🌟 Key Features
- Time Series Optimized: Efficiently handles high write and query loads for time-stamped data.
- Schema-less: Flexible data model; measurements, tags, and fields can be added dynamically.
- Powerful Query Language: InfluxQL and Flux for querying and data transformation.
- Retention Policies: Automatically expire old data based on configurable policies.
- Integrations: Works with visualization tools like Grafana and Telegraf for data collection.
🧩 Use Cases
- Application and infrastructure monitoring
- IoT sensor data collection
- Real-time analytics dashboards
- DevOps observability and alerting
🛠️ Basic Concepts
- Measurement: Similar to a table in relational databases.
- Tag: Indexed metadata for efficient queries (e.g., location, host).
- Field: Actual data values (e.g., temperature, CPU usage).
- Timestamp: Each data point is associated with a time.
📚 Resources
📝 Notes
- InfluxDB is ideal for high-frequency, time-stamped data.
- Use tags for metadata you’ll query often; use fields for actual measurements.
- Retention policies help manage storage and data lifecycle.