Meet Greptime at KubeCon 2025 â discover how one unified platform transforms observability for metrics, logs, and traces. đđť Register Now
15d:05h:54m:33s Skip to content 
About ClickHouse
ClickHouse is a high-performance, open-source columnar database management system (DBMS) specifically designed for online analytical processing (OLAP) workloads. Developed by Yandex, ClickHouse excels at real-time analytics on massive datasets through its column-oriented storage architecture and advanced compression techniques. It features SQL compatibility, distributed query processing, and specialized engines like MergeTree for time-series data. ClickHouse is widely adopted for business intelligence, real-time dashboards, log analysis, and data warehousing scenarios where sub-second query performance on petabyte-scale data is critical.
Click here to read the full report of GreptimeDB vs. ClickHouse in log scenarios.
Click here to read the full report of GreptimeDB vs. ClickHouse in log scenarios.
GreptimeDB vs. ClickHouse
| Feature/Aspect | GreptimeDB | ClickHouse |
|---|---|---|
| Data Model | Unified Observability Database | Columnar OLAP Database |
| Value Model | Multi-Value (supports complex data structures) | Multi-Value (columnar analytics-focused) |
| Multi-model Support | Metrics, Logs & Traces in one database | Primarily analytical data (requires separate systems for structured observability) |
| Ingestion Protocols | SQL gRPC InfluxDB Line Protocol Prometheus Remote Storage OpenTelemetry Loki Push API Elasticsearch Bulk API HTTP API | SQL HTTP interface Native TCP protocol Kafka integration Various connectors |
| Query Languages | SQL & PromQL (dual interface) | SQL (extended with ClickHouse-specific functions) |
| Data Retention | Flexible TTL policies with automatic tiering | TTL expressions and automatic data cleanup |
| Continuous Aggregation | Built-in SQL aggregation, Pipeline ETL engine & Flow streaming computation | Materialized views and aggregating MergeTree |
| Use Cases | Unified observability, real-time analytics, IoT monitoring, edge computing | Business intelligence, real-time analytics, data warehousing, log analysis |
| Architecture | Cloud-native distributed with compute-storage separation | Shared-nothing architecture with horizontal sharding |
| Storage Format | Apache Parquet (columnar, compressed) | MergeTree engine family with columnar storage |
| Query Performance | Sub-second queries with time-series optimization | Extremely fast analytical queries on large datasets |
| Compression | Advanced Parquet compression with smart encoding | Highly efficient columnar compression (LZ4, ZSTD) |
| Real-time Processing | Native real-time ingestion and querying | Near real-time with some ingestion latency |
| License | Apache 2.0 | Apache 2.0 |
| Deployment Complexity | Single system for observability workloads | Complex setup for distributed deployments |
| Resource Efficiency | Optimized for time-series workloads | High memory usage for complex analytical queries |
| Written Language | Rust (memory safety, performance) | C++ (high performance, complex optimization) |
Join our community
Get the latest updates and discuss with other users.