为什么达克比潘达读取同样的拼花文件要慢得多？

Question

我正在用Dask和python测试地板文件上的阅读规范，我发现用熊猫阅读同样的文件要比Dask快得多。我想了解为什么会这样，如果有一个方法可以达到同样的效果，

所有相关软件包的版本

print(dask.__version__) print(pd.__version__) print(pyarrow.__version__) print(fastparquet.__version__)

2.6.0 0.25.2 0.15.1 0.3.2

import pandas as pd 
import numpy as np
import dask.dataframe as dd

col = [str(i) for i in list(np.arange(40))]
df = pd.DataFrame(np.random.randint(0,100,size=(5000000, 4 * 10)), columns=col)

df.to_parquet('large1.parquet', engine='pyarrow')
 # Wall time: 3.86 s
df.to_parquet('large2.parquet', engine='fastparquet')
 # Wall time: 27.1 s
df = dd.read_parquet('large2.parquet', engine='fastparquet').compute()
 # Wall time: 5.89 s
df = dd.read_parquet('large1.parquet', engine='pyarrow').compute()
 # Wall time: 4.84 s
df = pd.read_parquet('large1.parquet',engine='pyarrow')
 # Wall time: 503 ms 
df = pd.read_parquet('large2.parquet',engine='fastparquet')
 # Wall time: 4.12 s

在使用混合数据类型时，差异更大。

dtypes: category(7), datetime64[ns](2), float64(1), int64(1), object(9)
memory usage: 973.2+ MB
# df.shape == (8575745, 20)

df.to_parquet('large1.parquet', engine='pyarrow')
 # Wall time: 9.67 s

df.to_parquet('large2.parquet', engine='fastparquet')
 # Wall time: 33.3 s

# read with Dask
df = dd.read_parquet('large1.parquet', engine='pyarrow').compute()
 # Wall time: 34.5 s

df = dd.read_parquet('large2.parquet', engine='fastparquet').compute()
 # Wall time: 1min 22s

# read with pandas 
df = pd.read_parquet('large1.parquet',engine='pyarrow')
 # Wall time: 8.67 s

df = pd.read_parquet('large2.parquet',engine='fastparquet')
 # Wall time: 21.8 s

Answer 1

我的第一个猜测是，Pandas将Parquet数据集保存到一个行组中，这将不允许像Dask这样的系统并行化。这并不能解释为什么速度更慢，但也解释了为什么它不能更快。

要了解更多信息，我建议进行分析。您可能对本文件感兴趣：

• https://docs.dask.org/en/latest/understanding-performance.html