关于机器学习:并行计算框架PolarsDask的数据处理性能对比

在 Pandas 2.0 公布当前，咱们公布过一些评测的文章，这次咱们看看，除了 Pandas 以外，罕用的两个都是为了大数据处理的并行数据框架的比照测试。

本文咱们应用两个相似的脚本来执行提取、转换和加载 (ETL) 过程。

测试内容

这两个脚本次要性能包含:

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)连贯两个 DF，b)依据 PULocationID 计算行程间隔的平均值，c)只抉择某些条件的行，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

将最终的后果保留到新的文件

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

保留

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

其余代码

 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

函数性能与下面一样，所以咱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()

测试后果比照

1、小数据集

咱们应用 164 Mb 的数据集，这样大小的数据集对咱们来说比拟小，在日常中也时十分常见的。

上面是每个库运行五次的后果:

Polars

Dask

2、中等数据集

咱们应用 1.1 Gb 的数据集，这种类型的数据集是 GB 级别，尽管能够残缺的加载到内存中，然而数据体量要比小数据集大很多。

Polars

Dask

3、大数据集

咱们应用一个 8gb 的数据集，这样大的数据集可能一次性加载不到内存中，须要框架的解决。

Polars

Dask

总结

从后果中能够看出，Polars 和 Dask 都能够应用惰性求值。所以读取和转换十分快，执行它们的工夫简直不随数据集大小而变动;

能够看到这两个库都十分善于解决中等规模的数据集。

因为 polar 和 Dask 都是应用惰性运行的，所以上面展现了残缺 ETL 的后果(均匀运行 5 次)。

Polars 在小型数据集和中型数据集的测试中都获得了胜利。然而，Dask 在大型数据集上的均匀工夫性能为 26 秒。

这可能和 Dask 的并行计算优化无关，因为官网的文档说“Dask 工作的运行速度比 Spark ETL 查问快三倍，并且应用更少的 CPU 资源”。

下面是测试应用的电脑配置，Dask 在计算时占用的 CPU 更多，能够说并行性能更好。

https://avoid.overfit.cn/post/74128cd8803b43f2a51ca4ff4fed4a95

作者：Luís Oliveira