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Thanks for the pointer. I truly understand that TimescaleDB is a general-purpose time-series DB and I understand that most use-cases are unique in that it makes sense to make these decisions about what and how to downsample consciously. However, I feel that there is a large audience of people who "just" want a database that they can point their system-metrics collector at (Telegraf), point their dashboard at (Grafana) and just hit "go", much like would with something like Datadog, and have the confidence that they can still scale the database if its ever necessary. Much like ElasticSearch provides default mappings (text/keyword/date/number), this would a great 80-20 solution for the default use-case of "I want to collect system metrics from my hundreds of servers and have a few sensible defaults about granularity, downsampling and data-retention, and only then will I start to worry about whether that data will eventually exceed my one-server deployment."





Yep, that's exactly what the "Timescale Observability" stack is about. Type "helm install", and a full stack is spun-up and auto-configures to scrape information. You have graphs up in Grafana within 2 minutes, zero configuration.

- See https://github.com/timescale/timescale-observability

- Or join the #prometheus channel at https://slack.timescale.com




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