print(records[key])
print(records[key])
for key in keys:
record = query.select(key, 0, [1, 1, 1, 1, 1])[0]
error = False
for i, column in enumerate(record.columns):
if column != records[key][i]:
error = True
if error:
print('select error on', key, ':', record, ', correct:', records[key])
print("Select finished")
deleted_keys = sample(keys, 100)
for key in deleted_keys:
query.delete(key)
records.pop(key, None)
for i in range(0, 100):
r = sorted(sample(range(0, len(keys)), 2))
column_sum = sum(
map(lambda x: records[x][0]
if x in records else 0, keys[r[0]:r[1] + 1]))
result = query.sum(keys[r[0]], keys[r[1]], 0)
if column_sum != result:
print('sum error on [', keys[r[0]], ',', keys[r[1]], ']: ', result,
', correct: ', column_sum)
print("Aggregate finished")
db.close()
[None, None, randrange(0, 100), None, None],
[None, None, None, randrange(0, 100), None],
[None, None, None, None, randrange(0, 100)],
]
update_time_0 = process_time()
for i in range(0, 10000):
query.update(choice(keys), *(choice(update_cols)))
update_time_1 = process_time()
print("Updating 10k records took: \t\t\t", update_time_1 - update_time_0)
# Measuring Select Performance
select_time_0 = process_time()
for i in range(0, 10000):
query.select(choice(keys), 0, [1, 1, 1, 1, 1])
select_time_1 = process_time()
print("Selecting 10k records took: \t\t\t", select_time_1 - select_time_0)
# Measuring Aggregate Performance
agg_time_0 = process_time()
for i in range(0, 10000, 100):
result = query.sum(i, 100, randrange(0, 5))
agg_time_1 = process_time()
print("Aggregate 10k of 100 record batch took:\t", agg_time_1 - agg_time_0)
# Measuring Delete Performance
delete_time_0 = process_time()
for i in range(0, 10000):
query.delete(906659671 + i)
delete_time_1 = process_time()
print("Deleting 10k records took: \t\t\t", delete_time_1 - delete_time_0)
