In the previous article,
https://dataget.ai/wp-admin/post.php?post=576&action=edit
we discussed a core principle of data systems: data creates value only when it is continuously queried and used.
Among all data utilization methods, querying is the most direct and effective. However, in big data environments, querying massive datasets while maintaining low latency introduces a critical challenge—performance.
To address this challenge, engineers have made different architectural trade-offs depending on workload patterns. One of the most successful outcomes of these efforts is ClickHouse, a database designed specifically for high-performance analytical queries.
This article explains ClickHouse from four perspectives:
- Technical principles
- Architecture design
- Core advantages
- Typical application scenarios
What Is ClickHouse?
ClickHouse (short for Clickstream Data Warehouse) is an open-source, high-performance OLAP (Online Analytical Processing) database developed by a Russian engineering team and released in 2016.
Since its release, ClickHouse has rapidly gained popularity in the big data ecosystem due to its extreme query speed, efficient resource usage, and flexible deployment architecture.
Unlike traditional row-oriented relational databases, ClickHouse stores data in a columnar format. This design allows analytical queries to read only the columns they need, which dramatically reduces disk I/O and improves performance on large datasets.
Its performance advantages come from a combination of:
Distributed and parallel architecture
Columnar storage
High-efficiency data compression
Vectorized execution
Technical Principles Behind ClickHouse Performance
ClickHouse achieves high-speed querying through several tightly integrated technical mechanisms.
1. Columnar Storage
Traditional row-based databases are optimized for transactional workloads but inefficient for analytics. Analytical queries often scan only a few columns across billions of rows.
ClickHouse stores data by column instead of by row, allowing queries to read only the required columns. As a result, disk reads decrease significantly, and query latency drops accordingly.
2. Data Compression
Because each column stores values of the same type, ClickHouse achieves a very high compression ratio. Compression not only reduces storage costs but also speeds up queries, since less data must be read from disk into memory.
In many real-world workloads, compression ratios of 5× or higher are common.
3. Vectorized Execution
ClickHouse uses vectorized processing, meaning it operates on batches of rows at once instead of processing rows one by one.
By leveraging modern CPU SIMD (Single Instruction, Multiple Data) capabilities, ClickHouse significantly improves computation throughput and CPU efficiency.
4. Distributed Architecture
ClickHouse supports horizontal scaling through sharding and replication. Data can be distributed across multiple nodes, while queries execute in parallel both within a single node and across a cluster.
This design enables ClickHouse to handle petabyte-scale datasets while maintaining predictable query performance.
5. Sparse Indexing and Data Partitioning
Instead of traditional B+Tree indexes, ClickHouse uses sparse indexes combined with partitioning strategies.
These indexes allow ClickHouse to skip large blocks of irrelevant data efficiently during range queries and filtering operations, minimizing unnecessary scans.
Architecture Design Overview
Although ClickHouse integrates multiple advanced technologies, its architecture remains relatively simple and modular. It can be understood as three logical layers.
Storage Layer
- Stores data in partitions and shards
- Uses columnar data files
- Automatically merges and sorts data for optimized reads
Compute Layer
- Parses and optimizes SQL queries
- Builds execution plans
- Executes queries using a vectorized, multi-threaded engine
Distributed Layer
- Enables horizontal scalability
- Supports sharding and replication
- Uses Distributed Tables to aggregate results across nodes
Core Advantages of ClickHouse
Thanks to its design, ClickHouse offers several key advantages in big data querying scenarios:
- Exceptional Query Performance Aggregations over billions of rows often complete in seconds.
- High Compression Efficiency Columnar storage combined with compression significantly reduces storage footprint.
- Strong Scalability Works efficiently in both single-node and distributed environments.
- SQL Compatibility Supports familiar SQL syntax, reducing the learning curve for analysts and engineers.
Typical Application Scenarios
ClickHouse is widely adopted in production systems across many industries.
Log Analysis
Centralized storage and fast querying of large-scale system and application logs.
User Behavior Analytics
Real-time and historical analysis of clickstream data, conversion funnels, and retention metrics.
Metrics Monitoring
Powering dashboards, monitoring platforms, and alerting systems with second-level latency.
BI and Reporting
Supporting ad-hoc analytical queries and interactive dashboards for business intelligence teams.
Conclusion
By combining columnar storage, vectorized computation, and distributed execution, ClickHouse has redefined what is possible in big data querying.
It excels as an analytical engine for scenarios that demand fast aggregation, high concurrency, and massive data volumes. However, no single system fits all workloads.
As data architectures continue to evolve, ClickHouse often works alongside other query engines and storage systems. In upcoming articles, we will explore how these tools complement ClickHouse in modern data ecosystems.