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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
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