Decoding Memory Management in ClickHouse


ClickHouse uses a memory management system to control the allocation and deallocation of memory resources. The main components of the memory management system are:

  1. Memory Pool: ClickHouse uses a memory pool to manage the allocation of memory resources. The memory pool is divided into blocks of fixed size, and each block can be allocated or deallocated as needed. When a block is allocated, it is removed from the free list and added to the allocated list. When a block is deallocated, it is removed from the allocated list and added back to the free list.
  2. Memory Tracker: ClickHouse uses a memory tracker to keep track of the memory usage of different parts of the system, such as query execution and data structures. The memory tracker assigns a unique id to each memory allocation, and it keeps track of the size, owner, and other metadata associated with each allocation.
  3. Memory Limit: ClickHouse uses a memory limit to control the amount of memory that can be allocated by the system. The memory limit is set in the configuration file, and it can be adjusted as needed. When the memory limit is reached, ClickHouse will start to deallocate memory by releasing blocks from the free list or by flushing data from memory to disk.
  4. Garbage Collection: ClickHouse uses a garbage collection mechanism to periodically deallocate memory that is no longer in use. The garbage collector scans the allocated list for blocks that are no longer in use, and it releases them back to the free list.
  5. Memory-efficient Data Structures: ClickHouse uses memory-efficient data structures to minimize memory usage. For example, it uses columnar storage and data compression to reduce the amount of memory required to store data.

Overall, ClickHouse memory management system is responsible for managing the memory resources in an efficient way, by allocating the memory to the right parts of the system, releasing memory when it’s not needed, and keeping track of the memory usage. The system is designed to minimize the amount of memory used while still providing good performance and scalability. The memory limit, garbage collection, memory-efficient data structures, and memory tracker components work together to ensure that the system uses memory resources efficiently and avoid out of memory errors.

Monitoring ClickHouse Memory Usage in Real-time

Python code to monitor ClickHouse memory usage in real-time and send alerts:

import clickhouse_driver
import smtplib

# Connect to the ClickHouse server
connection = clickhouse_driver.connect(host='hostname', port='port', user='username', password='password')

# Set the threshold for memory usage (in bytes)
threshold = 1000000000

while True:
    # Execute a query to retrieve memory usage information
    query = 'SELECT memory_usage FROM system.metrics WHERE metric = "MemoryTracking"'
    result = connection.execute(query)

    # Check if the memory usage exceeds the threshold
    for row in result:
        if row[0] > threshold:
            # Send an alert email
            sender = ''
            receivers = ['']
            message = 'Subject: ClickHouse Memory Usage Alert\n\nThe memory usage of the ClickHouse server has exceeded the threshold of {} bytes.'.format(threshold)
            smtp_server = smtplib.SMTP('')
            smtp_server.sendmail(sender, receivers, message)
            print("alert sent")
    sleep(60) # time in seconds for the script to sleep before running the next check.

# Close the connection


This script runs an infinite loop and periodically retrieves memory usage information from the ClickHouse server. It checks if the memory usage exceeds a threshold, and if it does, it sends an alert email to the specified address.

You can adjust the threshold value to the desired value and also you can use a library like schedule to schedule the script to run at a specific time.

You can also use other libraries like Twilio to send text messages, slack-sdk to send notifications on Slack and many more.

You may also want to consider adding some error handling, such as a try-except block around the query execution, in case the query fails or the connection to the ClickHouse server is lost.

To know more about ClickHouse Memory,  please do consider reading the below articles: 

About Shiv Iyer 227 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.