Python – 使用HUGE数据集避免内存错误

我有一个连接到PostGresql 数据库的python程序.在这个数据库中,我有很多数据(大约12亿行).幸运的是,我不必同时分析所有这些行.

这12亿行分布在几张桌子上(大约30张).目前我正在访问一个名为table_3的表,我想在其中访问具有特定“did”值的所有行(如调用该列).

我使用sql命令计算了行数：

SELECT count(*) FROM table_3 WHERE did='356002062376054';

返回1.57亿行.

我将对所有这些行执行一些“分析”(提取2个特定值)并对这些值进行一些计算,然后将它们写入字典,然后将它们保存在另一个表中的PostGresql上.

问题是我正在创建大量列表和字典来管理所有这些我最终耗尽内存,即使我使用的是Python 3 64位并且具有64 GB的RAM.

一些代码：

CONNECTION = psycopg2.connect('<psycopg2 formatted string>')
CURSOR = CONNECTION.cursor()

DID_LIST = ["357139052424715",
            "353224061929963",
            "356002064810514",
            "356002064810183",
            "358188051768472",
            "358188050598029",
            "356002061925067",
            "358188056470108",
            "356002062376054",
            "357460064130045"]

SENSOR_LIST = [1, 2, 3, 4, 5, 6, 7, 8, 9,
               10, 11, 12, 13, 801, 900, 901,
               902, 903, 904, 905, 906, 907,
               908, 909, 910, 911]

for did in did_list:
    table_name = did
    for sensor_id in sensor_list:
        rows = get_data(did, sensor_id)
        list_object = create_standard_list(sensor_id, rows)  # Happens here
        formatted_list = format_table_dictionary(list_object) # Or here
        pushed_rows = write_to_table(table_name, formatted_list) #write_to_table method is omitted as that is not my problem.

def get_data(did, table_id):
    """Getting data from postgresql."""
    table_name = "table_{0}".format(table_id)
    query = """SELECT * FROM {0} WHERE did='{1}'
               ORDER BY timestamp""".format(table_name, did)

    CURSOR.execute(query)
    CONNECTION.commit()

    return CURSOR

def create_standard_list(sensor_id, data):
    """Formats DB data to dictionary"""
    list_object = []

    print("Create standard list")
    for row in data: # data is the psycopg2 CURSOR
        row_timestamp = row[2]
        row_data = row[3]

        temp_object = {"sensor_id": sensor_id, "timestamp": row_timestamp,
                       "data": row_data}

        list_object.append(temp_object)

    return list_object


def format_table_dictionary(list_dict):
    """Formats dictionary to simple data
       table_name = (dates, data_count, first row)"""
    print("Formatting dict to DB")
    temp_today = 0
    dict_list = []
    first_row = {}
    count = 1

    for elem in list_dict:
        # convert to seconds
        date = datetime.fromtimestamp(elem['timestamp'] / 1000)
        today = int(date.strftime('%d'))
        if temp_today is not today:
            if not first_row:
                first_row = elem['data']
            first_row_str = str(first_row)
            dict_object = {"sensor_id": elem['sensor_id'],
                           "date": date.strftime('%d/%m-%Y'),
                           "reading_count": count,
                           # size in MB of data
                           "approx_data_size": (count*len(first_row_str)/1000),
                           "time": date.strftime('%H:%M:%s'),
                           "first_row": first_row}

            dict_list.append(dict_object)
            first_row = {}
            temp_today = today
            count = 0
        else:
            count += 1

    return dict_list

我的错误发生在创建两个列表中的任何一个,在我的代码中用注释标记.它代表我的电脑停止响应,并最终让我退出.我正在运行Windows 10,如果这是重要的.

我知道我使用“create_standard_list”方法创建的第一个列表可以被排除,并且该代码可以在“format_table_dictionary”代码中运行,从而避免在内存中包含157 mio元素的列表,但我认为其他一些表我将遇到类似的问题,可能会更大,所以我想现在就优化它,但我不确定我能做什么？

我想写一个文件并不会真正有用,因为我必须读取该文件,从而将它重新放回内存中？

极简主义的例子

我有一张桌子

---------------------------------------------------------------
|Row 1 | did | timestamp | data | unused value | unused value |
|Row 2 | did | timestamp | data | unused value | unused value |
....
---------------------------------

table = [{ values from above row1 }, { values from above row2},...]

connection = psycopg2.connect(<connection string>)
cursor = connection.cursor()

table = cursor.execute("""SELECT * FROM table_3 WHERE did='356002062376054'
                          ORDER BY timestamp""")

extracted_list = extract(table)
calculated_list = calculate(extracted_list)
... write to db ...

def extract(table):
    """extract all but unused values"""
    new_list = []
    for row in table:
        did = row[0]
        timestamp = row[1]
        data = row[2]

        a_dict = {'did': did, 'timestamp': timestamp, 'data': data}
        new_list.append(a_dict)

    return new_list


def calculate(a_list):
    """perform calculations on values"""
    dict_list = []
    temp_today = 0
    count = 0
    for row in a_list:
        date = datetime.fromtimestamp(row['timestamp'] / 1000) # from ms to sec
        today = int(date.strfime('%d'))
        if temp_today is not today:
            new_dict = {'date': date.strftime('%d/%m-%Y'),
                        'reading_count': count,
                        'time': date.strftime('%H:%M:%s')}
            dict_list.append(new_dict)

    return dict_list

解决方法:

create_standard_list()和format_table_dictionary()可以构建生成器(产生每个项而不是返回完整列表),这会停止将整个列表保存在内存中,因此应该解决您的问题,例如：

def create_standard_list(sensor_id, data):
    for row in data:
        row_timestamp = row[2]
        row_data = row[3]

        temp_object = {"sensor_id": sensor_id, "timestamp": row_timestamp,
                       "data": row_data}
        yield temp_object
       #^ yield each item instead of appending to a list

有关generators和yield keyword的更多信息.

Python – 使用HUGE数据集避免内存错误

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