ClickHouse
Terms related to simplyblock
ClickHouse Designed for Fast Analytics on Massive Datasets
ClickHouse is an open-source, column-oriented database management system (DBMS) developed to enable fast and efficient online analytical processing (OLAP). Known for its ability to process billions of rows per second, ClickHouse is optimized for real-time analytics on large datasets and is widely used in observability, log analysis, and telemetry platforms.
How ClickHouse Works
ClickHouse is designed for speed at every layer of its architecture. From storage layout to query execution, each component is optimized for real-time analytics.
Columnar Storage Architecture
Unlike row-based databases, ClickHouse stores data by columns rather than rows. This layout provides better compression, faster scan times, and improved performance for analytical queries that target specific columns. This structure is highly efficient for aggregations, filtering, and time-series operations.
Parallel and Vectorized Execution
ClickHouse uses vectorized query execution and multi-threaded parallelism to deliver high throughput. By processing blocks of data instead of individual rows, it reduces CPU overhead and accelerates compute-bound tasks.
Real-Time Inserts and Querying
Though OLAP systems are traditionally batch-oriented, ClickHouse supports high-frequency inserts and concurrent queries, making it suitable for real-time dashboards and monitoring systems. Its support for materialized views and TTL-based data retention also aligns with time-series data lifecycle requirements.
ClickHouse vs Traditional Databases
ClickHouse is designed specifically for analytical workloads, not transactional processing. Here’s how it compares to traditional and other analytical databases:
Comparison Table
ClickHouse is purpose-built for analytical workloads with high performance at scale. Here’s how it compares:
| Feature | ClickHouse | PostgreSQL | Snowflake |
|---|---|---|---|
| Database Type | OLAP | OLTP | Cloud OLAP |
| Storage Format | Columnar | Row-based | Columnar |
| Performance | Sub-second queries | Slower for OLAP | Optimized (cloud) |
| Open Source | Yes | Yes | No |
| Best Use Case | Analytics, Logs | Transactions | Warehousing |
Use Cases for ClickHouse
ClickHouse is widely adopted in scenarios where speed, scale, and analytics precision matter:
- Application and infrastructure observability (e.g., logs, traces, metrics)
- Real-time dashboards and BI tools
- Network traffic analytics
- Event data warehousing
- Time-series financial or telemetry data
It is frequently paired with high-throughput streaming sources and can be deployed in containerized and cloud environments, including Kubernetes.
ClickHouse and Storage Infrastructure
ClickHouse relies on fast, consistent storage to maintain low-latency query performance at scale. The right storage layer directly impacts throughput and reliability.
Block Storage with NVMe/TCP
ClickHouse requires fast storage for sustained performance during ingestion and querying. Solutions like NVMe over TCP eliminate traditional storage bottlenecks by offering low-latency access over Ethernet networks without requiring RDMA.
By deploying ClickHouse with simplyblock’s™ NVMe-TCP-based storage, users can achieve:
- Faster data ingestion at high concurrency
- Erasure-coded redundancy for fault tolerance with lower overhead
- Dynamic volume provisioning in Kubernetes environments
- QoS and tenant isolation for multi-team deployments
- Snapshots and clones for development and rollback scenarios
ClickHouse in Kubernetes and Edge Environments
ClickHouse supports stateless scaling of queries, but requires persistent storage for long-term data retention. With Kubernetes-native storage, ClickHouse can be deployed with:
- StatefulSets and persistent volumes (via CSI drivers)
- Volume-level snapshots for faster backup and test cycles
- Multi-cloud and hybrid deployment readiness
- Secure operations via built-in encryption and access controls
For edge analytics use cases, ClickHouse benefits from NVMe-accelerated storage at the edge to maintain low latency and local query performance.
Related Links and Technologies
ClickHouse Kubernetes Operator
Questions and Answers
ClickHouse is a column-oriented database used for real-time analytics and data warehousing. It’s optimized for fast read-heavy workloads, making it ideal for dashboards, log analytics, metrics processing, and business intelligence use cases.
Yes, ClickHouse can see significant gains when using NVMe over TCP. Its high read throughput benefits from lower latency and increased IOPS, helping accelerate large-scale analytical queries on distributed setups.
ClickHouse runs efficiently on Kubernetes when paired with high-performance persistent volumes. Simplyblock’s CSI driver allows you to provision encrypted NVMe-backed storage directly in your Kubernetes clusters.
To secure ClickHouse data at rest, use volume-level encryption via data-at-rest encryption (DARE). With Simplyblock, you can isolate tenants or volumes using unique keys while maintaining high storage performance.
ClickHouse is highly optimized for high-ingest analytics workloads. Combined with fast NVMe storage, it can handle large data volumes at speed, making it a great choice for time-series and observability platforms.