Time-Series Database Benchmarking: InfluxDB vs TimescaleDB vs VictoriaMetrics - NextGenBeing Time-Series Database Benchmarking: InfluxDB vs TimescaleDB vs VictoriaMetrics - NextGenBeing
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Benchmarking Time-Series Databases: InfluxDB 2.6 vs TimescaleDB 2.10 vs VictoriaMetrics 1.83 for IoT and Industrial Monitoring Applications

Benchmarking InfluxDB 2.6, TimescaleDB 2.10, and VictoriaMetrics 1.83 for IoT and industrial monitoring applications reveals significant performance differences.

Operating Systems Premium Content 3 min read
NextGenBeing Founder

NextGenBeing Founder

Dec 15, 2025 • 47 views
Benchmarking Time-Series Databases: InfluxDB 2.6 vs TimescaleDB 2.10 vs VictoriaMetrics 1.83 for IoT and Industrial Monitoring Applications
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Introduction to Time-Series Databases

When I first started working with IoT and industrial monitoring applications, I realized the importance of efficient time-series data storage and retrieval. Last quarter, our team discovered that our initial database choice was bottlenecking our entire system. We tried using a traditional relational database, but it wasn't designed to handle the high volume and velocity of time-series data. This led us to explore dedicated time-series databases.

The Problem with Traditional Databases

Traditional relational databases are not optimized for time-series data, which typically involves large amounts of structured data with timestamps. When we tried to use a relational database for our IoT application, we encountered significant performance issues and high storage costs. The database was not designed to handle the constant stream of data from sensors and devices, leading to slow query performance and increased latency.

Enter Time-Series Databases

Time-series databases are designed specifically for handling large amounts of time-stamped data.

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Tags: IoT benchmarking time-series databases industrial monitoring

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