s.add(execStats[i])
else:
s.update(getAllStages(execStats[i]))
elif type(execStats) == type([]):
for i in execStats:
s.update(getAllStages(i))
return s
if __name__ == "__main__":
p = Workload()
mongo = Mongo()
db = mongo.getDb()
list_of_collections = db.list_collection_names()
list_of_collections.sort()
pipeline = p.getWorkload()
f = open("list_operations.md", "w")
for i in list_of_collections:
f.write('## ')
f.write(i)
f.write('\n')
f.write('\n')
f.write('| Query | Operations |')
f.write('\n')
f.write('|---|---|')
f.write('\n')
for idx, j in enumerate(pipeline):
q = Query(db, i, j)
rs = q.getExecStats()
operations = list(getAllStages(rs))
f.write('| Query ' + str(idx) + '|' + ', '.join(operations) + '|')
from mongo import Mongo
from workload import Workload
from query import Query
import pandas as pd
from table import Table
if __name__ == "__main__":
mongo = Mongo()
db = mongo.getDb()
list_of_collections = db.list_collection_names()
workload = Workload()
pipeline = workload.getWorkload()
data = []
for i in pipeline:
dct = {}
for j in list_of_collections:
q = Query(db, j, i)
dct[j] = q.getQueryExecTime()
data.append(dct)
df = pd.DataFrame(data)
df_total = pd.DataFrame([dict(df.sum())])
list_of_queries= []
for i in range(len(pipeline)):
list_of_queries.append('Query '+str(i+1))
df['Query'] = list_of_queries
df.set_index('Query', inplace=True)