updated_columns = [None, None, None, None, None]
for i in range(1, grades_table.num_columns):
value = randint(0, 20)
updated_columns[i] = value
original = records[key].copy()
records[key][i] = value
query.update(key, *updated_columns)
record = query.select(key, [1, 1, 1, 1, 1])[0]
error = False
for j, column in enumerate(record.columns):
if column != records[key][j]:
error = True
if error:
print('update error on', original, 'and', updated_columns, ':',
record, ', correct:', records[key])
else:
print('update on', original, 'and', updated_columns, ':', record)
updated_columns[i] = None
keys = sorted(list(records.keys()))
for c in range(0, grades_table.num_columns):
for i in range(0, 20):
r = sorted(sample(range(0, len(keys)), 2))
column_sum = sum(map(lambda key: records[key][c], keys[r[0]:r[1] + 1]))
result = query.sum(keys[r[0]], keys[r[1]], c)
if column_sum != result:
print('sum error on [', keys[r[0]], ',', keys[r[1]], ']: ', result,
', correct: ', column_sum)
else:
print('sum on [', keys[r[0]], ',', keys[r[1]], ']: ', column_sum)
[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), [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):
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)
key = keys[k * 5 + j]
q = Query(grades_table)
transaction.add_query(q.select, key, 0, [1, 1, 1, 1, 1])
q = Query(grades_table)
transaction.add_query(q.increment, key, 1)
transaction_workers[i % num_threads].add_transaction(transaction)
threads = []
for transaction_worker in transaction_workers:
threads.append(threading.Thread(target=transaction_worker.run, args=()))
for i, thread in enumerate(threads):
print('Thread', i, 'started')
thread.start()
for i, thread in enumerate(threads):
thread.join()
print('Thread', i, 'finished')
num_committed_transactions = sum(t.result for t in transaction_workers)
print(num_committed_transactions, 'transaction committed.')
query = Query(grades_table)
s = query.sum(keys[0], keys[-1], 1)
if s != num_committed_transactions * 5:
print('Expected sum:', num_committed_transactions * 5, ', actual:', s,
'. Failed.')
else:
print('Pass.')