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Add tests for DataFrame.__iter__ and .iter_partitions()
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Xiayue Charles Lin
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Jun 22, 2023
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from __future__ import annotations | ||
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import pytest | ||
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import daft | ||
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class MockException(Exception): | ||
pass | ||
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@pytest.mark.parametrize("materialized", [False, True]) | ||
def test_iter_rows(materialized): | ||
# Test that df.__iter__ produces the correct rows in the correct order. | ||
# It should work regardless of whether the dataframe has already been materialized or not. | ||
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df = daft.from_pydict({"a": list(range(10))}).into_partitions(5).with_column("b", daft.col("a") + 100) | ||
if materialized: | ||
df = df.collect() | ||
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rows = [_ for _ in df] | ||
assert rows == [{"a": x, "b": x + 100} for x in range(10)] | ||
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@pytest.mark.parametrize("materialized", [False, True]) | ||
def test_iter_partitions(materialized): | ||
# Test that df.iter_partitions() produces partitions in the correct order. | ||
# It should work regardless of whether the dataframe has already been materialized or not. | ||
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df = daft.from_pydict({"a": list(range(10))}).into_partitions(5).with_column("b", daft.col("a") + 100) | ||
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if materialized: | ||
df = df.collect() | ||
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parts = [_.to_pydict() for _ in df.iter_partitions()] | ||
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assert parts == [ | ||
{"a": [0, 1], "b": [100, 101]}, | ||
{"a": [2, 3], "b": [102, 103]}, | ||
{"a": [4, 5], "b": [104, 105]}, | ||
{"a": [6, 7], "b": [106, 107]}, | ||
{"a": [8, 9], "b": [108, 109]}, | ||
] | ||
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def test_iter_exception(): | ||
# Test that df.__iter__ actually returns results before completing execution. | ||
# We test this by raising an exception in a UDF if too many partitions are executed. | ||
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@daft.udf(return_dtype=daft.DataType.int64()) | ||
def echo_or_trigger(s): | ||
if max(s.to_pylist()) > 100: | ||
raise MockException | ||
else: | ||
return s | ||
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df = daft.from_pydict({"a": list(range(200))}).into_partitions(100).with_column("b", echo_or_trigger(daft.col("a"))) | ||
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it = iter(df) | ||
assert next(it) == {"a": 0, "b": 0} | ||
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# Ensure the exception does trigger if execution continues. | ||
with pytest.raises(MockException): | ||
list(it) | ||
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def test_iter_partitions_exception(): | ||
# Test that df.iter_partitions actually returns results before completing execution. | ||
# We test this by raising an exception in a UDF if too many partitions are executed. | ||
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@daft.udf(return_dtype=daft.DataType.int64()) | ||
def echo_or_trigger(s): | ||
if max(s.to_pylist()) > 100: | ||
raise MockException | ||
else: | ||
return s | ||
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df = daft.from_pydict({"a": list(range(200))}).into_partitions(100).with_column("b", echo_or_trigger(daft.col("a"))) | ||
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it = df.iter_partitions() | ||
assert next(it).to_pydict() == {"a": [0, 1], "b": [0, 1]} | ||
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# Ensure the exception does trigger if execution continues. | ||
with pytest.raises(MockException): | ||
list(it) |