chore: add query context data tests (#32157)

tests/unit_tests/common/test_query_context_processor.py

@@ -0,0 +1,238 @@
+# 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.
+
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from superset.common.chart_data import ChartDataResultFormat
+from superset.common.query_context_processor import QueryContextProcessor
+from superset.utils.core import GenericDataType
+
+
+@pytest.fixture
+def mock_query_context():
+    with patch(
+        "superset.common.query_context_processor.QueryContextProcessor"
+    ) as mock_query_context_processor:
+        yield mock_query_context_processor
+
+
+@pytest.fixture
+def processor(mock_query_context):
+    mock_query_context.datasource.data = MagicMock()
+    mock_query_context.datasource.data.get.return_value = {
+        "col1": "Column 1",
+        "col2": "Column 2",
+    }
+    return QueryContextProcessor(mock_query_context)
+
+
+def test_get_data_table_like(processor, mock_query_context):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+
+    result = processor.get_data(df, coltypes)
+    expected = [
+        {"col1": 1, "col2": "a"},
+        {"col1": 2, "col2": "b"},
+        {"col1": 3, "col2": "c"},
+    ]
+    assert result == expected
+
+
+@patch("superset.common.query_context_processor.csv.df_to_escaped_csv")
+def test_get_data_csv(mock_df_to_escaped_csv, processor, mock_query_context):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.CSV
+
+    mock_df_to_escaped_csv.return_value = "col1,col2\n1,a\n2,b\n3,c\n"
+    result = processor.get_data(df, coltypes)
+    assert result == "col1,col2\n1,a\n2,b\n3,c\n"
+    mock_df_to_escaped_csv.assert_called_once_with(df, index=False, encoding="utf-8")
+
+
+@patch("superset.common.query_context_processor.excel.df_to_excel")
+@patch("superset.common.query_context_processor.excel.apply_column_types")
+def test_get_data_xlsx(
+    mock_apply_column_types, mock_df_to_excel, processor, mock_query_context
+):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.XLSX
+
+    mock_df_to_excel.return_value = b"binary data"
+    result = processor.get_data(df, coltypes)
+    assert result == b"binary data"
+    mock_apply_column_types.assert_called_once_with(df, coltypes)
+    mock_df_to_excel.assert_called_once_with(df)
+
+
+def test_get_data_json(processor, mock_query_context):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+
+    result = processor.get_data(df, coltypes)
+    expected = [
+        {"col1": 1, "col2": "a"},
+        {"col1": 2, "col2": "b"},
+        {"col1": 3, "col2": "c"},
+    ]
+    assert result == expected
+
+
+def test_get_data_invalid_dataframe(processor, mock_query_context):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+
+    with patch.object(df, "to_dict", side_effect=ValueError("Invalid DataFrame")):
+        with pytest.raises(ValueError, match="Invalid DataFrame"):
+            processor.get_data(df, coltypes)
+
+
+def test_get_data_non_unique_columns(processor, mock_query_context):
+    data = [[1, "a"], [2, "b"], [3, "c"]]
+    df = pd.DataFrame(data, columns=["col1", "col1"])
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+
+    with pytest.warns(
+        UserWarning,
+        match="DataFrame columns are not unique, some columns will be omitted",
+    ):
+        processor.get_data(df, coltypes)
+
+
+def test_get_data_empty_dataframe_json(processor, mock_query_context):
+    df = pd.DataFrame(columns=["col1", "col2"])
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+    result = processor.get_data(df, coltypes)
+    assert result == []
+
+
+@patch("superset.common.query_context_processor.csv.df_to_escaped_csv")
+def test_get_data_empty_dataframe_csv(
+    mock_df_to_escaped_csv, processor, mock_query_context
+):
+    df = pd.DataFrame(columns=["col1", "col2"])
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.CSV
+    mock_df_to_escaped_csv.return_value = "col1,col2\n"
+    result = processor.get_data(df, coltypes)
+    assert result == "col1,col2\n"
+    mock_df_to_escaped_csv.assert_called_once_with(df, index=False, encoding="utf-8")
+
+
+@patch("superset.common.query_context_processor.excel.df_to_excel")
+@patch("superset.common.query_context_processor.excel.apply_column_types")
+def test_get_data_empty_dataframe_xlsx(
+    mock_apply_column_types, mock_df_to_excel, processor, mock_query_context
+):
+    df = pd.DataFrame(columns=["col1", "col2"])
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.XLSX
+    mock_df_to_excel.return_value = b"binary data empty"
+    result = processor.get_data(df, coltypes)
+    assert result == b"binary data empty"
+    mock_apply_column_types.assert_called_once_with(df, coltypes)
+    mock_df_to_excel.assert_called_once_with(df)
+
+
+def test_get_data_nan_values_json(processor, mock_query_context):
+    df = pd.DataFrame({"col1": [1, np.nan, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+    result = processor.get_data(df, coltypes)
+    assert result[0]["col1"] == 1
+    assert pd.isna(result[1]["col1"])
+    assert result[2]["col1"] == 3
+
+
+def test_get_data_invalid_input(processor, mock_query_context):
+    df = "not a dataframe"
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+    with pytest.raises(AttributeError):
+        processor.get_data(df, coltypes)
+
+
+def test_get_data_default_format_when_result_format_is_none(
+    processor, mock_query_context
+):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = None
+    result = processor.get_data(df, coltypes)
+    expected = [
+        {"col1": 1, "col2": "a"},
+        {"col1": 2, "col2": "b"},
+        {"col1": 3, "col2": "c"},
+    ]
+    assert result == expected
+
+
+def fake_apply_column_types(df, coltypes):
+    if len(coltypes) != len(df.columns):
+        raise ValueError("Mismatch between column types and dataframe columns")
+    return df
+
+
+@patch("superset.common.query_context_processor.excel.df_to_excel")
+@patch(
+    "superset.common.query_context_processor.excel.apply_column_types",
+    side_effect=fake_apply_column_types,
+)
+def test_get_data_invalid_coltypes_length_xlsx(
+    mock_apply_column_types, mock_df_to_excel, processor, mock_query_context
+):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC]  # Mismatched length
+    mock_query_context.result_format = ChartDataResultFormat.XLSX
+    with pytest.raises(
+        ValueError, match="Mismatch between column types and dataframe columns"
+    ):
+        processor.get_data(df, coltypes)
+
+
+def test_get_data_does_not_mutate_dataframe(processor, mock_query_context):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    original_df = df.copy(deep=True)
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.JSON
+    _ = processor.get_data(df, coltypes)
+    pd.testing.assert_frame_equal(df, original_df)
+
+
+@patch(
+    "superset.common.query_context_processor.excel.apply_column_types",
+    side_effect=ValueError("Conversion error"),
+)
+def test_get_data_xlsx_apply_column_types_error(
+    mock_apply_column_types, processor, mock_query_context
+):
+    df = pd.DataFrame({"col1": [1, 2, 3], "col2": ["a", "b", "c"]})
+    coltypes = [GenericDataType.NUMERIC, GenericDataType.STRING]
+    mock_query_context.result_format = ChartDataResultFormat.XLSX
+    with pytest.raises(ValueError, match="Conversion error"):
+        processor.get_data(df, coltypes)