Python 多处理处理列表的速度较慢，即使使用共享内存也是如此

Question

当我使用 Manager 对象跨进程共享列表时，此代码的非并行版本如何比并行版本运行得更快。我这样做是为了避免任何序列化，而且我不需要编辑列表。

我从 Oracle 返回一个 800,000 行的数据集，将其转换为列表并使用 Manager.list() 将其存储在共享内存中。

我并行地遍历查询结果中的每一列，以获得一些统计信息(我知道我可以在 SQL 中完成)。

主要代码:

import cx_Oracle
import csv
import os
import glob
import datetime
import multiprocessing as mp
import get_column_stats as gs;
import pandas as pd
import pandas.io.sql as psql


def get_data():
    print("Starting Job: " + str(datetime.datetime.now()));
    manager = mp.Manager()

    # Step 1: Init multiprocessing.Pool()   
    pool = mp.Pool(mp.cpu_count())
    print("CPU Count: " + str(mp.cpu_count()))

    dsn_tns = cx_Oracle.makedsn('myserver.net', '1521', service_name='PARIELGX');
    con = cx_Oracle.connect(user='fred', password='password123', dsn=dsn_tns);


    stats_results = [["OWNER","TABLE","COLUMN_NAME","RECORD_COUNT","DISTINCT_VALUES","MIN_LENGTH","MAX_LENGTH","MIN_VAL","MAX_VAL"]];

    sql = "SELECT * FROM ARIEL.DIM_REGISTRATION_SET"

    cur = con.cursor();

    print("Start Executing SQL: " + str(datetime.datetime.now()));
    cur.execute(sql);
    print("End SQL Execution: " + str(datetime.datetime.now()));

    print("Start SQL Fetch: " + str(datetime.datetime.now()));
    rs = cur.fetchall();
    print("End SQL Fetch: " + str(datetime.datetime.now()));

    print("Start Creation of Shared Memory List: " + str(datetime.datetime.now()));    
    lrs = manager.list(list(rs)) # shared memory list
    print("End Creation of Shared Memory List: " + str(datetime.datetime.now()));

    col_names = [];
    for field in cur.description:   
        col_names.append(field[0]); 

    #print(col_names)
    #print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')
    #print(rs)
    #print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')
    #print(lrs)




    col_index = 0;


    print("Start In-Memory Iteration of Dataset: " + str(datetime.datetime.now()));
      # we go through every field
    for field in cur.description:   
        col_names.append(field[0]);       

    # start at column 0
    col_index = 0;

    # iterate through each column, to gather stats from each column using parallelisation
    pool_results = pool.map_async(gs.get_column_stats_rs, [(lrs, col_name, col_names) for col_name in col_names]).get()

    for i in pool_results:
        stats_results.append(i)

    # Step 3: Don't forget to close
    pool.close() 


    print("End In-Memory Iteration of Dataset: " + str(datetime.datetime.now()));
    # end filename for
    cur.close();           

    outfile = open('C:\jupyter\Experiment\stats_dim_registration_set.csv','w');
    writer=csv.writer(outfile,quoting=csv.QUOTE_ALL, lineterminator='\n');
    writer.writerows(stats_results);
    outfile.close()
    print("Ending Job: " + str(datetime.datetime.now()));





get_data();

并行调用的代码:

def get_column_stats_rs(args):
    # rs is a list recordset of the results
    rs, col_name, col_names = args

    col_index = col_names.index(col_name)

    sys.stdout = open("col_" + col_name + ".out", "a")

    print("Starting Iteration of Column: " + col_name)
    max_length = 0
    min_length = 100000  # abitrarily large number!!

    max_value = ""
    min_value = "zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz"  # abitrarily large number!!

    distinct_value_count = 0

    has_values = False  # does the column have any non-null values
    has_null_values = False

    row_count = 0

    # create a dictionary into which we can add the individual items present in each row of data
    # a dictionary will not let us add the same value more than once, so we can simply count the
    # dictionary values at the end
    distinct_values = {}

    row_index = 0

    # go through every row, for the current column being processed to gather the stats
    for val in rs:
        row_value = val[col_index]

        row_count += 1

        if row_value is None:
            value_length = 0
        else:
            value_length = len(str(row_value))

        if value_length > max_length:
            max_length = value_length

        if value_length < min_length:
            if value_length > 0:
                min_length = value_length

        if row_value is not None:
            if str(row_value) > max_value:
                max_value = str(row_value)
            if str(row_value) < min_value:
                min_value = str(row_value)

        # capture distinct values
        if row_value is None:
            row_value = "Null"
            has_null_values = True
        else:
            has_values = True
            distinct_values[row_value] = 1

        row_index += 1
        # end row for

    distinct_value_count = len(distinct_values)

    if has_values == False:
        distinct_value_count = None
        min_length = None
        max_length = None
        min_value = None
        max_value = None
    elif has_null_values == True and distinct_value_count > 0:
        distinct_value_count -= 1

    if min_length == 0 and max_length > 0 and has_values == True:
        min_length = max_length

    print("Ending Iteration of Column: " + col_name)

    return ["ARIEL", "DIM_REGISTRATION_SET", col_name, row_count, distinct_value_count, min_length, max_length,
        strip_crlf(str(min_value)), strip_crlf(str(max_value))]

辅助函数:

def strip_crlf(value):
    return value.replace('\n', ' ').replace('\r', '')

我正在使用 Manager.list() 对象在进程之间共享状态:

lrs = manager.list(list(rs)) # shared memory list

并在 map_async() 方法中传递列表:

pool_results = pool.map_async(gs.get_column_stats_rs, [(lrs, col_name, col_names) for col_name in col_names]).get()

Answer 1

管理器开销会增加您的运行时间。此外，您并未在此处直接使用共享内存。您只使用多处理管理器，它比共享内存或单线程实现慢。如果您的代码不需要同步，这意味着您没有修改共享数据，只需跳过管理器并直接使用共享内存对象。

https://docs.python.org/3.7/library/multiprocessing.html

Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.