python - Python multiprocessing 处理列表的速度较慢,即使在使用共享内存时也是如此
问题描述
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快得多。我这样做是为了避免任何序列化,我不需要编辑列表。
我从 Oracle 返回一个 800,000 行数据集,将其转换为列表并使用 Manager.list() 将其存储在共享内存中。
我正在并行迭代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 中做到这一点)。
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))]
辅助功能:
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()
解决方案
管理器开销增加了您的运行时。此外,您在这里也没有直接使用共享内存。您只使用多处理管理器,已知它比共享内存或单线程实现慢。如果您不需要在代码中进行同步,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
服务器进程管理器比使用共享内存对象更灵活,因为它们可以支持任意对象类型。此外,单个管理器可以由网络上不同计算机上的进程共享。但是,它们比使用共享内存要慢。
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