avro reader integration tests (#7156)

Added some avro reader integration tests for fastavro. These cover type detection, single-value parsing, and null value parsing, but do not cover parsing multiple values.

Authors:
  - Christopher Harris (@cwharris)

Approvers:
  - Vukasin Milovanovic (@vuule)
  - GALI PREM SAGAR (@galipremsagar)

URL: #7156

python/cudf/cudf/tests/test_avro_reader_fastavro_integration.py

@@ -0,0 +1,209 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+#
+# Licensed 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 io
+
+import fastavro
+import pytest
+
+import cudf
+from cudf.tests.utils import assert_eq
+
+
+def cudf_from_avro_util(schema, records):
+
+    schema = [] if schema is None else fastavro.parse_schema(schema)
+    buffer = io.BytesIO()
+    fastavro.writer(buffer, schema, records)
+    buffer.seek(0)
+    return cudf.read_avro(buffer)
+
+
+avro_type_params = [
+    ("boolean", "bool"),
+    ("int", "int32"),
+    ("long", "int64"),
+    ("float", "float32"),
+    ("double", "float64"),
+    ("bytes", "str"),
+    ("string", "str"),
+]
+
+
+@pytest.mark.parametrize("avro_type, expected_dtype", avro_type_params)
+@pytest.mark.parametrize("namespace", [None, "root_ns"])
+@pytest.mark.parametrize("nullable", [True, False])
+def test_can_detect_dtype_from_avro_type(
+    avro_type, expected_dtype, namespace, nullable
+):
+    avro_type = avro_type if not nullable else ["null", avro_type]
+
+    schema = fastavro.parse_schema(
+        {
+            "type": "record",
+            "name": "test",
+            "namespace": namespace,
+            "fields": [{"name": "prop", "type": avro_type}],
+        }
+    )
+
+    actual = cudf_from_avro_util(schema, [])
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(None, None, expected_dtype)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("avro_type, expected_dtype", avro_type_params)
+@pytest.mark.parametrize("namespace", [None, "root_ns"])
+@pytest.mark.parametrize("nullable", [True, False])
+def test_can_detect_dtype_from_avro_type_nested(
+    avro_type, expected_dtype, namespace, nullable
+):
+    avro_type = avro_type if not nullable else ["null", avro_type]
+
+    schema_leaf = {
+        "name": "leaf",
+        "type": "record",
+        "fields": [{"name": "prop3", "type": avro_type}],
+    }
+
+    schema_child = {
+        "name": "child",
+        "type": "record",
+        "fields": [{"name": "prop2", "type": schema_leaf}],
+    }
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "namespace": namespace,
+        "fields": [{"name": "prop1", "type": schema_child}],
+    }
+
+    actual = cudf_from_avro_util(schema_root, [])
+
+    col_name = "{ns}child.{ns}leaf.prop3".format(
+        ns="" if namespace is None else namespace + "."
+    )
+
+    expected = cudf.DataFrame(
+        {col_name: cudf.Series(None, None, expected_dtype)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize(
+    "avro_type, cudf_type, avro_val, cudf_val",
+    [
+        ("boolean", "bool", True, True),
+        ("boolean", "bool", False, False),
+        ("int", "int32", 1234, 1234),
+        ("long", "int64", 1234, 1234),
+        ("float", "float32", 12.34, 12.34),
+        ("double", "float64", 12.34, 12.34),
+        ("string", "str", "heyϴ", "heyϴ"),
+        # ("bytes", "str", "heyϴ", "heyϴ"),
+    ],
+)
+def test_can_parse_single_value(avro_type, cudf_type, avro_val, cudf_val):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [{"name": "prop", "type": ["null", avro_type]}],
+    }
+
+    records = [
+        {"prop": avro_val},
+    ]
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(data=[cudf_val], dtype=cudf_type)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("avro_type, cudf_type", avro_type_params)
+def test_can_parse_single_null(avro_type, cudf_type):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [{"name": "prop", "type": ["null", avro_type]}],
+    }
+
+    records = [{"prop": None}]
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame(
+        {"prop": cudf.Series(data=[None], dtype=cudf_type)}
+    )
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.parametrize("avro_type, cudf_type", avro_type_params)
+def test_can_parse_no_data(avro_type, cudf_type):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [{"name": "prop", "type": ["null", avro_type]}],
+    }
+
+    records = []
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame({"prop": cudf.Series(data=[], dtype=cudf_type)})
+
+    assert_eq(expected, actual)
+
+
+@pytest.mark.xfail(
+    reason="cudf avro reader is unable to parse zero-field metadata."
+)
+@pytest.mark.parametrize("avro_type, cudf_type", avro_type_params)
+def test_can_parse_no_fields(avro_type, cudf_type):
+
+    schema_root = {
+        "name": "root",
+        "type": "record",
+        "fields": [],
+    }
+
+    records = []
+
+    actual = cudf_from_avro_util(schema_root, records)
+
+    expected = cudf.DataFrame()
+
+    assert_eq(expected, actual)
+
+
+def test_can_parse_no_schema():
+
+    schema_root = None
+    records = []
+    actual = cudf_from_avro_util(schema_root, records)
+    expected = cudf.DataFrame()
+    assert_eq(expected, actual)