├── .gitignore ├── LICENSE ├── README.md ├── intensity.py ├── model ├── metric.py └── similarity.py ├── requirements.txt └── utils ├── dataloader.py ├── logger.py ├── time.py └── ts.py /.gitignore: -------------------------------------------------------------------------------- 1 | data 2 | *.csv 3 | *.pdf 4 | intermediate 5 | .vscode 6 | logs 7 | 8 | # Byte-compiled / optimized / DLL files 9 | __pycache__/ 10 | *.py[cod] 11 | *$py.class 12 | 13 | # C extensions 14 | *.so 15 | 16 | # Distribution / packaging 17 | .Python 18 | build/ 19 | develop-eggs/ 20 | dist/ 21 | downloads/ 22 | eggs/ 23 | .eggs/ 24 | lib/ 25 | lib64/ 26 | parts/ 27 | sdist/ 28 | var/ 29 | wheels/ 30 | pip-wheel-metadata/ 31 | share/python-wheels/ 32 | *.egg-info/ 33 | .installed.cfg 34 | *.egg 35 | MANIFEST 36 | 37 | # PyInstaller 38 | # Usually these files are written by a python script from a template 39 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 40 | *.manifest 41 | *.spec 42 | 43 | # Installer logs 44 | pip-log.txt 45 | pip-delete-this-directory.txt 46 | 47 | # Unit test / coverage reports 48 | htmlcov/ 49 | .tox/ 50 | .nox/ 51 | .coverage 52 | .coverage.* 53 | .cache 54 | nosetests.xml 55 | coverage.xml 56 | *.cover 57 | *.py,cover 58 | .hypothesis/ 59 | .pytest_cache/ 60 | 61 | # Translations 62 | *.mo 63 | *.pot 64 | 65 | # Django stuff: 66 | *.log 67 | local_settings.py 68 | db.sqlite3 69 | db.sqlite3-journal 70 | 71 | # Flask stuff: 72 | instance/ 73 | .webassets-cache 74 | 75 | # Scrapy stuff: 76 | .scrapy 77 | 78 | # Sphinx documentation 79 | docs/_build/ 80 | 81 | # PyBuilder 82 | target/ 83 | 84 | # Jupyter Notebook 85 | .ipynb_checkpoints 86 | 87 | # IPython 88 | profile_default/ 89 | ipython_config.py 90 | 91 | # pyenv 92 | .python-version 93 | 94 | # pipenv 95 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 96 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 97 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 98 | # install all needed dependencies. 99 | #Pipfile.lock 100 | 101 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 102 | __pypackages__/ 103 | 104 | # Celery stuff 105 | celerybeat-schedule 106 | celerybeat.pid 107 | 108 | # SageMath parsed files 109 | *.sage.py 110 | 111 | # Environments 112 | .env 113 | .venv 114 | env/ 115 | venv/ 116 | ENV/ 117 | env.bak/ 118 | venv.bak/ 119 | 120 | # Spyder project settings 121 | .spyderproject 122 | .spyproject 123 | 124 | # Rope project settings 125 | .ropeproject 126 | 127 | # mkdocs documentation 128 | /site 129 | 130 | # mypy 131 | .mypy_cache/ 132 | .dmypy.json 133 | dmypy.json 134 | 135 | # Pyre type checker 136 | .pyre/ 137 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AID 2 | 3 | This repository contains the code for ASE'21 paper *AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems*. 4 | 5 | ## Data 6 | 7 | Please download the data from https://doi.org/10.5281/zenodo.5638238. 8 | 9 | The file contains a small portion of the preprocessed traces used in our paper. The traces are sanitized subsets of the first-party microservice invocations in one of Huawei Cloud's geographical regions on April 11, 2021. The service names are desensitized. 10 | 11 | ## Usage 12 | 13 | 1. `pip install -r requirements.txt` 14 | 2. `python intensity.py` 15 | 16 | ## Reference 17 | 18 | If you use our data or code, please kindly cite our paper. 19 | -------------------------------------------------------------------------------- /intensity.py: -------------------------------------------------------------------------------- 1 | import json 2 | from typing import List, Optional, Tuple 3 | 4 | import pandas as pd 5 | import numpy as np 6 | from scipy.special import softmax 7 | 8 | from utils.time import TimestampAgg 9 | from utils.ts import TSTransform, CompoundTransform 10 | from utils.logger import setupLogging 11 | from utils.dataloader import HuaweiDataset 12 | from model.similarity import DTW, Aggregator 13 | 14 | 15 | class AID: 16 | def __init__(self): 17 | # initialize logger 18 | loggerName = "AID" 19 | self._logger = setupLogging('logs', loggerName) 20 | # initialize data loader 21 | self._loader = HuaweiDataset() 22 | 23 | def _filterCandidate(self, candidateList): 24 | """Only return candidate calls whose parents appear as others' children 25 | Args: 26 | candidateList: a list of dict indicating calls 27 | 28 | Returns: 29 | candidateList: a list of filtered calls 30 | """ 31 | childSet = set(map(lambda x: x['c'], candidateList)) 32 | parentSet = set(map(lambda x: x['p'], candidateList)) 33 | self._logger.info(f"No. of child services: {len(childSet)}") 34 | self._logger.info(f"No. of parent services: {len(parentSet)}") 35 | self._logger.info( 36 | f"No. of services in both child and parent: {len(childSet & parentSet)}") 37 | self._logger.info( 38 | f"No. of parent not in child: {len(parentSet-childSet)}") 39 | self._logger.info( 40 | f"No. of child not in parent: {len(childSet-parentSet)}") 41 | filteredCand = sorted(filter(lambda x: x['p'] in childSet, candidateList), 42 | key=lambda x: x['cnt'], 43 | reverse=True) 44 | return filteredCand 45 | 46 | def _calculateKPIDistance(self, 47 | filteredCand, 48 | TSDict, 49 | kpiList, 50 | rowIdx, 51 | transformOperations, 52 | mpw: int, 53 | metricAggFunc=Aggregator.mean_agg, 54 | kpiNorm: str = "minmax"): 55 | """Calculate the intensity of dependency 56 | Args: 57 | filteredCand: a list of filtered candidates, see self.eval() 58 | TSDict: kpi series, see self.eval() 59 | kpiList: name of kpis to use, see self.eval() 60 | rowIdx: the time index (bin index), see self.eval() 61 | transformOperations: the normalization of time series, see self.eval() 62 | mpw: max propagation window, check the DSW algorithm for details 63 | metricAggFunc: how to aggregate the metrics in each bin, default is mean aggregation 64 | kpiNorm: normalize the distances of the same kpi, can be "minmax" or "softmax" 65 | 66 | Returns: 67 | candidateList: a list of filtered calls 68 | """ 69 | def transform(TSDict, cmdbId, kpi, rowIdx): 70 | srs = pd.Series(TSDict.loc[cmdbId][kpi], index=rowIdx).fillna(0) 71 | return CompoundTransform(srs, transformOperations) 72 | 73 | for item in filteredCand: 74 | for kpi in kpiList: 75 | # TODO 76 | # if the input array is constant (usually because we cannot detect any error) 77 | # then we should mark it as UNKNOWN 78 | item[f'dsw-{kpi}'] = DTW.dsw_distance( 79 | transform(TSDict, item['c'], kpi, rowIdx), 80 | transform(TSDict, item['p'], kpi, rowIdx), 81 | mpw=mpw) 82 | 83 | if kpiNorm == "softmax": 84 | for kpi in kpiList: 85 | allValues = np.array( 86 | list(map(lambda x: x[f'dsw-{kpi}'], filteredCand))) 87 | x = softmax(allValues) 88 | assert len(x) == len(filteredCand) 89 | for idx, candidate in enumerate(filteredCand): 90 | candidate[f'normalized-dsw-{kpi}'] = x[idx] 91 | elif kpiNorm == "minmax": 92 | for kpi in kpiList: 93 | allValues = list(map(lambda x: x[f'dsw-{kpi}'], filteredCand)) 94 | maxValue = np.max(allValues) 95 | minValue = np.min(allValues) 96 | for candidate in filteredCand: 97 | candidate[f'normalized-dsw-{kpi}'] = candidate[f'dsw-{kpi}'] - minValue 98 | if maxValue - minValue > 0: 99 | candidate[f'normalized-dsw-{kpi}'] /= maxValue - minValue 100 | else: 101 | raise NotImplementedError 102 | 103 | # calculate intensity 104 | for candidate in filteredCand: 105 | sims_dsw = [] 106 | for kpi in kpiList: 107 | sims_dsw.append(candidate[f'normalized-dsw-{kpi}']) 108 | # distance = 0 -> most similar, so need to use 1-agg 109 | candidate[f'intensity'] = 1-metricAggFunc(sims_dsw) 110 | 111 | filteredCand.sort(key=lambda x: x[f'intensity'], reverse=True) 112 | return filteredCand 113 | 114 | def eval(self, 115 | path: str, 116 | start: str, 117 | end: str, 118 | interval: int = 1, 119 | transformOperations: List[Tuple] = [('ZN',), ("MA", 15)], 120 | mpw: int = 5): 121 | """interface for evaluating dependency intensity 122 | 123 | Args: 124 | path: csv file name 125 | start: start date or time, eight-digit date YYYYMMDD 126 | end: end date or time, eight-digit date YYYYMMDD 127 | interval: aggregation interval. 1 minute is recommeneded. 128 | transformOperations 129 | 130 | Returns: 131 | intensity: a list of dicts, sorted by intensity value, higher 132 | value indicates higher dependency intensity 133 | """ 134 | # 1. load file 135 | self._logger.info(f"File name: {path}") 136 | candidateList, TSDict, cmdbList, kpiList = self._loader.load( 137 | path, 138 | tsAggFunc=TimestampAgg.toFreqMinute, 139 | tsAggFreq=int(interval)) 140 | self._logger.info(f"Finish loading dataset") 141 | 142 | # 2. preprocess 143 | # filter candidate 144 | self._logger.info( 145 | f"No. of candidates before filter: {len(candidateList)}") 146 | candidateList = self._filterCandidate(candidateList) 147 | self._logger.info( 148 | f"No. of candidates after filter: {len(candidateList)}") 149 | 150 | # filter data point 151 | def genDate(datestr): 152 | return f"{datestr[:4]}-{datestr[4:6]}-{datestr[6:8]}" 153 | 154 | rowIdx = pd.date_range(f"{genDate(start)} 00:00:00", 155 | f"{genDate(end)} 23:59:00", freq=f'{interval}T') 156 | 157 | self._logger.info(f"Time start: {rowIdx[0]}") 158 | self._logger.info(f"Time end: {rowIdx[-1]}") 159 | 160 | # 3. Calculate intensity 161 | self._logger.info("Calculate inensity") 162 | self._logger.info(f"Applied Transformations: {transformOperations}") 163 | self._logger.info(f"DSW Max Propagation Window: {mpw}") 164 | intensityList = self._calculateKPIDistance(candidateList, TSDict, kpiList, rowIdx, 165 | transformOperations=transformOperations, 166 | mpw=mpw, 167 | metricAggFunc=Aggregator.mean_agg) 168 | self._logger.info("Finish calculating intensity") 169 | 170 | # remove unnecessary attributes 171 | intensityList = list(map( 172 | lambda x: {"c": x["c"], 173 | "p": x["p"], 174 | "intensity": x["intensity"]}, 175 | intensityList) 176 | ) 177 | 178 | return intensityList 179 | 180 | 181 | if __name__ == "__main__": 182 | # uasge example 183 | aid = AID() 184 | intensity = aid.eval("data/industry/status_1min_20210411.csv.xz", 185 | start="20210411", 186 | end='20210411') 187 | with open("intensity.json", 'w') as f: 188 | json.dump(intensity, f, indent=4) 189 | -------------------------------------------------------------------------------- /model/metric.py: -------------------------------------------------------------------------------- 1 | import math 2 | import numpy as np 3 | 4 | 5 | def cross_entropy(label, pred): 6 | """ 7 | Two lists: label, pred 8 | Returns the float corresponding to their cross-entropy 9 | """ 10 | assert len(label) == len( 11 | pred), "label and pred should have the same length" 12 | sum = 0.0 13 | for x in map(lambda y, p: (1-y)*math.log(1-p)+y*math.log(p), label, pred): 14 | sum += x 15 | return -sum/len(label) 16 | 17 | 18 | def mean_absolute_error(label, pred): 19 | label, pred = np.array(label), np.array(pred) 20 | diff = pred - label 21 | abs_diff = np.absolute(diff) 22 | mean_diff = abs_diff.mean() 23 | return mean_diff 24 | 25 | 26 | def root_mean_squared_error(label, pred): 27 | label, pred = np.array(label), np.array(pred) 28 | diff = pred - label 29 | differences_squared = diff ** 2 30 | mean_diff = differences_squared.mean() 31 | rmse_val = np.sqrt(mean_diff) 32 | return rmse_val 33 | 34 | 35 | if __name__ == "__main__": 36 | # Tests 37 | Y = [1, 1, 0, 0] 38 | P = [0.6, 0.2, 0.9, 0.2] 39 | print(cross_entropy(Y, P)) 40 | print(mean_absolute_error(Y, P)) 41 | print(root_mean_squared_error(Y, P)) 42 | -------------------------------------------------------------------------------- /model/similarity.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | import numpy as np 3 | from scipy.stats import pearsonr, spearmanr, kendalltau 4 | 5 | 6 | class DTW: 7 | @staticmethod 8 | def naive_dtw_distance(ts_a, ts_b, mww, d=lambda x, y: abs(x-y)**2): 9 | """Computes dtw distance between two time series 10 | 11 | Args: 12 | ts_a: time series a 13 | ts_b: time series b 14 | mww: max warping window, int 15 | d: distance function 16 | 17 | Returns: 18 | dtw distance 19 | """ 20 | 21 | # Create cost matrix via broadcasting with large int 22 | ts_a, ts_b = np.array(ts_a), np.array(ts_b) 23 | M, N = len(ts_a), len(ts_b) 24 | cost = np.ones((M, N)) 25 | 26 | # Initialize the first row and column 27 | cost[0, 0] = d(ts_a[0], ts_b[0]) 28 | for i in range(1, M): 29 | cost[i, 0] = cost[i-1, 0] + d(ts_a[i], ts_b[0]) 30 | 31 | for j in range(1, N): 32 | cost[0, j] = cost[0, j-1] + d(ts_a[0], ts_b[j]) 33 | 34 | # Populate rest of cost matrix within window 35 | for i in range(1, M): 36 | for j in range(max(1, i - mww), min(N, i + mww)): 37 | choices = cost[i-1, j-1], cost[i, j-1], cost[i-1, j] 38 | cost[i, j] = min(choices) + d(ts_a[i], ts_b[j]) 39 | 40 | # Return DTW distance given window 41 | return cost[-1, -1] 42 | 43 | @staticmethod 44 | def dsw_distance(ts_c, ts_p, mpw, delta=1, d=lambda x, y: abs(x-y)**2): 45 | """Computes dsw distance between parent and child 46 | 47 | Args: 48 | ts_c: time series child 49 | ts_p: time series parent 50 | mpw: max propagation window, int 51 | delta: allowed time shift in the system 52 | d: distance function 53 | 54 | Returns: 55 | dsw distance 56 | """ 57 | 58 | # Create cost matrix via broadcasting with large int 59 | ts_p, ts_c = np.array(ts_p), np.array(ts_c) 60 | M, N = len(ts_c), len(ts_p) 61 | cost = np.ones((M, N)) 62 | 63 | # Initialize the first row and column 64 | cost[0, 0] = d(ts_p[0], ts_c[0]) 65 | for i in range(1, M): 66 | cost[i, 0] = cost[i-1, 0] + d(ts_c[i], ts_p[0]) 67 | 68 | for j in range(1, N): 69 | cost[0, j] = cost[0, j-1] + d(ts_c[0], ts_p[j]) 70 | 71 | # Populate rest of cost matrix within window 72 | for i in range(1, M): 73 | for j in range(max(1, i - mpw - delta), min(N, i + delta)): 74 | choices = cost[i-1, j-1], cost[i, j-1], cost[i-1, j] 75 | cost[i, j] = min(choices) + d(ts_c[i], ts_p[j]) 76 | 77 | # Return DSW 78 | return cost[-1, -1] 79 | 80 | 81 | class Correlation: 82 | @staticmethod 83 | def pearson(ts_a, ts_b): 84 | """Computes pearson correlation between two time series 85 | 86 | Args: 87 | ts_a: time series a 88 | ts_b: time series b 89 | 90 | Returns: 91 | r: correlation of a and b 92 | p: p value 93 | """ 94 | ts_a, ts_b = np.array(ts_a), np.array(ts_b) 95 | r, p = pearsonr(ts_a, ts_b) 96 | return r, p 97 | 98 | @staticmethod 99 | def spearman(ts_a, ts_b): 100 | """Computes spearman correlation between two time series 101 | 102 | Args: 103 | ts_a: time series a 104 | ts_b: time series b 105 | 106 | Returns: 107 | r: correlation of a and b 108 | p: p value 109 | """ 110 | ts_a, ts_b = np.array(ts_a), np.array(ts_b) 111 | r, p = spearmanr(ts_a, ts_b) 112 | return r, p 113 | 114 | @staticmethod 115 | def kendall(ts_a, ts_b): 116 | """Computes kendall correlation between two time series 117 | 118 | Args: 119 | ts_a: time series a 120 | ts_b: time series b 121 | 122 | Returns: 123 | r: correlation of a and b 124 | p: p value 125 | """ 126 | ts_a, ts_b = np.array(ts_a), np.array(ts_b) 127 | r, p = kendalltau(ts_a, ts_b) 128 | return r, p 129 | 130 | 131 | class Aggregator: 132 | @staticmethod 133 | def mean_agg(metrics: List[float]): 134 | return np.mean(metrics) 135 | 136 | @staticmethod 137 | def max_agg(metrics: List[float]): 138 | return np.max(metrics) 139 | 140 | @staticmethod 141 | def min_agg(metrics: List[float]): 142 | return np.min(metrics) 143 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas 2 | numpy 3 | scipy 4 | pyyaml -------------------------------------------------------------------------------- /utils/dataloader.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | 4 | from .time import TimestampAgg 5 | 6 | 7 | class TTDataset: 8 | def loadRawData(self, filename): 9 | df = pd.read_json(filename) 10 | df['timestamp'] = pd.to_datetime(df['timestamp']).astype(int) / (10**9) 11 | return df 12 | 13 | def getCandidateListByDF(self, df): 14 | df_c = df[['parent_id', 'span_id', 'cmdb_id']] 15 | df_p = df[['span_id', 'cmdb_id']] 16 | df = pd.merge(df_c, df_p, how='left', left_on='parent_id', 17 | right_on='span_id', suffixes=('_c', '_p')) 18 | df = df[['cmdb_id_c', 'cmdb_id_p']] 19 | df = df.dropna(axis=0, how='any') 20 | df = df[df['cmdb_id_c'] != df['cmdb_id_p']] 21 | df_g = df.groupby(['cmdb_id_c', 'cmdb_id_p']) 22 | candidateList = [] 23 | for g in list(df_g): 24 | tmp = {} 25 | tmp['c'] = g[0][0] 26 | tmp['p'] = g[0][1] 27 | tmp['cnt'] = len(g[1]) 28 | candidateList.append(tmp) 29 | return candidateList 30 | 31 | def countHttp(self, codes): 32 | cnt = 0 33 | for c in codes: 34 | cnt += (c != 200) 35 | return cnt 36 | 37 | def getTSDictByDF(self, trace_df, tsAggFunc=TimestampAgg.toMinute): 38 | cmdbList = list(trace_df['cmdb_id'].unique()) 39 | 40 | TSDict = trace_df[['cmdb_id', 'timestamp', 41 | 'duration', 'httpCode']].copy(deep=True) 42 | TSDict['timestamp'] = TSDict['timestamp'].apply( 43 | tsAggFunc).apply(pd.to_datetime) 44 | TSDict = TSDict.groupby(['cmdb_id', 'timestamp']).agg( 45 | {'duration': [np.max, np.mean, np.std, 'count'], 'httpCode': [self.countHttp]}) 46 | 47 | # TSDict.columns = ['_'.join(col) for col in TSDict.columns] 48 | TSDict.columns = ['duration_max', 49 | 'duration_avg', 50 | 'duration_std', 51 | 'call_cnt', 52 | 'http_err_cnt'] 53 | TSDict['http_err_rate'] = TSDict['http_err_cnt'] / TSDict['call_cnt'] 54 | TSDict.drop(columns=['http_err_cnt'], inplace=True) 55 | return TSDict, cmdbList 56 | 57 | def load(self, fileName, tsAggFunc=TimestampAgg.toMinute): 58 | trace = self.loadRawData(fileName) 59 | candidateList = self.getCandidateListByDF(trace) 60 | TSDict, cmdbList = self.getTSDictByDF(trace, tsAggFunc) 61 | kpiList = list(TSDict.columns) 62 | return candidateList, TSDict, cmdbList, kpiList 63 | 64 | 65 | class HuaweiDataset: 66 | """ 67 | Huawei Trace Data 68 | """ 69 | 70 | def loadRawData(self, filename): 71 | trace = pd.read_csv(filename) 72 | trace['parent_csvc_name'].fillna("Source") 73 | trace['parent_cmpt_name'].fillna("Source") 74 | trace['parent_id'] = trace['parent_csvc_name'] + \ 75 | "::" + trace['parent_cmpt_name'] 76 | trace['child_id'] = trace['child_csvc_name'] + \ 77 | "::" + trace['child_cmpt_name'] 78 | trace = trace[trace['parent_id'] != trace['child_id']] 79 | trace.drop(columns=['parent_csvc_name', 'parent_cmpt_name', 80 | 'child_csvc_name', 'child_cmpt_name'], 81 | inplace=True) 82 | return trace 83 | 84 | def getCandidateListByDF(self, df): 85 | df = df.groupby(['parent_id', 'child_id']).agg( 86 | {'call_num_sum': np.sum}).reset_index() 87 | candidateList = [] 88 | for i in range(df.shape[0]): 89 | candidateList.append({ 90 | 'c': df.iloc[i]['child_id'], 91 | 'p': df.iloc[i]['parent_id'], 92 | 'cnt': df.iloc[i]['call_num_sum'] 93 | }) 94 | return candidateList 95 | 96 | def getTSDictByDF(self, df, tsAggFunc, tsAggFreq): 97 | # need to process trace 98 | cmdbList = list(df['child_id'].unique()) 99 | 100 | df['from_duration_sum'] = df['from_duration_avg'] * df['call_num_sum'] 101 | df['to_duration_sum'] = df['to_duration_avg'] * df['call_num_sum'] 102 | df['from_err_num_sum'] = df['from_err_num_avg'] * df['call_num_sum'] 103 | df['to_err_num_sum'] = df['to_err_num_avg'] * df['call_num_sum'] 104 | # df['timeout_num_sum'] = df['timeout_num_avg'] * df['call_num_sum'] 105 | df['ts'] = df['ts'].apply(tsAggFunc, args=( 106 | tsAggFreq,)).apply(pd.to_datetime) 107 | tmpdf = df.groupby(['child_id', 'ts']).agg({ 108 | 'call_num_sum': np.sum, 109 | 'from_duration_sum': np.sum, 110 | 'from_duration_max': np.max, 111 | 'to_duration_sum': np.sum, 112 | 'to_duration_max': np.max, 113 | 'from_err_num_sum': np.sum, 114 | 'from_err_num_max': np.max, 115 | 'to_err_num_sum': np.sum, 116 | 'to_err_num_max': np.max 117 | }) 118 | tmpdf['from_duration_avg'] = tmpdf['from_duration_sum'] / \ 119 | tmpdf['call_num_sum'] 120 | tmpdf['to_duration_avg'] = tmpdf['to_duration_sum'] / \ 121 | tmpdf['call_num_sum'] 122 | tmpdf['from_err_rate'] = tmpdf['from_err_num_sum'] / \ 123 | tmpdf['call_num_sum'] 124 | tmpdf['to_err_rate'] = tmpdf['to_err_num_sum'] / \ 125 | tmpdf['call_num_sum'] 126 | tmpdf.drop(columns=['from_duration_sum', 127 | 'to_duration_sum', 128 | 'from_err_num_sum', 129 | 'to_err_num_sum'], inplace=True) 130 | 131 | TSDict = tmpdf 132 | return TSDict, cmdbList 133 | 134 | def load(self, fileName, tsAggFunc, tsAggFreq): 135 | trace = self.loadRawData(fileName) 136 | candidateList = self.getCandidateListByDF(trace) 137 | TSDict, cmdbList = self.getTSDictByDF(trace, tsAggFunc, tsAggFreq) 138 | kpiList = list(TSDict.columns) 139 | return candidateList, TSDict, cmdbList, kpiList 140 | -------------------------------------------------------------------------------- /utils/logger.py: -------------------------------------------------------------------------------- 1 | import os 2 | import logging 3 | from logging.handlers import RotatingFileHandler 4 | 5 | 6 | def setupLogging(logDir=None, name=None, logFmt="lite"): 7 | """ 8 | Set up logging 9 | 10 | :logDir: the dir to store log file, if not specified then it will not log to file 11 | :name: name of logger, you need to call getLogger when the name is set, used for surpressing unrelated logs 12 | :logFmt: can be lite or full 13 | """ 14 | assert logFmt in ["lite", "full"], "Undefined log format" 15 | assert name is not None or logFmt == "lite", "You need to specify logger name in full log format" 16 | 17 | # Predefiend log format 18 | # https://www.programcreek.com/python/example/192/logging.Formatter 19 | log_file_format = { 20 | "full": "%(asctime)s - %(levelname)s - %(name)s - %(filename)s:%(funcName)s:%(lineno)d - %(message)s", 21 | "lite": "%(asctime)s - %(levelname)s - %(filename)s - %(message)s" 22 | } 23 | 24 | log_console_format = { 25 | "full": "[%(levelname)s] %(name)s - %(filename)s:%(funcName)s:%(lineno)d - %(message)s", 26 | "lite": "[%(levelname)s] %(filename)s - %(message)s" 27 | } 28 | 29 | # Main logger 30 | mainLogger = logging.getLogger(name) 31 | mainLogger.setLevel(logging.DEBUG) 32 | 33 | # Console logger (must) 34 | consoleHandler = logging.StreamHandler() 35 | consoleHandler.setLevel(logging.INFO) 36 | consoleHandler.setFormatter(logging.Formatter(log_console_format[logFmt])) 37 | mainLogger.addHandler(consoleHandler) 38 | 39 | # File logger (optional) 40 | if logDir: 41 | if not os.path.isdir(logDir): 42 | os.mkdir(logDir) 43 | 44 | debugFileHandler = RotatingFileHandler( 45 | os.path.join(logDir, f'{name+"_" if name else ""}debug.log'), maxBytes=10**6, backupCount=5) 46 | debugFileHandler.setLevel(logging.DEBUG) 47 | debugFileHandler.setFormatter( 48 | logging.Formatter(log_file_format[logFmt])) 49 | mainLogger.addHandler(debugFileHandler) 50 | 51 | errorFileHandler = RotatingFileHandler( 52 | os.path.join(logDir, f'{name+"_" if name else ""}warning.log'), maxBytes=10**6, backupCount=5) 53 | errorFileHandler.setLevel(logging.WARNING) 54 | errorFileHandler.setFormatter( 55 | logging.Formatter(log_file_format[logFmt])) 56 | mainLogger.addHandler(errorFileHandler) 57 | 58 | return mainLogger 59 | 60 | 61 | if __name__ == '__main__': 62 | setupLogging("logs") 63 | logging.info("information") 64 | logging.debug("oops bug") 65 | logging.warning("warning") 66 | 67 | setupLogging("logs", "somename", "full") 68 | logger = logging.getLogger("somename") 69 | logger.info("information") 70 | logger.debug("oops bug") 71 | logger.warning("warning") 72 | -------------------------------------------------------------------------------- /utils/time.py: -------------------------------------------------------------------------------- 1 | from math import ceil, floor 2 | import time 3 | 4 | 5 | class TimestampAgg: 6 | @staticmethod 7 | def toSecond(ts): 8 | """ 9 | change timestamp to time string 10 | """ 11 | timeArray = time.localtime(ts) 12 | otherStyleTime = time.strftime("%Y-%m-%d %H:%M:%S", timeArray) 13 | return otherStyleTime 14 | 15 | @staticmethod 16 | def toMinute(ts): 17 | """ 18 | change timestamp to time string 19 | """ 20 | timeArray = time.localtime(ts) 21 | otherStyleTime = time.strftime("%Y-%m-%d %H:%M", timeArray) 22 | return otherStyleTime 23 | 24 | @staticmethod 25 | def toFreqMinute(ts, freq): 26 | """ 27 | change timestamp to time string 28 | """ 29 | seconds = freq * 60 30 | timeArray = time.localtime(floor(ts / float(seconds)) * seconds) 31 | otherStyleTime = time.strftime("%Y-%m-%d %H:%M", timeArray) 32 | return otherStyleTime 33 | -------------------------------------------------------------------------------- /utils/ts.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | from typing import List, Tuple 4 | 5 | 6 | class TSTransform: 7 | @staticmethod 8 | def DIFF(ts: pd.Series): 9 | """differential""" 10 | if not len(ts.shape) == 1: 11 | raise NotImplementedError('Only support 1-d time series currently') 12 | new_ts = ts.diff() 13 | new_ts.iloc[0] = ts.iloc[0] 14 | return new_ts 15 | 16 | @staticmethod 17 | def OT(ts: pd.Series): 18 | """ 19 | offset_translation 20 | """ 21 | if not len(ts.shape) == 1: 22 | raise NotImplementedError('Only support 1-d time series currently') 23 | new_ts = ts - ts.mean() 24 | return new_ts 25 | 26 | @staticmethod 27 | def ZN(ts: pd.Series): 28 | """ 29 | z_normalize 30 | """ 31 | if not len(ts.shape) == 1: 32 | raise NotImplementedError('Only support 1-d time series currently') 33 | new_ts = ts - ts.mean() 34 | if ts.std() != 0: 35 | new_ts /= ts.std() 36 | return new_ts 37 | 38 | @staticmethod 39 | def MM(ts: pd.Series): 40 | """ 41 | minmax_normalizem map to [0, 1] 42 | """ 43 | if not len(ts.shape) == 1: 44 | raise NotImplementedError('Only support 1-d time series currently') 45 | new_ts = ts - ts.min() 46 | if (ts.max()-ts.min()) != 0: 47 | new_ts /= (ts.max()-ts.min()) 48 | return new_ts 49 | 50 | @staticmethod 51 | def MA(ts: pd.Series, w=15, type=None): 52 | """Moving average on time series 53 | Args: 54 | ts: time series 55 | w: window size 56 | type: window type, average window by default 57 | See https://pandas.pydata.org/docs/reference/api/pandas.Series.rolling.html 58 | 59 | Returns: 60 | ts after moving average 61 | """ 62 | if not len(ts.shape) == 1: 63 | raise NotImplementedError('Only support 1-d time series currently') 64 | new_ts = ts.rolling(window=w, win_type=type).mean() 65 | new_ts.iloc[:w-1] = ts.iloc[:w-1] 66 | return new_ts 67 | 68 | @staticmethod 69 | def EMA(ts: pd.Series, w=15): 70 | """Exponential moving average on time series 71 | Args: 72 | ts: time series 73 | w: window size 74 | 75 | Returns: 76 | ts after moving average 77 | """ 78 | if not len(ts.shape) == 1: 79 | raise NotImplementedError('Only support 1-d time series currently') 80 | new_ts = ts.rolling(window=w, win_type='exponential').mean() 81 | new_ts.iloc[:w-1] = ts.iloc[:w-1] 82 | return new_ts 83 | 84 | 85 | def CompoundTransform(ts: pd.Series, transforms: List[Tuple]): 86 | new_ts = ts.copy() 87 | for transform in transforms: 88 | operator = getattr(TSTransform, transform[0]) 89 | new_ts = operator(new_ts, *transform[1:]) 90 | return new_ts 91 | 92 | 93 | def test(): 94 | import matplotlib.pyplot as plt 95 | 96 | ts = pd.Series(np.array([333.53, 334.3, 340.98, 343.55, 338.55, 343.51, 347.64, 352.15, 354.87, 348, 353.54, 356.71, 357.55, 360.5, 97 | 356.52, 349.52, 337.72, 338.61, 338.37, 344.8, 351.12, 347.68, 348.4, 355.92, 357.75, 351.31, 352.25, 350.6, 98 | 344.9, 345]), index=range(100, 130)) 99 | 100 | fig, ax = plt.subplots(2, 1) 101 | df = TSTransform.DIFF(ts) 102 | ax[0].plot(df, label='df') 103 | ot = TSTransform.OT(ts) 104 | ax[0].plot(ot, label='ot') 105 | zn = TSTransform.ZN(ts) 106 | ax[0].plot(zn, label='zn') 107 | mm = TSTransform.MM(ts) 108 | ax[0].plot(mm, label='mm') 109 | compound1 = TSTransform.MA(TSTransform.ZN(ts), 4) 110 | ax[0].plot(compound1, label='zn+ma') 111 | compound2 = CompoundTransform(ts, [('ZN',), ("MA", 4)]) 112 | ax[0].plot(compound2, label='zn+ma 2') 113 | ax[0].legend() 114 | ax[1].plot(ts, label='original') 115 | ma = TSTransform.MA(ts, 4) 116 | ax[1].plot(ma, label='ma') 117 | ema = TSTransform.EMA(ts, 4) 118 | ax[1].plot(ema, label='ema') 119 | ax[1].legend() 120 | fig.show() 121 | 122 | 123 | if __name__ == "__main__": 124 | # some tests 125 | test() 126 | --------------------------------------------------------------------------------