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056e6368d0
* refactor!: change partition metadata structure for table entities * refactor!: updated json schema for TypeScript code gen * chore: migration of partition for table entities * style: python & java linting * updated ui side change for table partitioned key * miner fix * addressing comments * fixed ci error --------- Co-authored-by: Shailesh Parmar <shailesh.parmar.webdev@gmail.com>
129 lines
4.6 KiB
Python
129 lines
4.6 KiB
Python
# Copyright 2021 Collate
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Interfaces with database for all database engine
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supporting sqlalchemy abstraction layer
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"""
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import math
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import random
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from typing import cast
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from metadata.data_quality.validations.table.pandas.tableRowInsertedCountToBeBetween import (
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TableRowInsertedCountToBeBetweenValidator,
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)
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from metadata.generated.schema.entity.data.table import (
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PartitionIntervalTypes,
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PartitionProfilerConfig,
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ProfileSampleType,
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)
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from metadata.readers.dataframe.models import DatalakeTableSchemaWrapper
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from metadata.utils.datalake.datalake_utils import fetch_dataframe
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from metadata.utils.logger import test_suite_logger
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logger = test_suite_logger()
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class PandasInterfaceMixin:
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"""Interface mixin grouping shared methods between test suite and profiler interfaces"""
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def get_partitioned_df(self, dfs):
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"""Get partitioned dataframe
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Returns:
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DataFrame
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"""
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self.table_partition_config = cast(
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PartitionProfilerConfig, self.table_partition_config
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)
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partition_field = self.table_partition_config.partitionColumnName
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if (
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self.table_partition_config.partitionIntervalType
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== PartitionIntervalTypes.COLUMN_VALUE
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):
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return [
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df[
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df[partition_field].isin(
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self.table_partition_config.partitionValues
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)
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]
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for df in dfs
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]
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if (
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self.table_partition_config.partitionIntervalType
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== PartitionIntervalTypes.INTEGER_RANGE
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):
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return [
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df[
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df[partition_field].between(
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self.table_partition_config.partitionIntegerRangeStart,
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self.table_partition_config.partitionIntegerRangeEnd,
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)
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]
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for df in dfs
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]
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return [
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df[
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df[partition_field]
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>= TableRowInsertedCountToBeBetweenValidator._get_threshold_date( # pylint: disable=protected-access
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self.table_partition_config.partitionIntervalUnit.value,
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self.table_partition_config.partitionInterval,
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)
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]
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for df in dfs
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]
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def return_ometa_dataframes_sampled(
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self, service_connection_config, client, table, profile_sample_config
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):
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"""
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returns sampled ometa dataframes
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"""
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data = fetch_dataframe(
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config_source=service_connection_config.configSource,
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client=client,
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file_fqn=DatalakeTableSchemaWrapper(
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key=table.name.__root__,
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bucket_name=table.databaseSchema.name,
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file_extension=table.fileFormat,
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),
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)
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if data:
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random.shuffle(data)
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# sampling data based on profiler config (if any)
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if hasattr(profile_sample_config, "profile_sample"):
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if (
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profile_sample_config.profile_sample_type
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== ProfileSampleType.PERCENTAGE
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):
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return [
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df.sample(
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frac=profile_sample_config.profile_sample / 100,
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random_state=random.randint(0, 100),
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replace=True,
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)
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for df in data
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]
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if profile_sample_config.profile_sample_type == ProfileSampleType.ROWS:
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sample_rows_per_chunk: int = math.floor(
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profile_sample_config.profile_sample / len(data)
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)
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return [
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df.sample(
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n=sample_rows_per_chunk,
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random_state=random.randint(0, 100),
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replace=True,
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)
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for df in data
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]
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return data
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raise TypeError(f"Couldn't fetch {table.name.__root__}")
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