python - 购买内存以避免 30-50Gb 以上的文件分块

Question

我使用 pandas 来读取非常大的 csv 文件，这些文件也是经过 gzip 压缩的。我解压成大约 30-50GB 的 csv 文件。我对文件进行分块并处理/操作它们。最后将相关数据添加到我压缩的HDF5文件中

它工作正常，但速度很慢，因为我每天必须处理一个文件，并且有几年的数据(600TB 未压缩的 csv)

购买更多内存是否是避免分块并加快进程(例如 64GB/128GB)的好方法？但这会让 Pandas 变得缓慢且笨重吗？我是否正确地说，切换到 C++ 可以加快该过程，但我仍然遭受读取过程的困扰，并且必须处理 block 中的数据。最后，有人对处理这个问题的最佳方法有任何想法吗？

顺便说一句，一旦工作完成，我就不必返回并再次处理数据，所以只想让它在合理的时间内工作，所以写一些并行进程可能很好但经验有限的东西我需要花一些时间来构建它，所以我宁愿不要这样做，除非这是唯一的选择。

更新。我认为看代码会更容易。无论如何，我不认为代码特别慢。我认为技术/方法可能是。

def txttohdf(path, contract):
    #create dataframes for trade and quote
    dftrade = pd.DataFrame(columns = ["datetime", "Price", "Volume"])
    dfquote = pd.DataFrame(columns = ["datetime", "BidPrice", "BidSize","AskPrice", "AskSize"])
    #create an hdf5 file with high compression and table so we can append
    hdf = pd.HDFStore(path + contract + '.h5', complevel=9, complib='blosc')
    hdf.put('trade', dftrade, format='table', data_columns=True)
    hdf.put('quote', dfquote, format='table', data_columns=True)
    #date1 = date(start).strftime('%Y%m%d')
    #date2 = date(end).strftime('%Y%m%d')
    #dd = [date1 + timedelta(days=x) for x in range((date2-date1).days + 1)]
    #walkthrough directories
    for subdir, dir, files in os.walk(path):
        for file in files:
            #check if contract has name
            #print(file)
                #create filename from directory and file 

            filename = os.path.join(subdir, file)
                #read in csv
            if filename.endswith('.gz'):

                df = pd.read_csv(gzip.open(filename),header=0,iterator=True,chunksize = 10000, low_memory =False,  names = ['RIC','Date','Time','GMTOffset','Type','ExCntrbID','LOC','Price','Volume','MarketVWAP','BuyerID','BidPrice','BidSize','NoBuyers','SellerID','AskPrice','AskSize','NoSellers','Qualifiers','SeqNo','ExchTime','BlockTrd','FloorTrd','PERatio','Yield','NewPrice','NewVol','NewSeqNo','BidYld','AskYld','ISMABidYld','ISMAAskYld','Duration','ModDurtn','BPV','AccInt','Convexity','BenchSpd','SwpSpd','AsstSwpSpd','SwapPoint','BasePrice','UpLimPrice','LoLimPrice','TheoPrice','StockPrice','ConvParity','Premium','BidImpVol','AskImpVol','ImpVol','PrimAct','SecAct','GenVal1','GenVal2','GenVal3','GenVal4','GenVal5','Crack','Top','FreightPr','1MnPft','3MnPft','PrYrPft','1YrPft','3YrPft','5YrPft','10YrPft','Repurch','Offer','Kest','CapGain','Actual','Prior','Revised','Forecast','FrcstHigh','FrcstLow','NoFrcts','TrdQteDate','QuoteTime','BidTic','TickDir','DivCode','AdjClose','PrcTTEFlag','IrgTTEFlag','PrcSubMktId','IrgSubMktId','FinStatus','DivExDate','DivPayDate','DivAmt','Open','High','Low','Last','OpenYld','HighYld','LowYld','ShortPrice','ShortVol','ShortTrdVol','ShortTurnnover','ShortWeighting','ShortLimit','AccVolume','Turnover','ImputedCls','ChangeType','OldValue','NewValue','Volatility','Strike','Premium','AucPrice','Auc Vol','MidPrice','FinEvalPrice','ProvEvalPrice','AdvancingIssues','DecliningIssues','UnchangedIssues','TotalIssues','AdvancingVolume','DecliningVolume','UnchangedVolume','TotalVolume','NewHighs','NewLows','TotalMoves','PercentageChange','AdvancingMoves','DecliningMoves','UnchangedMoves','StrongMarket','WeakMarket','ChangedMarket','MarketVolatility','OriginalDate','LoanAskVolume','LoanAskAmountTradingPrice','PercentageShortVolumeTradedVolume','PercentageShortPriceTradedPrice','ForecastNAV','PreviousDaysNAV','FinalNAV','30DayATMIVCall','60DayATMIVCall','90DayATMIVCall','30DayATMIVPut','60DayATMIVPut','90DayATMIVPut','BackgroundReference','DataSource','BidSpread','AskSpread','ContractPhysicalUnits','Miniumumquantity','NumberPhysicals','ClosingReferencePrice','ImbalanceQuantity','FarClearingPrice','NearClearingPrice','OptionAdjustedSpread','ZSpread','ConvexityPremium','ConvexityRatio','PercentageDailyReturn','InterpolatedCDSBasis','InterpolatedCDSSpread','ClosesttoMaturityCDSBasis','SettlementDate','EquityPrice','Parity','CreditSpread','Delta','InputVolatility','ImpliedVolatility','FairPrice','BondFloor','Edge','YTW','YTB','SimpleMargin','DiscountMargin','12MonthsEPS','UpperTradingLimit','LowerTradingLimit','AmountOutstanding','IssuePrice','GSpread','MiscValue','MiscValueDescription'])
                #parse date time this is quicker than doing it while we read it in
                for chunk in df:
                    chunk['datetime'] = chunk.apply(lambda row: datetime.datetime.strptime(row['Date']+ ':' + row['Time'],'%d-%b-%Y:%H:%M:%S.%f'), axis=1)
                    #df = df[~df.comment.str.contains('ALIAS')]
                #drop uneeded columns inc date and time
                    chunk = chunk.drop(['Date','Time','GMTOffset','ExCntrbID','LOC','MarketVWAP','BuyerID','NoBuyers','SellerID','NoSellers','Qualifiers','SeqNo','ExchTime','BlockTrd','FloorTrd','PERatio','Yield','NewPrice','NewVol','NewSeqNo','BidYld','AskYld','ISMABidYld','ISMAAskYld','Duration','ModDurtn','BPV','AccInt','Convexity','BenchSpd','SwpSpd','AsstSwpSpd','SwapPoint','BasePrice','UpLimPrice','LoLimPrice','TheoPrice','StockPrice','ConvParity','Premium','BidImpVol','AskImpVol','ImpVol','PrimAct','SecAct','GenVal1','GenVal2','GenVal3','GenVal4','GenVal5','Crack','Top','FreightPr','1MnPft','3MnPft','PrYrPft','1YrPft','3YrPft','5YrPft','10YrPft','Repurch','Offer','Kest','CapGain','Actual','Prior','Revised','Forecast','FrcstHigh','FrcstLow','NoFrcts','TrdQteDate','QuoteTime','BidTic','TickDir','DivCode','AdjClose','PrcTTEFlag','IrgTTEFlag','PrcSubMktId','IrgSubMktId','FinStatus','DivExDate','DivPayDate','DivAmt','Open','High','Low','Last','OpenYld','HighYld','LowYld','ShortPrice','ShortVol','ShortTrdVol','ShortTurnnover','ShortWeighting','ShortLimit','AccVolume','Turnover','ImputedCls','ChangeType','OldValue','NewValue','Volatility','Strike','Premium','AucPrice','Auc Vol','MidPrice','FinEvalPrice','ProvEvalPrice','AdvancingIssues','DecliningIssues','UnchangedIssues','TotalIssues','AdvancingVolume','DecliningVolume','UnchangedVolume','TotalVolume','NewHighs','NewLows','TotalMoves','PercentageChange','AdvancingMoves','DecliningMoves','UnchangedMoves','StrongMarket','WeakMarket','ChangedMarket','MarketVolatility','OriginalDate','LoanAskVolume','LoanAskAmountTradingPrice','PercentageShortVolumeTradedVolume','PercentageShortPriceTradedPrice','ForecastNAV','PreviousDaysNAV','FinalNAV','30DayATMIVCall','60DayATMIVCall','90DayATMIVCall','30DayATMIVPut','60DayATMIVPut','90DayATMIVPut','BackgroundReference','DataSource','BidSpread','AskSpread','ContractPhysicalUnits','Miniumumquantity','NumberPhysicals','ClosingReferencePrice','ImbalanceQuantity','FarClearingPrice','NearClearingPrice','OptionAdjustedSpread','ZSpread','ConvexityPremium','ConvexityRatio','PercentageDailyReturn','InterpolatedCDSBasis','InterpolatedCDSSpread','ClosesttoMaturityCDSBasis','SettlementDate','EquityPrice','Parity','CreditSpread','Delta','InputVolatility','ImpliedVolatility','FairPrice','BondFloor','Edge','YTW','YTB','SimpleMargin','DiscountMargin','12MonthsEPS','UpperTradingLimit','LowerTradingLimit','AmountOutstanding','IssuePrice','GSpread','MiscValue','MiscValueDescription'], axis=1)
                # convert to datetime explicitly and add nanoseconds to same time stamps
                    chunk['datetime'] = pd.to_datetime(chunk.datetime)
                #nanoseconds = df.groupby(['datetime']).cumcount()
                #df['datetime'] += np.array(nanoseconds, dtype='m8[ns]')  
                # drop empty prints and make sure all prices are valid
                    dfRic = chunk[(chunk["RIC"] == contract)]
                    if len(dfRic)>0:
                        print(dfRic)
                    if ~chunk.empty:
                        dft = dfRic[(dfRic["Type"] == "Trade")]
                        dft.dropna(subset = ["Volume"], inplace =True)
                        dft = dft.drop(["RIC","Type","BidPrice", "BidSize", "AskPrice", "AskSize"], axis=1)
                        dft = dft[(dft["Price"] > 0)]

                    # clean up bid and ask
                        dfq = dfRic[(dfRic["Type"] == "Quote")]
                        dfq.dropna(how = 'all', subset = ["BidSize","AskSize"], inplace =True)
                        dfq = dfq.drop(["RIC","Type","Price", "Volume"], axis=1)
                        dfq = dfq[(dfq["BidSize"] > 0) | (dfq["AskSize"] > 0)]
                        dfq = dfq.ffill()
                    else:
                        print("Empty")    
    #add to hdf and close if loop finished
                    hdf.append('trade', dft, format='table', data_columns=True)
                    hdf.append('quote', dfq, format='table', data_columns=True)
    hdf.close()

Answer 1

我认为您有很多可以优化的地方:

• 首先只读取您真正需要的列，而不是读取然后删除它们 - 使用 usecols=list_of_needed_columns 参数

• 增加 block 大小 - 尝试使用不同的值 - 我会从 10**5

• 不要使用 chunk.apply(...) 来转换日期时间 - 它非常慢 - 使用 pd.to_datetime(column, format= '...')而不是

• 组合多个条件时，您可以更有效地过滤数据，而不是一步步进行: