How to implement Lazy Expressions in ClickHouse for Query Performance?

Lazy Expression Evaluation in ClickHouse

Table of Contents


Lazy expression evaluation has several use cases in data processing and analytics. Here are some common scenarios where lazy evaluation is beneficial:

  1. Query Optimization: Lazy evaluation allows query optimizers to defer computations until necessary. This enables the optimizer to rearrange and optimize expressions, filters, and joins in the query execution plan to reduce unnecessary computations and improve query performance.
  2. Resource Efficiency: By deferring the computation of expressions until needed, lazy evaluation helps conserve computational resources such as CPU and memory. It avoids unnecessary calculations for expressions that may not contribute to the final result, optimizing resource utilization.
  3. Predicate Pushdown: Lazy evaluation is particularly useful in pushing down predicates (filters) closer to the data source. Predicates can be evaluated as late as possible, minimizing the amount of data that needs to be processed and improving query performance.
  4. Short Circuit Evaluation: Lazy evaluation allows short circuiting of expressions. In boolean expressions, for example, if the result can be determined based on the evaluation of the first condition, the remaining conditions are not evaluated. This optimization improves performance by avoiding unnecessary computations.
  5. Columnar Processing: Lazy evaluation works well with columnar storage formats where data is stored and processed column-wise. By selectively loading and processing only the required columns during query execution, lazy evaluation minimizes I/O and improves query performance in columnar databases.
  6. Iterative Computations: Lazy evaluation is beneficial for iterative computations and data transformations. It avoids unnecessary re-computations by deferring the evaluation until the results are required. This can significantly improve the efficiency of iterative algorithms and data processing pipelines.

Lazy expression evaluation is widely used in various data processing systems, including databases, analytics frameworks, and query engines. It offers optimizations that improve query performance, reduce resource consumption, and enable efficient data processing in a variety of use cases, ranging from ad-hoc querying to large-scale data analytics and machine learning.

By default, ClickHouse’s lazy evaluation ensures that expressions are only evaluated when needed, optimizing query performance by minimizing unnecessary computations. However, you can also explicitly control the evaluation of expressions using functions like if, case, and or. ClickHouse pushes predicates (filters) as close to the data source as possible, leveraging lazy evaluation. It evaluates the predicates as late as possible, filtering out unnecessary data early in the query execution process and reducing the amount of data processed.

Here’s an example that demonstrates lazy evaluation in ClickHouse:

SELECT column1, column2
FROM table
WHERE column1 = 'value' AND column2 + column3 > 10

In this example, ClickHouse will first apply the filter column1 = ‘value’ to reduce the dataset before evaluating the expression column2 + column3 > 10. The evaluation of the expression is deferred until necessary, allowing ClickHouse to optimize the query execution plan based on the query structure and available data.

Overall, ClickHouse’s built-in lazy evaluation ensures efficient query execution by deferring computations and optimizing the processing of expressions based on the query structure and data access patterns.


Lazy expression evaluation in ClickHouse optimizes query performance by deferring computations until necessary, conserving resources and improving efficiency in data processing. Leveraging lazy evaluation enables ClickHouse to efficiently handle various use cases, from query optimization to iterative computations, enhancing overall performance in data analytics and processing.

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About Shiv Iyer 225 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.