Time-series data has a particular profile: writes are constant and append-only, reads are almost always over a time range, and old data is queried less as it ages. A relational database will accept this workload happily for a while and then become the bottleneck.
The first fix is not a new database. It is partitioning by time. Splitting a table into daily or monthly partitions means queries touch only the relevant ranges and old data can be dropped by removing a partition rather than deleting rows, which is dramatically cheaper. PostgreSQL supports this natively, and the TimescaleDB extension automates much of it.
The second is downsampling. Nobody needs per-second readings from two years ago. Rolling raw data into hourly and then daily aggregates as it ages keeps the useful signal and discards the volume. Decide the retention ladder early, because deciding it later means processing an enormous backlog.
Purpose-built stores — InfluxDB, Prometheus for metrics, ClickHouse for analytical workloads — earn their complexity at genuine scale, and each brings a different operational burden. Prometheus is excellent for infrastructure metrics and deliberately not designed for long-term storage of business data. Choosing it for the latter is a common and painful mistake.
Cardinality is the thing that breaks these systems, and it breaks them suddenly. Every unique combination of tags creates a separate series. Tagging a metric with a user ID or a request ID turns thousands of series into millions, and the system that was comfortable yesterday will not start today. Treat tag values as a bounded set, always.
For most Indian IIoT and monitoring projects we see, partitioned PostgreSQL with a downsampling job carries the load for far longer than teams expect. Reach for a specialised store when you can point at the specific query that has become too slow, not in anticipation.
