并行计算框架Polars、并行比Dask的计算据处数据处理性能对比

作者：Luís Oliveira 2023-07-10 13:51:45开发 测试 本文我们使用两个类似的脚本来执行提取、转换和加载(ETL)过程。框架

在Pandas 2.0发布以后，理性我们发布过一些评测的并行比文章，这次我们看看，计算据处除了Pandas以外，框架常用的理性两个都是为了大数据处理的并行数据框架的对比测试。

本文我们使用两个类似的并行比脚本来执行提取、转换和加载(ETL)过程。计算据处

测试内容

这两个脚本主要功能包括:

从两个parquet 文件中提取数据，框架对于小型数据集，理性变量path1将为“yellow_tripdata/ yellow_tripdata_2014-01”，并行比对于中等大小的计算据处数据集，变量path1将是框架“yellow_tripdata/yellow_tripdata”。对于大数据集，变量path1将是“yellow_tripdata/yellow_tripdata*.parquet”;

进行数据转换:a)连接两个DF，b)根据PULocationID计算行程距离的平均值，c)只选择某些条件的行，d)将步骤b的值四舍五入为2位小数，e)将列“trip_distance”重命名为“mean_trip_distance”，f)对列“mean_trip_distance”进行排序。

将最终的结果保存到新的文件。

脚本

1、Polars

数据加载读取

def extraction():    """    Extract two datasets from parquet files    """    path1="yellow_tripdata/yellow_tripdata_2014-01.parquet"    df_trips= pl_read_parquet(path1,)    path2 = "taxi+_zone_lookup.parquet"    df_zone = pl_read_parquet(path2,)     return df_trips, df_zone  def pl_read_parquet(path, ):    """    Converting parquet file into Polars dataframe    """    df= pl.scan_parquet(path,)    return df

转换函数

def transformation(df_trips, df_zone):    """    Proceed to several transformations    """    df_trips= mean_test_speed_pl(df_trips, )         df = df_trips.join(df_zone,how="inner", left_on="PULocationID", right_on="LocationID",)    df = df.select(["Borough","Zone","trip_distance",])       df = get_Queens_test_speed_pd(df)    df = round_column(df, "trip_distance",2)    df = rename_column(df, "trip_distance","mean_trip_distance")     df = sort_by_columns_desc(df, "mean_trip_distance")    return df   def mean_test_speed_pl(df_pl,):    """    Getting Mean per PULocationID    """    df_pl = df_pl.groupby('PULocationID').agg(pl.col(["trip_distance",]).mean())    return df_pl  def get_Queens_test_speed_pd(df_pl):    """    Only getting Borough in Queens    """     df_pl = df_pl.filter(pl.col("Borough")=='Queens')     return df_pl  def round_column(df, column,to_round):    """    Round numbers on columns    """    df = df.with_columns(pl.col(column).round(to_round))    return df  def rename_column(df, column_old, column_new):    """    Renaming columns    """    df = df.rename({ column_old: column_new})    return df  def sort_by_columns_desc(df, column):    """    Sort by column    """    df = df.sort(column, descending=True)    return df

保存

def loading_into_parquet(df_pl):    """    Save dataframe in parquet    """    df_pl.collect(streaming=True).write_parquet(f'yellow_tripdata_pl.parquet')

其他代码

import polars as pl import time  def pl_read_parquet(path, ):    """    Converting parquet file into Polars dataframe    """    df= pl.scan_parquet(path,)    return df  def mean_test_speed_pl(df_pl,):    """    Getting Mean per PULocationID    """    df_pl = df_pl.groupby('PULocationID').agg(pl.col(["trip_distance",]).mean())    return df_pl  def get_Queens_test_speed_pd(df_pl):    """    Only getting Borough in Queens    """     df_pl = df_pl.filter(pl.col("Borough")=='Queens')     return df_pl  def round_column(df, column,to_round):    """    Round numbers on columns    """    df = df.with_columns(pl.col(column).round(to_round))    return df  def rename_column(df, column_old, column_new):    """    Renaming columns    """    df = df.rename({ column_old: column_new})    return df   def sort_by_columns_desc(df, column):    """    Sort by column    """    df = df.sort(column, descending=True)    return df   def main():         print(f'Starting ETL for Polars')    start_time = time.perf_counter()     print('Extracting...')    df_trips, df_zone =extraction()            end_extract=time.perf_counter()     time_extract =end_extract- start_time     print(f'Extraction Parquet end in { round(time_extract,5)} seconds')    print('Transforming...')    df = transformation(df_trips, df_zone)    end_transform = time.perf_counter()     time_transformation =time.perf_counter() - end_extract    print(f'Transformation end in { round(time_transformation,5)} seconds')    print('Loading...')    loading_into_parquet(df,)    load_transformation =time.perf_counter() - end_transform    print(f'Loading end in { round(load_transformation,5)} seconds')    print(f"End ETL for Polars in { str(time.perf_counter()-start_time)}")   if __name__ == "__main__":         main()

2、Dask

函数功能与上面一样，所以我们把代码整合在一起：

import dask.dataframe as dd from dask.distributed import Client import time  def extraction():    path1 = "yellow_tripdata/yellow_tripdata_2014-01.parquet"    df_trips = dd.read_parquet(path1)    path2 = "taxi+_zone_lookup.parquet"    df_zone = dd.read_parquet(path2)     return df_trips, df_zone  def transformation(df_trips, df_zone):    df_trips = mean_test_speed_dask(df_trips)    df = df_trips.merge(df_zone, how="inner", left_on="PULocationID", right_on="LocationID")    df = df[["Borough", "Zone", "trip_distance"]]     df = get_Queens_test_speed_dask(df)    df = round_column(df, "trip_distance", 2)    df = rename_column(df, "trip_distance", "mean_trip_distance")     df = sort_by_columns_desc(df, "mean_trip_distance")    return df  def loading_into_parquet(df_dask):    df_dask.to_parquet("yellow_tripdata_dask.parquet", engine="fastparquet")  def mean_test_speed_dask(df_dask):    df_dask = df_dask.groupby("PULocationID").agg({ "trip_distance": "mean"})    return df_dask  def get_Queens_test_speed_dask(df_dask):    df_dask = df_dask[df_dask["Borough"] == "Queens"]    return df_dask  def round_column(df, column, to_round):    df[column] = df[column].round(to_round)    return df  def rename_column(df, column_old, column_new):    df = df.rename(columns={ column_old: column_new})    return df  def sort_by_columns_desc(df, column):    df = df.sort_values(column, ascending=False)    return df    def main():    print("Starting ETL for Dask")    start_time = time.perf_counter()     client = Client() # Start Dask Client     df_trips, df_zone = extraction()     end_extract = time.perf_counter()    time_extract = end_extract - start_time     print(f"Extraction Parquet end in { round(time_extract, 5)} seconds")    print("Transforming...")    df = transformation(df_trips, df_zone)    end_transform = time.perf_counter()    time_transformation = time.perf_counter() - end_extract    print(f"Transformation end in { round(time_transformation, 5)} seconds")    print("Loading...")    loading_into_parquet(df)    load_transformation = time.perf_counter() - end_transform    print(f"Loading end in { round(load_transformation, 5)} seconds")    print(f"End ETL for Dask in { str(time.perf_counter() - start_time)}")     client.close() # Close Dask Client  if __name__ == "__main__":    main()

测试结果对比

1、小数据集

我们使用164 Mb的数据集，这样大小的数据集对我们来说比较小，在日常中也时非常常见的。

下面是每个库运行五次的结果:

Polars

Dask

2、中等数据集

我们使用1.1 Gb的数据集，这种类型的数据集是GB级别，虽然可以完整的加载到内存中，但是数据体量要比小数据集大很多。

Polars

Dask

3、大数据集

我们使用一个8gb的数据集，这样大的数据集可能一次性加载不到内存中，需要框架的处理。

Polars

Dask

总结

从结果中可以看出，Polars和Dask都可以使用惰性求值。所以读取和转换非常快，执行它们的时间几乎不随数据集大小而变化;

可以看到这两个库都非常擅长处理中等规模的数据集。

由于polar和Dask都是使用惰性运行的，所以下面展示了完整ETL的结果(平均运行5次)。

Polars在小型数据集和中型数据集的测试中都取得了胜利。但是，Dask在大型数据集上的平均时间性能为26秒。

这可能和Dask的并行计算优化有关，因为官方的文档说“Dask任务的运行速度比Spark ETL查询快三倍，并且使用更少的CPU资源”。

上面是测试使用的电脑配置，Dask在计算时占用的CPU更多，可以说并行性能更好。

责任编辑：华轩 来源： DeepHub IMBA 测试并行计算框架

(责任编辑：时尚)