def get_stats():
for log_type in get_log_types():
db = connect_db(log_type)
log_stats = db.command("dbstats")
stats_coll = connect_collection('tools', 'db_stats')
stats_object = stats_coll.update({'db':log_type}, log_stats, upsert=True)
def tests(self):
for case in self.test_list:
case["dumper"].dump(test=True)
names = connect_db(case["logtype"], local=True).collection_names()
coll = None
names.remove("system.indexes")
try:
coll = connect_collection(case["logtype"], names[0], local=True)
self.assertEqual(coll.count() % 5, 0)
for test in self.test_keys:
assert coll.distinct(test) is not 0
except Exception as e:
print case["logtype"] + " had an error"
print str(e)
pass
def print_last_inserts():
""" return time of last insert for each logtype """
for log_type in get_log_types():
newest_coll_name = connect_db(log_type, local=False).collection_names()[-1]
coll = connect_collection(log_type, newest_coll_name, local=False)
for document in coll.find().sort([('time', DESCENDING)]).limit(1):
line = document['line'].split(' ')
try:
line.remove('')
except:
pass
print log_type + '\t\t' + str(line[0:3])
def compact(test=False):
"""
Compact (defrag/free) every collection
"""
for log_type in get_log_types():
try:
db = connect_db(log_type, local=test, master=True)
for coll_name in db.collection_names():
coll = connect_collection(log_type, coll_name)
try:
print "compacting " + coll_name
db.eval("db.runCommand({ compact : '%s'})" % (coll_name))
except Exception as e:
print str(e)
continue
except Exception as e:
print str(e)
continue
# for log_type in get_log_types():
>>>>>>> Stashed changes
for log_type in ['aruba']:
# find all collections in the database
database = connect_db(log_type, local=True)
collnames = database.collection_names()
try:
collnames.remove('system.indexes')
collnames.remove('system.users')
except:
pass
# loop over collections names
for collname in collnames:
collection = connect_collection(log_type, collname, local=True)
<<<<<<< Updated upstream
>>>>>>> Stashed changes
=======
>>>>>>> Stashed changes
# use map/reduce to count the frequency of each field
result = collection.map_reduce(map_fn, reduce_fn, "key_frequency")
<<<<<<< Updated upstream
# print out the summary data
print coll_name.center(60, '=')
for item in result.find():
key = item['_id']
count = int(item['value'])
freq = (count / float(total)) * 100.0