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

👤 [email protected] • 📅 May 8, 2026 • 👁️ 39 views • 🔄 Updated May 31, 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. --- **Want to deploy apps, clusters, or Customize hosting services?** 👉 Explore our services at [**siliconpin.com**](https://siliconpin.com) and start building your own edge infrastructure today.