GreptimeDB: Unified Time-Series Database | Generated by AI
Overview of GreptimeDB
GreptimeDB is an open-source, cloud-native, distributed time-series database designed primarily for observability data, including metrics, logs, and traces. It unifies these data types into a single database, enabling real-time insights at massive scales (up to petabytes) with sub-second query responses. It’s built to handle high-cardinality data efficiently, supporting queries via SQL, PromQL, and streaming processing. The database is optimized for IoT, edge computing, and cloud environments, with seamless integration into tools like Prometheus, OpenTelemetry, and Grafana.
Infrastructure Architecture
GreptimeDB features a cloud-native architecture that separates compute from storage, allowing for elastic scaling and cost efficiency by leveraging object storage (e.g., AWS S3 or Azure Blob) for data persistence. This design reduces storage costs by 3-5 times compared to traditional block storage while maintaining high performance through optimizations like caching and columnar formats.
Key components include:
- Metasrv: The central metadata server that manages database schemas, table information, and data distribution (sharding). It monitors datanode health, updates routing tables, and ensures cluster reliability. In cluster mode, it requires at least three nodes for high availability.
- Frontend: A stateless layer that handles incoming requests, performs authentication, translates protocols (e.g., MySQL, PostgreSQL, REST API) into internal gRPC, and routes queries to datanodes based on metasrv guidance. It scales horizontally for increased load.
- Datanodes: Responsible for storing and processing data regions (shards). They execute read/write operations, manage local caches, and return results. Data is persisted in object storage for durability and scalability.
Interactions: Requests enter via the frontend, which consults the metasrv for routing. The frontend forwards to relevant datanodes, which process and respond. This setup supports standalone mode (all components in one binary for local/embedded use) or cluster mode (Kubernetes-friendly for production).
Storage specifics: It uses a customized Log-Structured Merge (LSM) tree tailored for time-series data, with Write-Ahead Logging (WAL) for durability. Data is partitioned by time, compressed in Parquet format, and cached in a multi-tiered system (write cache for recent data, read cache with LRU eviction for historical data, and metadata caching). This mitigates object storage latency, enabling low-latency queries on hot data (sub-millisecond) and efficient handling of cold data via prefetching. Reliability features include multi-replica storage, checksums, and cross-region replication.
Technology Stack and Offerings
- Written in Rust: Yes, the entire database is implemented in Rust for high performance, memory safety, and efficiency. It leverages libraries like Apache DataFusion and Arrow for vectorized execution and parallel processing, optimizing CPU usage with SIMD instructions.
- Open Source on GitHub: Fully open-source under the Apache 2.0 license, hosted at https://github.com/GreptimeTeam/greptimedb. The project is in beta as of 2025, with general availability targeted for mid-2025. It’s actively maintained with regular releases (e.g., v0.14 in April 2025), focusing on features like full-text indexing and dual-engine support. Community involvement includes external contributors, and it’s used in production by early adopters.
- GreptimeDB Cloud: A fully managed, serverless cloud service built on the open-source core, offering pay-as-you-go pricing, automatic scaling, and zero operational overhead. It provides enterprise-grade features like enhanced security, high availability, and professional support, while supporting multi-cloud object storage. The cloud version relates to the open-source one by extending it for large-scale, business-critical use cases, with the same unified APIs for easy migration.
Innovation and Quality of Work
GreptimeDB stands out as innovative in the observability space by unifying metrics, logs, and traces into one database, reducing the complexity of traditional multi-tool stacks (e.g., replacing combinations like Prometheus + Loki + Jaeger). Its compute-storage separation enables “infinity scalability” in Kubernetes environments, handling massive cardinality without performance degradation, and achieves up to 50x lower operational/storage costs through object storage integration and smart caching. The Rust implementation contributes to exceptional reliability and speed, with benchmarks showing it outperforming competitors: #1 in cold runs and #4 in hot runs on ClickHouse’s JSONBench, and superior to InfluxDB, Elasticsearch, and Grafana Mimir in ingestion throughput, query latency, and resource efficiency (e.g., 6x faster in TSBS tests). Features like rich indexing (inverted, full-text, vector) and native OpenTelemetry support further enhance its edge in real-time IoT and monitoring scenarios.
Overall, the team has done great work: The project has grown from a new entrant in 2022 to a robust solution with real-world adoptions (e.g., at Poizon for observability and OB Cloud for log management). Active development, positive community feedback, and strong benchmark results indicate high-quality engineering focused on performance, cost, and usability. While still in beta, its trajectory suggests it’s poised for broader impact in cloud-native databases.
Greptime Official Website
GreptimeDB Documentation - Architecture
GreptimeDB GitHub Repository
GreptimeDB Storage Architecture Deep Dive
GreptimeDB vs. Competitors Benchmark
GreptimeDB v0.14 Release
Scaling Observability at Poizon