def __getLatest(self, dt=None):
print(f'{self.dateFrom} - {self.dateTo} Grab latest data', dt)
if not dt: dt = hp.GetTimeRanges(self.dateFrom, self.dateTo)
allNodesDf = qrs.allTestedNodes(dt)
allNodesDf = allNodesDf[(allNodesDf['ip'] != '')
& ~(allNodesDf['ip'].isnull())]
# in some cases there is one IP having 2 different hostanames
allNodesDf = self.__removeDuplicates(allNodesDf)
rows = []
# run a query for each ip because it is not a trivial task (if possible at all) to aggreagate the geolocation fields
for item in allNodesDf.sort_values('host',
ascending=False).to_dict('records'):
lastRec = qrs.mostRecentMetaRecord(item['ip'], item['ipv6'], dt)
if len(lastRec) > 0:
rows.append(lastRec)
else:
item['site_index'] = item['site']
rows.append(item)
columns = [
'ip', 'timestamp', 'host', 'site', 'administrator', 'email', 'lat',
'lon', 'site_meta', 'site_index'
]
df = pd.DataFrame(rows, columns=columns)
df = df.drop_duplicates()
df['last_update'] = df['timestamp'].apply(
lambda ts: self.convertTime(ts))
df['last_update'].fillna(self.convertTime(dt[1]), inplace=True)
return df
def runInParallel(dateFrom, dateTo):
# query the past 12 hours and split the period into 8 time ranges
# dateFrom, dateTo = hp.defaultTimeRange(12)
# dateFrom, dateTo = ['2022-05-17 20:15', '2022-05-18 08:15']
print(f' Run for period: {dateFrom} - {dateTo}')
dtList = hp.GetTimeRanges(dateFrom, dateTo, 12)
with ProcessPoolExecutor(max_workers=4) as pool:
result = pool.map(getTraceData, [[dtList[i], dtList[i+1]] for i in range(len(dtList)-1)])
def queryData(self, idx):
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo) / 60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(len(time_list) - 1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i + 1]))
return data
def queryData(dateFrom, dateTo):
data = []
# query in portions since ES does not allow aggregations with more than 10000 bins
intv = int(hp.CalcMinutes4Period(dateFrom, dateTo) / 60)
time_list = hp.GetTimeRanges(dateFrom, dateTo, intv)
for i in range(len(time_list) - 1):
data.extend(query4Avg(time_list[i], time_list[i + 1]))
return data
def loadPacketLossData(dateFrom, dateTo):
data = []
intv = int(hp.CalcMinutes4Period(dateFrom, dateTo) / 60)
time_list = hp.GetTimeRanges(dateFrom, dateTo, intv)
for i in range(len(time_list) - 1):
data.extend(
qrs.query4Avg('ps_packetloss', time_list[i], time_list[i + 1]))
return pd.DataFrame(data)
def InOutDf(self, idx, idx_df):
print(idx)
in_out_values = []
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for t in ['dest_host', 'src_host']:
meta_df = idx_df.copy()
df = pd.DataFrame(
qrs.queryDailyAvg(idx, t, time_list[0],
time_list[1])).reset_index()
df['index'] = pd.to_datetime(df['index'],
unit='ms').dt.strftime('%d/%m')
df = df.transpose()
header = df.iloc[0]
df = df[1:]
df.columns = ['day-3', 'day-2', 'day-1', 'day']
meta_df = pd.merge(meta_df, df, left_on="host", right_index=True)
three_days_ago = meta_df.groupby('site').agg(
{
'day-3': lambda x: x.mean(skipna=False)
}, axis=1).reset_index()
two_days_ago = meta_df.groupby('site').agg(
{
'day-2': lambda x: x.mean(skipna=False)
}, axis=1).reset_index()
one_day_ago = meta_df.groupby('site').agg(
{
'day-1': lambda x: x.mean(skipna=False)
}, axis=1).reset_index()
today = meta_df.groupby('site').agg(
{
'day': lambda x: x.mean(skipna=False)
}, axis=1).reset_index()
site_avg_df = reduce(
lambda x, y: pd.merge(x, y, on='site', how='outer'),
[three_days_ago, two_days_ago, one_day_ago, today])
site_avg_df.set_index('site', inplace=True)
change = site_avg_df.pct_change(axis='columns')
site_avg_df = pd.merge(site_avg_df,
change,
left_index=True,
right_index=True,
suffixes=('_val', ''))
site_avg_df['direction'] = 'IN' if t == 'dest_host' else 'OUT'
in_out_values.append(site_avg_df)
site_df = pd.concat(in_out_values).reset_index()
site_df = site_df.round(2)
return {"data": site_df, "dates": header}
def getData(self, src, dest):
time_list = hp.GetTimeRanges(self.root_parent.dateFrom,
self.root_parent.dateTo)
df = pd.DataFrame(qrs.queryAllValues(self._idx, src, dest, time_list))
df.rename(columns={hp.getValueField(self._idx): 'value'}, inplace=True)
if len(df) > 0:
df['log_value'] = np.log(df['value'].replace(0, np.nan))
df['sqrt'] = df['value']**(1 / 2)
return df
def queryNodesGeoLocation():
include=["geolocation","external_address.ipv4_address", "external_address.ipv6_address", "config.site_name", "host"]
period = hp.GetTimeRanges(*hp.defaultTimeRange(days=30))
query = {
"query": {
"bool": {
"filter": [
{
"range": {
"timestamp": {
"gte": period[0],
"lte": period[1]
}
}
}
]
}
}
}
data = scan(client=hp.es, index='ps_meta', query=query,
_source=include, filter_path=['_scroll_id', '_shards', 'hits.hits._source'])
count = 0
neatdata = []
ddict = {}
for res in data:
if not count%100000: print(count)
data = res['_source']
if 'config' in data:
site = data['config']['site_name']
else: site = None
if 'ipv4_address' in data['external_address']:
ip = data['external_address']['ipv4_address']
else: ip = data['external_address']['ipv6_address']
geoip = [None, None]
if 'geolocation' in data:
geoip = data['geolocation'].split(",")
# if 'speed' in data['external_address']:
# speed = data['external_address']['speed']
if (ip in ddict) and (site is not None):
ddict[ip]['site'] = site
else: ddict[ip] = {'lat': geoip[0], 'lon': geoip[1], 'site': site, 'host':data['host']}
count=count+1
return ddict
def __updateDataset(self):
self.metaDf = self.getMetafromES()
if len(self.metaDf) > 1:
print('Update meta data')
self.metaDf = self.__updateMetaData(self.metaDf,
self.__getLatest())
else:
# Initially, grab one year of data split into 6 chunks in order to
# fill in info that may not appear in the most recent data
print('No data found. Query a year back.')
dateTo = datetime.strftime(self.now, '%Y-%m-%d %H:%M')
dateFrom = datetime.strftime(self.now - timedelta(days=365),
'%Y-%m-%d %H:%M')
timeRange = hp.GetTimeRanges(dateFrom, dateTo, 10)
self.metaDf = self.__getLatest([timeRange[0], timeRange[1]])
for i in range(2, len(timeRange) - 1):
print(
f'Period: {timeRange[i]}, {timeRange[i+1]}, data size before update: {len(self.metaDf)}'
)
self.metaDf = self.__updateMetaData(
self.metaDf,
self.__getLatest([timeRange[i], timeRange[i + 1]]))
print(f'Size after update: {len(self.metaDf)}')
print()
# self.metaDf.loc[self.metaDf.site == 'CERN-PROD', 'lat'] = 46.2416566
# self.metaDf.loc[self.metaDf.site == 'CERN-PROD', 'lon'] = 6.0468415
# Finally, try to fix empty fields by searching for similar host names and assign their value
try:
self.metaDf = self.__fixMissingSites(self.metaDf)
self.metaDf = self.metaDf.fillna(
self.metaDf[['site', 'lat', 'lon']].groupby('site').ffill())
self.metaDf = self.metaDf.drop_duplicates(subset='ip', keep="last")
# remove all >1500 nodes for which there is no meaningful info
self.metaDf.fillna('', inplace=True)
toRemoveIds = self.metaDf[~(self.metaDf['lat'].astype(bool))&~(self.metaDf['lon'].astype(bool))\
&~(self.metaDf['administrator'].astype(bool))&~(self.metaDf['site_index'].astype(bool))\
&~(self.metaDf['site_meta'].astype(bool))&~(self.metaDf['email'].astype(bool))].index.values
self.metaDf = self.metaDf[~self.metaDf.index.isin(toRemoveIds)]
except Exception as e:
print(traceback.format_exc())
finally:
print('Meta data done')
def getValues(self, probdf):
# probdf = markNodes()
df = pd.DataFrame(columns=['timestamp', 'value', 'idx', 'hash'])
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for item in probdf[['src', 'dest', 'idx']].values:
tempdf = pd.DataFrame(
qrs.queryAllValues(item[2], item, time_list[0], time_list[1]))
tempdf['idx'] = item[2]
tempdf['hash'] = item[0] + "-" + item[1]
tempdf['src'] = item[0]
tempdf['dest'] = item[1]
tempdf.rename(columns={hp.getValueField(item[2]): 'value'},
inplace=True)
df = df.append(tempdf, ignore_index=True)
return df
def run(dateFrom, dateTo):
# query the past 24 hours and split the period into 8 time ranges
dtList = hp.GetTimeRanges(dateFrom, dateTo, 8)
with ProcessPoolExecutor(max_workers=4) as pool:
result = pool.map(getTraceData, [[dtList[i], dtList[i+1]] for i in range(len(dtList)-1)])
