apply patch for SQL operations
            assert session.sql("select 1,2,3").collect() == [Row(1,2,3)]
    else:
        assert session.sql("select 1,2,3").collect() == [Row(1,2,3)]

When local testing is enabled, all tables created by DataFrame.save_as_table() are saved as temporary tables in memory and can be retrieved using Session.table(). You can use the supported DataFrame operations on the table as usual.

Patching built-in functions¶

Some of the built-in functions under snowflake.snowpark.functions are not supported in the local testing framework. If you use a function that is not supported, you can use the @patch decorator from snowflake.snowpark.mock to create a patch.

For the patched function to be defined and implemented, the signature (parameter list) must align with the built-in function’s parameters. The local testing framework passes parameters to the patched function using the following rules:

• For parameters of type ColumnOrName in the signature of built-in functions, ColumnEmulator is passed as the parameter of the patched functions. ColumnEmulator is similar to a pandas.Series object that contains the column data.

• For parameters of type LiteralType in the signature of built-in functions, the literal value is passed as the parameter of the patched functions.

• Otherwise, the raw value is passed as the parameter of the patched functions.

As for the returning type of the patched functions, returning an instance of ColumnEmulator is expected in correspondence with the returning type of Column of built-in functions.

For example, the built-in function to_timestamp() could be patched like this:

import datetime
from snowflake.snowpark.mock import patch, ColumnEmulator, ColumnType
from snowflake.snowpark.functions import to_timestamp
from snowflake.snowpark.types import TimestampType

@patch(to_timestamp)
def mock_to_timestamp(column: ColumnEmulator, format = None) -> ColumnEmulator:
    ret_column = ColumnEmulator(data=[datetime.datetime.strptime(row, '%Y-%m-%dT%H:%M:%S%z') for row in column])
    ret_column.sf_type = ColumnType(TimestampType(), True)
    return ret_column

Skipping test cases¶

If your PyTest test suite contains a test case that is not well supported by local testing, you can skip those cases by using PyTest’s mark.skipif decorator. The following example assumes that you configured your session and parameters as described earlier. The condition checks whether the local_testing_mode is set to local; if so, the test case is skipped with an explanatory message.

import pytest

@pytest.mark.skipif(
    condition="config.getvalue('local_testing_mode') == 'local'",
reason="Test case disabled for local testing"
)
def test_case(session):
    ...

Registering UDFs and stored procedures¶

You can create and call user-defined functions (UDFs) and stored procedures in the local testing framework. To create the objects, you can use the following syntax options:

Example

The following code example creates a UDF and stored procedure using the decorators, and then calls both by name:

from snowflake.snowpark.session import Session
from snowflake.snowpark.dataframe import col, DataFrame
from snowflake.snowpark.functions import udf, sproc, call_udf
from snowflake.snowpark.types import IntegerType, StringType

# Create local session
session = Session.builder.config('local_testing', True).create()

# Create local table
table = 'example'
session.create_dataframe([[1,2],[3,4]],['a','b']).write.save_as_table(table)

# Register a UDF, which is called from the stored procedure
@udf(name='example_udf', return_type=IntegerType(), input_types=[IntegerType(), IntegerType()])
def example_udf(a, b):
    return a + b

# Register stored procedure
@sproc(name='example_proc', return_type=IntegerType(), input_types=[StringType()])
def example_proc(session, table_name):
    return session.table(table_name)\
        .with_column('c', call_udf('example_udf', col('a'), col('b')))\
        .count()

# Call the stored procedure by name
output = session.call('example_proc', table)

print(output)

Limitations¶

The following list contains the known limitations and behavior gaps in the local testing framework. Snowflake currently has no plans to address these items.

• Raw SQL strings and operations that require parsing SQL strings, such as session.sql and DataFrame.filter("col1 > 12"), are not supported.

• Asynchronous operations are not supported.

• Database objects such as tables, stored procedures, and UDFs are not persisted beyond the session level, and all operations are performed in memory. For example, permanent stored procedures registered in one mock session are not visible to other mock sessions.

• String collation related features, such as Column.collate, are not supported.

• Variant, Array, and Object data types are only supported with standard JSON encoding and decoding. Expressions like [1,2,,3,] are considered valid JSON in Snowflake but not in local testing, where Python’s built-in JSON functionalities are used. You can specify the module-level variables snowflake.snowpark.mock.CUSTOM_JSON_ENCODER and snowflake.snowpark.mock.CUSTOM_JSON_DECODER to override the default settings.

• Only a subset of Snowflake’s functions (including window functions) are implemented. To learn how to inject your own function definition, see Patching built-in functions.

  • Patching rank-related functions is currently not supported.

• SQL format models are not supported. For example, the mock implementation of to_decimal does not handle the optional parameter format.

• The Snowpark Python library does not have a built-in Python API to create or drop stages, so the local testing framework assumes that every incoming stage has already been created.

• The current implementation of UDFs and stored procedures does not perform any package validation. All packages referenced in your code need to be installed before the program is executed.

• Query tags are not supported.

• Query history is not supported.

• Lineage is not supported.

• When a UDF or stored procedure is registered, optional parameters such as parallel, execute_as, statement_params, source_code_display, external_access_integrations, secrets, and comment are ignored.

• For Table.sample, SYSTEM or BLOCK sampling is the same as ROW sampling.

• Snowflake does not officially support running the local testing framework inside stored procedures. Sessions of local testing mode inside stored procedures might encounter or trigger unexpected errors.

Unsupported features¶

The following is a list of features that are currently not implemented in the local testing framework. Snowflake is actively working to address these items.

In general, any reference to these functionalities should raise a NotImplementedError:

• UDTFs (user-defined table functions)

• UDAFs (user-defined aggregate functions)

• Vectorized UDFs and UDTFs

• Built-in table functions

• Table stored procedures

• Geometry, Geography, and Vector data types

• Interval expressions

• Read file formats other than JSON and CSV

  • For a supported file format, not all read options are supported. For example, infer_schema is not supported for the CSV format.

For any features not listed here as unsupported or as a known limitation, check the latest list of feature requests for local testing, or create a feature request in the snowpark-python GitHub repository.

Known issues¶

The following is a list of known issues or behavior gaps that exist in the local testing framework. Snowflake is actively planning to address these issues.

• Using window functions inside DataFrame.groupby or other aggregation operations is not supported.

  # Selecting window function expressions is supported
  df.select("key", "value", sum_("value").over(), avg("value").over())
  
  # Aggregating window function expressions is NOT supported
  df.group_by("key").agg([sum_("value"), sum_(sum_("value")).over(window) - sum_("value")])
  

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• Selecting columns with the same name will only return one column. As a workaround, use Column.alias to rename the columns to have distinct names.

  df.select(lit(1), lit(1)).show() # col("a"), col("a")
  #---------
  #|"'1'"  |
  #---------
  #|1      |
  #|...    |
  #---------
  
  # Workaround: Column.alias
  DataFrame.select(lit(1).alias("col1_1"), lit(1).alias("col1_2"))
  # "col1_1", "col1_2"
  

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• For Table.merge and Table.update, the session parameters ERROR_ON_NONDETERMINISTIC_UPDATE and ERROR_ON_NONDETERMINISTIC_MERGE must be set to False. This means that for multi-joins, one of the matched rows is updated.

• Fully qualified stage names in GET and PUT file operations are not supported. Database and schema names are treated as part of the stage name.

• The mock_to_char implementation only supports timestamps in a format that has separators between different time parts.

• DataFrame.pivot has a parameter called values that allows a pivot to be limited to specific values. Only statistically defined values can be used at this time. Values that are provided using a subquery will raise an error.

• Creating a DataFrame from a pandas DataFrame that contains a timestamp with timezone information is not supported.

For any issues not mentioned in this list, check the latest list of open issues, or create a bug report in the snowpark-python GitHub repository.