How do we implement intelligent Caching on ClickHouse with machine learning?

Implementing intelligent caching with machine learning in a ClickHouse environment involves predicting data access patterns and optimizing cache usage based on these predictions. This approach helps to ensure that the most frequently accessed or soon-to-be-accessed data is readily available, enhancing performance and reducing latency. Here’s how you can approach building and deploying a machine learning-based caching strategy for ClickHouse:

Step 1: Data Collection and Analysis

Collect Usage Data:

  • Gather data on query frequency, types, and access patterns from ClickHouse’s system.query_log.
  • Collect metrics on disk I/O, cache hit rates, and query performance.

Analyze Access Patterns:

  • Analyze the collected data to identify patterns or trends. Look for regularities such as peak usage times, most frequently accessed tables or columns, and queries that consume significant resources.

Step 2: Feature Engineering

Create Features:

  • Develop features from the query logs and system metrics that could help predict query load and data access patterns. Possible features include:
    • Time of day, day of the week, and other temporal features.
    • Query type (SELECT, INSERT, etc.).
    • Table or column names accessed.
    • Historical load and cache miss rates.

Step 3: Model Development

Choose a Model:

  • Select appropriate machine learning models based on the nature of the prediction. Time series forecasting models like ARIMA, SARIMA, or more advanced deep learning models such as LSTM (Long Short-Term Memory networks) can be used to predict future data access patterns.

Train the Model:

  • Use historical data to train the model. Ensure that the data is split into training and validation sets to evaluate the model’s performance accurately.

Model Evaluation:

  • Test the model against the validation set to evaluate its predictive accuracy. Adjust model parameters or features based on performance metrics such as MAE (Mean Absolute Error) or RMSE (Root Mean Square Error).

Step 4: Integration with ClickHouse

Deploy the Model:

  • Implement the model within a service or script that runs at regular intervals, predicting which data will be accessed soon.
  • Based on the model’s predictions, preload data into the cache. This can be done by executing queries that touch the data blocks or rows predicted to be needed soon.

Step 5: Automation and Scheduling

Automate Preloading:

  • Use cron jobs or workflow automation tools like Apache Airflow to schedule and run the preloading scripts during off-peak hours.
  • Ensure that the system can dynamically adjust the data being preloaded based on the latest predictions and system state.

Step 6: Monitoring and Feedback Loop

Monitor System Performance:

  • Continuously monitor the performance impact of the ML-based caching strategy. Track metrics such as query response times, cache hit rates, and overall system load.

Feedback Loop:

  • Create a feedback loop where the system’s performance impacts the machine learning model. Use new data to retrain the model periodically or adjust its predictions, enhancing its accuracy and relevance.


  • Regularly review the effectiveness of the caching strategy and refine the machine learning model. Update the model with new data, tweak features, or try different algorithms based on observed performance and changes in data access patterns.

Additional Considerations

  • Security and Data Governance: Ensure that any data used for model training complies with data privacy regulations and company policies.
  • Cost-Benefit Analysis: Regularly evaluate the cost of maintaining the ML system against the performance benefits it provides. Adjust your strategy as needed to ensure cost-effectiveness.

By leveraging machine learning to enhance caching strategies, ClickHouse administrators can significantly boost database performance and user satisfaction while managing costs effectively. This proactive approach requires ongoing management but offers substantial benefits in optimizing data retrieval and resource usage.

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About Shiv Iyer 222 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.