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superset2/tests/unit_tests/commands/databases/csv_reader_test.py
Beto Dealmeida a82e310600 feat: improve perf of CSV uploads (#34603)
2025-08-20 08:53:02 -04:00

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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import io
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from werkzeug.datastructures import FileStorage
from superset.commands.database.exceptions import DatabaseUploadFailed
from superset.commands.database.uploaders.csv_reader import CSVReader, CSVReaderOptions
from tests.unit_tests.fixtures.common import create_csv_file
CSV_DATA = [
["Name", "Age", "City", "Birth"],
["name1", "30", "city1", "1990-02-01"],
["name2", "25", "city2", "1995-02-01"],
["name3", "20", "city3", "2000-02-01"],
]
CSV_DATA_CHANGED_HEADER = [
["name1", "30", "city1", "1990-02-01"],
["Name", "Age", "City", "Birth"],
["name2", "25", "city2", "1995-02-01"],
["name3", "20", "city3", "2000-02-01"],
]
CSV_DATA_WITH_NULLS = [
["Name", "Age", "City", "Birth"],
["name1", "N/A", "city1", "1990-02-01"],
["name2", "25", "None", "1995-02-01"],
["name3", "20", "city3", "2000-02-01"],
]
CSV_DATA_DAY_FIRST = [
["Name", "Age", "City", "Birth"],
["name1", "30", "city1", "01-02-1990"],
]
CSV_DATA_DECIMAL_CHAR = [
["Name", "Age", "City", "Birth"],
["name1", "30,1", "city1", "1990-02-01"],
]
CSV_DATA_SKIP_INITIAL_SPACE = [
[" Name", "Age", "City", "Birth"],
[" name1", "30", "city1", "1990-02-01"],
]
@pytest.mark.parametrize(
"file, options, expected_cols, expected_values",
[
(
create_csv_file(CSV_DATA),
CSVReaderOptions(),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", "1990-02-01"],
["name2", 25, "city2", "1995-02-01"],
["name3", 20, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA, delimiter="|"),
CSVReaderOptions(delimiter="|"),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", "1990-02-01"],
["name2", 25, "city2", "1995-02-01"],
["name3", 20, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
columns_read=["Name", "Age"],
),
["Name", "Age"],
[
["name1", 30],
["name2", 25],
["name3", 20],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
columns_read=["Name", "Age"],
column_data_types={"Age": "float"},
),
["Name", "Age"],
[
["name1", 30.0],
["name2", 25.0],
["name3", 20.0],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
columns_read=[],
),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", "1990-02-01"],
["name2", 25, "city2", "1995-02-01"],
["name3", 20, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
columns_read=[],
column_data_types={"Age": "float"},
),
["Name", "Age", "City", "Birth"],
[
["name1", 30.0, "city1", "1990-02-01"],
["name2", 25.0, "city2", "1995-02-01"],
["name3", 20.0, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
rows_to_read=1,
),
["Name", "Age", "City", "Birth"],
[
["name1", 30.0, "city1", "1990-02-01"],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
rows_to_read=1,
columns_read=["Name", "Age"],
),
["Name", "Age"],
[
["name1", 30.0],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
skip_rows=1,
),
["name1", "30", "city1", "1990-02-01"],
[
["name2", 25.0, "city2", "1995-02-01"],
["name3", 20.0, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA),
CSVReaderOptions(
column_dates=["Birth"],
),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", datetime(1990, 2, 1, 0, 0)],
["name2", 25, "city2", datetime(1995, 2, 1, 0, 0)],
["name3", 20, "city3", datetime(2000, 2, 1, 0, 0)],
],
),
(
create_csv_file(CSV_DATA_CHANGED_HEADER),
CSVReaderOptions(
header_row=1,
),
["Name", "Age", "City", "Birth"],
[
["name2", 25, "city2", "1995-02-01"],
["name3", 20, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA_WITH_NULLS),
CSVReaderOptions(
null_values=["N/A", "None"],
),
["Name", "Age", "City", "Birth"],
[
["name1", np.nan, "city1", "1990-02-01"],
["name2", 25.0, np.nan, "1995-02-01"],
["name3", 20.0, "city3", "2000-02-01"],
],
),
(
create_csv_file(CSV_DATA_DAY_FIRST),
CSVReaderOptions(
day_first=False,
column_dates=["Birth"],
),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", datetime(1990, 1, 2, 0, 0)],
],
),
(
create_csv_file(CSV_DATA_DAY_FIRST),
CSVReaderOptions(
day_first=True,
column_dates=["Birth"],
),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", datetime(1990, 2, 1, 0, 0)],
],
),
(
create_csv_file(CSV_DATA_DECIMAL_CHAR),
CSVReaderOptions(
decimal_character=",",
),
["Name", "Age", "City", "Birth"],
[
["name1", 30.1, "city1", "1990-02-01"],
],
),
(
create_csv_file(CSV_DATA_SKIP_INITIAL_SPACE),
CSVReaderOptions(
skip_initial_space=True,
),
["Name", "Age", "City", "Birth"],
[
["name1", 30, "city1", "1990-02-01"],
],
),
],
)
def test_csv_reader_file_to_dataframe(file, options, expected_cols, expected_values):
csv_reader = CSVReader(
options=options,
)
df = csv_reader.file_to_dataframe(file)
assert df.columns.tolist() == expected_cols
actual_values = df.values.tolist()
for i in range(len(expected_values)):
for j in range(len(expected_values[i])):
expected_val = expected_values[i][j]
actual_val = actual_values[i][j]
# Check if both values are NaN
if isinstance(expected_val, float) and isinstance(actual_val, float):
assert np.isnan(expected_val) == np.isnan(actual_val)
else:
assert expected_val == actual_val
file.close()
def test_csv_reader_index_column():
csv_reader = CSVReader(
options=CSVReaderOptions(index_column="Name"),
)
df = csv_reader.file_to_dataframe(create_csv_file(CSV_DATA))
assert df.index.name == "Name"
def test_csv_reader_wrong_index_column():
csv_reader = CSVReader(
options=CSVReaderOptions(index_column="wrong"),
)
with pytest.raises(DatabaseUploadFailed) as ex:
csv_reader.file_to_dataframe(create_csv_file(CSV_DATA))
assert str(ex.value) == "Parsing error: Index wrong invalid"
def test_csv_reader_broken_file_no_columns():
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
with pytest.raises(DatabaseUploadFailed) as ex:
csv_reader.file_to_dataframe(create_csv_file([""]))
assert str(ex.value) == "Parsing error: No columns to parse from file"
def test_csv_reader_wrong_columns_to_read():
csv_reader = CSVReader(
options=CSVReaderOptions(columns_read=["xpto"]),
)
with pytest.raises(DatabaseUploadFailed) as ex:
csv_reader.file_to_dataframe(create_csv_file(CSV_DATA))
assert str(ex.value) == (
"Parsing error: Usecols do not match columns, "
"columns expected but not found: ['xpto']"
)
def test_csv_reader_invalid_file():
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
with pytest.raises(DatabaseUploadFailed) as ex:
csv_reader.file_to_dataframe(
FileStorage(
io.StringIO("c1,c2,c3\na,b,c\n1,2,3,4,5,6,7\n1,2,3"), filename=""
)
)
assert str(ex.value) == (
"Parsing error: Error tokenizing data. C error:"
" Expected 3 fields in line 3, saw 7\n"
)
def test_csv_reader_invalid_encoding():
"""Test that encoding detection automatically handles problematic encoding."""
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
binary_data = b"col1,col2,col3\nv1,v2,\xba\nv3,v4,v5\n"
# The new encoding detection should automatically handle this
df = csv_reader.file_to_dataframe(FileStorage(io.BytesIO(binary_data)))
assert df.columns.tolist() == ["col1", "col2", "col3"]
assert len(df) == 2 # Should have 2 data rows
def test_csv_reader_encoding_detection_latin1():
"""Test automatic encoding detection for Latin-1 encoded files."""
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
# Create a Latin-1 encoded file with special characters
binary_data = "col1,col2,col3\nCafé,Résumé,naïve\n".encode("latin-1")
df = csv_reader.file_to_dataframe(FileStorage(io.BytesIO(binary_data)))
assert df.columns.tolist() == ["col1", "col2", "col3"]
assert df.values.tolist() == [["Café", "Résumé", "naïve"]]
def test_csv_reader_encoding_detection_iso88591():
"""Test automatic encoding detection for ISO-8859-1 encoded files."""
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
# Create an ISO-8859-1 encoded file with special characters
binary_data = "col1,col2\nCafé,naïve\n".encode("iso-8859-1")
df = csv_reader.file_to_dataframe(FileStorage(io.BytesIO(binary_data)))
assert df.columns.tolist() == ["col1", "col2"]
assert df.values.tolist() == [["Café", "naïve"]]
def test_csv_reader_explicit_encoding():
"""Test that explicit encoding is respected."""
csv_reader = CSVReader(
options=CSVReaderOptions(encoding="latin-1"),
)
# Create a Latin-1 encoded file
binary_data = "col1,col2\nCafé,naïve\n".encode("latin-1")
df = csv_reader.file_to_dataframe(FileStorage(io.BytesIO(binary_data)))
assert df.columns.tolist() == ["col1", "col2"]
assert df.values.tolist() == [["Café", "naïve"]]
def test_csv_reader_encoding_detection_failure():
"""Test that undecodable files raise appropriate error."""
csv_reader = CSVReader(
options=CSVReaderOptions(encoding="ascii"), # Force ASCII encoding
)
# Create data that can't be decoded as ASCII
binary_data = b"col1,col2\n\xff\xfe,test\n"
with pytest.raises(DatabaseUploadFailed) as ex:
csv_reader.file_to_dataframe(FileStorage(io.BytesIO(binary_data)))
assert "Parsing error" in str(ex.value)
def test_csv_reader_file_metadata():
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
file = create_csv_file(CSV_DATA)
metadata = csv_reader.file_metadata(file)
assert metadata == {
"items": [
{"column_names": ["Name", "Age", "City", "Birth"], "sheet_name": None}
]
}
file.close()
file = create_csv_file(CSV_DATA, delimiter="|")
csv_reader = CSVReader(
options=CSVReaderOptions(delimiter="|"),
)
metadata = csv_reader.file_metadata(file)
assert metadata == {
"items": [
{"column_names": ["Name", "Age", "City", "Birth"], "sheet_name": None}
]
}
file.close()
def test_csv_reader_file_metadata_invalid_file():
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
with pytest.raises(DatabaseUploadFailed) as ex:
csv_reader.file_metadata(
FileStorage(io.StringIO("c1,c2,c3\na,b,c\n1,2,3,4,5,6,7\n1,2,3"))
)
assert str(ex.value) == (
"Parsing error: Error tokenizing data. C error:"
" Expected 3 fields in line 3, saw 7\n"
)
def test_csv_reader_chunking_large_file():
"""Test that chunking is used for large files."""
# Create a large CSV with more than 100k rows
large_data = [["col1", "col2", "col3"]]
for i in range(100001):
large_data.append([f"val{i}", str(i), f"data{i}"])
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
df = csv_reader.file_to_dataframe(create_csv_file(large_data))
assert len(df) == 100001
assert df.columns.tolist() == ["col1", "col2", "col3"]
assert df.iloc[0].tolist() == ["val0", 0, "data0"]
assert df.iloc[-1].tolist() == ["val100000", 100000, "data100000"]
def test_csv_reader_chunking_with_rows_limit():
"""Test that chunking respects rows_to_read limit."""
# Create a CSV with more than the chunk size
large_data = [["col1", "col2"]]
for i in range(60000): # More than chunk size of 50000
large_data.append([f"val{i}", str(i)])
csv_reader = CSVReader(
options=CSVReaderOptions(rows_to_read=55000),
)
df = csv_reader.file_to_dataframe(create_csv_file(large_data))
assert len(df) == 55000
assert df.columns.tolist() == ["col1", "col2"]
def test_csv_reader_no_chunking_small_file():
"""Test that chunking is not used for small files."""
# Create a small CSV (less than 2 * chunk size)
small_data = [["col1", "col2"]]
for i in range(1000): # Much less than chunk size
small_data.append([f"val{i}", str(i)])
csv_reader = CSVReader(
options=CSVReaderOptions(rows_to_read=1000),
)
df = csv_reader.file_to_dataframe(create_csv_file(small_data))
assert len(df) == 1000
assert df.columns.tolist() == ["col1", "col2"]
def test_csv_reader_engine_selection():
"""Test engine selection based on feature flag."""
from unittest.mock import MagicMock, patch
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
# Test 1: Feature flag disabled (default) - should use c engine
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
with patch(
"superset.commands.database.uploaders.csv_reader.is_feature_enabled"
) as mock_flag:
mock_flag.return_value = False
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(return_value=pd.DataFrame({"col1": [1, 2, 3]}))
mock_pd.DataFrame = pd.DataFrame
file = create_csv_file([["col1"], ["1"], ["2"], ["3"]])
csv_reader.file_to_dataframe(file)
# Check that c engine is selected when feature flag is disabled
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("engine") == "c"
# Test 2: Feature flag enabled - pyarrow would be used but chunking prevents it
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
with patch(
"superset.commands.database.uploaders.csv_reader.is_feature_enabled"
) as mock_flag:
with patch("importlib.util") as mock_util:
mock_flag.return_value = True
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(
return_value=pd.DataFrame({"col1": [1, 2, 3]})
)
mock_pd.DataFrame = pd.DataFrame
mock_pd.concat = MagicMock(
return_value=pd.DataFrame({"col1": [1, 2, 3]})
)
mock_util.find_spec = MagicMock(return_value=True)
file = create_csv_file([["col1"], ["1"], ["2"], ["3"]])
csv_reader.file_to_dataframe(file)
# Check that c engine is selected due to chunking (default behavior)
# Even with feature flag enabled, chunking prevents pyarrow usage
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("engine") == "c"
# Test 3: Feature flag enabled but unsupported options - should use c engine
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
with patch(
"superset.commands.database.uploaders.csv_reader.is_feature_enabled"
) as mock_flag:
mock_flag.return_value = True
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(return_value=pd.DataFrame({"col1": [1, 2, 3]}))
mock_pd.DataFrame = pd.DataFrame
# Create reader with date parsing (unsupported by pyarrow)
csv_reader_with_dates = CSVReader(
options=CSVReaderOptions(column_dates=["date_col"]),
)
file = create_csv_file([["date_col"], ["2023-01-01"]])
csv_reader_with_dates.file_to_dataframe(file)
# Check that c engine is selected due to unsupported options
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("engine") == "c"
def test_csv_reader_low_memory_setting():
"""Test that low_memory is set to False."""
from unittest.mock import MagicMock, patch
csv_reader = CSVReader(
options=CSVReaderOptions(),
)
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(return_value=pd.DataFrame({"col1": [1, 2, 3]}))
mock_pd.DataFrame = pd.DataFrame
file = create_csv_file([["col1"], ["1"], ["2"], ["3"]])
csv_reader.file_to_dataframe(file)
# Check that low_memory=False was set
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("low_memory") is False
def test_csv_reader_cache_dates_setting():
"""Test that cache_dates is set to True for performance."""
from unittest.mock import MagicMock, patch
csv_reader = CSVReader(
options=CSVReaderOptions(column_dates=["date_col"]),
)
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(
return_value=pd.DataFrame({"date_col": ["2023-01-01"]})
)
mock_pd.DataFrame = pd.DataFrame
file = create_csv_file([["date_col"], ["2023-01-01"]])
csv_reader.file_to_dataframe(file)
# Check that cache_dates=True was set
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("cache_dates") is True
def test_csv_reader_pyarrow_feature_flag():
"""
Test that the CSV_UPLOAD_PYARROW_ENGINE feature flag controls engine selection.
"""
import io
from unittest.mock import MagicMock, patch
from werkzeug.datastructures import FileStorage
# Test _read_csv directly to avoid the file_to_dataframe chunking logic
with patch(
"superset.commands.database.uploaders.csv_reader.is_feature_enabled"
) as mock_flag:
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
with patch.object(
CSVReader, "_select_optimal_engine"
) as mock_engine_select:
# Test 1: FF enabled, pyarrow available, no unsupported options
mock_flag.return_value = True
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(return_value=pd.DataFrame({"col1": [1]}))
mock_engine_select.return_value = "pyarrow"
# Create clean kwargs without any problematic options
clean_kwargs = {
"encoding": "utf-8",
"low_memory": False,
# No chunksize, iterator, nrows, parse_dates, or na_values
}
file = FileStorage(io.StringIO("col1\nval1"))
CSVReader._read_csv(file, clean_kwargs)
# Verify feature flag was checked
mock_flag.assert_called_with("CSV_UPLOAD_PYARROW_ENGINE")
# Verify engine selection method was called
mock_engine_select.assert_called_once()
# Verify pyarrow engine was selected
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("engine") == "pyarrow"
# Test 2: Feature flag disabled
with patch(
"superset.commands.database.uploaders.csv_reader.is_feature_enabled"
) as mock_flag:
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
mock_flag.return_value = False
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(return_value=pd.DataFrame({"col1": [1]}))
clean_kwargs = {
"encoding": "utf-8",
"low_memory": False,
}
file = FileStorage(io.StringIO("col1\nval1"))
CSVReader._read_csv(file, clean_kwargs)
# Verify feature flag was checked
mock_flag.assert_called_with("CSV_UPLOAD_PYARROW_ENGINE")
# Verify c engine was selected when flag is disabled
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("engine") == "c"
# Test 3: Feature flag enabled but unsupported options present
with patch(
"superset.commands.database.uploaders.csv_reader.is_feature_enabled"
) as mock_flag:
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
mock_flag.return_value = True
mock_pd.__version__ = "2.0.0"
mock_pd.read_csv = MagicMock(return_value=pd.DataFrame({"col1": [1]}))
# Include unsupported options
unsupported_kwargs = {
"encoding": "utf-8",
"low_memory": False,
"nrows": 100, # Unsupported by pyarrow
}
file = FileStorage(io.StringIO("col1\nval1"))
CSVReader._read_csv(file, unsupported_kwargs)
# Verify c engine was selected due to unsupported options
call_kwargs = mock_pd.read_csv.call_args[1]
assert call_kwargs.get("engine") == "c"
def test_csv_reader_select_optimal_engine():
"""Test the _select_optimal_engine method with different scenarios."""
from unittest.mock import MagicMock, patch
# Test 1: PyArrow available, no built-in support
with patch("superset.commands.database.uploaders.csv_reader.util") as mock_util:
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
with patch("superset.commands.database.uploaders.csv_reader.logger"):
mock_util.find_spec = MagicMock(
return_value=MagicMock()
) # PyArrow found
mock_pd.__version__ = "2.0.0" # No pyarrow in version
# Mock successful pyarrow import
with patch.dict("sys.modules", {"pyarrow": MagicMock()}):
result = CSVReader._select_optimal_engine()
assert result == "pyarrow"
# Test 2: PyArrow not available
with patch("superset.commands.database.uploaders.csv_reader.util") as mock_util:
with patch("superset.commands.database.uploaders.csv_reader.logger"):
mock_util.find_spec = MagicMock(return_value=None) # PyArrow not found
result = CSVReader._select_optimal_engine()
assert result == "c"
# Test 3: Pandas with built-in pyarrow
with patch("superset.commands.database.uploaders.csv_reader.util") as mock_util:
with patch("superset.commands.database.uploaders.csv_reader.pd") as mock_pd:
with patch("superset.commands.database.uploaders.csv_reader.logger"):
mock_util.find_spec = MagicMock(
return_value=MagicMock()
) # PyArrow found
mock_pd.__version__ = "2.0.0+pyarrow" # Has pyarrow in version
# Mock successful pyarrow import
with patch.dict("sys.modules", {"pyarrow": MagicMock()}):
result = CSVReader._select_optimal_engine()
assert result == "c"
# Test 4: PyArrow import fails
with patch("superset.commands.database.uploaders.csv_reader.util") as mock_util:
with patch("superset.commands.database.uploaders.csv_reader.logger"):
mock_util.find_spec = MagicMock(return_value=MagicMock()) # PyArrow found
# Mock import error
with patch(
"builtins.__import__", side_effect=ImportError("PyArrow import failed")
):
result = CSVReader._select_optimal_engine()
assert result == "c"
def test_csv_reader_progressive_encoding_detection():
"""Test that progressive encoding detection uses multiple sample sizes."""
import io
from werkzeug.datastructures import FileStorage
# Create a file with latin-1 encoding that will require detection
content = "col1,col2,col3\n" + "café,résumé,naïve\n"
binary_data = content.encode("latin-1")
file = FileStorage(io.BytesIO(binary_data))
# Track read calls to verify progressive sampling
original_read = file.read
read_calls = []
read_sizes = []
def track_read(size):
read_calls.append(size)
read_sizes.append(size)
file.seek(0) # Reset position for consistent reading
result = original_read(size)
file.seek(0) # Reset again
return result
file.read = track_read
# Call encoding detection
detected_encoding = CSVReader._detect_encoding(file)
# Should detect the correct encoding
assert detected_encoding in [
"latin-1",
"utf-8",
], f"Should detect valid encoding, got {detected_encoding}"
# Should have made multiple read attempts with different sizes
# (The method tries multiple sample sizes until it finds a working encoding)
assert len(read_calls) >= 1, f"Should have made read calls, got {read_calls}"
# Test that the method handles the sample sizes properly
assert all(size > 0 for size in read_sizes), "All sample sizes should be positive"
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