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"""
Test for issue #152 (BUG-004): SQL column aliases not properly parsed in SELECT statements.
Issue #152 reports that SQL SELECT statements with column aliases (e.g., `SELECT col AS alias`)
are not properly parsed. The executor tries to access columns using the full alias expression
instead of parsing it correctly.
Error:
SparkColumnNotFoundError: 'DataFrame' object has no attribute 'name as dept_name'.
Available columns: dept_id, name
"""
from sparkless.testing import get_imports
# Get the appropriate imports based on backend (sparkless or PySpark)
imports = get_imports()
F = imports.F
class TestIssue152SQLColumnAliases:
"""Test cases for issue #152: SQL column aliases parsing."""
def test_sql_with_inner_join_and_aliases(self, spark, table_prefix):
"""Test that SQL queries with JOIN and column aliases work correctly.
This test verifies the fix for issue #152 where queries like:
SELECT e.name, d.name as dept_name
FROM employees e
INNER JOIN departments d ON e.dept_id = d.id
would fail with "name as dept_name" not being parsed correctly.
"""
emp_tbl, dept_tbl = f"{table_prefix}_employees", f"{table_prefix}_departments"
# Create test data
employees_data = [("Alice", 1), ("Bob", 2)]
employees_df = spark.createDataFrame(employees_data, ["name", "dept_id"])
employees_df.write.mode("overwrite").saveAsTable(emp_tbl)
departments_data = [(1, "IT"), (2, "HR")]
departments_df = spark.createDataFrame(departments_data, ["id", "name"])
departments_df.write.mode("overwrite").saveAsTable(dept_tbl)
# Execute SQL query with aliases
result = spark.sql(
f"""
SELECT e.name, d.name as dept_name
FROM {emp_tbl} e
INNER JOIN {dept_tbl} d ON e.dept_id = d.id
"""
)
# Verify the query executes without errors
rows = result.collect()
assert len(rows) == 2
# Verify column names are correct
# After JOIN, columns are prefixed with table alias (e.name -> e_name)
# But aliased columns use their alias (d.name as dept_name -> dept_name)
assert "e_name" in result.columns or "name" in result.columns
assert "dept_name" in result.columns
# Verify data is correct
row_dicts = [row.asDict() for row in rows]
# Use the actual column name (e_name or name)
name_col = "e_name" if "e_name" in result.columns else "name"
assert any(
row[name_col] == "Alice" and row["dept_name"] == "IT" for row in row_dicts
)
assert any(
row[name_col] == "Bob" and row["dept_name"] == "HR" for row in row_dicts
)
def test_sql_with_left_join_and_aliases(self, spark, table_prefix):
"""Test that SQL queries with LEFT JOIN and column aliases work correctly."""
emp_tbl, dept_tbl = f"{table_prefix}_employees", f"{table_prefix}_departments"
# Create test data
employees_data = [("Alice", 1), ("Bob", 99)] # Bob has invalid dept_id
employees_df = spark.createDataFrame(employees_data, ["name", "dept_id"])
employees_df.write.mode("overwrite").saveAsTable(emp_tbl)
departments_data = [(1, "IT")]
departments_df = spark.createDataFrame(departments_data, ["id", "name"])
departments_df.write.mode("overwrite").saveAsTable(dept_tbl)
# Execute SQL query with aliases
result = spark.sql(
f"""
SELECT e.name, d.name as dept_name
FROM {emp_tbl} e
LEFT JOIN {dept_tbl} d ON e.dept_id = d.id
"""
)
# Verify the query executes without errors
rows = result.collect()
assert len(rows) == 2
# Verify column names are correct
assert "e_name" in result.columns or "name" in result.columns
assert "dept_name" in result.columns
# Verify data (Bob should have NULL dept_name)
row_dicts = [row.asDict() for row in rows]
name_col = "e_name" if "e_name" in result.columns else "name"
alice_row = next(row for row in row_dicts if row[name_col] == "Alice")
assert alice_row["dept_name"] == "IT"
bob_row = next(row for row in row_dicts if row[name_col] == "Bob")
assert bob_row["dept_name"] is None