FleetDB
FleetDB is an open-source time series database optimized for fast reads and writes of numeric data. It is useful for capturing metrics, sensor data, and events. FleetDB is lightweight, scalable, and easy to deploy in Docker and Kubernetes.
FleetDB: Open-Source Time Series Database for Fast Reads & Writes
FleetDB is an open-source time series database optimized for fast reads and writes of numeric data. It is useful for capturing metrics, sensor data, and events. FleetDB is lightweight, scalable, and easy to deploy in Docker and Kubernetes.
What is FleetDB?
FleetDB is an open-source time series database designed specifically for capturing high-velocity streams of numeric data like metrics, sensor readings, and events. It provides low-latency reads and writes, making it well-suited for monitoring and observability data.
Some key capabilities and benefits of FleetDB include:
- Optimized for time series workloads - Time series data has unique characteristics, like typically being append-only. FleetDB incorporates tailored compaction algorithms resulting in efficiency gains.
- Lightweight and scalable - Its small resource footprint enables scaling out affordably to manage heavy ingest loads. It scales predictably with minimal overhead.
- Simple deployment - FleetDB runs seamlessly in containers like Docker and orchestrators like Kubernetes. This makes deployment and management straightforward.
- Tunable for resources - Different tuning profiles allow optimizing FleetDB for ingest rate, compression ratio, and query flexibility. Cater deployment to specific needs.
- Developer friendly - Its data model uses familiar concepts like tags and fields. A REST API and client libraries in Go, Python, JavaScript and more accelerate building applications.
If you need to store and analyze numeric time series data, FleetDB is purpose-built for the job. Its versatility makes it suitable for monitoring infrastructure and applications, storing IoT data streams, aggregating metrics, and more.
FleetDB Features
Features
- Time series data storage
- High write throughput
- Fast reads
- Numeric data optimization
- Lightweight
- Scalable
- Docker and Kubernetes deployments
Pricing
- Open Source
Pros
Open source
Fast performance
Scalable
Lightweight
Easy deployment
Cons
Limited documentation
Not ideal for non-numeric data
No built-in monitoring or alerting
Limited ecosystem compared to more established databases
Official Links
Reviews & Ratings
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