/
groupby_benchmark.py
39 lines (30 loc) · 1.22 KB
/
groupby_benchmark.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import argparse
import ray
import os
import modin.pandas as pd
from utils import time_logger
parser = argparse.ArgumentParser(description='groupby benchmark')
parser.add_argument('--path', dest='path', help='path to the csv data file')
parser.add_argument('--logfile', dest='logfile', help='path to the log file')
args = parser.parse_args()
file = args.path
file_size = os.path.getsize(file)
if not os.path.exists(os.path.split(args.logfile)[0]):
os.makedirs(os.path.split(args.logfile)[0])
logging.basicConfig(filename=args.logfile, level=logging.INFO)
df = pd.read_csv(file)
blocks = df._block_partitions.flatten().tolist()
ray.wait(blocks, len(blocks))
with time_logger("Groupby + sum aggregation on axis=0: {}; Size: {} bytes"
.format(file, file_size)):
df_groupby = df.groupby('1')
blocks = df_groupby.sum()._block_partitions.flatten().tolist()
ray.wait(blocks, len(blocks))
with time_logger("Groupby mean on axis=0: {}; Size: {} bytes"
.format(file, file_size)):
blocks = df_groupby.mean()._block_partitions.flatten().tolist()
ray.wait(blocks, len(blocks))