并行计算框架Polars、Dask的数据处理性能对比

在Pandas 2.0发布以后，我们发布过一些评测的文章，这次我们看看，除了Pandas以外，常用的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脚本来执行提取、转换和加载(ETL)过程。

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，对于小型数据集，变量path1将为“yellow_tripdata/ yellow_tripdata_2014-01”，对于中等大小的数据集，变量path1将是“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。