1 # Copyright (c) 2023 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """Prepare data for Plotly Dash applications.
19 import awswrangler as wr
22 from yaml import load, FullLoader, YAMLError
23 from datetime import datetime, timedelta
26 from awswrangler.exceptions import EmptyDataFrame, NoFilesFound
30 """Gets the data from parquets and stores it for further use by dash
34 def __init__(self, data_spec_file: str) -> None:
35 """Initialize the Data object.
37 :param data_spec_file: Path to file specifying the data to be read from
39 :type data_spec_file: str
40 :raises RuntimeError: if it is not possible to open data_spec_file or it
41 is not a valid yaml file.
45 self._data_spec_file = data_spec_file
47 # Specification of data to be read from parquets:
48 self._data_spec = list()
50 # Data frame to keep the data:
52 "statistics": pd.DataFrame(),
53 "trending": pd.DataFrame(),
54 "iterative": pd.DataFrame(),
55 "coverage": pd.DataFrame()
60 with open(self._data_spec_file, "r") as file_read:
61 self._data_spec = load(file_read, Loader=FullLoader)
62 except IOError as err:
64 f"Not possible to open the file {self._data_spec_file,}\n{err}"
66 except YAMLError as err:
68 f"An error occurred while parsing the specification file "
69 f"{self._data_spec_file,}\n"
78 def _get_list_of_files(
80 last_modified_begin=None,
81 last_modified_end=None,
84 """Get list of interested files stored in S3 compatible storage and
87 :param path: S3 prefix (accepts Unix shell-style wildcards)
88 (e.g. s3://bucket/prefix) or list of S3 objects paths
89 (e.g. [s3://bucket/key0, s3://bucket/key1]).
90 :param last_modified_begin: Filter the s3 files by the Last modified
91 date of the object. The filter is applied only after list all s3
93 :param last_modified_end: Filter the s3 files by the Last modified date
94 of the object. The filter is applied only after list all s3 files.
95 :param days: Number of days to filter.
96 :type path: Union[str, List[str]]
97 :type last_modified_begin: datetime, optional
98 :type last_modified_end: datetime, optional
99 :type days: integer, optional
100 :returns: List of file names.
105 last_modified_begin = datetime.now(tz=UTC) - timedelta(days=days)
107 file_list = wr.s3.list_objects(
110 last_modified_begin=last_modified_begin,
111 last_modified_end=last_modified_end
113 logging.debug("\n".join(file_list))
114 except NoFilesFound as err:
115 logging.error(f"No parquets found.\n{err}")
116 except EmptyDataFrame as err:
117 logging.error(f"No data.\n{err}")
122 def _create_dataframe_from_parquet(
123 path, partition_filter=None,
125 validate_schema=False,
126 last_modified_begin=None,
127 last_modified_end=None,
130 """Read parquet stored in S3 compatible storage and returns Pandas
133 :param path: S3 prefix (accepts Unix shell-style wildcards)
134 (e.g. s3://bucket/prefix) or list of S3 objects paths
135 (e.g. [s3://bucket/key0, s3://bucket/key1]).
136 :param partition_filter: Callback Function filters to apply on PARTITION
137 columns (PUSH-DOWN filter). This function MUST receive a single
138 argument (Dict[str, str]) where keys are partitions names and values
139 are partitions values. Partitions values will be always strings
140 extracted from S3. This function MUST return a bool, True to read
141 the partition or False to ignore it. Ignored if dataset=False.
142 :param columns: Names of columns to read from the file(s).
143 :param validate_schema: Check that individual file schemas are all the
144 same / compatible. Schemas within a folder prefix should all be the
145 same. Disable if you have schemas that are different and want to
147 :param last_modified_begin: Filter the s3 files by the Last modified
148 date of the object. The filter is applied only after list all s3
150 :param last_modified_end: Filter the s3 files by the Last modified date
151 of the object. The filter is applied only after list all s3 files.
152 :param days: Number of days to filter.
153 :type path: Union[str, List[str]]
154 :type partition_filter: Callable[[Dict[str, str]], bool], optional
155 :type columns: List[str], optional
156 :type validate_schema: bool, optional
157 :type last_modified_begin: datetime, optional
158 :type last_modified_end: datetime, optional
159 :type days: integer, optional
160 :returns: Pandas DataFrame or None if DataFrame cannot be fetched.
166 last_modified_begin = datetime.now(tz=UTC) - timedelta(days=days)
168 df = wr.s3.read_parquet(
170 path_suffix="parquet",
172 validate_schema=validate_schema,
176 partition_filter=partition_filter,
177 last_modified_begin=last_modified_begin,
178 last_modified_end=last_modified_end
180 df.info(verbose=True, memory_usage="deep")
182 f"\nCreation of dataframe {path} took: {time() - start}\n"
184 except NoFilesFound as err:
186 f"No parquets found in specified time period.\n"
187 f"Nr of days: {days}\n"
188 f"last_modified_begin: {last_modified_begin}\n"
191 except EmptyDataFrame as err:
193 f"No data in parquets in specified time period.\n"
194 f"Nr of days: {days}\n"
195 f"last_modified_begin: {last_modified_begin}\n"
201 def read_all_data(self, days: int=None) -> dict:
202 """Read all data necessary for all applications.
204 :param days: Number of days to filter. If None, all data will be
207 :returns: A dictionary where keys are names of parquets and values are
208 the pandas dataframes with fetched data.
209 :rtype: dict(str: pandas.DataFrame)
212 lst_trending = list()
213 lst_iterative = list()
214 lst_coverage = list()
216 for data_set in self._data_spec:
218 f"Reading data for {data_set['data_type']} "
219 f"{data_set['partition_name']} {data_set.get('release', '')}"
221 partition_filter = lambda part: True \
222 if part[data_set["partition"]] == data_set["partition_name"] \
224 if data_set["data_type"] in ("trending", "statistics"):
228 data = Data._create_dataframe_from_parquet(
229 path=data_set["path"],
230 partition_filter=partition_filter,
231 columns=data_set.get("columns", None),
235 if data_set["data_type"] == "statistics":
236 self._data["statistics"] = data
237 elif data_set["data_type"] == "trending":
238 lst_trending.append(data)
239 elif data_set["data_type"] == "iterative":
240 data["release"] = data_set["release"]
241 data["release"] = data["release"].astype("category")
242 lst_iterative.append(data)
243 elif data_set["data_type"] == "coverage":
244 data["release"] = data_set["release"]
245 data["release"] = data["release"].astype("category")
246 lst_coverage.append(data)
248 raise NotImplementedError(
249 f"The data type {data_set['data_type']} is not implemented."
252 self._data["iterative"] = pd.concat(
257 self._data["trending"] = pd.concat(
262 self._data["coverage"] = pd.concat(
268 for key in self._data.keys():
270 f"\nData frame {key}:"
271 f"\n{self._data[key].memory_usage(deep=True)}\n"
273 self._data[key].info(verbose=True, memory_usage="deep")
276 resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1000
277 logging.info(f"Memory allocation: {mem_alloc:.0f}MB")