108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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"""Utility module."""
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import logging
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import sys
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from pymongo import MongoClient
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# constants
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REFS_FACTOR = 1.2 # 120%
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TIMELINE_SIMILARITY = ('model', 'device', 'test_exe', 'os', 'cpu_info', 'target_branch')
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def _transpose_dicts(items, template=None):
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""" Build dictionary of arrays from array of dictionaries
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Example:
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> in = [{'a':1, 'b':3}, {'a':2}]
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> _transpose_dicts(in, template=in[0])
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{'a':[1,2], 'b':[3, None]}
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"""
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result = {}
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if not items:
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return result
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if not template:
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template = items[0]
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for key, template_val in template.items():
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if isinstance(template_val, dict):
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result[key] = _transpose_dicts(
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[item[key] for item in items if key in item], template_val)
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else:
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result[key] = [item.get(key, None) for item in items]
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return result
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def query_memory_timeline(records, db_url, db_name, db_collection, max_items=20, similarity=TIMELINE_SIMILARITY):
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""" Query database for similar memory items committed previously
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"""
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def timeline_key(item):
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""" Defines order for timeline report entries
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"""
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order = 0
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for step_name, _ in item['results'].items():
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if len(item['results'][step_name]['vmhwm']) <= 1:
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return 1
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order = item['results'][step_name]['vmhwm']["avg"][-1] - item['results'][step_name]['vmhwm']["avg"][-2] + \
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item['results'][step_name]['vmrss']["avg"][-1] - item['results'][step_name]['vmrss']["avg"][-2]
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if not item['status']:
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# ensure failed cases are always on top
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order += sys.maxsize / 2
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return order
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client = MongoClient(db_url)
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collection = client[db_name][db_collection]
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result = []
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for record in records:
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items = []
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try:
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query = dict((key, record[key]) for key in similarity)
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query['commit_date'] = {'$lt': record['commit_date']}
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pipeline = [
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{'$match': query},
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{'$addFields': {
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'commit_date': {'$dateFromString': {'dateString': '$commit_date'}}}},
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{'$sort': {'commit_date': -1}},
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{'$limit': max_items},
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{'$sort': {'commit_date': 1}},
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]
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items += list(collection.aggregate(pipeline))
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except KeyError:
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pass # keep only the record if timeline failed to generate
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items += [record]
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for item in items:
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item["status"] = {"passed": True, "failed": False, "not_finished": False}[item["status"]]
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timeline = _transpose_dicts(items, template=record)
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result += [timeline]
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result.sort(key=timeline_key, reverse=True)
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return result
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def compare_with_references(aggr_stats: dict, reference: dict):
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"""Compare values with provided reference"""
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vm_metrics_to_compare = {"vmrss", "vmhwm"}
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stat_metrics_to_compare = {"avg"}
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status = 0
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for step_name, vm_records in reference.items():
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for vm_metric, stat_metrics in vm_records.items():
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if vm_metric not in vm_metrics_to_compare:
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continue
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for stat_metric_name, reference_val in stat_metrics.items():
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if stat_metric_name not in stat_metrics_to_compare:
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continue
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if aggr_stats[step_name][vm_metric][stat_metric_name] > reference_val * REFS_FACTOR:
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logging.error(f"Comparison failed for '{step_name}' step for '{vm_metric}' for"
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f" '{stat_metric_name}' metric. Reference: {reference_val}."
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f" Current values: {aggr_stats[step_name][vm_metric][stat_metric_name]}")
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status = 1
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else:
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logging.info(f"Comparison passed for '{step_name}' step for '{vm_metric}' for"
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f" '{stat_metric_name}' metric. Reference: {reference_val}."
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f" Current values: {aggr_stats[step_name][vm_metric][stat_metric_name]}")
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return status
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