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timeseries-databases

Provide time-series database patterns for TimescaleDB and InfluxDB.

Updated Mar 12, 2026
Search Tags:#database#data modeling#observability#timescaledb#timeseries#influxdb
One-click install
npx skills add https://github.com/mefardales/skillbox --skill timeseries-databases
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: timeseries-databases
Source: https://github.com/mefardales/skillbox/tree/main/skills/data/timeseries-databases
Command: npx skills add https://github.com/mefardales/skillbox --skill timeseries-databases

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides patterns and best practices for efficiently storing, querying, and analyzing time-series data, crucial for applications dealing with metrics, IoT, and financial data.

Core Features & Use Cases

  • Database Patterns: Covers TimescaleDB (PostgreSQL extension) and InfluxDB.
  • Data Modeling: Guidance on wide vs. narrow table structures.
  • Optimization: Strategies for downsampling, compression, and retention policies.
  • Querying: Examples for time bucketing, continuous aggregates, moving averages, and rate of change.
  • Use Case: Building a system to monitor server metrics, ingest IoT sensor readings, or analyze high-frequency financial trading data.

Quick Start

Use the timeseries-databases skill to learn how to create a hypertable in TimescaleDB for storing sensor data.

Frequently Asked Questions about timeseries-databases

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I model time-series data for IoT sensor readings in TimescaleDB?▼

Model time-series data by creating a PostgreSQL hypertable optimized for time-stamped IoT sensor readings. Choose between wide and narrow table structures based on your query patterns to ensure efficient data ingestion and retrieval.

What's the best way to downsample high-frequency metrics in InfluxDB?▼

Downsample high-frequency metrics in InfluxDB using Flux query language to aggregate data into larger time buckets. Implement retention policies to automatically drop raw data while keeping downsampled aggregates for long-term analysis.

How do continuous aggregates work for time-series data analysis?▼

Continuous aggregates in time-series databases automatically pre-compute and materialize summarized data, enabling fast querying of historical metrics. They refresh incrementally as new data ingests, reducing query latency for dashboarding and reporting.

Does this time-series database approach require knowing SQL and Flux?▼

Yes, effective implementation requires understanding SQL for TimescaleDB and Flux for InfluxDB. These query languages are necessary to execute time bucketing, continuous aggregates, moving averages, and rate of change calculations.

When should I use TimescaleDB vs InfluxDB for financial data?▼

Use TimescaleDB for financial data if you need PostgreSQL compatibility and SQL queries, or InfluxDB for high-throughput metrics ingestion using Flux. Both support downsampling and compression, but differ in ecosystem integration and query syntax.

Why are my time-series database queries slow on high-frequency trading data?▼

Queries on high-frequency trading data slow down without proper time bucketing, compression, and retention policies. Apply downsampling strategies and continuous aggregates to reduce the volume of scanned rows during analytical lookups.

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