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# 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.
import gzip
from pathlib import Path
import numpy as np
import pyarrow as pa
import pyarrow.dataset as ds
import pytest
from datafusion import col, udf
from datafusion.object_store import Http
from pyarrow.csv import write_csv
from . import generic as helpers
def test_no_table(ctx):
with pytest.raises(Exception, match="DataFusion error"):
ctx.sql("SELECT a FROM b").collect()
def test_register_csv(ctx, tmp_path):
path = tmp_path / "test.csv"
gzip_path = tmp_path / "test.csv.gz"
table = pa.Table.from_arrays(
[
[1, 2, 3, 4],
["a", "b", "c", "d"],
[1.1, 2.2, 3.3, 4.4],
],
names=["int", "str", "float"],
)
write_csv(table, path)
with Path.open(path, "rb") as csv_file, gzip.open(gzip_path, "wb") as gzipped_file:
gzipped_file.writelines(csv_file)
ctx.register_csv("csv", path)
ctx.register_csv("csv1", str(path))
ctx.register_csv(
"csv2",
path,
has_header=True,
delimiter=",",
schema_infer_max_records=10,
)
ctx.register_csv(
"csv_gzip",
gzip_path,
file_extension="gz",
file_compression_type="gzip",
)
alternative_schema = pa.schema(
[
("some_int", pa.int16()),
("some_bytes", pa.string()),
("some_floats", pa.float32()),
]
)
ctx.register_csv("csv3", path, schema=alternative_schema)
assert ctx.catalog().schema().names() == {
"csv",
"csv1",
"csv2",
"csv3",
"csv_gzip",
}
for table in ["csv", "csv1", "csv2", "csv_gzip"]:
result = ctx.sql(f"SELECT COUNT(int) AS cnt FROM {table}").collect()
result = pa.Table.from_batches(result)
assert result.to_pydict() == {"cnt": [4]}
result = ctx.sql("SELECT * FROM csv3").collect()
result = pa.Table.from_batches(result)
assert result.schema == alternative_schema
with pytest.raises(ValueError, match="Delimiter must be a single character"):
ctx.register_csv("csv4", path, delimiter="wrong")
with pytest.raises(
ValueError,
match="file_compression_type must one of: gzip, bz2, xz, zstd",
):
ctx.register_csv("csv4", path, file_compression_type="rar")
def test_register_csv_list(ctx, tmp_path):
path = tmp_path / "test.csv"
int_values = [1, 2, 3, 4]
table = pa.Table.from_arrays(
[
int_values,
["a", "b", "c", "d"],
[1.1, 2.2, 3.3, 4.4],
],
names=["int", "str", "float"],
)
write_csv(table, path)
ctx.register_csv("csv", path)
csv_df = ctx.table("csv")
expected_count = csv_df.count() * 2
ctx.register_csv(
"double_csv",
path=[
path,
path,
],
)
double_csv_df = ctx.table("double_csv")
actual_count = double_csv_df.count()
assert actual_count == expected_count
int_sum = ctx.sql("select sum(int) from double_csv").to_pydict()[
"sum(double_csv.int)"
][0]
assert int_sum == 2 * sum(int_values)
def test_register_http_csv(ctx):
url = "https://raw.githubusercontent.com/ibis-project/testing-data/refs/heads/master/csv/diamonds.csv"
ctx.register_object_store("", Http(url))
ctx.register_csv("remote", url)
assert ctx.table_exist("remote")
res, *_ = ctx.sql("SELECT COUNT(*) AS total FROM remote").to_pylist()
assert res["total"] > 0
def test_register_parquet(ctx, tmp_path):
path = helpers.write_parquet(tmp_path / "a.parquet", helpers.data())
ctx.register_parquet("t", path)
ctx.register_parquet("t1", str(path))
assert ctx.catalog().schema().names() == {"t", "t1"}
result = ctx.sql("SELECT COUNT(a) AS cnt FROM t").collect()
result = pa.Table.from_batches(result)
assert result.to_pydict() == {"cnt": [100]}
@pytest.mark.parametrize(
("path_to_str", "legacy_data_type"), [(True, False), (False, False), (False, True)]
)
def test_register_parquet_partitioned(ctx, tmp_path, path_to_str, legacy_data_type):
dir_root = tmp_path / "dataset_parquet_partitioned"
dir_root.mkdir(exist_ok=False)
(dir_root / "grp=a").mkdir(exist_ok=False)
(dir_root / "grp=b").mkdir(exist_ok=False)
table = pa.Table.from_arrays(
[
[1, 2, 3, 4],
["a", "b", "c", "d"],
[1.1, 2.2, 3.3, 4.4],
],
names=["int", "str", "float"],
)
pa.parquet.write_table(table.slice(0, 3), dir_root / "grp=a/file.parquet")
pa.parquet.write_table(table.slice(3, 4), dir_root / "grp=b/file.parquet")
dir_root = str(dir_root) if path_to_str else dir_root
partition_data_type = "string" if legacy_data_type else pa.string()
ctx.register_parquet(
"datapp",
dir_root,
table_partition_cols=[("grp", partition_data_type)],
parquet_pruning=True,
file_extension=".parquet",
)
assert ctx.catalog().schema().names() == {"datapp"}
result = ctx.sql("SELECT grp, COUNT(*) AS cnt FROM datapp GROUP BY grp").collect()
result = pa.Table.from_batches(result)
rd = result.to_pydict()
assert dict(zip(rd["grp"], rd["cnt"])) == {"a": 3, "b": 1}
@pytest.mark.parametrize("path_to_str", [True, False])
def test_register_dataset(ctx, tmp_path, path_to_str):
path = helpers.write_parquet(tmp_path / "a.parquet", helpers.data())
path = str(path) if path_to_str else path
dataset = ds.dataset(path, format="parquet")
ctx.register_dataset("t", dataset)
assert ctx.catalog().schema().names() == {"t"}
result = ctx.sql("SELECT COUNT(a) AS cnt FROM t").collect()
result = pa.Table.from_batches(result)
assert result.to_pydict() == {"cnt": [100]}
def test_register_json(ctx, tmp_path):
path = Path(__file__).parent.resolve()
test_data_path = Path(path) / "data_test_context" / "data.json"
gzip_path = tmp_path / "data.json.gz"
with (
Path.open(test_data_path, "rb") as json_file,
gzip.open(gzip_path, "wb") as gzipped_file,
):
gzipped_file.writelines(json_file)
ctx.register_json("json", test_data_path)
ctx.register_json("json1", str(test_data_path))
ctx.register_json(
"json2",
test_data_path,
schema_infer_max_records=10,
)
ctx.register_json(
"json_gzip",
gzip_path,
file_extension="gz",
file_compression_type="gzip",
)
ctx.register_json(
"json_gzip1",
str(gzip_path),
file_extension="gz",
file_compression_type="gzip",
)
alternative_schema = pa.schema(
[
("some_int", pa.int16()),
("some_bytes", pa.string()),
("some_floats", pa.float32()),
]
)
ctx.register_json("json3", path, schema=alternative_schema)
assert ctx.catalog().schema().names() == {
"json",
"json1",
"json2",
"json3",
"json_gzip",
"json_gzip1",
}
for table in ["json", "json1", "json2", "json_gzip"]:
result = ctx.sql(f'SELECT COUNT("B") AS cnt FROM {table}').collect()
result = pa.Table.from_batches(result)
assert result.to_pydict() == {"cnt": [3]}
result = ctx.sql("SELECT * FROM json3").collect()
result = pa.Table.from_batches(result, alternative_schema)
assert result.schema == alternative_schema
with pytest.raises(
ValueError,
match="file_compression_type must one of: gzip, bz2, xz, zstd",
):
ctx.register_json("json4", gzip_path, file_compression_type="rar")
def test_register_avro(ctx):
path = "testing/data/avro/alltypes_plain.avro"
ctx.register_avro("alltypes_plain", path)
result = ctx.sql(
"SELECT SUM(tinyint_col) as tinyint_sum FROM alltypes_plain"
).collect()
result = pa.Table.from_batches(result).to_pydict()
assert result["tinyint_sum"][0] > 0
alternative_schema = pa.schema(
[
pa.field("id", pa.int64()),
]
)
ctx.register_avro(
"alltypes_plain_schema",
path,
schema=alternative_schema,
)
result = ctx.sql("SELECT * FROM alltypes_plain_schema").collect()
result = pa.Table.from_batches(result)
assert result.schema == alternative_schema
def test_execute(ctx, tmp_path):
data = [1, 1, 2, 2, 3, 11, 12]
# single column, "a"
path = helpers.write_parquet(tmp_path / "a.parquet", pa.array(data))
ctx.register_parquet("t", path)
assert ctx.catalog().schema().names() == {"t"}
# count
result = ctx.sql("SELECT COUNT(a) AS cnt FROM t WHERE a IS NOT NULL").collect()
ctx.sql("SELECT COUNT(a) AS cnt FROM t WHERE a IS NOT NULL").show()
expected_schema = pa.schema([("cnt", pa.int64(), False)])
expected_values = pa.array([7], type=pa.int64())
expected = [pa.RecordBatch.from_arrays([expected_values], schema=expected_schema)]
assert result == expected
# where
expected_values = pa.array([2], type=pa.int64())
expected = [pa.RecordBatch.from_arrays([expected_values], schema=expected_schema)]
result = ctx.sql("SELECT COUNT(a) AS cnt FROM t WHERE a > 10").collect()
assert result == expected
# group by
results = ctx.sql(
"SELECT CAST(a as int) AS a, COUNT(a) AS cnt FROM t GROUP BY a"
).collect()
# group by returns batches
result_keys = []
result_values = []
for result in results:
pydict = result.to_pydict()
result_keys.extend(pydict["a"])
result_values.extend(pydict["cnt"])
result_keys, result_values = (
list(t) for t in zip(*sorted(zip(result_keys, result_values)))
)
assert result_keys == [1, 2, 3, 11, 12]
assert result_values == [2, 2, 1, 1, 1]
# order by
result = ctx.sql(
"SELECT a, CAST(a AS int) AS a_int FROM t ORDER BY a DESC LIMIT 2"
).collect()
expected_a = pa.array([50.0219, 50.0152], pa.float64())
expected_cast = pa.array([50, 50], pa.int32())
expected = [pa.RecordBatch.from_arrays([expected_a, expected_cast], ["a", "a_int"])]
np.testing.assert_equal(expected[0].column(1), expected[0].column(1))
def test_cast(ctx, tmp_path):
"""Verify that we can cast"""
path = helpers.write_parquet(tmp_path / "a.parquet", helpers.data())
ctx.register_parquet("t", path)
valid_types = [
"smallint",
"int",
"bigint",
"float(32)",
"float(64)",
"float",
]
select = ", ".join([f"CAST(9 AS {t}) AS A{i}" for i, t in enumerate(valid_types)])
# can execute, which implies that we can cast
ctx.sql(f"SELECT {select} FROM t").collect()
@pytest.mark.parametrize(
("fn", "input_types", "output_type", "input_values", "expected_values"),
[
(
lambda x: x,
[pa.float64()],
pa.float64(),
[-1.2, None, 1.2],
[-1.2, None, 1.2],
),
(
lambda x: x.is_null(),
[pa.float64()],
pa.bool_(),
[-1.2, None, 1.2],
[False, True, False],
),
],
)
def test_udf(
ctx, tmp_path, fn, input_types, output_type, input_values, expected_values
):
# write to disk
path = helpers.write_parquet(tmp_path / "a.parquet", pa.array(input_values))
ctx.register_parquet("t", path)
func = udf(fn, input_types, output_type, name="func", volatility="immutable")
ctx.register_udf(func)
batches = ctx.sql("SELECT func(a) AS tt FROM t").collect()
result = batches[0].column(0)
assert result == pa.array(expected_values)
_null_mask = np.array([False, True, False])
@pytest.mark.parametrize(
"arr",
[
pa.array(["a", "b", "c"], pa.utf8(), _null_mask),
pa.array(["a", "b", "c"], pa.large_utf8(), _null_mask),
pa.array([b"1", b"2", b"3"], pa.binary(), _null_mask),
pa.array([b"1111", b"2222", b"3333"], pa.large_binary(), _null_mask),
pa.array([False, True, True], None, _null_mask),
pa.array([0, 1, 2], None),
helpers.data_binary_other(),
helpers.data_date32(),
helpers.data_with_nans(),
pytest.param(
pa.array([b"1111", b"2222", b"3333"], pa.binary(4), _null_mask),
id="binary4",
),
# `timestamp[s]` does not roundtrip for pyarrow.parquet: https://github.com/apache/arrow/issues/41382
pytest.param(
helpers.data_datetime("s"),
id="datetime_s",
marks=pytest.mark.xfail(
reason="pyarrow.parquet does not support timestamp[s] roundtrips"
),
),
pytest.param(
helpers.data_datetime("ms"),
id="datetime_ms",
),
pytest.param(
helpers.data_datetime("us"),
id="datetime_us",
),
pytest.param(
helpers.data_datetime("ns"),
id="datetime_ns",
),
# Not writtable to parquet
pytest.param(helpers.data_timedelta("s"), id="timedelta_s"),
pytest.param(helpers.data_timedelta("ms"), id="timedelta_ms"),
pytest.param(helpers.data_timedelta("us"), id="timedelta_us"),
pytest.param(helpers.data_timedelta("ns"), id="timedelta_ns"),
],
)
def test_simple_select(ctx, tmp_path, arr):
path = helpers.write_parquet(tmp_path / "a.parquet", arr)
ctx.register_parquet("t", path)
batches = ctx.sql("SELECT a AS tt FROM t").collect()
result = batches[0].column(0)
# In DF 43.0.0 we now default to having BinaryView and StringView
# so the array that is saved to the parquet is slightly different
# than the array read. Convert to values for comparison.
if isinstance(result, (pa.BinaryViewArray, pa.StringViewArray)):
arr = arr.tolist()
result = result.tolist()
np.testing.assert_equal(result, arr)
@pytest.mark.parametrize(
"file_sort_order", [None, [[col("int").sort(ascending=True, nulls_first=True)]]]
)
@pytest.mark.parametrize("pass_schema", [True, False])
@pytest.mark.parametrize("path_to_str", [True, False])
def test_register_listing_table(
ctx, tmp_path, pass_schema, file_sort_order, path_to_str
):
dir_root = tmp_path / "dataset_parquet_partitioned"
dir_root.mkdir(exist_ok=False)
(dir_root / "grp=a/date=2020-10-05").mkdir(exist_ok=False, parents=True)
(dir_root / "grp=a/date=2021-10-05").mkdir(exist_ok=False, parents=True)
(dir_root / "grp=b/date=2020-10-05").mkdir(exist_ok=False, parents=True)
table = pa.Table.from_arrays(
[
[1, 2, 3, 4, 5, 6, 7],
["a", "b", "c", "d", "e", "f", "g"],
[1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7],
],
names=["int", "str", "float"],
)
pa.parquet.write_table(
table.slice(0, 3), dir_root / "grp=a/date=2020-10-05/file.parquet"
)
pa.parquet.write_table(
table.slice(3, 2), dir_root / "grp=a/date=2021-10-05/file.parquet"
)
pa.parquet.write_table(
table.slice(5, 10), dir_root / "grp=b/date=2020-10-05/file.parquet"
)
dir_root = f"file://{dir_root}/" if path_to_str else dir_root
ctx.register_listing_table(
"my_table",
dir_root,
table_partition_cols=[("grp", pa.string()), ("date", pa.date64())],
file_extension=".parquet",
schema=table.schema if pass_schema else None,
file_sort_order=file_sort_order,
)
assert ctx.catalog().schema().names() == {"my_table"}
result = ctx.sql(
"SELECT grp, COUNT(*) AS count FROM my_table GROUP BY grp"
).collect()
result = pa.Table.from_batches(result)
rd = result.to_pydict()
assert dict(zip(rd["grp"], rd["count"])) == {"a": 5, "b": 2}
result = ctx.sql(
"SELECT grp, COUNT(*) AS count FROM my_table WHERE date='2020-10-05' GROUP BY grp" # noqa: E501
).collect()
result = pa.Table.from_batches(result)
rd = result.to_pydict()
assert dict(zip(rd["grp"], rd["count"])) == {"a": 3, "b": 2}