Prometheus only handle aggregated data, though. While with influx you can store the events themselves with labels etc. While Prometheus is often good enough for standard metrics, it is just things it can't handle.
That's not true. You're referring to pull-based approach for metrics collection. It has its tradeoffs (like fixed interval scraping), but has a lot of benefits too (like higher reliability). Check the following link [0] from VictoriaMetrics docs, which supports both push and pull approaches. Prometheus also gained push support this year, though.
However, the main difference between Prometheus-like systems (Thanos, Mimir, VictoriaMetrics) and more traditional DBs for time series like InfluxDB or TimescaleDB is that first are designed to reflect system's state, and last are designed to reflect system's events. That's the main difference in paradigm, data model, and query languages. There is a reason why PromQL is so easy in 99% of cases, and so complex and annoying when users want to express what they get used to in traditional databases.
I'm saying this because I went through creating a Grafana datasource for ClickHouse [1] and I felt how complicated it is to express a most straightforward PromQL query in SQL, and vice versa.
If you'd like to learn more about differences between common queries for plotting time series in PromQL and SQL see my talk here [2].
[0] https://docs.victoriametrics.com/keyConcepts.html#write-data
[1] https://grafana.com/grafana/plugins/vertamedia-clickhouse-da...
interface_if_octets host=router,instance=eth0,rx=123584,tx=213956
interface_if_octets host=router,instance=eth0,type=rx 123584
interface_if_octets host=router,instance=eth0,type=tx 213956
>While Prometheus is often good enough for standard metrics, it is just things it can't handle.
My experience is that just anything made to ingest and analyze logs ends up mediocre for metrics and vice versa. I don't think I've seen single product that did both well or efficiently. So I'd rather have good metrics and just use ELK/Graylog/whatever else for logs.
With influx you can save every event. So you know exactly when it happened, the unique labels for that event etc. It's a completely different paradigm.
So in Prometheus you have
myCounter,pod=1,time=20:23,value=1000
myCounter,pod=2,time=20:23,value=500
myCounter,pod=1,time=20:24,value=1100
myCounter,pod=2,time=20:24,value=700