taipy/tests/core/data/test_parquet_data_node.py

# Copyright 2021-2025 Avaiga Private Limited
#
# 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 os
import pathlib
import re
import uuid
from datetime import datetime, timedelta
from importlib import util
from time import sleep

import freezegun
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal

from taipy import Scope
from taipy.common.config import Config
from taipy.common.config.exceptions.exceptions import InvalidConfigurationId
from taipy.core.common._utils import _normalize_path
from taipy.core.data._data_manager import _DataManager
from taipy.core.data._data_manager_factory import _DataManagerFactory
from taipy.core.data.data_node_id import DataNodeId
from taipy.core.data.parquet import ParquetDataNode
from taipy.core.exceptions.exceptions import (
    InvalidExposedType,
    UnknownCompressionAlgorithm,
    UnknownParquetEngine,
)
from taipy.core.reason import NoFileToDownload, NotAFile


@pytest.fixture(scope="function", autouse=True)
def cleanup():
    yield
    path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/temp.parquet")
    if os.path.isfile(path):
        os.remove(path)


class MyCustomObject:
    def __init__(self, id, integer, text):
        self.id = id
        self.integer = integer
        self.text = text


class MyOtherCustomObject:
    def __init__(self, id, sentence):
        self.id = id
        self.sentence = sentence


def create_custom_class(**kwargs):
    return MyOtherCustomObject(id=kwargs["id"], sentence=kwargs["text"])


class TestParquetDataNode:
    __engine = ["pyarrow"]
    if util.find_spec("fastparquet"):
        __engine.append("fastparquet")

    def test_create(self):
        path = "data/node/path"
        compression = "snappy"
        parquet_dn_config = Config.configure_parquet_data_node(
            id="foo_bar", default_path=path, compression=compression, name="super name"
        )
        dn = _DataManagerFactory._build_manager()._create(parquet_dn_config, None, None)
        assert isinstance(dn, ParquetDataNode)
        assert dn.storage_type() == "parquet"
        assert dn.config_id == "foo_bar"
        assert dn.name == "super name"
        assert dn.scope == Scope.SCENARIO
        assert dn.id is not None
        assert dn.owner_id is None
        assert dn.last_edit_date is None
        assert dn.job_ids == []
        assert not dn.is_ready_for_reading
        assert dn.path == path
        assert dn.properties["exposed_type"] == "pandas"
        assert dn.properties["compression"] == "snappy"
        assert dn.properties["engine"] == "pyarrow"

        parquet_dn_config_1 = Config.configure_parquet_data_node(
            id="bar", default_path=path, compression=compression, exposed_type=MyCustomObject
        )
        dn_1 = _DataManagerFactory._build_manager()._create(parquet_dn_config_1, None, None)
        assert isinstance(dn_1, ParquetDataNode)
        assert dn_1.properties["exposed_type"] == MyCustomObject

        parquet_dn_config_2 = Config.configure_parquet_data_node(
            id="bar", default_path=path, compression=compression, exposed_type=np.ndarray
        )
        dn_2 = _DataManagerFactory._build_manager()._create(parquet_dn_config_2, None, None)
        assert isinstance(dn_2, ParquetDataNode)
        assert dn_2.properties["exposed_type"] == np.ndarray

        parquet_dn_config_3 = Config.configure_parquet_data_node(
            id="bar", default_path=path, compression=compression, exposed_type=pd.DataFrame
        )
        dn_3 = _DataManagerFactory._build_manager()._create(parquet_dn_config_3, None, None)
        assert isinstance(dn_3, ParquetDataNode)
        assert dn_3.properties["exposed_type"] == pd.DataFrame

        with pytest.raises(InvalidConfigurationId):
            dn = ParquetDataNode("foo bar", Scope.SCENARIO, properties={"path": path, "name": "super name"})

    def test_get_user_properties(self, parquet_file_path):
        dn_1 = ParquetDataNode("dn_1", Scope.SCENARIO, properties={"path": parquet_file_path})
        assert dn_1._get_user_properties() == {}

        dn_2 = ParquetDataNode(
            "dn_2",
            Scope.SCENARIO,
            properties={
                "exposed_type": "numpy",
                "default_data": "foo",
                "default_path": parquet_file_path,
                "engine": "pyarrow",
                "compression": "snappy",
                "read_kwargs": {"columns": ["a", "b"]},
                "write_kwargs": {"index": False},
                "foo": "bar",
            },
        )

        # exposed_type, default_data, default_path, path, engine, compression, read_kwargs, write_kwargs
        # are filtered out
        assert dn_2._get_user_properties() == {"foo": "bar"}

    def test_new_parquet_data_node_with_existing_file_is_ready_for_reading(self, parquet_file_path):
        not_ready_dn_cfg = Config.configure_data_node(
            "not_ready_data_node_config_id", "parquet", path="NOT_EXISTING.parquet"
        )
        not_ready_dn = _DataManager._bulk_get_or_create([not_ready_dn_cfg])[not_ready_dn_cfg]
        assert not not_ready_dn.is_ready_for_reading

        ready_dn_cfg = Config.configure_data_node("ready_data_node_config_id", "parquet", path=parquet_file_path)
        ready_dn = _DataManager._bulk_get_or_create([ready_dn_cfg])[ready_dn_cfg]
        assert ready_dn.is_ready_for_reading

    @pytest.mark.parametrize(
        ["properties", "exists"],
        [
            ({}, False),
            ({"default_data": {"a": ["foo", "bar"]}}, True),
        ],
    )
    def test_create_with_default_data(self, properties, exists):
        dn = ParquetDataNode("foo", Scope.SCENARIO, DataNodeId(f"dn_id_{uuid.uuid4()}"), properties=properties)
        assert dn.path == f"{Config.core.storage_folder}parquets/{dn.id}.parquet"
        assert os.path.exists(dn.path) is exists

    @pytest.mark.parametrize("engine", __engine)
    def test_modin_deprecated_in_favor_of_pandas(self, engine, parquet_file_path):
        # Create ParquetDataNode with modin exposed_type
        props = {"path": parquet_file_path, "exposed_type": "modin", "engine": engine}
        parquet_data_node_as_modin = ParquetDataNode("bar", Scope.SCENARIO, properties=props)
        assert parquet_data_node_as_modin.properties["exposed_type"] == "pandas"
        data_modin = parquet_data_node_as_modin.read()
        assert isinstance(data_modin, pd.DataFrame)

    def test_set_path(self):
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": "foo.parquet"})
        _DataManagerFactory._build_manager()._repository._save(dn)
        assert dn.path == "foo.parquet"
        dn.path = "bar.parquet"
        assert dn.path == "bar.parquet"

    def test_raise_error_unknown_parquet_engine(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/example.parquet")
        with pytest.raises(UnknownParquetEngine):
            ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "engine": "foo"})

    def test_raise_error_unknown_compression_algorithm(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/example.parquet")
        with pytest.raises(UnknownCompressionAlgorithm):
            ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "compression": "foo"})

    def test_raise_error_invalid_exposed_type(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/example.parquet")
        with pytest.raises(InvalidExposedType):
            ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "exposed_type": "foo"})

    def test_get_system_file_modified_date_instead_of_last_edit_date(self, tmpdir_factory):
        temp_file_path = str(tmpdir_factory.mktemp("data").join("temp.parquet"))
        pd.DataFrame([]).to_parquet(temp_file_path)
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": temp_file_path, "exposed_type": "pandas"})
        _DataManagerFactory._build_manager()._repository._save(dn)

        dn.write(pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}))
        previous_edit_date = dn.last_edit_date

        sleep(0.1)

        pd.DataFrame(pd.DataFrame(data={"col1": [5, 6], "col2": [7, 8]})).to_parquet(temp_file_path)
        new_edit_date = datetime.fromtimestamp(os.path.getmtime(temp_file_path))

        assert previous_edit_date < dn.last_edit_date
        assert new_edit_date == dn.last_edit_date

        sleep(0.1)

        dn.write(pd.DataFrame(data={"col1": [9, 10], "col2": [10, 12]}))
        assert new_edit_date < dn.last_edit_date
        os.unlink(temp_file_path)

    def test_get_system_folder_modified_date_instead_of_last_edit_date(self, tmpdir_factory):
        temp_folder_path = tmpdir_factory.mktemp("data").strpath
        temp_file_path = os.path.join(temp_folder_path, "temp.parquet")
        pd.DataFrame([]).to_parquet(temp_file_path)

        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": temp_folder_path})
        _DataManagerFactory._build_manager()._repository._save(dn)
        initial_edit_date = dn.last_edit_date

        # Sleep so that the file can be created successfully on Ubuntu
        sleep(0.1)

        pd.DataFrame(pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})).to_parquet(temp_file_path)
        first_edit_date = datetime.fromtimestamp(os.path.getmtime(temp_file_path))
        assert dn.last_edit_date > initial_edit_date
        assert dn.last_edit_date == first_edit_date

        sleep(0.1)

        pd.DataFrame(pd.DataFrame(data={"col1": [5, 6], "col2": [7, 8]})).to_parquet(temp_file_path)
        second_edit_date = datetime.fromtimestamp(os.path.getmtime(temp_file_path))
        assert dn.last_edit_date > first_edit_date
        assert dn.last_edit_date == second_edit_date

        os.unlink(temp_file_path)

    def test_migrate_to_new_path(self, tmp_path):
        _base_path = os.path.join(tmp_path, ".data")
        path = os.path.join(_base_path, "test.parquet")
        # create a file on old path
        os.mkdir(_base_path)
        with open(path, "w"):
            pass

        dn = ParquetDataNode("foo_bar", Scope.SCENARIO, properties={"path": path, "name": "super name"})

        assert ".data" not in dn.path
        assert os.path.exists(dn.path)

    def test_is_downloadable(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/example.parquet")
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "exposed_type": "pandas"})
        reasons = dn.is_downloadable()
        assert reasons
        assert reasons.reasons == ""

    def test_is_not_downloadable_no_file(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/wrong_path.parquet")
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "exposed_type": "pandas"})
        reasons = dn.is_downloadable()
        assert not reasons
        assert len(reasons._reasons) == 1
        assert str(NoFileToDownload(_normalize_path(path), dn.id)) in reasons.reasons

    def test_is_not_downloadable_not_a_file(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample")
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "exposed_type": "pandas"})
        reasons = dn.is_downloadable()
        assert not reasons
        assert len(reasons._reasons) == 1
        assert str(NotAFile(_normalize_path(path), dn.id)) in reasons.reasons

    def test_get_downloadable_path(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/example.parquet")
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path, "exposed_type": "pandas"})
        assert re.split(r"[\\/]", dn._get_downloadable_path()) == re.split(r"[\\/]", path)

    def test_get_downloadable_path_with_not_existing_file(self):
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": "NOT_EXISTING.parquet"})
        assert dn._get_downloadable_path() == ""

    def test_get_downloadable_path_as_directory_should_return_nothing(self):
        path = os.path.join(pathlib.Path(__file__).parent.resolve(), "data_sample/parquet_example")
        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": path})
        assert dn._get_downloadable_path() == ""

    def test_upload(self, parquet_file_path, tmpdir_factory):
        old_parquet_path = tmpdir_factory.mktemp("data").join("df.parquet").strpath
        old_data = pd.DataFrame([{"a": 0, "b": 1, "c": 2}, {"a": 3, "b": 4, "c": 5}])

        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": old_parquet_path, "exposed_type": "pandas"})
        _DataManagerFactory._build_manager()._repository._save(dn)
        dn.write(old_data)
        old_last_edit_date = dn.last_edit_date

        upload_content = pd.read_parquet(parquet_file_path)

        with freezegun.freeze_time(old_last_edit_date + timedelta(seconds=1)):
            dn._upload(parquet_file_path)

        assert_frame_equal(dn.read(), upload_content)  # The content of the dn should change to the uploaded content
        assert dn.last_edit_date > old_last_edit_date
        assert dn.path == _normalize_path(old_parquet_path)  # The path of the dn should not change

    def test_upload_with_upload_check_pandas(self, parquet_file_path, tmpdir_factory):
        old_parquet_path = tmpdir_factory.mktemp("data").join("df.parquet").strpath
        old_data = pd.DataFrame([{"a": 0, "b": 1, "c": 2}, {"a": 3, "b": 4, "c": 5}])

        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": old_parquet_path, "exposed_type": "pandas"})
        _DataManagerFactory._build_manager()._repository._save(dn)
        dn.write(old_data)
        old_last_edit_date = dn.last_edit_date

        def check_data_column(upload_path, upload_data):
            return upload_path.endswith(".parquet") and upload_data.columns.tolist() == ["a", "b", "c"]

        not_exists_parquet_path = tmpdir_factory.mktemp("data").join("not_exists.parquet").strpath
        reasons = dn._upload(not_exists_parquet_path, upload_checker=check_data_column)
        assert bool(reasons) is False
        assert (
            str(list(reasons._reasons[dn.id])[0]) == "The uploaded file 'not_exists.parquet' can not be read,"
            f" therefore is not a valid data file for data node '{dn.id}'"
        )

        not_parquet_path = tmpdir_factory.mktemp("data").join("wrong_format_df.not_parquet").strpath
        old_data.to_parquet(not_parquet_path, index=False)
        # The upload should fail when the file is not a parquet
        reasons = dn._upload(not_parquet_path, upload_checker=check_data_column)
        assert bool(reasons) is False
        assert (
            str(list(reasons._reasons[dn.id])[0])
            == f"The uploaded file 'wrong_format_df.not_parquet' has invalid data for data node '{dn.id}'"
        )

        wrong_format_parquet_path = tmpdir_factory.mktemp("data").join("wrong_format_df.parquet").strpath
        pd.DataFrame([{"a": 1, "b": 2, "d": 3}, {"a": 4, "b": 5, "d": 6}]).to_parquet(
            wrong_format_parquet_path, index=False
        )
        # The upload should fail when check_data_column() return False
        reasons = dn._upload(wrong_format_parquet_path, upload_checker=check_data_column)
        assert bool(reasons) is False
        assert (
            str(list(reasons._reasons[dn.id])[0])
            == f"The uploaded file 'wrong_format_df.parquet' has invalid data for data node '{dn.id}'"
        )

        assert_frame_equal(dn.read(), old_data)  # The content of the dn should not change when upload fails
        assert dn.last_edit_date == old_last_edit_date  # The last edit date should not change when upload fails
        assert dn.path == _normalize_path(old_parquet_path)  # The path of the dn should not change

        # The upload should succeed when check_data_column() return True
        assert dn._upload(parquet_file_path, upload_checker=check_data_column)

    def test_upload_with_upload_check_numpy(self, tmpdir_factory):
        old_parquet_path = tmpdir_factory.mktemp("data").join("df.parquet").strpath
        old_data = np.array([[1, 2, 3], [4, 5, 6]])

        new_parquet_path = tmpdir_factory.mktemp("data").join("new_upload_data.parquet").strpath
        new_data = np.array([[1, 2, 3], [4, 5, 6]])
        pd.DataFrame(new_data, columns=["a", "b", "c"]).to_parquet(new_parquet_path, index=False)

        dn = ParquetDataNode("foo", Scope.SCENARIO, properties={"path": old_parquet_path, "exposed_type": "numpy"})
        _DataManagerFactory._build_manager()._repository._save(dn)
        dn.write(old_data)
        old_last_edit_date = dn.last_edit_date

        def check_data_is_positive(upload_path, upload_data):
            return upload_path.endswith(".parquet") and np.all(upload_data > 0)

        not_exists_parquet_path = tmpdir_factory.mktemp("data").join("not_exists.parquet").strpath
        reasons = dn._upload(not_exists_parquet_path, upload_checker=check_data_is_positive)
        assert bool(reasons) is False
        assert (
            str(list(reasons._reasons[dn.id])[0]) == "The uploaded file 'not_exists.parquet' can not be read,"
            f" therefore is not a valid data file for data node '{dn.id}'"
        )

        not_parquet_path = tmpdir_factory.mktemp("data").join("wrong_format_df.not_parquet").strpath
        pd.DataFrame(old_data, columns=["a", "b", "c"]).to_parquet(not_parquet_path, index=False)
        # The upload should fail when the file is not a parquet
        reasons = dn._upload(not_parquet_path, upload_checker=check_data_is_positive)
        assert (
            str(list(reasons._reasons[dn.id])[0])
            == f"The uploaded file 'wrong_format_df.not_parquet' has invalid data for data node '{dn.id}'"
        )

        wrong_format_parquet_path = tmpdir_factory.mktemp("data").join("wrong_format_df.parquet").strpath
        pd.DataFrame(np.array([[-1, 2, 3], [-4, -5, -6]]), columns=["a", "b", "c"]).to_parquet(
            wrong_format_parquet_path, index=False
        )
        # The upload should fail when check_data_is_positive() return False
        reasons = dn._upload(wrong_format_parquet_path, upload_checker=check_data_is_positive)
        assert (
            str(list(reasons._reasons[dn.id])[0])
            == f"The uploaded file 'wrong_format_df.parquet' has invalid data for data node '{dn.id}'"
        )

        np.array_equal(dn.read(), old_data)  # The content of the dn should not change when upload fails
        assert dn.last_edit_date == old_last_edit_date  # The last edit date should not change when upload fails
        assert dn.path == _normalize_path(old_parquet_path)  # The path of the dn should not change

        # The upload should succeed when check_data_is_positive() return True
        assert dn._upload(new_parquet_path, upload_checker=check_data_is_positive)