ClickHouse Performance: Implementing Dynamic Disks in ClickHouse 23.2

Introduction

Dynamic disks in ClickHouse 23.2 refer to a feature that allows for more efficient use of disk space and improved performance when working with large amounts of data.

Traditionally, ClickHouse used a file-based storage system where each table is stored as a separate set of files on disk. With dynamic disks, however, tables are split into smaller, fixed-size chunks called “parts,” and these parts are stored on a shared pool of disk space. This allows for more efficient use of disk space, as smaller parts can be packed more tightly together than larger files.

In addition to more efficient use of disk space, dynamic disks can also improve performance in several ways:

  1. Faster data loading: Because data is split into smaller parts, data can be loaded into ClickHouse more quickly, as each part can be loaded independently.
  2. Faster query performance: Dynamic disks allow for more efficient data retrieval, as ClickHouse can read data from multiple parts in parallel. This can lead to faster query performance, particularly for complex queries that require access to multiple tables.
  3. Better resource utilization: By reducing the size of individual parts, dynamic disks can help to reduce the amount of memory and CPU resources required to process data. This can help to improve overall system performance and reduce the risk of resource contention.

How to implement dynamic disks in ClickHouse 23.2?

To implement dynamic disks in ClickHouse 23.2, you need to follow these general steps:

  1. Choose a disk that will be used as a pool of dynamic disks. This can be a single disk or a group of disks that will be managed as a single pool.
  2. Set up ClickHouse to use the dynamic disks feature by enabling the appropriate settings in your ClickHouse configuration file. You will need to specify the path to the pool of dynamic disks and the maximum size of each individual part.
  3. Create a table that uses dynamic disks by specifying the ENGINE as MergeTree and using the –on-clust option to specify the path to the pool of dynamic disks. For example:
CREATE TABLE my_table (
    ...
) ENGINE = MergeTree()
PARTITION BY ...
ORDER BY ...
SETTINGS index_granularity = ...
PRIMARY KEY ...
SAMPLE BY ...
--on-clust '/path/to/dynamic/disks/pool/';
  1. Insert data into the table as usual. ClickHouse will automatically split the data into smaller parts and store them in the dynamic disk pool.
  2. Query the table as usual. ClickHouse will automatically read the relevant parts from the dynamic disk pool and return the results.

Technically, implementing dynamic disks in ClickHouse 23.2 involves configuring ClickHouse to use a pool of disks for storage, creating tables that use the dynamic disk engine, and inserting and querying data as usual.

Script for monitoring disk I/O performance of dynamic disks in ClickHouse 23.2

Here is an example script for monitoring disk I/O performance of dynamic disks in ClickHouse 23.2 using the iostat command:

#!/bin/bash

# Set the device name of the dynamic disk pool
device_name="/dev/disk/by-id/.../dynamic_disk_pool"

# Set the interval for monitoring (in seconds)
interval=5

while true; do
    # Get the current time
    current_time=$(date +%s)

    # Get the disk I/O statistics using the iostat command
    iostat_output=$(iostat -dx -k -p ${device_name} ${interval} 1 | tail -n1)

    # Extract the relevant metrics from the iostat output
    read_ops=$(echo ${iostat_output} | awk '{print $4}')
    write_ops=$(echo ${iostat_output} | awk '{print $5}')
    read_kbps=$(echo ${iostat_output} | awk '{print $6}')
    write_kbps=$(echo ${iostat_output} | awk '{print $7}')

    # Output the metrics to a log file
    echo "${current_time},${read_ops},${write_ops},${read_kbps},${write_kbps}" >> disk_io.log
done

This script uses the iostat command to monitor the disk I/O performance of a dynamic disk pool specified by device_name. The script outputs the read and write operations per second (OPS) and the read and write kilobytes per second (KB/s) to a log file at regular intervals specified by interval.

You can run this script in the background using a tool like nohup to continue monitoring the disk I/O performance even if the terminal session is closed. Note that you may need to modify the script to match the specific configuration of your ClickHouse installation and disk pool.

Algorithms used for implementing dynamic disks in ClickHouse 23.2

ClickHouse 23.2 uses several algorithms to implement dynamic disks and improve storage efficiency and query performance. Here are some of the key algorithms used in ClickHouse 23.2 for implementing dynamic disks:

  1. Dynamic partition pruning: ClickHouse dynamically partitions data at the block level, which allows it to store data more efficiently and minimize the amount of data that needs to be read from disk during queries. Dynamic partition pruning works by dividing data into blocks of fixed size and then dynamically partitioning those blocks into smaller parts based on the data distribution.
  2. Merging algorithm: ClickHouse uses a merging algorithm to efficiently merge smaller parts into larger ones. This algorithm works by merging two parts into a larger one if they have overlapping ranges and similar sizes. By merging parts in this way, ClickHouse can reduce the number of parts that need to be read from disk during queries and improve query performance.
  3. Index granularity optimization: ClickHouse optimizes index granularity to improve query performance and reduce disk I/O. Index granularity determines the size of the index blocks used to store index data. By optimizing the index granularity, ClickHouse can reduce the number of index blocks that need to be read from disk during queries and improve query performance.
  4. Space reclamation: ClickHouse periodically reclaims space from deleted or overwritten data to prevent disk usage from growing too large. Space reclamation works by merging adjacent parts that have been deleted or overwritten, and then compressing and deleting the resulting space.

Conclusion

These algorithms work together to implement dynamic disks in ClickHouse 23.2 and improve storage efficiency and query performance. By using dynamic partition pruning, a merging algorithm, index granularity optimization, and space reclamation, ClickHouse can store data more efficiently, reduce disk I/O, and improve query performance.

To read more about ClickHouse, do give the following a read

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.