Introduction
Creating a Python CLI (Command-Line Interface) application for custom profiling of ClickHouse Server queries involves several components. The application will allow users to input queries via the command line, execute them on a ClickHouse server, and then provide performance metrics. Below is a Python script that achieves this:
Python Script for ClickHouse Query Performance Profiling</h2>
import argparse
import time
import psutil
from clickhouse_driver import Client
def profile_query(query, host, port, user, password):
"""
Execute a query on ClickHouse server and profile its performance.
Args:
query (str): The ClickHouse SQL query to be executed.
host (str): Hostname of the ClickHouse server.
port (int): Port of the ClickHouse server.
user (str): Username for the ClickHouse server.
password (str): Password for the ClickHouse server.
Returns:
dict: Execution time, CPU usage, and I/O stats.
"""
client = Client(host=host, port=port, user=user, password=password)
start_time = time.time()
start_cpu = psutil.cpu_times()
start_io = psutil.disk_io_counters()
# Execute the query
client.execute(query)
end_time = time.time()
end_cpu = psutil.cpu_times()
end_io = psutil.disk_io_counters()
# Calculate performance metrics
duration = end_time - start_time
cpu_usage = {'user': end_cpu.user - start_cpu.user, 'system': end_cpu.system - start_cpu.system}
io_usage = {'read_bytes': end_io.read_bytes - start_io.read_bytes,
'write_bytes': end_io.write_bytes - start_io.write_bytes}
return {'duration': duration, 'cpu_usage': cpu_usage, 'io_usage': io_usage}
def main():
"""
Main function to handle command-line arguments and execute query profiling.
"""
parser = argparse.ArgumentParser(description='Profile a ClickHouse query for performance.')
parser.add_argument('query', help='The ClickHouse SQL query to profile.')
parser.add_argument('--host', default='localhost', help='Hostname of the ClickHouse server.')
parser.add_argument('--port', type=int, default=9000, help='Port of the ClickHouse server.')
parser.add_argument('--user', default='default', help='Username for the ClickHouse server.')
parser.add_argument('--password', default='', help='Password for the ClickHouse server.')
args = parser.parse_args()
# Execute and profile the query
result = profile_query(args.query, args.host, args.port, args.user, args.password)
print(f"Query Execution Time: {result['duration']} seconds")
print(f"CPU Usage: {result['cpu_usage']}")
print(f"I/O Usage: {result['io_usage']}")
if __name__ == "__main__":
main()
How to Use the Script
- Install Dependencies: This script requires
clickhouse-driverandpsutil. You can install them using pip:
pip install clickhouse-driver psutil
2. Running the Script: Use the command line to run the script. For example:
python profiler.py "SELECT * FROM my_table" --host myclickhousehost --user myuser --password mypassword
- Output: The script will output the query execution time, CPU usage, and I/O statistics.
Script Features
- Command-Line Arguments: The script uses
argparseto handle command-line arguments for flexibility. - Query Execution: Connects to ClickHouse using provided credentials and executes the specified query.
- Performance Profiling</strong>: Gathers CPU and I/O metrics during query execution.
- Output: Displays the profiling results in a readable format.
Note
- Security: Ensure that sensitive information like passwords is handled securely, especially in a production environment.
Conclusion
In this article, we have learned to use a simple python script for ClickHouse query profiling, for performance monitoring & optimization.
To learn more about ClickHouse Query Profiling, consider reading the following articles:
- Configuring ClickHouse Query Profiler to Monitor Performance
- ChistaDATA’s Anansi Query Profiler for ClickHouse Performance: Part 1
- ChistaDATA’s Anansi Query Profiler for ClickHouse Performance: Part 2
