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[GLUTEN-7641][VL] Add Gluten benchmark scripts #7642

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38 changes: 38 additions & 0 deletions tools/workload/benchmark_velox/README.md
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# Setup, Build and Benchmark Spark/Gluten with Jupyter Notebook

This guide provides notebooks and scripts for conducting performance testing in Gluten. The standard approach involves setting up the test environment on a bare-metal machine or cloud instance and running performance tests with TPC-H/TPC-DS workloads. These scripts enable users to reproduce our performance results in their own environment.

## Environment Setup

The recommended OS is ubuntu22.04 with kernel 5.15. To prepare the environment, run [initialize.ipynb](./initialize.ipynb), which will:

- Install system dependencies and set up jupyter notebook
- Configure Hadoop and Spark
- Configure kernel parameters
- Build Gluten using Docker
- Generate TPC-H/TPC-DS tables

## Running TPC-H/TPC-DS Benchmarks

To run TPC-H/TPC-DS benchmarks, use [tpc_workload.ipynb](./tpc_workload.ipynb). You can create a copy of the notebook and modify the parameters defined in this notebook to run different workloads. However, creating and modifying a copy each time you change workloads can be inconvenient. Instead, it's recommended to use Papermill to pass parameters via the command line for greater flexibility.

The required parameters are specified in [params.yaml.template](./params.yaml.template). To use it, create your own YAML file by copying and modifying the template. The command to run the notebook is:

```bash
papermill tpc_workload.ipynb --inject-output-path -f params.yaml gluten_tpch.ipynb
```
After execution, the output notebook will be saved as `gluten_tpch.ipynb`.

If you want to use different parameters, you can specify them via the `-f` option. It will overwrite the previously defined parameters in `params.yaml`. e.g. To switch to the TPC-DS workload, run:
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specify them via the -p


```bash
papermill tpc_workload.ipynb --inject-output-path -f params.yaml -p workoad tpcds gluten_tpcds.ipynb
```

Please refer to the Papermill documentation for additional usage details.

We also provide a script [run_tpc_workload.sh](./run_tpc_workload.sh). This script wraps the Papermill command, automatically renaming the output notebook with a timestamp and application ID to prevent overwriting existing output files.

## Analyzing Performance Results

You can check the **Show Performance** section in the output notebook after execution. It shows the cpu% per query, and draws charts for the cpu%, memory throughput, disk throughput/util%, network throughput and pagefaults.
96 changes: 96 additions & 0 deletions tools/workload/benchmark_velox/init_disks.py
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# Licensed to the Apache Software Foundation (ASF) under one or more
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Do we require the python version? python3 or python2?

# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import subprocess
import questionary
import json

def yes_or_no(question):
while True:
user_input = input(question + '(yes/no/quit): ')
if user_input.lower() == 'yes':
return True
elif user_input.lower() == 'no':
return False
elif user_input.lower() == 'quit':
sys.exit(0)
else:
continue

def filter_empty_str(l):
return [x for x in l if x]

def run_and_log(cmd):
print('\033[92m' + '>>> Running command: ' + repr(cmd) + '\033[0m')
result = subprocess.run(cmd, check=True, shell=True, capture_output=True, text=True)
print(result.stdout)
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Can we add print("=======stdout============") to indicate it is stdout log, so as stderr

print(result.stderr)

def init_disks():
all_disks = filter_empty_str(subprocess.run("lsblk -I 7,8,259 -npd --output NAME".split(' '), capture_output=True, text=True).stdout.split('\n'))
if not all_disks:
print("No disks found on system. Exit.")
sys.exit(0)
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sys.exit(1), I assume it is not a normal state.


answer = False
disks = []
while not answer:
disks = questionary.checkbox('Select disks to initialize:', choices=all_disks).ask()
answer = yes_or_no('Confirm selected:\n' + '\n'.join(disks) + '\n')

if not disks:
print('No disks are selected.')
return

for d in disks:
print('Initializing {} ...'.format(d))
run_and_log('wipefs -a {}'.format(d))
run_and_log('echo "g\nw\n" | fdisk {}'.format(d))
run_and_log('echo "n\n\n\n\nw\n" | fdisk {}'.format(d))
run_and_log('mkfs.ext4 {}p1'.format(d))

def mount_partitions():
subprocess.run('lsblk -pf --json > lsblk.json', shell=True)
partitions = []
with open('lsblk.json', 'r') as f:
data = json.load(f)
for d in data['blockdevices']:
if 'children' in d:
for c in d['children']:
if c['fstype'] == 'ext4':
partitions.append(c['name'])
answer = False
while not answer:
partitions = questionary.checkbox('Select partitions to create mount points:', choices=partitions).ask()
answer = yes_or_no('Confirm selected:\n' + '\n'.join(partitions) + '\n')

for i, p in enumerate(partitions):
d = 'data{}'.format(i)
run_and_log('e2label {} ""'.format(p))
run_and_log('e2label {} {}'.format(p, d))
run_and_log('mkdir -p /{}'.format(d))
run_and_log('mount -L {} /{}'.format(d, d))

def choose():
choice = questionary.select('Select operation:', choices=['Format disks', 'Mount partitions']).ask()
print(choice)
if choice == 'Format disks':
init_disks()
elif choice == 'Mount partitions':
mount_partitions()

if __name__ == '__main__':
choose()
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