1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
|
# 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."""
import logging
from time import time
import awswrangler as wr
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):
"""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()
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"
f"{df}"
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):
"""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.
)
def read_trending_mrr(self):
"""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")
)
def read_trending_ndrpdr(self):
"""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")
)
def read_iterative_mrr(self):
"""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")
)
def read_iterative_ndrpdr(self):
"""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")
)
|
