Learnitweb

Introduction to ClickHouse

1. What is ClickHouse?

ClickHouse is a high-performance, open-source, column-oriented database management system (DBMS) designed specifically for Online Analytical Processing (OLAP) workloads where very large volumes of data must be queried and aggregated extremely fast.

ClickHouse was originally developed by Yandex to power real-time analytics for web-scale applications, and it is now widely used in domains such as observability, finance, ad-tech, IoT analytics, and business intelligence systems that need sub-second responses on billions of rows.

Unlike traditional transactional databases that focus on many small reads and writes, ClickHouse is optimized for:

  • Large scans
  • Heavy aggregations
  • Analytical queries
  • Time-series data
  • Log analytics
  • Real-time dashboards

If your use case involves counting, grouping, filtering, or aggregating massive datasets quickly, ClickHouse is often a strong candidate.

2. OLTP vs OLAP: Why ClickHouse Exists

To understand ClickHouse, you must first understand the difference between OLTP and OLAP systems.

2.1 OLTP (Online Transaction Processing)

OLTP databases are designed for handling many small, concurrent transactions where data consistency and fast row-level operations are critical.

Examples include:

  • Banking systems
  • E-commerce order processing
  • Reservation systems

Typical characteristics:

  • Frequent inserts/updates/deletes
  • Small queries affecting few rows
  • Strong ACID guarantees
  • Row-oriented storage

Examples: MySQL, PostgreSQL, Oracle DB.

2.2 OLAP (Online Analytical Processing)

OLAP databases are designed for analyzing large volumes of historical data where queries scan millions or billions of rows and compute aggregates.

Examples include:

  • Analytics dashboards
  • Data warehousing
  • Monitoring and observability systems
  • Business intelligence reports

Typical characteristics:

  • Large data scans
  • Complex aggregations
  • Fewer updates, more reads
  • Column-oriented storage

ClickHouse is purpose-built for OLAP.

3. Column-Oriented Storage Explained

ClickHouse stores data by columns instead of rows, which is one of the biggest reasons for its performance advantage in analytics.

3.1 Row-Oriented Example

Imagine a table:

IDNameAgeCity

Row-based storage stores data like:

(1, Sam, 30, Delhi)
(2, Ravi, 25, Mumbai)

When you query only the Age column, the database still reads Name and City data unnecessarily.

3.2 Column-Oriented Example

Column-based storage stores:

ID:   1, 2
Name: Sam, Ravi
Age:  30, 25
City: Delhi, Mumbai

When you query Age, ClickHouse reads only the Age column, dramatically reducing disk I/O and improving performance.

3.3 Why Column Storage is Faster

Column storage enables efficient compression, faster scans, and better CPU cache utilization because similar data types are stored together.

Advantages:

  • Reads only required columns
  • Better compression ratios
  • Faster aggregation operations
  • Reduced I/O
  • Optimized for analytical queries

4. Key Features of ClickHouse

4.1 Extremely High Query Performance

ClickHouse can process billions of rows per second on a single server under the right conditions because it is designed for vectorized query execution and efficient columnar storage.

4.2 Real-Time Analytics

ClickHouse allows near real-time ingestion and querying, which makes it suitable for live dashboards and monitoring systems.

4.3 Data Compression

ClickHouse uses advanced compression techniques that significantly reduce storage footprint while also improving read speed.

Compression methods include:

  • LZ4
  • ZSTD
  • Delta encoding
  • DoubleDelta

4.4 Horizontal Scalability

ClickHouse supports distributed clusters where data can be sharded and replicated across nodes, allowing it to scale to petabytes of data.

4.5 SQL Support

ClickHouse provides a rich SQL dialect with support for joins, subqueries, aggregations, window functions, and materialized views.

4.6 Open Source

ClickHouse is open source, which means you can self-host it without licensing costs and customize it for your needs.

5. When NOT to Use ClickHouse

ClickHouse is not a replacement for transactional databases and should not be used where frequent row-level updates and strict transactional consistency are required.

Avoid for:

  • Heavy updates/deletes
  • Transaction-heavy workloads
  • OLTP systems
  • Complex relational constraints