How checkpointing works in ClickHouse?

Understanding Checkpointing Process in ClickHouse


Introduction – What is Checkpointing in Database Systems?

Checkpointing is a process of periodically saving the state of a database system to disk, so that in the event of a failure, the system can be quickly restored to a known good state.

In a database system, data is stored in memory (RAM) and on disk. While the system is running, data is constantly being modified and added to. In order to ensure that the data is always consistent and can be easily restored in the event of a failure, it is important to periodically save the state of the system to disk. This is where checkpointing comes in.

During the checkpoint process, the database system writes the data stored in memory (RAM) to disk, including the data stored in the buffer, the data stored in the index, and the metadata associated with the system, such as the configuration settings and the state of the system. This allows the system to quickly restore the state of the system in the event of a failure.

Checkpointing is typically performed in the background, and does not affect the performance of the system. The interval at which checkpointing is performed can be configured based on the specific requirements of the system.

There are different types of checkpointing in databases, such as full checkpoint, incremental checkpoint and Merge checkpoint. The specific implementation of checkpointing can vary depending on the type of database system, but the goal is always the same: to ensure that the data is always consistent and can be easily restored in the event of a failure.

In summary, checkpointing is a process of periodically saving the state of a database system to disk, so that in the event of a failure, the system can be quickly restored to a known good state, including data stored in memory (RAM), data stored in the buffer, the data stored in the index, and the metadata associated with the system. It is typically performed in the background and the frequency of checkpointing can be configured based on the specific requirements of the system.

Here’s an example of a C++ algorithm that can be used to implement checkpointing in a database system:

void checkpoint()
{
    // Step 1: Acquire a lock to prevent concurrent modifications to the data
    std::unique_lock<std::mutex> lock(data_mutex);

    // Step 2: Open a file to write the checkpoint
    std::ofstream checkpoint_file("checkpoint.dat", std::ios::binary);

    // Step 3: Serialize the data and write it to the file
    for (auto& data : data_vector)
    {
        checkpoint_file << data << " ";
    }

    // Step 4: Close the file
    checkpoint_file.close();

    // Step 5: Release the lock
    lock.unlock();
}

This example assumes that the data being checkpointed is stored in a vector called “data_vector” and protected by a mutex called “data_mutex”. The algorithm starts by acquiring a lock on the mutex to prevent concurrent modifications to the data. Then it opens a file to write the checkpoint, serializes the data and writes it to the file and then releases the lock.

It’s important to note that this is just a simple example of how checkpointing could be implemented in a database system. In a real-world scenario, the algorithm would likely be more complex and take into account factors such as data compression, encryption, and incremental checkpointing.

It is also important to note that this is a simple example, you should consider the specific requirements of your use case and adapt the algorithm accordingly. Also, you should test this algorithm on a test environment before deploying it to production.

It is also important to note that, depending on the database system you are using, it might have its own implementation of checkpointing. For example, PostgreSQL, MySQL, ClickHouse and many other DBMSs have their own implementation of checkpointing, which is more efficient and optimized for their specific storage engines.

How does checkpointing work in ClickHouse?

Checkpointing in ClickHouse is a process of periodically saving the state of the system to disk, so that in the event of a failure, the system can be quickly restored to a known good state.

There are two types of checkpointing in ClickHouse:

  1. Periodic checkpointing: This is the default method of checkpointing in ClickHouse. It periodically saves the state of the system to disk based on a specified interval. The interval is configurable and can be adjusted based on the specific requirements of the system.
  2. Merge checkpointing: This method of checkpointing is triggered when a merge process is performed on a table. During the merge process, the system creates a new version of the table, and saves the old version to disk. This allows the system to quickly restore to a previous version of the table in the event of a failure.

In both cases, the checkpoint process is performed in the background, and does not affect the performance of the system.

During the checkpoint process, ClickHouse writes the data stored in memory (RAM) to disk, including the data stored in the read and write buffer, the data stored in the compression and encoding buffer, and the data stored in the index. This allows the system to quickly restore the state of the system in the event of a failure.

In addition, ClickHouse also saves the metadata associated with the system, such as the configuration settings and the state of the system.

Conclusion

Checkpointing in ClickHouse is a process of periodically saving the state of the system to disk, so that in the event of a failure, the system can be quickly restored to a known good state. ClickHouse supports two types of checkpointing: periodic checkpointing, and merge checkpointing, which helps to ensure that the data is always consistent and can be easily restored in the event of a failure.

About Shiv Iyer 199 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.