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)
[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(906659671 + 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.')
[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): start_value = 906659671 + i end_value = start_value + 100 result = query.sum(start_value, end_value, 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)