OpenTSDB

OpenTSDB
Name	OpenTSDB
Title	OpenTSDB
Developer	StumbleUpon; community
Released	2010
Programming language	Java
Operating system	Cross-platform
License	GNU Lesser General Public License

OpenTSDB is a scalable, distributed time-series database designed for storing, indexing, and serving large volumes of numeric time-series data. It was created to handle metrics at datacenter and internet scale, providing long-term retention, fast writes, and real-time queries for monitoring and analytics. The project integrates with a range of systems for collection, visualization, and alerting, and is commonly deployed alongside other observability and big data technologies.

Overview

OpenTSDB is an open-source time-series datastore that emphasizes horizontal scalability and efficient storage of timestamped numeric measurements. Influenced by designs in distributed storage and indexing, it uses a key-value datastore backend to persist data and a stateless server model to expose APIs. The software targets workloads produced by telemetry systems in large organizations and is used in production in environments that require continuous metric collection across many hosts and services.

Architecture and Components

The architecture separates ingestion, storage, indexing, and query serving into modular components. The core server accepts metric writes over HTTP and opens endpoints for reads, relying on a backend for persistence. Commonly integrated backends include distributed datastores that provide replication and partitioning. The design mirrors concepts found in distributed systems research and incorporates techniques from projects in large-scale data infrastructure.

Key components include: - A write ingestion layer that accepts HTTP PUT/POST, supporting bulk writes and timestamped datapoints. - A storage adapter that maps metrics to a backend key-value store for persistent retention. - An indexing layer that maps metric names and tag dimensions to stored series identifiers. - A query engine that aggregates, down-samples, and serves time-series data via APIs.

Data Model and Storage

OpenTSDB's data model centers on metric names, timestamps, numeric values, and a set of key/value tags for series identification. Each unique combination of metric name and tagset corresponds to a series identifier used by the indexing layer. To achieve long-term retention and efficient reads, the system writes raw datapoints and supports roll-up/downsampling at query time.

Storage relies on an external distributed datastore for durability and scaling. The backend choice influences performance characteristics such as write throughput, read latency, and compaction behavior. Typical deployment patterns use a clustered, replicated storage layer to maintain availability across failures and to distribute shards of time-series data.

Querying and APIs

The server exposes HTTP-based RESTful APIs for writes, reads, metadata, and admin operations. Querying supports range scans, aggregations, rate computations, and downsampling functions. Clients can request multiple metrics in a single query and apply functions to combine series or compute derived metrics. The API also supports lookup operations for metric names and tag keys/values, enabling dynamic discovery for visualization and alerting systems.

APIs are designed to be language-agnostic; a wide range of client libraries and integrations provide bindings for common platforms and monitoring stacks. Query response formats aim for simplicity and interoperability with visualization tools and downstream processing systems.

Deployment and Scalability

Designed for horizontal scalability, the server tier is typically deployed as stateless processes behind load balancers, enabling elastic scaling of ingestion and query capacity. The choice and configuration of the backend datastore are central to achieving high throughput and low-latency reads. Operators tune replication, compaction, and shard placement to balance durability and performance goals.

High-availability deployments use clustering for the storage backend and redundant server instances for failover. For large clusters, techniques such as pre-sharding, capacity planning, and multi-datacenter replication are applied to maintain consistent performance under heavy write loads.

Use Cases and Integrations

OpenTSDB is used for infrastructure monitoring, application performance metrics, business analytics, and IoT telemetry. It integrates with collectors and exporters that poll or push metrics from hosts, containers, application frameworks, and network devices. Common integrations include visualization platforms, alerting engines, telemetry collectors, metric shippers, orchestration systems, and cloud monitoring services.

Typical integration points include metric collectors, dashboarding tools for time-series visualization, and alerting systems that evaluate queries over sliding windows. The system is frequently paired with components that handle high-cardinality tagging, compression, and long-term archival.

Community and Development History

Originating from engineers working on large-scale web infrastructure, the project evolved through contributions from a broad community of operators and developers. The codebase and ecosystem expanded with client libraries, plugins, and deployment tooling contributed by both individuals and organizations. Over time, the community has produced enhancements, performance optimizations, and ecosystem integrations that reflect operational experience from diverse production environments.

Category:Time series databases