def check_job_itemdetail_status(self):
selleritemdetailsjobs_completed = 0
newsellerlist=[]
filenamelist=[]
for seller in self.successsellers:
if seller.get('jobcompletecheck', True) == False:
newsellerlist.append(seller)
else:
cloud.cloud.cloudLog.critical("job failed for seller "+str(seller))
if seller.get('itemdetailjobid'):cloud.kill(seller['itemdetailjobid'])
if seller.get('itemdetailjoblist'):cloud.kill(seller['itemdetailjoblist'])
while selleritemdetailsjobs_completed != len(newsellerlist):
try:
for seller in newsellerlist:
if seller.get('jobcompletecheck', True) == False:
sellerjobfailedcount = 0
sellercompletedjobs = 0
for jobid in seller['itemdetailjoblist']:
if cloud.status(jobid) in ('error', 'stalled', 'killed'):
sellerjobfailedcount += 1
sellercompletedjobs += 1
if cloud.status(jobid) in ('done'):
sellercompletedjobs += 1
if sellerjobfailedcount > SELLERITEMDETAIL_ALLOWED_JOB_FAIL_COUNT:
update_sellers_status([seller], dict(phase="INDEXING", progress="ERROR"))
cloud.kill(seller['itemdetailjoblist'])
selleritemdetailsjobs_completed += 1
seller['jobcompletecheck'] = True
logger.exception(seller['sellerid'] + " jobs kill after two job failed")
elif sellercompletedjobs >= len(seller['itemdetailjoblist']):
selleritemdetailsjobs_completed += 1
seller['jobcompletecheck'] = True
cloud.delete(seller['itemdetailjobid'])
self.download_dumps(seller['filenamelist'])
filenamelist+=seller['filenamelist']
update_sellers_status([seller], dict(phase="INDEXING", progress="ENDED"))
print "Job detail wait loop"
time.sleep(3)
except Exception, e:
print e
def delete_job_related_data_in_picloud(self, sellerjobids, queuejobdis, itemdetailjobids):
if itemdetailjobids:
cloud.delete(sellerjobids)
if queuejobdis:
cloud.delete(queuejobdis)
if itemdetailjobids:
cloud.delete(itemdetailjobids)
def ProcessFile(filename):
"""Finds the frequencey of ELM instances in a FASTA file.
Returns a defaultdict key-ed by ('ELM_NAME', 'ELM_SEQ') and has a value of
counts/AA
"""
def ProcessSeq(inp):
seq_chunk, d = inp
count_dict = defaultdict(int)
elm_dict = {}
for key, val in d.items():
elm_dict[key] = re.compile(val)
for i, seq in enumerate(seq_chunk):
print i
for elm, reg in elm_dict.items():
m = reg.search(seq)
while m:
count_dict[(elm, m.group())] += 1
m = reg.search(seq, m.start()+1)
return count_dict
def ChunkGen(filename, per_block):
gen = SeqGen(filename)
block = take(per_block, gen)
c = per_block
while block:
#print 'yeilding block %d' % c
yield block
block = take(per_block, gen)
c += per_block
#if c > 3000: break
label = filename.split(os.sep)[-1]
elm_dict = ReadELMs_nocompile('elm_expressions.txt')
count_dict = defaultdict(int)
elm_count = defaultdict(int)
jids = []
for block in ChunkGen(filename, 100):
try:
jids.append(cloud.call(ProcessSeq, (block, elm_dict), _label=filename))
except cloud.cloud.CloudException:
try:
print 'tooooo big'
jids.append(cloud.call(ProcessSeq, (block[0::2], elm_dict), _label=filename))
jids.append(cloud.call(ProcessSeq, (block[1::2], elm_dict), _label=filename))
except cloud.cloud.CloudException:
print 'really tooo big'
jids.append(cloud.call(ProcessSeq, (block[0::3], elm_dict), _label=filename))
jids.append(cloud.call(ProcessSeq, (block[1::3], elm_dict), _label=filename))
jids.append(cloud.call(ProcessSeq, (block[2::3], elm_dict), _label=filename))
#print str(jids)
print 'waiting!'
for i, res in enumerate(cloud.iresult(jids)):
if i % 4 == 0: print 'processing result %d' % i
for key, item in res.iteritems():
elm, spec = key
count_dict[key] += item
elm_count[elm] += item
cloud.delete(jids)
outdict = {}
for key, count in count_dict.iteritems():
elm, spec = key
outdict[key] = (count, float(count) / float(elm_count[elm]))
return outdict
import cloud
import time
def square(x):
time.sleep(x)
return x*x
jids = cloud.map(square, range(4))
# delete information about these jobs from server
cloud.delete(jids)
def DictFromGen(GEN, elmfile, label = None, chunk_size = 10, stop_count = None):
"""Creates a dictionary of ELM frequencies from a generator"""
def ChunkGen(gen, per_block, stop_count):
block = take(per_block, gen)
c = per_block
while block:
logging.debug('yeilding block %d, len: %d' % (c, len(block)))
yield block
block = take(per_block, gen)
c += per_block
if stop_count != None and stop_count < c: break
def ProcessSeq(seq_chunk, d):
count_dict = defaultdict(int)
elm_dict = {}
for key, val in d.items():
elm_dict[key] = re.compile(val)
for i, seq in enumerate(seq_chunk):
#print i
for elm, reg in elm_dict.items():
m = reg.search(seq)
while m:
count_dict[(elm, m.group())] += 1
m = reg.search(seq, m.start()+1)
return count_dict
elm_dict = ReadELMs_nocompile(elmfile)
jids = []
for chunk in ChunkGen(GEN, chunk_size, stop_count):
submitted = False
s = 1
tids = []
while not submitted:
c = 1
while c<=s:
try:
#try to submit the current slice of the block
logging.debug('Submitting a chunk to the cloud')
id = cloud.call(ProcessSeq, chunk[c-1::s], elm_dict, _label = label)
tids.append(id)
c+=1
submitted = True
except cloud.cloud.CloudException:
#if there is an exception because there is too much info
#then kill, delete the cloud.call and then increase the
#slicing
submitted = False
cloud.kill(tids)
cloud.delete(tids)
logging.warning('Chunk was too big at slice: %d' %c)
s += 1
break
jids += tids
count_dict = defaultdict(int)
elm_count = defaultdict(int)
logging.warning('Waiting for the cloud')
for i, res in enumerate(cloud.iresult(jids)):
logging.debug('Processing result: %s' % i)
for key, item in res.iteritems():
elm, spec = key
count_dict[key] += item
elm_count[elm] += item
logging.info('Deleting jobs')
cloud.delete(jids)
logging.info('Creating output dictionary')
outdict = {}
for key, count in count_dict.iteritems():
elm, spec = key
outdict[key] = (count, float(count) / float(elm_count[elm]))
return outdict
