1288 Time-Series Databases for IoT
1288.1 Learning Objectives
By the end of this chapter series, you will be able to:
- Explain why traditional databases fail for high-velocity IoT time-series data
- Compare InfluxDB, TimescaleDB, and Prometheus architectures and use cases
- Design appropriate retention policies and downsampling strategies for IoT deployments
- Implement efficient queries for common IoT sensor data patterns
- Calculate storage requirements and optimize compression for production systems
- Select the right time-series database for specific IoT application requirements
Core concept: Time-series databases are purpose-built storage systems optimized for timestamped data with high write throughput, time-based queries, and efficient compression.
Why it matters: IoT sensors generate millions of readings per hour; traditional databases collapse under this load while TSDBs handle it with 10-100x better performance and 90%+ storage savings.
Key takeaway: Choose InfluxDB for pure metrics, TimescaleDB when you need SQL compatibility, or Prometheus for Kubernetes monitoring–and always implement retention policies from day one.
1288.2 Chapter Overview
This chapter has been organized into five focused sections for easier learning. Work through them in order, or jump to the topic most relevant to your current needs:
1288.2.1 1. Time-Series Fundamentals
Why traditional databases fail and how TSDBs solve it
Your smart factory generates 18 million sensor readings per hour. Traditional databases struggle with this volume because they optimize for transactional consistency, not the append-only, time-stamped nature of sensor data. This section explains:
- Why row-based storage causes write amplification
- How LSM trees and columnar storage optimize for IoT workloads
- The three architectural pillars of time-series databases
1288.2.2 2. Time-Series Platforms Comparison
InfluxDB vs TimescaleDB vs Prometheus
Not all time-series databases are created equal. Each platform makes different trade-offs between performance, query capabilities, and operational complexity. This section covers:
- InfluxDB: Native TSDB with Flux query language
- TimescaleDB: PostgreSQL extension with full SQL support
- Prometheus: Pull-based monitoring for Kubernetes environments
- Decision framework for platform selection
1288.2.3 3. Retention and Downsampling Strategies
Managing data lifecycle and time synchronization
IoT data grows exponentially. Without retention policies, storage costs spiral out of control. This section addresses:
- Multi-tier retention policies (hot/warm/cold)
- Downsampling strategies with continuous queries
- Time synchronization pitfalls and clock drift
- Edge processing for intelligent data reduction
1288.2.4 4. Query Optimization for IoT
Writing efficient queries and an interactive demo
Time-series queries differ fundamentally from traditional SQL. This section teaches:
- Common query patterns: last-value, time-range, anomaly detection
- Optimization techniques for high-cardinality data
- Best practices for production IoT workloads
- Interactive query builder and performance comparison tool
1288.2.5 5. Time-Series Practice
Case study, worked examples, and hands-on lab
Apply your knowledge with real-world examples:
- Tesla case study: 12 billion events per day with InfluxDB
- Worked example: Industrial sensor monitoring design
- Hands-on ESP32 lab: Stream sensor data to InfluxDB Cloud
1288.3 Quick Reference: Platform Selection
| Requirement | Recommended Platform |
|---|---|
| Pure metrics, simple deployment | InfluxDB |
| SQL compatibility, complex analytics | TimescaleDB |
| Kubernetes monitoring, pull-based | Prometheus |
| Edge deployment, minimal resources | InfluxDB Edge or QuestDB |
| Enterprise scale, managed service | InfluxDB Cloud or TimescaleDB Cloud |
1288.4 What’s Next
Start with Time-Series Fundamentals to understand why specialized databases are essential for IoT. If you’re already familiar with TSDB architecture, skip to Time-Series Platforms Comparison to choose the right database for your use case.
After completing this chapter series, proceed to:
- Stream Processing for real-time analytics pipelines
- Anomaly Detection for identifying sensor failures and anomalies