KairosdbEdit
KairosDB is a scalable, open-source time-series database designed to store and query large volumes of time-stamped data, such as metrics, telemetry, and sensor readings. It is built to handle high ingestion rates and to provide fast, expressive queries over long historical windows. Data in KairosDB is stored on a backend storage system, most commonly on top of a distributed database like Cassandra, and is organized by metric name plus a set of tags (key-value pairs) that describe dimensions such as host, service, region, or application. This tag-based model enables flexible filtering and aggregation without creating a separate structure for every dimension. KairosDB is typically deployed in on-premises data centers or private clouds where operators value control over hardware, configuration, and data retention policies. It is also used in hybrid setups that blend traditional IT monitoring with increasingly common edge or IoT telemetry workloads. See Time-series database for context on the class of systems KairosDB belongs to.
KairosDB provides a REST-oriented API for both ingestion and querying, supports a range of aggregation functions, and offers downsampling to manage historical data at different resolutions. The project emphasizes predictable performance and operational simplicity when dealing with continuous streams of datapoints across many machines. Users can write data through HTTP requests and retrieve summarized results over defined time ranges, with the ability to group results by tag values to answer questions like “how did this service perform across regions?” See REST API and Aggregation for related concepts.
Overview
Data model
Metrics are identified by a name and a set of tags. Each datapoint comprises a timestamp and a value. This model supports ad-hoc querying over combinations of dimensions without predefining all possible queries. See Metric and Tag (computer science) for related ideas.
Tag-based filtering enables dynamic slicing of data, such as isolating datapoints by host, service, or deployment environment. This approach contrasts with rigid, columnar schemas that hard-wire every possible dimension.
Storage and backend
The default storage backend is a distributed columnar store, with Cassandra as the most common choice in deployments that require scalability and fault tolerance. The combination of KairosDB’s data model with Cassandra’s distributed architecture aims to deliver linear write throughput as data volumes grow.
Data distribution and replication rely on the underlying backend’s semantics. Cassandra uses tunable consistency, which gives operators a balance between latency and data durability according to the needs of their use case. See Apache Cassandra for the underlying technology.
Ingestion and querying
Ingestion is typically performed via a REST API that accepts batched datapoints, allowing efficient throughput for large fleets of hosts or sensors. Some deployments also employ optional input plugins or adapters to support alternative ingestion channels.
Queries support aggregation over time windows, such as average, minimum, maximum, sum, and count. Downsampling can be used to reduce data resolution for long-term storage while preserving representative trends. Users can group results by tag values to produce multi-dimensional summaries suitable for dashboards and alerts. See Query language and Downsampling for related topics.
Administration and deployment
KairosDB is designed for horizontal scalability, which means scaling out by adding more nodes to the cluster rather than simply upgrading a single machine. This fits environments where organizations want to avoid single points of failure and to support peak ingestion during busy monitoring periods.
Because it relies on a backend like Cassandra, operational considerations include cluster management, backup and restore, capacity planning, and monitoring of query latency and write throughput. See Operational database administration and Monitoring for broader context.
Use cases and ecosystem
Enterprise IT monitoring: KairosDB is commonly used to store metrics from applications, services, and infrastructure, enabling long-term retention and historical analysis. See Monitoring (information technology).
IoT and telemetry: Telemetry from devices and edge sensors can generate large volumes of time-stamped data, making KairosDB’s scalable ingestion and tag-based queries attractive for filtering and aggregating signals.
Dashboards and alerting: By providing fast access to aggregated data over defined periods, KairosDB supports dashboards and alerting systems that rely on timely insights from long-running time series. See Dashboard (business) and Alerting for related concepts.
Comparisons with other time-series databases: In the ecosystem, KairosDB is often discussed alongside systems like OpenTSDB, InfluxDB, and TimescaleDB. Each project has its own architectural trade-offs, language ecosystems, and deployment footprints. See Time-series database for a broader landscape.
Controversies and debates
Operational complexity and backbone choices: Critics note that relying on an ecosystem like Cassandra can introduce operational complexity and maintenance overhead, especially for teams without deep distributed-database expertise. Proponents argue that the payoff is horizontal scalability, fault tolerance, and cost control through commodity hardware. The choice between a Cassandra-backed stack and alternatives with different consistency and operational profiles is a key debate for teams evaluating long-term viability.
SQL vs. no-SQL trade-offs in time-series work: Some observers prefer SQL-based time-series slices or newer SQL-first approaches for ease of ad-hoc querying and integration with existing data tooling. Advocates of KairosDB’s approach emphasize the efficiency of tag-based filtering and the maturity of the underlying distributed storage layer, arguing that performance and scalability matter more than strict SQL familiarity in large telemetry workloads. See SQL and NoSQL for related discussion.
Competition from cloud-native options: As managed, cloud-native time-series offerings become more common, there is a debate about on-premises control versus offsite hosting. Supporters of open-source, self-hosted solutions emphasize sovereignty, data governance, and the ability to avoid ongoing cloud costs and vendor lock-in. Critics may point to the convenience and automatic maintenance of managed services. See Cloud computing and Open-source software for context.
Ecosystem maturity and community governance: Some critics question the breadth of community contributions and long-term stewardship of project direction. Proponents stress that open-source projects with broad participation and clear licensing (e.g., Apache License 2.0) offer durable foundations free from exclusive vendor influence. See Open source and Apache License 2.0.
Data consistency considerations: With distributed backends, operators must decide on consistency settings that balance latency and durability. The right mix depends on the criticality of metrics and the tolerance for occasional out-of-order data. See Consistency model and Cassandra for background on these choices.