robin-sparkless 4.3.0

"""JDBC read/write tests with SQLite.

These tests use an in-memory SQLite database so no external server is required.
They verify PySpark-compatible JDBC options work correctly.
"""

from __future__ import annotations

import tempfile
import os

import pytest

from tests.sql.conftest import jdbc_available

pytestmark = pytest.mark.skipif(
    not jdbc_available(),
    reason="JDBC/SQLite support not enabled in this build",
)


def test_read_jdbc_sqlite_basic(spark) -> None:
    """spark.read.jdbc() works with SQLite file database."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE test_table (id INTEGER PRIMARY KEY, name TEXT)")
        conn.execute("INSERT INTO test_table (id, name) VALUES (1, 'Alice')")
        conn.execute("INSERT INTO test_table (id, name) VALUES (2, 'Bob')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="test_table", properties={})
        rows = df.collect()
        assert len(rows) == 2
        names = {r["name"] for r in rows}
        assert names == {"Alice", "Bob"}
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_read_jdbc_with_options_api(spark) -> None:
    """spark.read.option().jdbc() passes options correctly."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE users (id INTEGER PRIMARY KEY, email TEXT)")
        conn.execute("INSERT INTO users (id, email) VALUES (1, 'a@test.com')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.option("fetchsize", "100").jdbc(
            url=url, table="users", properties={}
        )
        rows = df.collect()
        assert len(rows) == 1
        assert rows[0]["email"] == "a@test.com"
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_write_jdbc_sqlite_append(spark) -> None:
    """df.write.jdbc(..., mode='append') appends rows to SQLite."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE append_test (id INTEGER, value TEXT)")
        conn.execute("INSERT INTO append_test VALUES (1, 'initial')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        df = spark.createDataFrame([(2, "appended")], schema="id int, value string")
        df.write.jdbc(url=url, table="append_test", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="append_test", properties={})
        rows = read_df.collect()
        assert len(rows) == 2
        values = {r["value"] for r in rows}
        assert values == {"initial", "appended"}
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_write_jdbc_sqlite_overwrite(spark) -> None:
    """df.write.jdbc(..., mode='overwrite') replaces existing data."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE overwrite_test (id INTEGER, value TEXT)")
        conn.execute("INSERT INTO overwrite_test VALUES (1, 'old')")
        conn.execute("INSERT INTO overwrite_test VALUES (2, 'data')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        df = spark.createDataFrame([(100, "new")], schema="id int, value string")
        df.write.jdbc(url=url, table="overwrite_test", properties={}, mode="overwrite")

        read_df = spark.read.jdbc(url=url, table="overwrite_test", properties={})
        rows = read_df.collect()
        assert len(rows) == 1
        assert rows[0]["value"] == "new"
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_format_jdbc_load(spark) -> None:
    """spark.read.format('jdbc').option('url', ...).option('dbtable', ...).load() works."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE format_test (id INTEGER, name TEXT)")
        conn.execute("INSERT INTO format_test VALUES (1, 'test')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = (
            spark.read.format("jdbc")
            .option("url", url)
            .option("dbtable", "format_test")
            .load(".")
        )
        rows = df.collect()
        assert len(rows) == 1
        assert rows[0]["name"] == "test"
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_with_query_option(spark) -> None:
    """Using query option instead of dbtable executes custom SQL."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute(
            "CREATE TABLE query_test (id INTEGER, category TEXT, amount INTEGER)"
        )
        conn.execute("INSERT INTO query_test VALUES (1, 'A', 100)")
        conn.execute("INSERT INTO query_test VALUES (2, 'B', 200)")
        conn.execute("INSERT INTO query_test VALUES (3, 'A', 150)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = (
            spark.read.format("jdbc")
            .option("url", url)
            .option(
                "query",
                "SELECT category, SUM(amount) as total FROM query_test GROUP BY category",
            )
            .load(".")
        )
        rows = df.collect()
        assert len(rows) == 2
        totals = {r["category"]: r["total"] for r in rows}
        assert totals["A"] == 250
        assert totals["B"] == 200
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_session_init_statement(spark) -> None:
    """sessionInitStatement executes SQL after connection is opened."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE session_init_test (id INTEGER, value TEXT)")
        conn.execute("INSERT INTO session_init_test VALUES (1, 'original')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = (
            spark.read.format("jdbc")
            .option("url", url)
            .option("dbtable", "session_init_test")
            .option("sessionInitStatement", "SELECT 1")
            .load(".")
        )
        rows = df.collect()
        assert len(rows) == 1
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_batchsize_write(spark) -> None:
    """batchsize option controls write batching."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE batch_test (id INTEGER, value TEXT)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        data = [(i, f"row_{i}") for i in range(100)]
        df = spark.createDataFrame(data, schema="id int, value string")
        df.write.jdbc(
            url=url, table="batch_test", properties={"batchsize": "10"}, mode="append"
        )

        read_df = spark.read.jdbc(url=url, table="batch_test", properties={})
        assert read_df.count() == 100
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_truncate_option(spark) -> None:
    """truncate=true uses TRUNCATE (or DELETE for SQLite) before overwrite."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE truncate_test (id INTEGER, name TEXT)")
        conn.execute("INSERT INTO truncate_test VALUES (1, 'old')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        df = spark.createDataFrame([(2, "new")], schema="id int, name string")
        df.write.jdbc(
            url=url,
            table="truncate_test",
            properties={"truncate": "true"},
            mode="overwrite",
        )

        read_df = spark.read.jdbc(url=url, table="truncate_test", properties={})
        rows = read_df.collect()
        assert len(rows) == 1
        assert rows[0]["name"] == "new"
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_error_if_exists_mode(spark) -> None:
    """mode='error' raises exception if table has data."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE error_test (id INTEGER, name TEXT)")
        conn.execute("INSERT INTO error_test VALUES (1, 'exists')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        df = spark.createDataFrame([(2, "new")], schema="id int, name string")
        with pytest.raises(Exception) as exc_info:
            df.write.jdbc(url=url, table="error_test", properties={}, mode="error")

        err_msg = str(exc_info.value).lower()
        assert "exists" in err_msg or "not empty" in err_msg or "data" in err_msg
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_ignore_mode(spark) -> None:
    """mode='ignore' does nothing if table has data."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE ignore_test (id INTEGER, name TEXT)")
        conn.execute("INSERT INTO ignore_test VALUES (1, 'original')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        df = spark.createDataFrame([(2, "new")], schema="id int, name string")
        df.write.jdbc(url=url, table="ignore_test", properties={}, mode="ignore")

        read_df = spark.read.jdbc(url=url, table="ignore_test", properties={})
        rows = read_df.collect()
        assert len(rows) == 1
        assert rows[0]["name"] == "original"
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_prepare_query(spark) -> None:
    """prepareQuery option executes SQL before the main query."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE prepare_source (id INTEGER, value INTEGER)")
        conn.execute("INSERT INTO prepare_source VALUES (1, 100)")
        conn.execute("INSERT INTO prepare_source VALUES (2, 200)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = (
            spark.read.format("jdbc")
            .option("url", url)
            .option(
                "prepareQuery",
                "CREATE TEMP VIEW prepare_view AS SELECT id, value * 2 as doubled FROM prepare_source",
            )
            .option("dbtable", "prepare_view")
            .load(".")
        )
        rows = df.collect()
        assert len(rows) == 2
        doubled_values = {r["doubled"] for r in rows}
        assert doubled_values == {200, 400}
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_memory_database(spark) -> None:
    """Using memory database URL works for ephemeral testing."""
    url = "jdbc:sqlite::memory:"

    df = spark.createDataFrame([(1, "test")], schema="id int, name string")

    # Memory database is per-connection so write then read within same session
    # may not work as expected - this test mainly verifies no crash occurs
    try:
        df.write.jdbc(url=url, table="mem_test", properties={}, mode="overwrite")
    except Exception:
        # Memory DB may not persist between operations - that's expected
        pass


def test_jdbc_integer_types_roundtrip(spark) -> None:
    """Integer values roundtrip correctly through SQLite."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE int_test (small_int INTEGER, big_int INTEGER)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        data = [
            (0, 0),
            (1, 100),
            (-1, -100),
            (32767, 2147483647),
            (-32768, -2147483648),
        ]
        df = spark.createDataFrame(data, schema="small_int int, big_int bigint")
        df.write.jdbc(url=url, table="int_test", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="int_test", properties={})
        rows = read_df.collect()
        assert len(rows) == 5

        small_vals = {r["small_int"] for r in rows}
        assert 0 in small_vals
        assert 1 in small_vals
        assert -1 in small_vals
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_float_types_roundtrip(spark) -> None:
    """Float/double values roundtrip correctly through SQLite."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE float_test (value REAL)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        data = [(0.0,), (1.5,), (-1.5,), (3.14159,), (1e10,)]
        df = spark.createDataFrame(data, schema="value double")
        df.write.jdbc(url=url, table="float_test", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="float_test", properties={})
        rows = read_df.collect()
        assert len(rows) == 5

        values = [r["value"] for r in rows]
        assert any(abs(v - 3.14159) < 0.0001 for v in values)
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_null_handling(spark) -> None:
    """NULL values are handled correctly."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE null_test (id INTEGER, name TEXT, value REAL)")
        conn.execute("INSERT INTO null_test VALUES (1, NULL, 1.0)")
        conn.execute("INSERT INTO null_test VALUES (2, 'present', NULL)")
        conn.execute("INSERT INTO null_test VALUES (NULL, 'no_id', 2.0)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="null_test", properties={})
        rows = df.collect()

        assert len(rows) == 3
        # Check that we can read rows with NULL values
        names = [r["name"] for r in rows]
        assert None in names or any(n is None for n in names)
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_special_characters_in_strings(spark) -> None:
    """Special characters in strings are preserved."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE special_chars (id INTEGER, text TEXT)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        special_strings = [
            (1, "Hello, World!"),
            (2, "Quote's test"),
            (3, 'Double "quotes"'),
            (4, "Line\nbreak"),
            (5, "Tab\there"),
            (6, "Unicode: 日本語 🎉"),
            (7, "Backslash: \\path\\to\\file"),
        ]
        df = spark.createDataFrame(special_strings, schema="id int, text string")
        df.write.jdbc(url=url, table="special_chars", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="special_chars", properties={})
        rows = read_df.collect()

        assert len(rows) == 7
        texts = {r["text"] for r in rows}
        assert "Hello, World!" in texts
        assert "Quote's test" in texts
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_options_dict_api(spark) -> None:
    """spark.read.options({...}) works with JDBC."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE options_test (id INTEGER, name TEXT)")
        conn.execute("INSERT INTO options_test VALUES (1, 'test')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        opts = {
            "url": url,
            "dbtable": "options_test",
        }
        df = spark.read.format("jdbc").options(opts).load(".")
        rows = df.collect()

        assert len(rows) == 1
        assert rows[0]["name"] == "test"
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_schema_fields(spark) -> None:
    """DataFrame schema reflects database column names and types."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute(
            "CREATE TABLE schema_test (user_id INTEGER, user_name TEXT, balance REAL)"
        )
        conn.execute("INSERT INTO schema_test VALUES (1, 'Alice', 100.50)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="schema_test", properties={})

        field_names = [f.name for f in df.schema.fields]
        assert "user_id" in field_names
        assert "user_name" in field_names
        assert "balance" in field_names
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_count_operation(spark) -> None:
    """DataFrame.count() works on JDBC-sourced data."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE count_test (id INTEGER)")
        for i in range(50):
            conn.execute("INSERT INTO count_test VALUES (?)", (i,))
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="count_test", properties={})

        assert df.count() == 50
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_filter_after_read(spark) -> None:
    """DataFrame operations like filter work after JDBC read."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE filter_test (id INTEGER, category TEXT)")
        conn.execute("INSERT INTO filter_test VALUES (1, 'A')")
        conn.execute("INSERT INTO filter_test VALUES (2, 'B')")
        conn.execute("INSERT INTO filter_test VALUES (3, 'A')")
        conn.execute("INSERT INTO filter_test VALUES (4, 'C')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="filter_test", properties={})

        filtered = df.filter(df["category"] == "A")
        rows = filtered.collect()

        assert len(rows) == 2
        assert all(r["category"] == "A" for r in rows)
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_select_columns(spark) -> None:
    """DataFrame.select() works after JDBC read."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE select_test (id INTEGER, name TEXT, email TEXT)")
        conn.execute("INSERT INTO select_test VALUES (1, 'Alice', 'alice@test.com')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="select_test", properties={})

        selected = df.select("id", "name")
        rows = selected.collect()

        assert len(rows) == 1
        assert "id" in rows[0].asDict()
        assert "name" in rows[0].asDict()
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_multiple_writes(spark) -> None:
    """Multiple append writes accumulate data correctly."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE multi_write (id INTEGER, batch INTEGER)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        # Write batch 1
        df1 = spark.createDataFrame([(1, 1), (2, 1)], schema="id int, batch int")
        df1.write.jdbc(url=url, table="multi_write", properties={}, mode="append")

        # Write batch 2
        df2 = spark.createDataFrame([(3, 2), (4, 2)], schema="id int, batch int")
        df2.write.jdbc(url=url, table="multi_write", properties={}, mode="append")

        # Write batch 3
        df3 = spark.createDataFrame([(5, 3)], schema="id int, batch int")
        df3.write.jdbc(url=url, table="multi_write", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="multi_write", properties={})
        assert read_df.count() == 5

        rows = read_df.collect()
        batches = {r["batch"] for r in rows}
        assert batches == {1, 2, 3}
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_empty_table_read(spark) -> None:
    """Reading an empty table returns empty DataFrame."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE empty_table (id INTEGER, name TEXT)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="empty_table", properties={})

        assert df.count() == 0
        rows = df.collect()
        assert len(rows) == 0
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_properties_passed_via_jdbc_method(spark) -> None:
    """Properties dict passed to jdbc() method is used."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE props_test (id INTEGER)")
        conn.execute("INSERT INTO props_test VALUES (1)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        # Pass properties including fetchsize
        props = {"fetchsize": "500"}
        df = spark.read.jdbc(url=url, table="props_test", properties=props)

        rows = df.collect()
        assert len(rows) == 1
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_chained_options(spark) -> None:
    """Multiple chained .option() calls work correctly."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE chained_test (id INTEGER, val TEXT)")
        conn.execute("INSERT INTO chained_test VALUES (1, 'a')")
        conn.execute("INSERT INTO chained_test VALUES (2, 'b')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = (
            spark.read.format("jdbc")
            .option("url", url)
            .option("dbtable", "chained_test")
            .option("fetchsize", "100")
            .option("sessionInitStatement", "SELECT 1")
            .load(".")
        )

        rows = df.collect()
        assert len(rows) == 2
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_write_mode_default_append(spark) -> None:
    """Default write mode without explicit mode works as append."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE default_mode (id INTEGER)")
        conn.execute("INSERT INTO default_mode VALUES (1)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        df = spark.createDataFrame([(2,)], schema="id int")
        # mode defaults to "error" in PySpark, but append is common
        df.write.jdbc(url=url, table="default_mode", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="default_mode", properties={})
        assert read_df.count() == 2
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_boolean_via_integer(spark) -> None:
    """Boolean values stored as integers (0/1) in SQLite roundtrip correctly."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE bool_test (id INTEGER, active INTEGER)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        # Write booleans as integers
        data = [(1, True), (2, False), (3, True)]
        df = spark.createDataFrame(data, schema="id int, active boolean")
        df.write.jdbc(url=url, table="bool_test", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="bool_test", properties={})
        rows = read_df.collect()

        assert len(rows) == 3
        # Values should be readable (as int or bool depending on implementation)
        active_vals = [r["active"] for r in rows]
        assert len(active_vals) == 3
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_large_string_values(spark) -> None:
    """Large string values are handled correctly."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE large_str (id INTEGER, content TEXT)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        # Create strings of various sizes
        large_string = "x" * 10000
        medium_string = "y" * 1000

        data = [(1, large_string), (2, medium_string), (3, "small")]
        df = spark.createDataFrame(data, schema="id int, content string")
        df.write.jdbc(url=url, table="large_str", properties={}, mode="append")

        read_df = spark.read.jdbc(url=url, table="large_str", properties={})
        rows = read_df.collect()

        assert len(rows) == 3
        contents = {r["id"]: r["content"] for r in rows}
        assert len(contents[1]) == 10000
        assert len(contents[2]) == 1000
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_groupby_after_read(spark) -> None:
    """GroupBy operations work after JDBC read."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE groupby_test (category TEXT, amount INTEGER)")
        conn.execute("INSERT INTO groupby_test VALUES ('A', 10)")
        conn.execute("INSERT INTO groupby_test VALUES ('A', 20)")
        conn.execute("INSERT INTO groupby_test VALUES ('B', 30)")
        conn.execute("INSERT INTO groupby_test VALUES ('B', 40)")
        conn.execute("INSERT INTO groupby_test VALUES ('B', 50)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="groupby_test", properties={})

        from sparkless.sql import functions as F

        grouped = df.groupBy("category").agg(F.sum("amount").alias("total"))
        rows = grouped.collect()

        assert len(rows) == 2
        totals = {r["category"]: r["total"] for r in rows}
        assert totals["A"] == 30
        assert totals["B"] == 120
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_orderby_after_read(spark) -> None:
    """OrderBy operations work after JDBC read."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE orderby_test (id INTEGER, name TEXT)")
        conn.execute("INSERT INTO orderby_test VALUES (3, 'Charlie')")
        conn.execute("INSERT INTO orderby_test VALUES (1, 'Alice')")
        conn.execute("INSERT INTO orderby_test VALUES (2, 'Bob')")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"
        df = spark.read.jdbc(url=url, table="orderby_test", properties={})

        ordered = df.orderBy("id")
        rows = ordered.collect()

        assert len(rows) == 3
        ids = [r["id"] for r in rows]
        assert ids == [1, 2, 3] or ids == sorted(ids)
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)


def test_jdbc_write_options_via_option_method(spark) -> None:
    """Write options passed via .option() on writer work."""
    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
        db_path = f.name

    try:
        import sqlite3

        conn = sqlite3.connect(db_path)
        conn.execute("CREATE TABLE write_opts (id INTEGER, name TEXT)")
        conn.commit()
        conn.close()

        url = f"jdbc:sqlite:{db_path}"

        data = [(i, f"name_{i}") for i in range(20)]
        df = spark.createDataFrame(data, schema="id int, name string")

        # Use option on writer
        df.write.option("batchsize", "5").jdbc(
            url=url, table="write_opts", properties={}, mode="append"
        )

        read_df = spark.read.jdbc(url=url, table="write_opts", properties={})
        assert read_df.count() == 20
    finally:
        if os.path.exists(db_path):
            os.unlink(db_path)