| id | SKL-timescaledb-TIMESCALEDB |
| name | Timescaledb |
| description | TimescaleDB is a time-series database optimized for fast ingest and real-time analytics. It provides automatic time-based partitioning, built-in compression, and continuous aggregates, making it ideal |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Timescaledb
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Overview
TimescaleDB is a time-series database optimized for fast ingest and real-time analytics. It provides automatic time-based partitioning, built-in compression, and continuous aggregates, making it ideal for IoT monitoring, application metrics, and analytics workloads.
Why This Matters
TimescaleDB is critical because:
- Performance: Automatic partitioning enables fast queries on time ranges
- Scalability: Horizontal scaling through data nodes
- Real-time: Built-in real-time aggregation for dashboards
- Compression: Reduces storage costs significantly
- Analytics: Built-in aggregates for common time-series operations
- Ingestion: Optimized for high-volume data streams
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
Inputs:
- Time-series data points
- Time range queries
- Aggregation parameters
- Compression settings
- Partition configuration
Outputs:
- Query results with proper TypeScript types
- Aggregated data
- Compressed data
- Storage URLs
Contracts:
- Data must have timestamp column
- Queries must use time-based filters
- Aggregates must be pre-computed
