Why Major Tech House's Are Switching to Yandex’s ClickHouse as the Main Database

Modern applications generate massive amounts of data every second.
As applications grow, traditional databases like MySQL or PostgreSQL often become slower for large analytical queries. Dashboards take longer to load, reports become expensive, and aggregations over millions of rows start affecting performance.

This is where ClickHouse becomes powerful.

ClickHouse is a high-performance column-oriented database built specifically for analytics. It is designed to process massive datasets extremely fast, making it a popular choice for real-time analytics, dashboards, observability systems, and large-scale reporting platforms.

What is ClickHouse?

ClickHouse is a column-oriented OLAP database designed for extremely fast analytical queries on massive datasets.

It was originally developed by Yandex and later open-sourced.

Unlike traditional databases that focus mainly on transactions, ClickHouse is built specifically for:

• analytics
• reporting
• log processing
• observability
• real-time dashboards
• large aggregations
• event analytics

ClickHouse can process billions of rows in seconds.

That is why many modern analytics platforms rely on it heavily.

Understanding OLTP vs OLAP

Before understanding ClickHouse deeply, we need to understand the difference between OLTP and OLAP databases.

OLTP Example

Databases like MySQL and PostgreSQL are optimized for:

• INSERT
• UPDATE
• DELETE
• transactional systems

Example:

• banking systems
• e-commerce checkout
• user authentication
• order processing

Typical query:

SELECT * FROM users WHERE id = 10;

This type of query reads very little data.

OLAP Example

OLAP databases focus on:

• aggregations
• analytics
• reports
• trends
• metrics

Example:

SELECT country, COUNT(*)
FROM users
GROUP BY country;

This query may scan millions or billions of rows.

Traditional databases struggle here at large scale.

ClickHouse is optimized exactly for this type of workload.

The Analytics Challenge in Traditional Databases

Imagine a table with 2 billion user events.

CREATE TABLE events (
    id BIGINT,
    user_id BIGINT,
    event_type VARCHAR(255),
    country VARCHAR(100),
    device VARCHAR(100),
    created_at DATETIME
);

Now imagine running this query:

SELECT country, COUNT(*) FROM events GROUP BY country;

Traditional row-based databases must read huge amounts of unnecessary data.

Even if we only need the country column, the database often reads full rows internally.

This becomes expensive at scale.

Row-Based vs Column-Based Storage

This is the most important concept behind ClickHouse performance.

How Traditional Row-Based Storage Works

Traditional databases store data row by row.

Example table:

Internally:

[1, John, USA, 5000]
[2, Alex, UK, 7000]

This is excellent for transactional systems.

But analytical queries become slower because unnecessary columns are also read.

How ClickHouse Stores Data

ClickHouse stores data column by column.

Internally:

id: 		[1,2]
name:		[John,Alex]
country:	[USA,UK]
salary: 	[5000,7000]

Now if we run:

SELECT country, COUNT(*) FROM users GROUP BY country;

ClickHouse only reads the country column.

This dramatically reduces disk I/O.

Why Columnar Storage is Extremely Fast

Columnar storage provides several major performance advantages:

• Reads Less Data
  ClickHouse only scans the required columns instead of reading entire rows.

• Better Compression
  Since columns usually contain similar data types, compression becomes much more efficient.

• Faster CPU Processing
  Vectorized execution allows ClickHouse to process batches of rows together instead of one row at a time.

• Better Cache Efficiency
  Columnar storage improves CPU cache utilization, which helps queries execute faster.

ClickHouse Architecture

ClickHouse simplified architecture:

                 +----------------+
                 | Load Balancer  |
                 +----------------+
                          |
        +----------------------------------+
        |              Cluster             |
        +----------------------------------+
           |              |             |
           v              v             v
     +----------+   +----------+   +----------+
     | Node 1   |   | Node 2   |   | Node 3   |
     +----------+   +----------+   +----------+

Understanding MergeTree Engine

The MergeTree engine is the heart of ClickHouse.

Most production ClickHouse tables use MergeTree-based engines.

Example:

CREATE TABLE events
(
    id UInt64,
    user_id UInt64,
    event_type String,
    country String,
    created_at DateTime
)
ENGINE = MergeTree()
ORDER BY (user_id, created_at);

Why ‘ORDER BY’ Matters in ClickHouse

This is very different from MySQL.

In ClickHouse:

ORDER BY (user_id, created_at)

does not only sort results.

It physically organizes data on disk.

This allows ClickHouse to scan far less data during queries.

How MergeTree Stores Data Internally

ClickHouse stores data in immutable parts.

Example:

Part_1
Part_2
Part_3

Over time:

• small parts merge into bigger parts
• data gets compressed
• indexes get optimized

This process runs automatically in the background.

Data Parts Explained

Each part contains:

columns/
primary index/
checksums/
compression metadata/
min-max indexes/

This architecture allows very fast analytical scans.

Sparse Primary Index

ClickHouse does not use traditional B-Tree indexes.

Instead, it uses sparse indexes.

This is extremely important.

Instead of indexing every row, ClickHouse indexes blocks of rows.

Example:

Row 1
Row 8192
Row 16384

This drastically reduces memory usage while still allowing fast scans.

Data Skipping Indexes

ClickHouse can skip large amounts of irrelevant data.

Example:

If a partition only contains January data:

SELECT *
FROM events
WHERE created_at >= '2026-05-01'

January partitions can be skipped entirely.

This is one reason ClickHouse is so fast.

Partitioning in ClickHouse

Example:

CREATE TABLE logs
(
    timestamp DateTime,
    level String,
    message String
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY timestamp;

This creates monthly partitions.

Benefits:

• faster queries
• easier maintenance
• better pruning
• efficient deletion

Real-Time Analytics Example

Imagine a website with millions of visitors.

We want hourly traffic analytics.

SELECT
    toStartOfHour(created_at) AS hour,
    COUNT(*) AS visits
FROM page_views
GROUP BY hour
ORDER BY hour;

ClickHouse handles this extremely efficiently.

Top Pages Example

SELECT
    page_url,
    COUNT(*) AS views
FROM page_views
GROUP BY page_url
ORDER BY views DESC
LIMIT 10;

Perfect for:

• dashboards
• analytics panels
• admin systems

Error Monitoring Example

SELECT
    status_code,
    COUNT(*) AS total
FROM logs
GROUP BY status_code
ORDER BY total DESC;

This is commonly used in observability systems.

Event Analytics Example

SELECT
    event_type,
    COUNT(*) AS total
FROM events
GROUP BY event_type
ORDER BY total DESC;

Perfect for:

• product analytics
• user behavior tracking
• SaaS analytics

Compression in ClickHouse

Compression is another huge reason behind ClickHouse speed.

Because columns contain similar data:

USA
USA
USA
USA

Compression ratios become extremely high.

Benefits:

• less disk usage
• less I/O
• faster scans

ClickHouse commonly achieves massive compression ratios compared to traditional databases.

Vectorized Query Execution

Traditional databases often process rows one by one.

ClickHouse processes batches of rows together.

This is called vectorized execution.

Instead of:

Row -> CPU -> Result

ClickHouse does:

Batch -> CPU SIMD Operations -> Result

This uses modern CPU architectures much more efficiently.

Parallel Processing

ClickHouse heavily uses parallelism.
Queries can run across:

• multiple CPU cores
• multiple threads
• multiple partitions
• multiple servers

This allows extremely high throughput.

Distributed ClickHouse Clusters

ClickHouse can scale horizontally across multiple servers, which makes it suitable for very large analytics workloads.
A distributed ClickHouse architecture may look like this:

                 +----------------+
                 | Load Balancer  |
                 +----------------+
                          |
        +----------------------------------+
        |              Cluster             |
        +----------------------------------+
           |              |             |
           v              v             v
     +----------+   +----------+   +----------+
     | Node 1   |   | Node 2   |   | Node 3   |
     +----------+   +----------+   +----------+

This architecture allows queries to run across multiple nodes in parallel, enabling high availability and extremely fast analytics processing on massive datasets.

Materialized Views in ClickHouse

Materialized views can pre-aggregate data automatically.

Example:

CREATE MATERIALIZED VIEW daily_stats
ENGINE = SummingMergeTree()
ORDER BY date
AS
SELECT
    toDate(created_at) AS date,
    COUNT(*) AS total
FROM events
GROUP BY date;

Benefits:

• faster dashboards
• precomputed analytics
• lower query latency

Advanced ClickHouse Internals

Now let us go deeper into advanced internals.

Granules and Marks

ClickHouse divides data into granules.

A granule usually contains around 8192 rows.

Primary indexes store references to these granules.

Example:

Granule 1 -> rows 1-8192
Granule 2 -> rows 8193-16384

This enables efficient block scanning.

Immutable Storage Design

ClickHouse parts are immutable.

Instead of updating rows directly:

• new parts are written
• background merges happen later

Benefits:

• reduced locking
• faster inserts
• simpler concurrency

Background Merge Process

Background threads continuously:

• merge parts
• compress data
• optimize storage

Example:

Part_1 + Part_2 + Part_3        
			↓    
		Merged_Part

This is one of the most important internal mechanisms.

Late Materialization

ClickHouse delays reading unnecessary columns until required.

This reduces memory and CPU usage significantly.

Query Pipeline Internals

Simplified query flow:

SQL Query   
	↓
Parser   
	↓
Logical Plan   
	↓
Query Optimizer   
	↓
Execution Pipeline   
	↓
Parallel Processing   
	↓
Aggregations   
	↓
Compressed Result

ClickHouse vs MySQL

When You Should Use ClickHouse

When You Should NOT Use ClickHouse

It is designed primarily for analytics.

Real-World Companies Using ClickHouse

Many companies use ClickHouse for large-scale analytics workloads.

Examples include:

• Cloudflare
• Uber
• eBay
• Lyft

These companies process massive amounts of data daily.

Example: Website Analytics System

Imagine building your own Google Analytics-like platform.

Every page visit creates an event:

INSERT INTO page_views VALUES
(
    1,
    '/pricing',
    'USA',
    now()
);

After billions of events:

SELECT
    country,
    COUNT(*) AS visits
FROM page_views
GROUP BY country;

ClickHouse can still answer quickly.

This is where it shines.

Performance Optimization Tips

• Choose Good ORDER BY Keys
  This is one of the most important optimization techniques in ClickHouse. Poor ordering can force the database to scan much more data than necessary, which can significantly reduce query performance.

• Use Partitioning Carefully
  Partitioning can improve query performance and maintenance, but creating too many partitions may actually slow down the system and increase overhead.

• Avoid Excessive Joins
  ClickHouse performs best with denormalized data structures. Large or complex joins can become expensive on massive datasets.

• Use Materialized Views
  Materialized views help pre-aggregate expensive computations, which can dramatically improve dashboard and reporting performance.

• Read Only Required Columns
  Avoid unnecessary column scans whenever possible.

Avoid:

SELECT *

Instead:

SELECT country, COUNT(*)

Final Thoughts

ClickHouse is the most powerful analytical databases available today.
It is not simply “another SQL database.”
Its architecture is fundamentally different.

By combining:

• columnar storage
• vectorized execution
• compression
• sparse indexes
• parallel processing
• MergeTree internals

ClickHouse can analyze billions of rows incredibly fast.

If your application deals with:

• logs
• metrics
• analytics
• dashboards
• event tracking
• observability

then ClickHouse can dramatically improve your system performance.

As modern applications continue generating massive amounts of data, analytical databases like ClickHouse are becoming more important than ever.

And among them, ClickHouse stands out as a fastest and most impressive technologies in modern data engineering.

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