datafusion-python/python/tests/test_sql.py at main · vectorlink-ai/datafusion-python · GitHub

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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

import os

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 open(path, "rb") as csv_file:

with 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().database().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,

)

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().database().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", (True, False))

def test_register_parquet_partitioned(ctx, tmp_path, path_to_str):

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

ctx.register_parquet(

"datapp",

dir_root,

table_partition_cols=[("grp", "string")],

parquet_pruning=True,

file_extension=".parquet",

)

assert ctx.catalog().database().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().database().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 = os.path.dirname(os.path.abspath(__file__))

test_data_path = os.path.join(path, "data_test_context", "data.json")

gzip_path = tmp_path / "data.json.gz"

with open(test_data_path, "rb") as json_file:

with 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().database().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().database().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],

(

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", marks=pytest.mark.xfail

pytest.param(

helpers.data_timedelta("ms"), id="timedelta_ms", marks=pytest.mark.xfail

pytest.param(

helpers.data_timedelta("us"), id="timedelta_us", marks=pytest.mark.xfail

pytest.param(

helpers.data_timedelta("ns"), id="timedelta_ns", marks=pytest.mark.xfail

)

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) or isinstance(result, pa.StringViewArray):

arr = arr.tolist()

result = result.tolist()

np.testing.assert_equal(result, arr)

@pytest.mark.parametrize("file_sort_order", (None, [[col("int").sort(True, 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_id=20201005").mkdir(exist_ok=False, parents=True)

(dir_root / "grp=a/date_id=20211005").mkdir(exist_ok=False, parents=True)

(dir_root / "grp=b/date_id=20201005").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_id=20201005/file.parquet"

)

pa.parquet.write_table(

table.slice(3, 2), dir_root / "grp=a/date_id=20211005/file.parquet"

)

pa.parquet.write_table(

table.slice(5, 10), dir_root / "grp=b/date_id=20201005/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", "string"), ("date_id", "int")],

file_extension=".parquet",

schema=table.schema if pass_schema else None,

file_sort_order=file_sort_order,

)

assert ctx.catalog().database().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_id=20201005 GROUP BY grp"

).collect()

result = pa.Table.from_batches(result)

rd = result.to_pydict()

assert dict(zip(rd["grp"], rd["count"])) == {"a": 3, "b": 2}

datafusion-python/python/tests/test_sql.py at main · vectorlink-ai/datafusion-python · GitHub

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