def __init__(self, configfile, verbose=0):
"Main module"
self.config = parse_config(configfile)
self.storage = StorageManager(self.config)
self.sitedb = SiteDBService(self.config, verbose)
self.dbs = DBSService(self.config, verbose)
self.popdb = PopDBService(self.config, verbose)
self.phedex = PhedexService(self.config, verbose)
self.dashboard = DashboardService(self.config, verbose)
self.salt = self.config.get('core', {}).get('salt', 'secret sauce')
self.verbose = verbose
self.multitask = self.config.get('core', {}).get('multitask', False)
self.queue = mp.Queue()
if verbose:
print("DCAF multitask", self.multitask)
def __init__(self, configfile, verbose=0):
"Main module"
self.config = parse_config(configfile)
self.storage = StorageManager(self.config)
self.sitedb = SiteDBService(self.config, verbose)
self.dbs = DBSService(self.config, verbose)
self.popdb = PopDBService(self.config, verbose)
self.phedex = PhedexService(self.config, verbose)
self.dashboard = DashboardService(self.config, verbose)
self.salt = self.config.get('core', {}).get('salt', 'secret sauce')
self.verbose = verbose
self.multitask = self.config.get('core', {}).get('multitask', False)
self.queue = mp.Queue()
if verbose:
print("DCAF multitask", self.multitask)
class DCAF(object):
def __init__(self, configfile, verbose=0):
"Main module"
self.config = parse_config(configfile)
self.storage = StorageManager(self.config)
self.sitedb = SiteDBService(self.config, verbose)
self.dbs = DBSService(self.config, verbose)
self.popdb = PopDBService(self.config, verbose)
self.phedex = PhedexService(self.config, verbose)
self.dashboard = DashboardService(self.config, verbose)
self.salt = self.config.get('core', {}).get('salt', 'secret sauce')
self.verbose = verbose
self.multitask = self.config.get('core', {}).get('multitask', False)
self.queue = mp.Queue()
if verbose:
print("DCAF multitask", self.multitask)
def fetch(self, doc):
"""
Fetch method retrieves data from data-provide and store them into
internal data storage.
"""
source = doc.get('source', None)
params = doc.get('params', {})
if self.verbose:
print("Fetch data from %s(%s)" % (source, params))
if source == 'sitedb':
for item in self.sitedb.fetch('people', {}):
print(item)
break
def data_types(self):
"""Return list of data types dicts:
- dtypes is data types, e.g. mc/data
- stypes is site types, dict of Tier sites
- rtypes is release types
{'series':{'major':{'minor':}}
"""
dtypes = ['mc', 'data'] # data types, should be small list
tiers = self.dbs.data_tiers()
stypes = {'s_%s'%TIER0:0, 's_%s'%TIER1:0, 's_%s'%TIER2:0, 's_%s'%TIER3:0, 's_%s'%TIER_NA:0} # site types
rtypes = {} # release types
releases = self.dbs.releases()
series = set()
majors = set()
minors = set()
for row in releases:
rel = row['release']
sval, major, minor = rel_ver(rel)
if not cmssw_test(sval, major, minor):
continue
series.add(sval)
majors.add(major)
minors.add(minor)
serdict = {}
for val in series:
serdict['rel1_%s'%val] = 0
majdict = {}
for val in majors:
majdict['rel2_%s'%val] = 0
mindict = {}
for val in minors:
mindict['rel3_%s'%val] = 0
# release types as defined in rel_type function
typdict = {'relt_%s'%RFULL:0, 'relt_%s'%RPRE:0, 'relt_%s'%RPATCH:0}
rtypes = {'series': serdict, 'majors': majdict, 'minors': mindict, 'rtypes': typdict}
return dtypes, stypes, rtypes, tiers
def dataset_info_all(self, dataset, dbsinst, timeframe):
"Concurrently obtain information about dataset."
# NOTE: this function may need expansion if we'll need to obtain more information
# about given dataset/timeframe. To extend, please add new local
# function similar to procN
# Each procN function should have (pos, out, args) input arguments
# the pos is a position in queue, out is an output queue and args
# are arguments required to pass to internal function
# local functions to fetch info about dataset from different subsystems
def proc1(pos, out, dataset, inst):
try:
res = [rname for rname in \
self.dbs.dataset_release_versions(dataset, inst)]
except:
res = []
out.put((pos, res))
def proc2(pos, out, dataset, _inst):
try:
res = [sname for sname in self.phedex.sites(dataset)]
except:
res = []
out.put((pos, res))
def proc3(pos, out, dataset, inst):
try:
res = [r for r in self.dbs.dataset_parents(dataset, inst)]
except:
res = []
out.put((pos, res))
def proc4(pos, out, dataset, inst):
try:
res = self.dbs.dataset_summary(dataset, inst)
except:
res = dict()
out.put((pos, res))
def proc5(pos, out, dataset, tframe):
try:
res = self.dashboard.dataset_info(dataset, tframe[0], tframe[1])
except:
res = dict()
out.put((pos, res))
# concurrent processes to run, each args contains
# a position value, output queue and args for internal function call
processes = [
mp.Process(target=proc1, args=(1, self.queue, dataset, dbsinst)),
mp.Process(target=proc2, args=(2, self.queue, dataset, dbsinst)),
mp.Process(target=proc3, args=(3, self.queue, dataset, dbsinst)),
mp.Process(target=proc4, args=(4, self.queue, dataset, dbsinst)),
mp.Process(target=proc5, args=(5, self.queue, dataset, timeframe))
]
# Run processes
for proc in processes:
proc.start()
# Exit the completed processes
for proc in processes:
proc.join()
# Get process results from the self.queue queue
results = [self.queue.get() for _ in processes]
results.sort() # sort by position
results = [v for _, v in results] # extract values
return results
def dataset_info(self, timeframe, dataset, dtypes, stypes, rtypes, tiers, dformat, target=0):
"Return common dataset info in specified data format"
dbsinst = self.dbs.dataset_dbsinst(dataset)
if not dbsinst:
return
row = self.dbs.dataset_info(dataset, dbsinst)
if row:
if self.multitask:
releases, sites, parents, summary, dashboard = \
self.dataset_info_all(dataset, dbsinst, timeframe)
else:
releases = [rname for rname in self.dbs.dataset_release_versions(dataset, dbsinst)]
sites = [sname for sname in self.phedex.sites(dataset)]
parents = [r for r in self.dbs.dataset_parents(dataset, dbsinst)]
summary = self.dbs.dataset_summary(dataset, dbsinst)
dashboard = self.dashboard.dataset_info(dataset, timeframe[0], timeframe[1])
nrels = len(releases)
series = rtypes['series']
majors = rtypes['majors']
minors = rtypes['minors']
relclf = rtypes['rtypes']
for rel in releases:
rserie, rmajor, rminor = rel_ver(rel)
if not cmssw_test(rserie, rmajor, rminor):
continue
rtype = rel_type(rel)
try:
series['rel1_%s'%rserie] += 1
except:
pass
try:
majors['rel2_%s'%rmajor] += 1
except:
pass
try:
minors['rel3_%s'%rminor] += 1
except:
pass
try:
relclf['relt_%s'%rtype] += 1
except:
pass
nsites = len(sites)
for site in sites:
stier = site_tier(site)
stypes['s_%s'%stier] += 1
dataset_id = row['rid']
era = genkey(row['acquisition_era_name'], self.salt, 5)
create_dn = self.sitedb.dnid(row['create_by'])
dbsinstid = row['dbs_instance']
dtype = row['primary_ds_type']
# number of data types should be small and simple
# list look-up shouldn't be a problem
if dtype not in dtypes:
dtypes.append(dtype)
dtype = dtypes.index(dtype)
_, prim, proc, tier = dataset.split('/')
prim = genkey(prim, self.salt, 5)
proc = genkey(proc, self.salt, 5)
if tier not in tiers:
tiers.append(tier)
tier = genkey(tier, self.salt, 5)
parent = parents[0] if len(parents) else 0
uid = genuid(yyyymmdd(timeframe[0]), dbsinstid, dataset_id)
size_norm = 2**30 # normalization factor for file size
rec = dict(id=uid, dataset=dataset_id, primds=prim, procds=proc, tier=tier,
dtype=dtype, creator=create_dn, nrel=nrels, nsites=nsites,
parent=parent, era=era, dbs=dbsinstid,
nfiles=summary.get('num_file', 0),
nlumis=summary.get('num_lumi', 0),
nblk=summary.get('num_block', 0),
nevt=summary.get('num_event', 0),
size=summary.get('file_size', 0)/size_norm,
cpu=dashboard.get('cpu', 0),
wct=dashboard.get('wct', 0),
proc_evts=dashboard.get('nevt', 0))
if isinstance(target, dict):
rec.update(target)
for key,val in series.items():
rec.update({key:val})
for key, val in majors.items():
rec.update({key:val})
for key, val in minors.items():
rec.update({key:val})
for key, val in relclf.items():
rec.update({key:val})
for key, val in stypes.items():
rec.update({key:val})
headers = rec.keys()
headers.sort()
headers.remove('id')
headers = ['id'] + headers # let dataset id be the first column
if dformat == 'headers':
yield headers
elif dformat == 'csv':
res = [str(rec[h]) for h in headers]
yield ','.join(res)
elif dformat == 'vw':
target_str = target.get('rnaccess')
vals = ' '.join([str(rec[h]) for h in headers])
uid = genkey(vals, self.salt, 5) # unique row identified
vwrow = "%s '%s |f %s" % (target_str, uid, vals)
yield vwrow
def update(self):
# get fresh copy of hashed db's
self.dbs.update('datasets')
self.dbs.update('releases')
self.sitedb.update('people')
self.sitedb.update('sites')
def cleanup(self, cname):
"Clean-up given collection"
self.storage.cleanup(cname)
def remove(self, cname, docid):
"Remove given docid from given collection"
spec = {"_id": docid}
self.storage.cleanup(cname, spec)
def dataframe(self, timeframe, seed, dformat, dbs_extra, newdata=None):
"""Form a dataframe from various CMS data-providers"""
dtypes, stypes, rtypes, tiers = self.data_types()
pop_datasets = 0
dbs_datasets = 0
popdb_results = [r for r in self.popdb.dataset_stat(timeframe[0], timeframe[1])]
if dformat == 'csv':
row = popdb_results[0]
dataset = row['dataset']
target = dict(naccess=row['naccess'],nusers=row['nusers'],totcpu=row['totcpu'],
rnaccess=row['rnaccess'],rnusers=row['rnusers'],rtotcpu=row['rtotcpu'])
# seed dataset to determine headers of the dataframe
rows = self.dataset_info(timeframe, seed, dtypes, stypes, rtypes,
tiers, 'headers', target)
headers = [r for r in rows][0]
yield ','.join(headers)
tstamp = time.strftime("%Y-%m-%d %H:%M:%S GMT", time.gmtime())
if newdata: # request new dataset
if self.verbose:
print("Generate dataframe for new datasets", tstamp)
n_days = 7
if timeframe:
n_days = ndays(yyyymmdd(timeframe[0]), yyyymmdd(timeframe[1]))
new_datasets = self.dbs.new_datasets(n_days)
target = dict(naccess=0,nusers=0,totcpu=0,
rnaccess=0,rnusers=0,rtotcpu=0)
for row in new_datasets:
dataset = row['dataset']
rows = self.dataset_info(timeframe, dataset, dtypes, stypes, \
rtypes, tiers, dformat, target)
for row in rows:
yield row
return
# get list of popular datasets in certain time frame
# popdb_results = [r for r in self.popdb.dataset_stat(timeframe[0], timeframe[1])]
popdb_datasets = {} #
for row in popdb_results:
dataset = row['dataset']
if not DATASET_PAT.match(dataset):
continue
if self.verbose:
print("Generate dataframe for %s, timeframe: %s, %s" \
% (dataset, timeframe, tstamp))
target = dict(naccess=row['naccess'],nusers=row['nusers'],totcpu=row['totcpu'],
rnaccess=row['rnaccess'],rnusers=row['rnusers'],rtotcpu=row['rtotcpu'])
rows = self.dataset_info(timeframe, dataset, dtypes, stypes, \
rtypes, tiers, dformat, target)
popdb_datasets[dataset] = row
for row in rows:
yield row
pop_datasets += 1
# get list of datasets from DBS and discard from this list
# those who were presented in popdb
all_dbs_datasets = self.dbs.datasets()
dbsdatasets = [d for d in all_dbs_datasets if d not in popdb_datasets.keys()]
target = dict(naccess=0,nusers=0,totcpu=0,
rnaccess=0,rnusers=0,rtotcpu=0)
for dataset in random.sample(dbsdatasets, dbs_extra):
rows = self.dataset_info(timeframe, dataset, dtypes, stypes, \
rtypes, tiers, dformat, target)
for row in rows:
yield row
dbs_datasets += 1
if self.verbose:
print("DBS datasets : %s" % dbs_datasets)
print("PopDB datasets: %s out of %s" % (pop_datasets, len(popdb_results)))
def export(self, dformat):
"Export analytics dataframe into provided data format"
print("Export dataframe into %s format" % dformat.lower())
if dformat.lower() == 'csv':
print("Do CSV export")
headers = []
for row in self.dataframe():
if not headers:
headers = row
yield ','.join(headers)
continue
yield ','.join(row)
elif dformat.lower() == 'vw':
print("Do vw export")
else:
raise NotImplemented
class DCAF(object):
def __init__(self, configfile, verbose=0):
"Main module"
self.config = parse_config(configfile)
self.storage = StorageManager(self.config)
self.sitedb = SiteDBService(self.config, verbose)
self.dbs = DBSService(self.config, verbose)
self.popdb = PopDBService(self.config, verbose)
self.phedex = PhedexService(self.config, verbose)
self.dashboard = DashboardService(self.config, verbose)
self.salt = self.config.get('core', {}).get('salt', 'secret sauce')
self.verbose = verbose
self.multitask = self.config.get('core', {}).get('multitask', False)
self.queue = mp.Queue()
if verbose:
print("DCAF multitask", self.multitask)
def fetch(self, doc):
"""
Fetch method retrieves data from data-provide and store them into
internal data storage.
"""
source = doc.get('source', None)
params = doc.get('params', {})
if self.verbose:
print("Fetch data from %s(%s)" % (source, params))
if source == 'sitedb':
for item in self.sitedb.fetch('people', {}):
print(item)
break
def data_types(self):
"""Return list of data types dicts:
- dtypes is data types, e.g. mc/data
- stypes is site types, dict of Tier sites
- rtypes is release types
{'series':{'major':{'minor':}}
"""
dtypes = ['mc', 'data'] # data types, should be small list
tiers = self.dbs.data_tiers()
stypes = {
's_%s' % TIER0: 0,
's_%s' % TIER1: 0,
's_%s' % TIER2: 0,
's_%s' % TIER3: 0,
's_%s' % TIER_NA: 0
} # site types
rtypes = {} # release types
releases = self.dbs.releases()
series = set()
majors = set()
minors = set()
for row in releases:
rel = row['release']
sval, major, minor = rel_ver(rel)
if not cmssw_test(sval, major, minor):
continue
series.add(sval)
majors.add(major)
minors.add(minor)
serdict = {}
for val in series:
serdict['rel1_%s' % val] = 0
majdict = {}
for val in majors:
majdict['rel2_%s' % val] = 0
mindict = {}
for val in minors:
mindict['rel3_%s' % val] = 0
# release types as defined in rel_type function
typdict = {
'relt_%s' % RFULL: 0,
'relt_%s' % RPRE: 0,
'relt_%s' % RPATCH: 0
}
rtypes = {
'series': serdict,
'majors': majdict,
'minors': mindict,
'rtypes': typdict
}
return dtypes, stypes, rtypes, tiers
def dataset_info_all(self, dataset, dbsinst, timeframe):
"Concurrently obtain information about dataset."
# NOTE: this function may need expansion if we'll need to obtain more information
# about given dataset/timeframe. To extend, please add new local
# function similar to procN
# Each procN function should have (pos, out, args) input arguments
# the pos is a position in queue, out is an output queue and args
# are arguments required to pass to internal function
# local functions to fetch info about dataset from different subsystems
def proc1(pos, out, dataset, inst):
try:
res = [rname for rname in \
self.dbs.dataset_release_versions(dataset, inst)]
except:
res = []
out.put((pos, res))
def proc2(pos, out, dataset, _inst):
try:
res = [sname for sname in self.phedex.sites(dataset)]
except:
res = []
out.put((pos, res))
def proc3(pos, out, dataset, inst):
try:
res = [r for r in self.dbs.dataset_parents(dataset, inst)]
except:
res = []
out.put((pos, res))
def proc4(pos, out, dataset, inst):
try:
res = self.dbs.dataset_summary(dataset, inst)
except:
res = dict()
out.put((pos, res))
def proc5(pos, out, dataset, tframe):
try:
res = self.dashboard.dataset_info(dataset, tframe[0],
tframe[1])
except:
res = dict()
out.put((pos, res))
# concurrent processes to run, each args contains
# a position value, output queue and args for internal function call
processes = [
mp.Process(target=proc1, args=(1, self.queue, dataset, dbsinst)),
mp.Process(target=proc2, args=(2, self.queue, dataset, dbsinst)),
mp.Process(target=proc3, args=(3, self.queue, dataset, dbsinst)),
mp.Process(target=proc4, args=(4, self.queue, dataset, dbsinst)),
mp.Process(target=proc5, args=(5, self.queue, dataset, timeframe))
]
# Run processes
for proc in processes:
proc.start()
# Exit the completed processes
for proc in processes:
proc.join()
# Get process results from the self.queue queue
results = [self.queue.get() for _ in processes]
results.sort() # sort by position
results = [v for _, v in results] # extract values
return results
def dataset_info(self,
timeframe,
dataset,
dtypes,
stypes,
rtypes,
tiers,
dformat,
target=0):
"Return common dataset info in specified data format"
dbsinst = self.dbs.dataset_dbsinst(dataset)
if not dbsinst:
return
row = self.dbs.dataset_info(dataset, dbsinst)
if row:
if self.multitask:
releases, sites, parents, summary, dashboard = \
self.dataset_info_all(dataset, dbsinst, timeframe)
else:
releases = [
rname for rname in self.dbs.dataset_release_versions(
dataset, dbsinst)
]
sites = [sname for sname in self.phedex.sites(dataset)]
parents = [
r for r in self.dbs.dataset_parents(dataset, dbsinst)
]
summary = self.dbs.dataset_summary(dataset, dbsinst)
dashboard = self.dashboard.dataset_info(
dataset, timeframe[0], timeframe[1])
nrels = len(releases)
series = {}
for k in rtypes['series'].keys():
series[k] = 0
majors = {}
for k in rtypes['majors'].keys():
majors[k] = 0
minors = {}
for k in rtypes['minors'].keys():
minors[k] = 0
relclf = {}
for k in rtypes['rtypes'].keys():
relclf[k] = 0
for rel in releases:
rserie, rmajor, rminor = rel_ver(rel)
if not cmssw_test(rserie, rmajor, rminor):
continue
rtype = rel_type(rel)
try:
series['rel1_%s' % rserie] += 1
except:
pass
try:
majors['rel2_%s' % rmajor] += 1
except:
pass
try:
minors['rel3_%s' % rminor] += 1
except:
pass
try:
relclf['relt_%s' % rtype] += 1
except:
pass
nsites = len(sites)
for site in sites:
stier = site_tier(site)
stypes['s_%s' % stier] += 1
dataset_id = row['rid']
era = genkey(row['acquisition_era_name'], self.salt, 5)
create_dn = self.sitedb.dnid(row['create_by'])
dbsinstid = row['dbs_instance']
dtype = row['primary_ds_type']
# number of data types should be small and simple
# list look-up shouldn't be a problem
if dtype not in dtypes:
dtypes.append(dtype)
dtype = dtypes.index(dtype)
_, prim, proc, tier = dataset.split('/')
prim = genkey(prim, self.salt, 5)
proc = genkey(proc, self.salt, 5)
if tier not in tiers:
tiers.append(tier)
tier = genkey(tier, self.salt, 5)
parent = parents[0] if len(parents) else 0
uid = genuid(yyyymmdd(timeframe[0]), dbsinstid, dataset_id)
size_norm = 2**30 # normalization factor for file size
if not summary:
summary = {} # we need a dict type
rec = dict(id=uid,
dataset=dataset_id,
primds=prim,
procds=proc,
tier=tier,
dtype=dtype,
creator=create_dn,
nrel=nrels,
nsites=nsites,
parent=parent,
era=era,
dbs=dbsinstid,
nfiles=summary.get('num_file', 0),
nlumis=summary.get('num_lumi', 0),
nblk=summary.get('num_block', 0),
nevt=summary.get('num_event', 0),
size=summary.get('file_size', 0) / size_norm,
cpu=dashboard.get('cpu', 0),
wct=dashboard.get('wct', 0),
proc_evts=dashboard.get('nevt', 0))
if isinstance(target, dict):
rec.update(target)
for key, val in series.items():
rec.update({key: val})
for key, val in majors.items():
rec.update({key: val})
for key, val in minors.items():
rec.update({key: val})
for key, val in relclf.items():
rec.update({key: val})
for key, val in stypes.items():
rec.update({key: val})
headers = rec.keys()
headers.sort()
headers.remove('id')
headers = ['id'] + headers # let dataset id be the first column
if dformat == 'headers':
yield headers
elif dformat == 'csv':
res = [str(rec[h]) for h in headers]
yield ','.join(res)
elif dformat == 'vw':
target_str = target.get('rnaccess')
vals = ' '.join([str(rec[h]) for h in headers])
uid = genkey(vals, self.salt, 5) # unique row identified
vwrow = "%s '%s |f %s" % (target_str, uid, vals)
yield vwrow
def update(self):
# get fresh copy of hashed db's
self.dbs.update('datasets')
self.dbs.update('releases')
self.sitedb.update('people')
self.sitedb.update('sites')
def cleanup(self, cname):
"Clean-up given collection"
self.storage.cleanup(cname)
def remove(self, cname, docid):
"Remove given docid from given collection"
spec = {"_id": docid}
self.storage.cleanup(cname, spec)
def dataframe(self, timeframe, seed, dformat, dbs_extra, newdata=None):
"""Form a dataframe from various CMS data-providers"""
dtypes, stypes, rtypes, tiers = self.data_types()
pop_datasets = 0
dbs_datasets = 0
popdb_results = [
r for r in self.popdb.dataset_stat(timeframe[0], timeframe[1])
]
if dformat == 'csv':
dataset = None
for row in popdb_results:
if len(row['dataset'].split(
'/')) == 4: # dataset with 3 slashes
dataset = row['dataset']
break
if not dataset:
raise Exception(
"Unable to find valid dataset name in popdb output")
target = dict(naccess=row['naccess'],
nusers=row['nusers'],
totcpu=row['totcpu'],
rnaccess=row['rnaccess'],
rnusers=row['rnusers'],
rtotcpu=row['rtotcpu'])
# seed dataset to determine headers of the dataframe
rows = self.dataset_info(timeframe, seed, dtypes, stypes, rtypes,
tiers, 'headers', target)
headers = [r for r in rows][0]
yield ','.join(headers)
tstamp = time.strftime("%Y-%m-%d %H:%M:%S GMT", time.gmtime())
if newdata: # request new dataset
if self.verbose:
print("Generate dataframe for new datasets", tstamp)
n_days = 7
if timeframe:
n_days = ndays(yyyymmdd(timeframe[0]), yyyymmdd(timeframe[1]))
new_datasets = self.dbs.new_datasets(n_days)
target = dict(naccess=0,
nusers=0,
totcpu=0,
rnaccess=0,
rnusers=0,
rtotcpu=0)
for row in new_datasets:
dataset = row['dataset']
rows = self.dataset_info(timeframe, dataset, dtypes, stypes, \
rtypes, tiers, dformat, target)
for row in rows:
yield row
return
# get list of popular datasets in certain time frame
# popdb_results = [r for r in self.popdb.dataset_stat(timeframe[0], timeframe[1])]
popdb_datasets = {} #
for row in popdb_results:
dataset = row['dataset']
if not DATASET_PAT.match(dataset):
continue
if self.verbose:
print("Generate dataframe for %s, timeframe: %s, %s" \
% (dataset, timeframe, tstamp))
target = dict(naccess=row['naccess'],
nusers=row['nusers'],
totcpu=row['totcpu'],
rnaccess=row['rnaccess'],
rnusers=row['rnusers'],
rtotcpu=row['rtotcpu'])
rows = self.dataset_info(timeframe, dataset, dtypes, stypes, \
rtypes, tiers, dformat, target)
popdb_datasets[dataset] = row
for row in rows:
yield row
pop_datasets += 1
# get list of datasets from DBS and discard from this list
# those who were presented in popdb
all_dbs_datasets = self.dbs.datasets()
dbsdatasets = [
d for d in all_dbs_datasets if d not in popdb_datasets.keys()
]
target = dict(naccess=0,
nusers=0,
totcpu=0,
rnaccess=0,
rnusers=0,
rtotcpu=0)
for dataset in random.sample(dbsdatasets, dbs_extra):
rows = self.dataset_info(timeframe, dataset, dtypes, stypes, \
rtypes, tiers, dformat, target)
for row in rows:
yield row
dbs_datasets += 1
if self.verbose:
print("DBS datasets : %s" % dbs_datasets)
print("PopDB datasets: %s out of %s" %
(pop_datasets, len(popdb_results)))
def export(self, dformat):
"Export analytics dataframe into provided data format"
print("Export dataframe into %s format" % dformat.lower())
if dformat.lower() == 'csv':
print("Do CSV export")
headers = []
for row in self.dataframe():
if not headers:
headers = row
yield ','.join(headers)
continue
yield ','.join(row)
elif dformat.lower() == 'vw':
print("Do vw export")
else:
raise NotImplemented