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import pyarrow as pa
import pytest
from datafusion import ExecutionContext
from datafusion import functions as f


@pytest.fixture
def df():
    ctx = ExecutionContext()

    # create a RecordBatch and a new DataFrame from it
    batch = pa.RecordBatch.from_arrays(
        [pa.array([1, 2, 3]), pa.array([4, 5, 6])],
        names=["a", "b"],
    )

    return ctx.create_dataframe([[batch]])


def test_select(df):
    df = df.select(
        f.col("a") + f.col("b"),
        f.col("a") - f.col("b"),
    )

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert result.column(0) == pa.array([5, 7, 9])
    assert result.column(1) == pa.array([-3, -3, -3])


def test_filter(df):
    df = df.select(
        f.col("a") + f.col("b"),
        f.col("a") - f.col("b"),
    ).filter(f.col("a") > f.lit(2))

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert result.column(0) == pa.array([9])
    assert result.column(1) == pa.array([-3])


def test_sort(df):
    df = df.sort([f.col("b").sort(ascending=False)])

    table = pa.Table.from_batches(df.collect())
    expected = {"a": [3, 2, 1], "b": [6, 5, 4]}

    assert table.to_pydict() == expected


def test_limit(df):
    df = df.limit(1)

    # execute and collect the first (and only) batch
    result = df.collect()[0]

    assert len(result.column(0)) == 1
    assert len(result.column(1)) == 1


def test_udf(df):
    # is_null is a pa function over arrays
    udf = f.udf(lambda x: x.is_null(), [pa.int64()], pa.bool_())

    df = df.select(udf(f.col("a")))
    result = df.collect()[0].column(0)

    assert result == pa.array([False, False, False])


def test_join():
    ctx = ExecutionContext()

    batch = pa.RecordBatch.from_arrays(
        [pa.array([1, 2, 3]), pa.array([4, 5, 6])],
        names=["a", "b"],
    )
    df = ctx.create_dataframe([[batch]])

    batch = pa.RecordBatch.from_arrays(
        [pa.array([1, 2]), pa.array([8, 10])],
        names=["a", "c"],
    )
    df1 = ctx.create_dataframe([[batch]])

    df = df.join(df1, join_keys=(["a"], ["a"]), how="inner")
    df = df.sort([f.col("a").sort(ascending=True)])
    table = pa.Table.from_batches(df.collect())

    expected = {"a": [1, 2], "c": [8, 10], "b": [4, 5]}
    assert table.to_pydict() == expected