Deep Dive into ClickHouse Streaming and Blocking Operators


In the realm of data processing, efficiency and performance are paramount. ClickHouse, a popular columnar database management system, employs a variety of operators to optimize queries.
Two pivotal ClickHouse Operators – the ClickHouse Streaming Operator and ClickHouse Blocking Operator are central to its efficiency. But, how do they work and more importantly, when should one use them?
This quick deep dive unravels their unique roles and demonstrates their power through practical dataset examples.

Understanding ClickHouse Streaming Operator and ClickHouse Blocking Operator 

ClickHouse Streaming and Blocking Operators are fundamental components of query execution in ClickHouse, and they handle data in distinct ways.

  • ClickHouse Streaming Operator: These operators process data as it flows through the query pipeline, without the need to store intermediate results. They are memory-efficient and suitable for continuous data streams.

  • ClickHouse Blocking Operators: In contrast, Blocking Operators accumulate and process data in chunks, often requiring temporary storage for intermediate results. They are useful for complex analytical queries.

Practical Dataset Examples

Let’s illustrate the difference between these two operator types with real-world examples:

Scenario 1: Harnessing ClickHouse Streaming Operator for Real-Time Metrics

Consider the task of monitoring a website’s traffic in real-time, aiming to compute the average response time for each page visit. The ClickHouse Streaming Operator is perfectly suited for this task.

Here’s a simplified SQL query:

avg(response_time) as avg_response_time

In this scenario, the ClickHouse Streaming Operator meticulously processes each incoming data point, computes the average on-the-fly, and emits the results immediately. It’s like calculating the average response time as visitors hit your website.

Scenario 2: Leveraging ClickHouse Blocking Operator for Comprehensive Batch Analytics

Consider a financial institution poised to conduct a detailed analysis of a year’s worth of transaction data. This task demands the aggregation of data across multiple dimensions and the computation of various metrics.

Here’s a sample SQL query:

sum(transaction_amount) as total_amount
date BETWEEN '2022-01-01' AND '2022-12-31'
date, account_type


The choice between ClickHouse Streaming Operator and ClickHouse Blocking Operator is pivotal. While the ClickHouse Streaming Operator is your ally in real-time data processing and analytics, the ClickHouse Blocking Operator stands as the cornerstone for detailed batch analytics. Choosing the right operator type depends on your use case and query requirements. Understanding the strengths and limitations of Streaming and Blocking Operators empowers you to optimize your ClickHouse queries effectively.

To know more about ClickHouse Functions and Operators, do consider reading the following articles:

  1. Deep Dive into User Defined Functions
  2. Aggregate Functions via ClickHouse
  3. Implementing MetroHash function in ClickHouse for high performance
  4. Conditional Analysis in ClickHouse with ‘IS’ Operator

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.