Python多線程性能-改用C ++？

Question

因此，我有一個Python腳本，該腳本基本上可以寫入80GB以上的文件。 目前，它只是串行運行，而只有我實際運行它時，才在服務器上運行約13個小時。

我將對其進行並行化，以便它寫入許多文件，而不只是一個文件。

取走我已經擁有的並保留在Python中但合並多個線程會稍微容易一些（有一個共享數據映射，他們需要訪問這些共享數據，而沒人會寫入這些數據，因此不需要保護） 。

但是，將其保留在Python中是愚蠢的嗎？ 我也知道C ++，所以您認為我應該只用C ++重寫它嗎？ 我認為該程序比其他任何東西都具有更多的磁盤綁定（沒有大量邏輯可用於寫入文件），因此也許它並沒有多大區別。 我不確定C ++編寫相同的80GB文件（串行）需要花費多長時間。

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UPDATE 6/6 / 14，16：40 PST：我在下面發布我的代碼，以確定代碼本身是否存在瓶頸，而不是純粹由磁盤綁定。

我每個表調用一次writeEntriesToSql（），那里大約有30個表。 “大小”是要寫入表的插入數。 所有表的累積大小約為200,000,000。

我確實注意到我要一次又一次地編譯我的正則表達式，這可能會導致我很浪費，盡管我不確定多少。

def writeEntriesToSql(db, table, size, outputFile):

# get a description of the table
rows = queryDatabaseMultipleRows(db, 'DESC ' + table)
fieldNameCol = 0    # no enums in python 2.7 :(
typeCol = 1
nullCol = 2
keyCol = 3
defaultCol = 4
extraCol = 5

fieldNamesToTypes = {}  

for row in rows:
    if (row[extraCol].find("auto_increment") == -1):
        # insert this one
        fieldNamesToTypes[row[fieldNameCol]] = row[typeCol]    


for i in range(size):
    fieldNames = ""
    fieldVals = "" 
    count = 0

    # go through the fields
    for fieldName, type in fieldNamesToTypes.iteritems():
            # build a string of field names to be used in the INSERT statement
        fieldNames += table + "." + fieldName

        if fieldName in foreignKeys[table]:
            otherTable = foreignKeys[table][fieldName][0]
            otherTableKey = foreignKeys[table][fieldName][1]
            if len(foreignKeys[table][fieldName]) == 3:
                # we already got the value so we don't have to get it again
                val = foreignKeys[table][fieldName][2]
            else:
                # get the value from the other table and store it
                #### I plan for this to be an infrequent query - unless something is broken here!
                query = "SELECT " + otherTableKey + " FROM " + otherTable + " LIMIT 1"
                val = queryDatabaseSingleRowCol(db, query)
                foreignKeys[table][fieldName].append(val)
            fieldVals += val
        else:
            fieldVals += getDefaultFieldVal(type)
        count = count + 1
        if count != len(fieldNamesToTypes):
            fieldNames += ","
            fieldVals += ","        


# return the default field value for a given field type which will be used to prepopulate our tables
def getDefaultFieldVal(type):

    if not ('insertTime' in globals()):
        global insertTime
        insertTime = datetime.utcnow()
        # store this time in a file so that it can be retrieved by SkyReporterTest.perfoutput.py
        try:
            timeFileName = perfTestDir + "/dbTime.txt"
            timeFile = open(timeFileName, 'w')
            timeFile.write(str(insertTime))
        except:
            print "!!! cannot open file " + timeFileName + " for writing. Please make sure this is run where you have write permissions\n"
            os.exit(1)


    # many of the types are formatted with a typename, followed by a size in parentheses
    ##### Looking at this more closely, I suppose I could be compiling this once instead of over and over - a bit bottleneck here?
    p = re.compile("(.*)\(([0-9]+).*")


    size = 0
    if (p.match(type)):
        size = int(p.sub(r"\2", type))
        type = p.sub(r"\1", type)
    else:
        size = 0


    if (type == "tinyint"):
        return str(random.randint(1, math.pow(2,7)))
    elif (type == "smallint"):
        return str(random.randint(1, math.pow(2,15)))
    elif (type == "mediumint"):
        return str(random.randint(1, math.pow(2,23)))
    elif (type == "int" or type == "integer"):
        return str(random.randint(1, math.pow(2,31)))
    elif (type == "bigint"):
        return str(random.randint(1, math.pow(2,63)))
    elif (type == "float" or type == "double" or type == "doubleprecision" or type == "decimal" or type == "realdecimal" or type == "numeric"):
        return str(random.random() * 100000000) # random endpoints for this random
    elif (type == "date"):
        insertTime = insertTime - timedelta(seconds=1)
        return "'" + insertTime.strftime("%Y-%m-%d") + "'" 
    elif (type == "datetime"):
        insertTime = insertTime - timedelta(seconds=1)
        return "'" + insertTime.strftime("%Y-%m-%d %H:%M:%S") + "'" 
    elif (type == "timestamp"):
        insertTime = insertTime - timedelta(seconds=1)
        return "'" + insertTime.strftime("%Y%m%d%H%M%S") + "'" 
    elif (type == "time"):
        insertTime = insertTime - timedelta(seconds=1)
        return "'" + insertTime.strftime("%H:%M:%S") + "'" 
    elif (type == "year"):
        insertTime = insertTime - timedelta(seconds=1)
        return "'" + insertTime.strftime("%Y") + "'" 
    elif (type == "char" or type == "varchar" or type == "tinyblog" or type == "tinytext" or type == "blob" or type == "text" or type == "mediumblob"
    or type == "mediumtext" or type == "longblob" or type == "longtext"):
        if (size == 0): # not specified
            return "'a'"
        else:
            lst = [random.choice(string.ascii_letters + string.digits) for n in xrange(size)]
            strn = "".join(lst)
            return strn
    elif (type == "enum"):
        return "NULL"   # TBD if needed
    elif (type == "set"):
        return "NULL"   # TBD if needed
    else:
        print "!!! Unrecognized mysql type: " + type + "\n"
        os.exit(1)

Answer 1

Python的I / O不會比其他語言慢很多。 解釋器的啟動速度可能很慢，但是編寫如此大的文件將攤銷該效果。

我建議研究一下多處理模塊，該模塊將通過具有多個Python實例來使您具有真正的並行性，這將有助於解決GIL。 但是，這些將附加一些開銷，但同樣，對於80GB的文件來說，也沒有太大關系。 請記住，每個過程都是一個完整的過程，這意味着它將占用更多的計算資源。

Answer 2

還請記住，由於您的代碼已經是IO / Bound，因此根據您的配置，您可能會獲得低速/無速比。 如果您正在從多個線程向單個磁盤寫入數據，那么弊大於利。