# 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_integer_in_float_column(): csv_data = [ ["Name", "Score", "City"], ["name1", 25.5, "city1"], ["name2", 25, "city2"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Score": "float"}) ) df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (2, 3) assert df["Score"].dtype == "float64" def test_csv_reader_object_type_auto_inferring(): # this case below won't raise a error csv_data = [ ["Name", "id", "City"], ["name1", 25.5, "city1"], ["name2", 15, "city2"], ["name3", 123456789086, "city3"], ["name4", "abc", "city4"], ["name5", 4.75, "city5"], ] csv_reader = CSVReader() df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (5, 3) # pandas automatically infers the type if column_data_types is not informed # if there's only one string in the column it converts the whole column to object assert df["id"].dtype == "object" def test_csv_reader_float_type_auto_inferring(): csv_data = [ ["Name", "id", "City"], ["name1", "25", "city1"], ["name2", "15", "city2"], ["name3", "123456789086", "city3"], ["name5", "4.75", "city5"], ] csv_reader = CSVReader() df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (4, 3) # The type here is automatically inferred to float due to 4.75 value assert df["id"].dtype == "float64" def test_csv_reader_int_type_auto_inferring(): csv_data = [ ["Name", "id", "City"], ["name1", "0", "city1"], ["name2", "15", "city2"], ["name3", "123456789086", "city3"], ["name5", "45", "city5"], ] csv_reader = CSVReader() df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (4, 3) assert df["id"].dtype == "int64" def test_csv_reader_bigint_type_auto_inferring(): csv_data = [ ["Name", "id", "City"], ["name1", "9223372036854775807", "city1"], ["name2", "9223372036854775806", "city2"], ["name3", "1234567890123456789", "city3"], ["name4", "0", "city4"], ["name5", "-9223372036854775808", "city5"], ] csv_reader = CSVReader() df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (5, 3) assert df["id"].dtype == "int64" assert df.iloc[0]["id"] == 9223372036854775807 assert df.iloc[4]["id"] == -9223372036854775808 def test_csv_reader_int_typing(): csv_data = [ ["Name", "id", "City"], ["name1", "0", "city1"], ["name2", "15", "city2"], ["name3", "123456789086", "city3"], ["name5", "45", "city5"], ] csv_reader = CSVReader(options=CSVReaderOptions(column_data_types={"id": "int"})) df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (4, 3) assert df["id"].dtype == "int64" def test_csv_reader_float_typing(): csv_data = [ ["Name", "score", "City"], ["name1", "0", "city1"], ["name2", "15.3", "city2"], ["name3", "45", "city3"], ["name5", "23.1342", "city5"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"score": "float"}) ) df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (4, 3) assert df["score"].dtype == "float64" def test_csv_reader_multiple_errors_display(): """Test that multiple errors are displayed with proper formatting.""" csv_data = [ ["Name", "Age", "Score"], ["Alice", "25", "95.5"], ["Bob", "invalid1", "87.2"], ["Charlie", "invalid2", "92.1"], ["Diana", "invalid3", "88.5"], ["Eve", "invalid4", "90.0"], ["Frank", "30", "85.5"], ] csv_reader = CSVReader(options=CSVReaderOptions(column_data_types={"Age": "int64"})) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Age' to int64" in error_msg assert "Found 4 error(s):" in error_msg assert "Line 3: 'invalid1' cannot be converted to int64" in error_msg assert "Line 4: 'invalid2' cannot be converted to int64" in error_msg assert "Line 5: 'invalid3' cannot be converted to int64" in error_msg assert "Line 6: 'invalid4' cannot be converted to int64" in error_msg # With MAX_DISPLAYED_ERRORS = 5, all 4 errors should be shown without truncation assert "and" not in error_msg or "more error(s)" not in error_msg def test_csv_reader_non_numeric_in_integer_column(): csv_data = [ ["Name", "Age", "City"], ["name1", "abc", "city1"], ["name2", "25", "city2"], ] csv_reader = CSVReader(options=CSVReaderOptions(column_data_types={"Age": "int64"})) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Age' to int64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 2: 'abc' cannot be converted to int64" in error_msg def test_csv_reader_non_numeric_in_float_column(): csv_data = [ ["Name", "Score", "City"], ["name1", "5.3", "city1"], ["name2", "25.5", "city2"], ["name3", "24.5", "city3"], ["name4", "1.0", "city4"], ["name5", "one point five", "city5"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Score": "float64"}) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Score' to float64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 6: 'one point five' cannot be converted to float64" in error_msg def test_csv_reader_improved_error_detection_int32(): """Test improved error detection for int32 type casting.""" csv_data = [ ["Name", "ID", "City"], ["name1", "123", "city1"], ["name2", "456", "city2"], ["name3", "not_a_number", "city3"], ["name4", "789", "city4"], ] csv_reader = CSVReader(options=CSVReaderOptions(column_data_types={"ID": "int32"})) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'ID' to int32" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 4: 'not_a_number' cannot be converted to int32" in error_msg def test_csv_reader_improved_error_detection_float32(): """Test improved error detection for float32 type casting.""" csv_data = [ ["Name", "Score", "City"], ["name1", "1.5", "city1"], ["name2", "2.7", "city2"], ["name3", "invalid_float", "city3"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Score": "float32"}) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Score' to float32" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 4: 'invalid_float' cannot be converted to float32" in error_msg def test_csv_reader_error_detection_with_header_row(): """Test that line numbers are correctly calculated with custom header row.""" csv_data = [ ["skip_this_row", "skip", "skip"], ["Name", "Age", "City"], ["name1", "25", "city1"], ["name2", "invalid_age", "city2"], ] csv_reader = CSVReader( options=CSVReaderOptions(header_row=1, column_data_types={"Age": "int"}) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Age' to int" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 4: 'invalid_age' cannot be converted to int" in error_msg def test_csv_reader_error_detection_first_row_error(): """Test error detection when the first data row has the error.""" csv_data = [ ["Name", "Age", "City"], ["name1", "not_a_number", "city1"], ["name2", "25", "city2"], ] csv_reader = CSVReader(options=CSVReaderOptions(column_data_types={"Age": "int64"})) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Age' to int64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 2: 'not_a_number' cannot be converted to int64" in error_msg def test_csv_reader_error_detection_missing_column(): """Test that missing columns are handled gracefully.""" csv_data = [ ["Name", "City"], ["name1", "city1"], ["name2", "city2"], ] # Try to cast a column that doesn't exist csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"NonExistent": "int64"}) ) # Should not raise an error for missing columns df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (2, 2) assert df.columns.tolist() == ["Name", "City"] def test_csv_reader_error_detection_mixed_valid_invalid(): csv_data = [ ["Name", "Score", "City"], ["name1", "95.5", "city1"], ["name2", "87.2", "city2"], ["name3", "92.1", "city3"], ["name4", "eighty-five", "city4"], ["name5", "78.9", "city5"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Score": "float64"}) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Score' to float64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 5: 'eighty-five' cannot be converted to float64" in error_msg def test_csv_reader_error_detection_multiple_invalid_values(): """Test error detection with multiple invalid values showing first 5 + count.""" csv_data = [ ["Name", "Score", "City"], ["name1", "95.5", "city1"], ["name2", "87.2", "city2"], ["name3", "92.1", "city3"], ["name4", "eighty-five", "city4"], ["name4", "eighty-one", "city4"], ["name4", "eighty", "city4"], ["name4", "one", "city4"], ["name4", "two", "city4"], ["name4", "three", "city4"], ["name5", "78.9", "city5"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Score": "float64"}) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Score' to float64" in error_msg assert "Found 6 error(s):" in error_msg assert "Line 5: 'eighty-five' cannot be converted to float64" in error_msg assert "Line 6: 'eighty-one' cannot be converted to float64" in error_msg assert "Line 7: 'eighty' cannot be converted to float64" in error_msg assert "Line 8: 'one' cannot be converted to float64" in error_msg assert "Line 9: 'two' cannot be converted to float64" in error_msg assert "and 1 more error(s)" in error_msg def test_csv_reader_error_detection_non_numeric_types(): """Test error detection for non-numeric type casting.""" csv_data = [ ["Name", "Status", "City"], ["name1", "active", "city1"], ["name2", "inactive", "city2"], ["name3", 123, "city3"], # This should cause an error when casting to string ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Status": "string"}) ) # For non-numeric types, the error detection should still work # but might have different behavior depending on pandas version try: df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) # If no error is raised, the conversion succeeded assert df["Status"].dtype == "string" except DatabaseUploadFailed as ex: # If an error is raised, it should have proper formatting error_msg = str(ex.value) assert "Cannot convert" in error_msg assert "Status" in error_msg def test_csv_reader_error_detection_with_null_values(): csv_data = [ ["Name", "Age", "City"], ["name1", "25", "city1"], ["name2", "", "city2"], ["name3", "invalid_age", "city3"], ] csv_reader = CSVReader(options=CSVReaderOptions(column_data_types={"Age": "int64"})) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data)) error_msg = str(ex.value) assert "Cannot convert column 'Age' to int64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 4: 'invalid_age' cannot be converted to int64" in error_msg def test_csv_reader_successful_numeric_conversion(): csv_data = [ ["Name", "Age", "Score", "ID"], ["name1", "25", "95.5", "1001"], ["name2", "30", "87.2", "1002"], ["name3", "35", "92.1", "1003"], ] csv_reader = CSVReader( options=CSVReaderOptions( column_data_types={ "Age": "int64", "Score": "float64", "ID": "int32", } ) ) df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (3, 4) assert df["Age"].dtype == "int64" assert df["Score"].dtype == "float64" assert df["ID"].dtype == "int32" assert df.iloc[0]["Age"] == 25 assert df.iloc[0]["Score"] == 95.5 assert df.iloc[0]["ID"] == 1001 def test_csv_reader_successful_string_conversion_with_floats(): csv_data = [ ["id"], [1439403621518935563], [42286989], [1413660691875593351], [8.26839e17], ] csv_reader = CSVReader( options=CSVReaderOptions( column_data_types={ "id": "str", } ) ) df = csv_reader.file_to_dataframe(create_csv_file(csv_data)) assert df.shape == (4, 1) assert df["id"].dtype == "object" assert df.iloc[0]["id"] == "1439403621518935563" assert df.iloc[1]["id"] == "42286989" assert df.iloc[2]["id"] == "1413660691875593351" assert df.iloc[3]["id"] == "8.26839e+17" def test_csv_reader_error_detection_improvements_summary(): csv_data_with_custom_header = [ ["metadata_row", "skip", "this"], ["Name", "Age", "Score"], ["Alice", "25", "95.5"], ["Bob", "invalid_age", "87.2"], ["Charlie", "30", "92.1"], ] csv_reader = CSVReader( options=CSVReaderOptions( header_row=1, column_data_types={"Age": "int64", "Score": "float64"} ) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data_with_custom_header)) error_msg = str(ex.value) assert "Cannot convert column 'Age' to int64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 4: 'invalid_age' cannot be converted to int64" in error_msg # Test case 2: Multiple type errors - Age comes first alphabetically csv_data_multiple_errors = [ ["Name", "Age", "Score"], ["Alice", "25", "95.5"], ["Bob", "invalid_age", "invalid_score"], # Error in both columns (line 3) ["Charlie", "30", "92.1"], ] csv_reader = CSVReader( options=CSVReaderOptions(column_data_types={"Age": "int64", "Score": "float64"}) ) with pytest.raises(DatabaseUploadFailed) as ex: csv_reader.file_to_dataframe(create_csv_file(csv_data_multiple_errors)) error_msg = str(ex.value) # Should catch the Age error first (Age comes before Score alphabetically) assert "Cannot convert column 'Age' to int64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 3: 'invalid_age' cannot be converted to int64" in error_msg def test_csv_reader_cast_column_types_function(): """Test the _cast_column_types function directly for better isolation.""" # Create test DataFrame test_data = { "name": ["Alice", "Bob", "Charlie"], "age": ["25", "30", "invalid_age"], "score": ["95.5", "87.2", "92.1"], } df = pd.DataFrame(test_data) # Test successful casting types_success = {"age": "int64", "score": "float64"} kwargs = {"header": 0} # This should work for first two rows, but we'll only test the first two df_subset = df.iloc[:2].copy() result_df = CSVReader._cast_column_types(df_subset, types_success, kwargs) assert result_df["age"].dtype == "int64" assert result_df["score"].dtype == "float64" assert result_df.iloc[0]["age"] == 25 assert result_df.iloc[0]["score"] == 95.5 # Test error case with pytest.raises(DatabaseUploadFailed) as ex: CSVReader._cast_column_types(df, types_success, kwargs) error_msg = str(ex.value) assert "Cannot convert column 'age' to int64" in error_msg assert "Found 1 error(s):" in error_msg assert "Line 4: 'invalid_age' cannot be converted to int64" in error_msg def test_csv_reader_cast_column_types_missing_column(): """Test _cast_column_types with missing columns.""" test_data = { "name": ["Alice", "Bob"], "age": ["25", "30"], } df = pd.DataFrame(test_data) # Try to cast a column that doesn't exist types = {"age": "int64", "nonexistent": "float64"} kwargs = {"header": 0} # Should not raise an error for missing columns result_df = CSVReader._cast_column_types(df, types, kwargs) assert result_df["age"].dtype == "int64" assert "nonexistent" not in result_df.columns def test_csv_reader_cast_column_types_different_numeric_types(): """Test _cast_column_types with various numeric types.""" test_data = { "int32_col": ["1", "2", "3"], "int64_col": ["100", "200", "300"], "float32_col": ["1.5", "2.5", "3.5"], "float64_col": ["10.1", "20.2", "30.3"], } df = pd.DataFrame(test_data) types = { "int32_col": "int32", "int64_col": "int64", "float32_col": "float32", "float64_col": "float64", } kwargs = {"header": 0} result_df = CSVReader._cast_column_types(df, types, kwargs) assert result_df["int32_col"].dtype == "int32" assert result_df["int64_col"].dtype == "int64" assert result_df["float32_col"].dtype == "float32" assert result_df["float64_col"].dtype == "float64" 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" def test_csv_reader_chunk_concatenation_error_logging(): """Test that pd.concat errors during chunking are logged and re-raised.""" from unittest.mock import patch # Create a large CSV that will trigger chunking (>100k rows) large_data = [["col1", "col2"]] for i in range(100001): large_data.append([f"val{i}", str(i)]) csv_reader = CSVReader(options=CSVReaderOptions()) # Mock pd.concat to raise an exception with patch( "superset.commands.database.uploaders.csv_reader.pd.concat" ) as mock_concat: mock_concat.side_effect = ValueError( "Cannot concatenate chunks with different dtypes" ) with pytest.raises(DatabaseUploadFailed) as exc_info: csv_reader.file_to_dataframe(create_csv_file(large_data)) # Verify the exception is still raised (wrapped as DatabaseUploadFailed) assert "Cannot concatenate chunks with different dtypes" in str(exc_info.value) # Verify concat was called (meaning chunking happened) assert mock_concat.called def test_csv_reader_chunk_concatenation_error_warning(caplog): """Test that pd.concat errors during chunking log a warning message.""" from unittest.mock import patch # Create a large CSV that will trigger chunking (>100k rows) large_data = [["col1", "col2"]] for i in range(100001): large_data.append([f"val{i}", str(i)]) csv_reader = CSVReader(options=CSVReaderOptions()) # Mock pd.concat to raise an exception with patch( "superset.commands.database.uploaders.csv_reader.pd.concat" ) as mock_concat: mock_concat.side_effect = ValueError( "Cannot concatenate chunks with different dtypes" ) import logging with caplog.at_level(logging.WARNING): with pytest.raises(DatabaseUploadFailed): csv_reader.file_to_dataframe(create_csv_file(large_data)) # Verify warning was logged assert any( "Error concatenating CSV chunks" in record.message for record in caplog.records ) assert any( "inconsistent date parsing across chunks" in record.message for record in caplog.records )