# Copyright (c) 2022 Cisco and/or its affiliates. # 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. """Prepare data for Plotly Dash.""" from datetime import datetime, timedelta import logging from time import time import awswrangler as wr from pytz import UTC from yaml import load, FullLoader, YAMLError from awswrangler.exceptions import EmptyDataFrame, NoFilesFound class Data: """ """ def __init__(self, data_spec_file, debug=False): """ """ # Inputs: self._data_spec_file = data_spec_file self._debug = debug # Specification of data to be read from parquets: self._data_spec = None # Data frame to keep the data: self._data = None # Read from files: try: with open(self._data_spec_file, "r") as file_read: self._data_spec = load(file_read, Loader=FullLoader) except IOError as err: raise RuntimeError( f"Not possible to open the file {self._data_spec_file,}\n{err}" ) except YAMLError as err: raise RuntimeError( f"An error occurred while parsing the specification file " f"{self._data_spec_file,}\n" f"{err}" ) @property def data(self): return self._data def _get_columns(self, parquet): try: return self._data_spec[parquet]["columns"] except KeyError as err: raise RuntimeError( f"The parquet {parquet} is not defined in the specification " f"file {self._data_spec_file} or it does not have any columns " f"specified.\n{err}" ) def _get_path(self, parquet): try: return self._data_spec[parquet]["path"] except KeyError as err: raise RuntimeError( f"The parquet {parquet} is not defined in the specification " f"file {self._data_spec_file} or it does not have the path " f"specified.\n{err}" ) def _create_dataframe_from_parquet(self, path, partition_filter=None, columns=None, validate_schema=False, last_modified_begin=None, last_modified_end=None, days=None): """Read parquet stored in S3 compatible storage and returns Pandas Dataframe. :param path: S3 prefix (accepts Unix shell-style wildcards) (e.g. s3://bucket/prefix) or list of S3 objects paths (e.g. [s3://bucket/key0, s3://bucket/key1]). :param partition_filter: Callback Function filters to apply on PARTITION columns (PUSH-DOWN filter). This function MUST receive a single argument (Dict[str, str]) where keys are partitions names and values are partitions values. Partitions values will be always strings extracted from S3. This function MUST return a bool, True to read the partition or False to ignore it. Ignored if dataset=False. :param columns: Names of columns to read from the file(s). :param validate_schema: Check that individual file schemas are all the same / compatible. Schemas within a folder prefix should all be the same. Disable if you have schemas that are different and want to disable this check. :param last_modified_begin: Filter the s3 files by the Last modified date of the object. The filter is applied only after list all s3 files. :param last_modified_end: Filter the s3 files by the Last modified date of the object. The filter is applied only after list all s3 files. :type path: Union[str, List[str]] :type partition_filter: Callable[[Dict[str, str]], bool], optional :type columns: List[str], optional :type validate_schema: bool, optional :type last_modified_begin: datetime, optional :type last_modified_end: datetime, optional :returns: Pandas DataFrame or None if DataFrame cannot be fetched. :rtype: DataFrame """ df = None start = time() if days: last_modified_begin = datetime.now(tz=UTC) - timedelta(days=days) try: df = wr.s3.read_parquet( path=path, path_suffix="parquet", ignore_empty=True, validate_schema=validate_schema, use_threads=True, dataset=True, columns=columns, partition_filter=partition_filter, last_modified_begin=last_modified_begin, last_modified_end=last_modified_end ) if self._debug: df.info(verbose=True, memory_usage='deep') logging.info( u"\n" f"Creation of dataframe {path} took: {time() - start}" u"\n" ) except NoFilesFound as err: logging.error(f"No parquets found.\n{err}") except EmptyDataFrame as err: logging.error(f"No data.\n{err}") self._data = df return df def read_stats(self, days=None): """Read Suite Result Analysis data partition from parquet. """ lambda_f = lambda part: True if part["stats_type"] == "sra" else False return self._create_dataframe_from_parquet( path=self._get_path("statistics"), partition_filter=lambda_f, columns=None, # Get all columns. days=days ) def read_trending_mrr(self, days=None): """Read MRR data partition from parquet. """ lambda_f = lambda part: True if part["test_type"] == "mrr" else False return self._create_dataframe_from_parquet( path=self._get_path("trending-mrr"), partition_filter=lambda_f, columns=self._get_columns("trending-mrr"), days=days ) def read_trending_ndrpdr(self, days=None): """Read NDRPDR data partition from iterative parquet. """ lambda_f = lambda part: True if part["test_type"] == "ndrpdr" else False return self._create_dataframe_from_parquet( path=self._get_path("trending-ndrpdr"), partition_filter=lambda_f, columns=self._get_columns("trending-ndrpdr"), days=days ) def read_iterative_mrr(self, days=None): """Read MRR data partition from iterative parquet. """ lambda_f = lambda part: True if part["test_type"] == "mrr" else False return self._create_dataframe_from_parquet( path=self._get_path("iterative-mrr"), partition_filter=lambda_f, columns=self._get_columns("iterative-mrr"), days=days ) def read_iterative_ndrpdr(self, days=None): """Read NDRPDR data partition from parquet. """ lambda_f = lambda part: True if part["test_type"] == "ndrpdr" else False return self._create_dataframe_from_parquet( path=self._get_path("iterative-ndrpdr"), partition_filter=lambda_f, columns=self._get_columns("iterative-ndrpdr"), days=days )