Streamlining ClickHouse Queries: A Deep Dive into Streaming and Blocking Operators

Coding Chaos: A Deep Dive into Streaming and Blocking Operators in ClickHouse


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 key categories of these operators are Streaming and Blocking Operators. Let’s explore their roles and differences using practical dataset examples.

Understanding Streaming and Blocking Operators:

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

  • Streaming Operators: 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.
  • 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: Streaming Operators for Real-Time Metrics

Imagine you’re monitoring a website’s traffic in real-time. You need to calculate the average response time for each page hit. Streaming operators shine in this scenario.

Here’s a simplified SQL query:

avg(response_time) as avg_response_time

In this case, the streaming operator 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: Blocking Operators for Batch Analytics

Now, consider a financial institution that wants to perform a complex analysis of transaction data over a year. This involves aggregating data across multiple dimensions and calculating 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

In this case, the blocking operator accumulates data within specified date ranges and account types, processes it in chunks, and then calculates the sums. It’s like collecting all the transactions for each day and account type and then performing the calculations.


  • Streaming Operators are your go-to choice for real-time processing and analytics where data streams in continuously.
  • Blocking Operators are better suited for batch analytics, where data is collected and processed in chunks.

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.

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.