Chapter 2
GreptimeDB: System Architecture Overview

What makes GreptimeDB fast, reliable, and ready for the challenges of next-generation time-series workloads? This chapter peels back the layers of its architecture-revealing the engineering decisions and foundational principles that enable real-time analytics, elastic scalability, and operational predictability. Journey from storage internals to distributed query execution as we unravel the building blocks behind GreptimeDB's exceptional performance and resilience.

2.1 Design Principles and Motivations

The architecture of GreptimeDB is fundamentally shaped by its core design philosophies, which are driven by the specific demands and complexities of modern time-series data workloads. Central to these principles are priorities on time-series efficiency, modularity for extensibility and maintainability, fault isolation to ensure operational resilience, and cloud-native readiness to support scalable deployment environments. These guiding tenets dictate the trade-offs and engineering decisions embedded within the system, optimizing for user needs spanning data ingestion, query performance, and system flexibility.

• Prioritizing Time-Series Efficiency. Time-series data presents unique challenges distinct from traditional relational or key-value stores due to its volume, velocity, and the temporal dimension of queries. GreptimeDB's design places emphasis on storage and query mechanisms tailored for rapid ingestion of high-velocity event streams and efficient execution of time-bound analytical queries. Data is organized to exploit temporal locality and compression opportunities, using columnar storage formats optimized for sequential writes and scans. Indexing strategies favor time-based access patterns, minimizing the overhead of maintenance and enabling data skipping on queries with temporal predicates. These choices inherently favor ingestion throughput and query latency over generic workload elasticity, reflecting a commitment to the specialized nature of time-series use cases.
• Modularity and Extensibility. A modular architecture is essential for the longevity and adaptability of GreptimeDB, allowing components to evolve independently and incorporating new features without degradation to the core system. This is achieved through clearly defined interfaces between subsystems such as storage engines, query processors, and catalog management. Such separation enables the integration of pluggable storage backends or query optimizations tailored to emerging hardware and workload characteristics. Modularity also simplifies the testing, debugging, and scaling of individual components, directly reducing development complexity and improving reliability. The architectural design balances component independence with the overhead of inter-component communication, carefully partitioning responsibilities to avoid tight coupling.
• Fault Isolation and Resilience. Ensuring operational robustness in distributed and resource-variable environments is a foundational motivation. GreptimeDB employs fault isolation by architecting components that fail gracefully and recover independently, limiting cascading failures across the system. This includes logical data partitioning, replication strategies, and independent management of transaction states. By isolating faults within well-defined boundaries, the system can maintain availability and data integrity amidst node failures, network partitions, or resource contention. These design decisions often require sacrificing immediate consistency or introducing background reconciliation processes but are justified by the need for high uptime in production deployments of time-series workloads.
• Cloud-Native Readiness. The evolving infrastructure landscape mandates designs optimized for container orchestration, elastic scaling, and hybrid-cloud deployments. GreptimeDB's architecture incorporates cloud-native principles such as stateless service design where applicable, declarative configuration, and seamless horizontal scalability. Integration with service discovery, workload orchestration systems, and cloud storage APIs minimizes operational overhead and facilitates on-demand resource provisioning. This orientation imposes constraints on state management and inter-service communication, influencing protocols and storage assumptions. The trade-offs made favor robustness and manageability in dynamic environments over traditional monolithic efficiency, aligning the system with modern DevOps and SRE practices.
• Balancing User Requirements: Ingest, Query, and Flexibility. The interplay of these design pillars manifests in concrete trade-offs aimed at harmonizing the competing priorities of data ingest throughput, query responsiveness, and system flexibility. For ingest, buffering strategies and batch processing optimize throughput but introduce bounded latency; for queries, data layout and indexing optimize for common time-filtered access patterns at some cost to ad hoc query flexibility. Flexibility is balanced by modular APIs and pluggability, which may incur overheads in operational complexity. These design compromises are informed by empirical workload characterizations and user feedback, ensuring the system excels in scenarios most relevant for time-series applications such as monitoring, observability, and industrial telemetry.

Together, these design principles create a coherent framework guiding GreptimeDB's evolution, producing a system that addresses the intrinsic challenges of time-series databases while maintaining the adaptability and resilience demanded by contemporary cloud environments. Each architectural decision is a reflection of considered trade-offs, deeply informed by the interplay between theoretical frameworks and practical workload constraints.

2.2 Storage Layer Design

GreptimeDB's storage layer is architected to optimize for high-throughput ingestion, efficient querying, and long-term data retention through a sophisticated columnar storage engine, effective time-based partitioning algorithms, and carefully structured on-disk layouts. This combination addresses the critical needs of time-series workloads, balancing read and write performance while controlling storage footprint through compression and metadata management.

At the core of the storage system lies a columnar storage engine tailored for the temporal and multidimensional characteristics of time-series data. Data for each logical table is vertically partitioned into columns, enabling more efficient compression and predicate pushdown during query execution. Columns are stored in contiguous memory regions, providing excellent cache locality for scan operations and vectorized processing. This format markedly reduces I/O overhead by allowing selective column retrieval based on query predicates.

The data ingestion path is optimized for append-only writes, crucial for time-series workloads that predominantly ingest data in sequential temporal order. Writes are initially buffered in an in-memory write-ahead log and a memory store before being flushed in batches to immutable on-disk segments. This batching maximizes sequential disk writes while minimizing random I/O. Because columns are independently encoded, GreptimeDB employs specialized compression codecs that exploit redundant patterns within each column, such as delta encoding for timestamps and dictionary encoding for tags or dimensions.

To organize data physically on disk, GreptimeDB implements a time-based partitioning scheme leveraging the natural ordering of temporal data. Each table is split into multiple partitions, each corresponding to a fixed-length time window, such as hours or days, configurable according to workload and retention policies. This partitioning strategy ensures that queries targeting recent or specific time intervals only scan relevant partitions, reducing unnecessary I/O and latency.

Partitions are managed using a hierarchical index structure that maintains metadata about each time range including minimum and maximum timestamps, cardinality statistics, and column-level encodings. This index supports efficient predicate evaluation and enables rapid pruning of irrelevant partitions during query planning. The partitions themselves comprise multiple immutable segments, which represent the smallest units of on-disk data storage. These segments contain column chunks that hold compressed column data for a subset of rows within the time window.

Segment retention policies operate at the partition level, enabling fine-grained control over data lifecycle management. GreptimeDB supports automatic eviction and archiving of partitions exceeding configured retention durations, facilitating compliance with storage budgets and regulatory requirements. Additionally, the design allows for seamless compaction: older small segments within a partition are merged into larger segments to improve query performance and reduce metadata overhead, while balancing the costs associated with write amplification.

On-disk layout is carefully engineered to maximize throughput for both...