ClickHouse Query Profiling for Performance Monitoring

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

1. Install Dependencies: This script requires clickhouse-driver and psutil. 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

3. Output: The script will output the query execution time, CPU usage, and I/O statistics.

Script Features

• Command-Line Arguments: The script uses argparse to 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