Why Major Tech House's Are Switching to Yandex’s ClickHouse as the Main Database
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📅 May 8, 2026 •
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• 🔄 Updated May 11, 2026
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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.
| Type | Purpose | Best For | Example Databases |
|---|---|---|---|
| OLTP | Fast transactions and real-time operations | Banking systems, e-commerce, authentication, CRUD applications | MySQL, PostgreSQL |
| OLAP | Fast analytics and large aggregations | Dashboards, reporting, analytics, observability, event tracking | ClickHouse, BigQuery, Redshift |
### 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:
```SQL
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:
```SQL
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.
```SQL
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:
```SQL
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:
| id | name | country | salary |
|---|---|---|---|
| 1 | John | USA | 5000 |
| 2 | Alex | UK | 7000 |
Internally:
```JSON
[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:
```JSON
id: [1,2]
name: [John,Alex]
country: [USA,UK]
salary: [5000,7000]
```
Now if we run:
```SQL
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:
```text
+----------------+
| 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:
```SQL
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:
```text
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:
```text
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:
```text
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:
```SQL
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:
```SQL
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.
```SQL
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
```SQL
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
```SQL
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
```SQL
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:
```text
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:
```text
Row -> CPU -> Result
```
ClickHouse does:
```text
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:
```text
+----------------+
| 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:
```SQL
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:
```text
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:
```text
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:
```text
SQL Query
↓
Parser
↓
Logical Plan
↓
Query Optimizer
↓
Execution Pipeline
↓
Parallel Processing
↓
Aggregations
↓
Compressed Result
```
----------
## ClickHouse vs MySQL
| Feature | MySQL | ClickHouse |
|---|---|---|
| Database Type | OLTP | OLAP |
| Storage | Row-based | Column-based |
| Aggregation Speed | Medium | Extremely Fast |
| Real-time Analytics | Limited | Excellent |
| Compression | Lower | Very High |
| UPDATE/DELETE | Excellent | Limited |
| Dashboard Queries | Slower at scale | Very Fast |
| Billions of Rows | Difficult | Excellent |
----------
## When You Should Use ClickHouse
| Use Case | Why ClickHouse Works Well |
|---|---|
| Analytics Platforms | Handles large aggregations extremely fast |
| Observability Systems | Excellent for logs, monitoring, and tracing |
| Metrics Platforms | Optimized for time-series and metric queries |
| Dashboards | Fast real-time analytical queries |
| Ad-Tech Platforms | Processes huge event datasets efficiently |
| Financial Analytics | Works well for reporting and analytical workloads |
| IoT Systems | Handles massive streams of sensor data |
| Event Tracking | Perfect for user activity and event analytics |
| Large-Scale Reporting | Designed for high-performance reporting systems |
---
## When You Should NOT Use ClickHouse
| Scenario | Why It May Not Be Ideal |
|---|---|
| Banking Transactions | Not designed for transactional consistency workloads |
| Heavy UPDATE Workloads | Updates are less efficient compared to OLTP databases |
| Highly Relational Systems | Complex relational workloads are not its primary focus |
| Traditional CRUD Applications | Better suited for analytics than frequent row modifications |
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:
```SQL
INSERT INTO page_views VALUES
(
1,
'/pricing',
'USA',
now()
);
```
After billions of events:
```SQL
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:
```SQL
SELECT *
```
Instead:
```SQL
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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