Monitoring Load on ClickHouse Server

Monitoring the load on ClickHouse Server


Load can significantly impact the performance of a ClickHouse server. The load on a server is a measure of how busy it is, and it can be represented by several different metrics such as CPU usage, memory usage, and disk I/O. When the load on a server is high, it can lead to decreased performance and increased response times for queries.

  1. High CPU usage: When the load on a ClickHouse server is high, it can lead to increased CPU usage, which can cause the server to slow down and make it difficult to process queries in a timely manner.
  2. High memory usage: When the load on a ClickHouse server is high, it can lead to increased memory usage, which can cause the server to swap and slow down. Additionally, if the memory usage is high for a long time, it can cause the server to run out of memory and crash.
  3. High Disk I/O: When the load on a ClickHouse server is high, it can lead to increased disk I/O, which can cause the server to slow down and make it difficult to process queries in a timely manner.
  4. High Network I/O: When the load on a ClickHouse server is high, it can lead to increased Network I/O, which can cause the server to slow down and make it difficult to process queries in a timely manner.
  5. High number of queries: ClickHouse is designed to handle high query loads, but if you have too many queries running at the same time, it can lead to slow response times and increased resource usage.
  6. High number of concurrent connections: If you have a high number of concurrent connections to your ClickHouse server, it can lead to increased resource usage and slow response times.
  7. High number of table scans: If a query causes a large number of table scans, it can lead to slow response times and increased resource usage.

To avoid these problems, it’s important to monitor the load on your ClickHouse server and take steps to optimize performance when the load is high. This can be done by using tools like the system status endpoint of ClickHouse, which allows you to analyze the performance metrics of the system and identify potential performance bottlenecks. Additionally, using appropriate compression methods and partitioning tables can also help to optimize the performance of the server. Additionally, using a good monitoring tool that can provide real-time performance metrics and alerts, enabling you to quickly identify and diagnose performance issues.

Python script to monitor the load on ClickHouse Server

import requests

# Set the URL for the ClickHouse server status endpoint
url = 'http://hostname:8123/'

while True:
    # Make a GET request to the status endpoint
    response = requests.get(url)
    data = response.json()

    # Extract the load information from the response
    load = data['performance_counters']['system']['load_average']['value']

    # Print the load information
    print("Load:", load)

    # Wait for a few seconds before making the next request
    time.sleep(5)

This script uses the requests library to make a GET request to the ClickHouse server status endpoint, which returns a JSON object containing various performance metrics. The script then extracts the load information from the JSON object and prints it to the console. It also uses a while loop to continuously make requests to the status endpoint and print the load information, with a 5 seconds delay between each request.

It’s important to note that you need to replace <hostname> with the appropriate value for your ClickHouse server.

You can also customize this script as per your requirements, like storing the load information in a file or in a database for future reference or setting alert threshold when the load is high.

It’s also important to note that this script will only work if you have the HTTP interface enabled in your ClickHouse server. You may want to check this setting first, as it’s disabled by default.

About Shiv Iyer 56 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.

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