Add some missing tests

tests/libs/pyarrow/test_pyarrow.py

@@ -0,0 +1,191 @@
+from datetime import timezone, datetime, timedelta  # noqa: I251
+from copy import deepcopy
+from typing import List, Any
+
+import pytest
+import pyarrow as pa
+
+from dlt.common import pendulum
+from dlt.common.libs.pyarrow import (
+    py_arrow_to_table_schema_columns,
+    from_arrow_scalar,
+    get_py_arrow_timestamp,
+    to_arrow_scalar,
+    get_py_arrow_datatype,
+    remove_null_columns,
+    remove_columns,
+    append_column,
+    rename_columns,
+    is_arrow_item,
+)
+from dlt.common.destination import DestinationCapabilitiesContext
+from tests.cases import TABLE_UPDATE_COLUMNS_SCHEMA
+
+
+def test_py_arrow_to_table_schema_columns():
+    dlt_schema = deepcopy(TABLE_UPDATE_COLUMNS_SCHEMA)
+
+    caps = DestinationCapabilitiesContext.generic_capabilities()
+    # The arrow schema will add precision
+    dlt_schema["col4"]["precision"] = caps.timestamp_precision
+    dlt_schema["col6"]["precision"], dlt_schema["col6"]["scale"] = caps.decimal_precision
+    dlt_schema["col11"]["precision"] = caps.timestamp_precision
+    dlt_schema["col4_null"]["precision"] = caps.timestamp_precision
+    dlt_schema["col6_null"]["precision"], dlt_schema["col6_null"]["scale"] = caps.decimal_precision
+    dlt_schema["col11_null"]["precision"] = caps.timestamp_precision
+
+    # Ignoring wei as we can't distinguish from decimal
+    dlt_schema["col8"]["precision"], dlt_schema["col8"]["scale"] = (76, 0)
+    dlt_schema["col8"]["data_type"] = "decimal"
+    dlt_schema["col8_null"]["precision"], dlt_schema["col8_null"]["scale"] = (76, 0)
+    dlt_schema["col8_null"]["data_type"] = "decimal"
+    # No json type
+    dlt_schema["col9"]["data_type"] = "text"
+    del dlt_schema["col9"]["variant"]
+    dlt_schema["col9_null"]["data_type"] = "text"
+    del dlt_schema["col9_null"]["variant"]
+
+    # arrow string fields don't have precision
+    del dlt_schema["col5_precision"]["precision"]
+
+    # Convert to arrow schema
+    arrow_schema = pa.schema(
+        [
+            pa.field(
+                column["name"],
+                get_py_arrow_datatype(column, caps, "UTC"),
+                nullable=column["nullable"],
+            )
+            for column in dlt_schema.values()
+        ]
+    )
+
+    result = py_arrow_to_table_schema_columns(arrow_schema)
+
+    # Resulting schema should match the original
+    assert result == dlt_schema
+
+
+def test_to_arrow_scalar() -> None:
+    naive_dt = get_py_arrow_timestamp(6, tz=None)
+    # print(naive_dt)
+    # naive datetimes are converted as UTC when time aware python objects are used
+    assert to_arrow_scalar(datetime(2021, 1, 1, 5, 2, 32), naive_dt).as_py() == datetime(
+        2021, 1, 1, 5, 2, 32
+    )
+    assert to_arrow_scalar(
+        datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone.utc), naive_dt
+    ).as_py() == datetime(2021, 1, 1, 5, 2, 32)
+    assert to_arrow_scalar(
+        datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), naive_dt
+    ).as_py() == datetime(2021, 1, 1, 5, 2, 32) + timedelta(hours=8)
+
+    # naive datetimes are treated like UTC
+    utc_dt = get_py_arrow_timestamp(6, tz="UTC")
+    dt_converted = to_arrow_scalar(
+        datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), utc_dt
+    ).as_py()
+    assert dt_converted.utcoffset().seconds == 0
+    assert dt_converted == datetime(2021, 1, 1, 13, 2, 32, tzinfo=timezone.utc)
+
+    berlin_dt = get_py_arrow_timestamp(6, tz="Europe/Berlin")
+    dt_converted = to_arrow_scalar(
+        datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), berlin_dt
+    ).as_py()
+    # no dst
+    assert dt_converted.utcoffset().seconds == 60 * 60
+    assert dt_converted == datetime(2021, 1, 1, 13, 2, 32, tzinfo=timezone.utc)
+
+
+def test_from_arrow_scalar() -> None:
+    naive_dt = get_py_arrow_timestamp(6, tz=None)
+    sc_dt = to_arrow_scalar(datetime(2021, 1, 1, 5, 2, 32), naive_dt)
+
+    # this value is like UTC
+    py_dt = from_arrow_scalar(sc_dt)
+    assert isinstance(py_dt, pendulum.DateTime)
+    # and we convert to explicit UTC
+    assert py_dt == datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone.utc)
+
+    # converts to UTC
+    berlin_dt = get_py_arrow_timestamp(6, tz="Europe/Berlin")
+    sc_dt = to_arrow_scalar(
+        datetime(2021, 1, 1, 5, 2, 32, tzinfo=timezone(timedelta(hours=-8))), berlin_dt
+    )
+    py_dt = from_arrow_scalar(sc_dt)
+    assert isinstance(py_dt, pendulum.DateTime)
+    assert py_dt.tzname() == "UTC"
+    assert py_dt == datetime(2021, 1, 1, 13, 2, 32, tzinfo=timezone.utc)
+
+
+def _row_at_index(table: pa.Table, index: int) -> List[Any]:
+    return [table.column(column_name)[index].as_py() for column_name in table.column_names]
+
+
+@pytest.mark.parametrize("pa_type", [pa.Table, pa.RecordBatch])
+def test_remove_null_columns(pa_type: Any) -> None:
+    table = pa_type.from_pylist(
+        [
+            {"a": 1, "b": 2, "c": None},
+            {"a": 1, "b": None, "c": None},
+        ]
+    )
+    result = remove_null_columns(table)
+    assert result.column_names == ["a", "b"]
+    assert _row_at_index(result, 0) == [1, 2]
+    assert _row_at_index(result, 1) == [1, None]
+
+
+@pytest.mark.parametrize("pa_type", [pa.Table, pa.RecordBatch])
+def test_remove_columns(pa_type: Any) -> None:
+    table = pa_type.from_pylist(
+        [
+            {"a": 1, "b": 2, "c": 5},
+            {"a": 1, "b": 3, "c": 4},
+        ]
+    )
+    result = remove_columns(table, ["b"])
+    assert result.column_names == ["a", "c"]
+    assert _row_at_index(result, 0) == [1, 5]
+    assert _row_at_index(result, 1) == [1, 4]
+
+
+@pytest.mark.parametrize("pa_type", [pa.Table, pa.RecordBatch])
+def test_append_column(pa_type: Any) -> None:
+    table = pa_type.from_pylist(
+        [
+            {"a": 1, "b": 2},
+            {"a": 1, "b": 3},
+        ]
+    )
+    result = append_column(table, "c", pa.array([5, 6]))
+    assert result.column_names == ["a", "b", "c"]
+    assert _row_at_index(result, 0) == [1, 2, 5]
+    assert _row_at_index(result, 1) == [1, 3, 6]
+
+
+@pytest.mark.parametrize("pa_type", [pa.Table, pa.RecordBatch])
+def test_rename_column(pa_type: Any) -> None:
+    table = pa_type.from_pylist(
+        [
+            {"a": 1, "b": 2, "c": 5},
+            {"a": 1, "b": 3, "c": 4},
+        ]
+    )
+    result = rename_columns(table, ["one", "two", "three"])
+    assert result.column_names == ["one", "two", "three"]
+    assert _row_at_index(result, 0) == [1, 2, 5]
+    assert _row_at_index(result, 1) == [1, 3, 4]
+
+
+@pytest.mark.parametrize("pa_type", [pa.Table, pa.RecordBatch])
+def test_is_arrow_item(pa_type: Any) -> None:
+    table = pa_type.from_pylist(
+        [
+            {"a": 1, "b": 2, "c": 5},
+            {"a": 1, "b": 3, "c": 4},
+        ]
+    )
+    assert is_arrow_item(table)
+    assert not is_arrow_item(table.to_pydict())
+    assert not is_arrow_item("hello")

tests/libs/pyarrow/test_pyarrow_normalizer.py

@@ -0,0 +1,111 @@
+from typing import List, Any
+
+import pyarrow as pa
+import pytest
+
+from dlt.common.libs.pyarrow import normalize_py_arrow_item, NameNormalizationClash
+from dlt.common.normalizers import explicit_normalizers, import_normalizers
+from dlt.common.schema.utils import new_column, TColumnSchema
+from dlt.common.destination import DestinationCapabilitiesContext
+
+
+def _normalize(table: pa.Table, columns: List[TColumnSchema]) -> pa.Table:
+    _, naming, _ = import_normalizers(explicit_normalizers())
+    caps = DestinationCapabilitiesContext()
+    columns_schema = {c["name"]: c for c in columns}
+    return normalize_py_arrow_item(table, columns_schema, naming, caps)
+
+
+def _row_at_index(table: pa.Table, index: int) -> List[Any]:
+    return [table.column(column_name)[index].as_py() for column_name in table.column_names]
+
+
+def test_quick_return_if_nothing_to_do() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"a": 1, "b": 2},
+        ]
+    )
+    columns = [new_column("a", "bigint"), new_column("b", "bigint")]
+    result = _normalize(table, columns)
+    # same object returned
+    assert result == table
+
+
+def test_pyarrow_reorder_columns() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"col_new": "hello", "col1": 1, "col2": "a"},
+        ]
+    )
+    columns = [new_column("col2", "text"), new_column("col1", "bigint")]
+    result = _normalize(table, columns)
+    # new columns appear at the end
+    assert result.column_names == ["col2", "col1", "col_new"]
+    assert _row_at_index(result, 0) == ["a", 1, "hello"]
+
+
+def test_pyarrow_add_empty_types() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"col1": 1},
+        ]
+    )
+    columns = [new_column("col1", "bigint"), new_column("col2", "text")]
+    result = _normalize(table, columns)
+    # new columns appear at the end
+    assert result.column_names == ["col1", "col2"]
+    assert _row_at_index(result, 0) == [1, None]
+    assert result.schema.field(1).type == "string"
+
+
+def test_field_normalization_clash() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"col^New": "hello", "col_new": 1},
+        ]
+    )
+    with pytest.raises(NameNormalizationClash):
+        _normalize(table, [])
+
+
+def test_field_normalization() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"col^New": "hello", "col2": 1},
+        ]
+    )
+    result = _normalize(table, [])
+    assert result.column_names == ["col_new", "col2"]
+    assert _row_at_index(result, 0) == ["hello", 1]
+
+
+def test_default_dlt_columns_not_added() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"col1": 1},
+        ]
+    )
+    columns = [
+        new_column("_dlt_something", "bigint"),
+        new_column("_dlt_id", "text"),
+        new_column("_dlt_load_id", "text"),
+        new_column("col2", "text"),
+        new_column("col1", "text"),
+    ]
+    result = _normalize(table, columns)
+    # no dlt_id or dlt_load_id columns
+    assert result.column_names == ["_dlt_something", "col2", "col1"]
+    assert _row_at_index(result, 0) == [None, None, 1]
+
+
+@pytest.mark.skip(reason="Somehow this does not fail, should we add an exception??")
+def test_fails_if_adding_non_nullable_column() -> None:
+    table = pa.Table.from_pylist(
+        [
+            {"col1": 1},
+        ]
+    )
+    columns = [new_column("col1", "bigint"), new_column("col2", "text", nullable=False)]
+    with pytest.raises(Exception):
+        _normalize(table, columns)

tests/normalize/test_normalize.py

@@ -6,6 +6,7 @@
 from dlt.common import json
 from dlt.common.destination.capabilities import TLoaderFileFormat
 from dlt.common.schema.schema import Schema
+from dlt.common.schema.utils import new_table
 from dlt.common.storages.exceptions import SchemaNotFoundError
 from dlt.common.typing import StrAny
 from dlt.common.data_types import TDataType
@@ -601,15 +602,15 @@ def extract_and_normalize_cases(normalize: Normalize, cases: Sequence[str]) -> s
     return normalize_pending(normalize)
 
 
-def normalize_pending(normalize: Normalize) -> str:
+def normalize_pending(normalize: Normalize, schema: Schema = None) -> str:
     # pool not required for map_single
     load_ids = normalize.normalize_storage.extracted_packages.list_packages()
     assert len(load_ids) == 1, "Only one package allowed or rewrite tests"
     for load_id in load_ids:
         normalize._step_info_start_load_id(load_id)
         normalize.load_storage.new_packages.create_package(load_id)
         # read schema from package
-        schema = normalize.normalize_storage.extracted_packages.load_schema(load_id)
+        schema = schema or normalize.normalize_storage.extracted_packages.load_schema(load_id)
         # get files
         schema_files = normalize.normalize_storage.extracted_packages.list_new_jobs(load_id)
         # normalize without pool
@@ -708,3 +709,40 @@ def assert_timestamp_data_type(load_storage: LoadStorage, data_type: TDataType)
     event_schema = load_storage.normalized_packages.load_schema(loads[0])
     # in raw normalize timestamp column must not be coerced to timestamp
     assert event_schema.get_table_columns("event")["timestamp"]["data_type"] == data_type
+
+
+def test_removal_of_normalizer_schema_section_and_add_seen_data(raw_normalize: Normalize) -> None:
+    extract_cases(
+        raw_normalize,
+        [
+            "event.event.user_load_1",
+        ],
+    )
+    load_ids = raw_normalize.normalize_storage.extracted_packages.list_packages()
+    assert len(load_ids) == 1
+    extracted_schema = raw_normalize.normalize_storage.extracted_packages.load_schema(load_ids[0])
+
+    # add some normalizer blocks
+    extracted_schema.tables["event"] = new_table("event")
+    extracted_schema.tables["event__parse_data__intent_ranking"] = new_table(
+        "event__parse_data__intent_ranking"
+    )
+    extracted_schema.tables["event__random_table"] = new_table("event__random_table")
+
+    # add x-normalizer info (and other block to control)
+    extracted_schema.tables["event"]["x-normalizer"] = {"evolve-columns-once": True}  # type: ignore
+    extracted_schema.tables["event"]["x-other-info"] = "blah"  # type: ignore
+    extracted_schema.tables["event__parse_data__intent_ranking"]["x-normalizer"] = {"seen-data": True, "random-entry": 1234}  # type: ignore
+    extracted_schema.tables["event__random_table"]["x-normalizer"] = {"evolve-columns-once": True}  # type: ignore
+
+    normalize_pending(raw_normalize, extracted_schema)
+    schema = raw_normalize.schema_storage.load_schema("event")
+    # seen data gets added, schema settings get removed
+    assert schema.tables["event"]["x-normalizer"] == {"seen-data": True}  # type: ignore
+    assert schema.tables["event__parse_data__intent_ranking"]["x-normalizer"] == {  # type: ignore
+        "seen-data": True,
+        "random-entry": 1234,
+    }
+    # no data seen here, so seen-data is not set and evolve settings stays until first data is seen
+    assert schema.tables["event__random_table"]["x-normalizer"] == {"evolve-columns-once": True}  # type: ignore
+    assert "x-other-info" in schema.tables["event"]