Fix Your Slow Time Based Queries

Storing time series data in PostgreSQL or another relational database feels obvious – until the table hits 50 million rows and your queries take 15 seconds. The problem isn’t your schema or your indexes. You’re using the wrong tool. Time series databases are built especially for this usage pattern.

We cover:

Why relational databases struggle with high-frequency sequential writes and time-range aggregation queries
Append-optimized storage and automatic time-based partitioning – what they mean in practice
Gorilla compression and why a year of metrics that takes 500GB in PostgreSQL might take 40-50GB in InfluxDB
The ingest → store → query → visualize/alert pattern and concrete stacks: Prometheus + Grafana, InfluxDB + Grafana, Fluentd + TimescaleDB + Grafana
When NOT to use time series databases – complex entity relationships, low volume, strong transactional needs
The tipping point: it’s not about data size, it’s about whether you’re fighting your database

If you’re manually partitioning tables or writing cleanup scripts, your database is telling you something.