GreptimeDB vs. ClickHouse | Greptime

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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.

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 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)

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