VictoriaMetrics core developer here. Intetesting paper! It mentions VictoriaMetrics, but doesn't compare its write and read performance and scalability to Mach

VictoriaMetrics core developer here. Intetesting paper! It mentions VictoriaMetrics, but doesn't compare its write and read performance and scalability to Mach. It would be great performing such a comparison, since VictoriaMetrics should scale better than both Prometheus and ImfluxDB (the systems used in performance comparisons) with the number of available CPU cores according to [1]. It looks like Mach maintains an in-memory append-only buffer per each active time series with 4KiB size. This means that its memory usage can be estimated as 4KiB * active_series. For example, it needs at least 4GiB of RAM for a million of active time series. This is 4x bigger than VictoriaMetrics' memory usage. A 4KiB in-memory buffer may contain up to 4KiB/16=256 (timestamp,value) samples. If a time series receives every new sample every 10 seconds, then 256 samples may cover up to 42 minutes worth of data. If per-series in-memory buffers are persisted when they become full, then Mach may lose up to 42 minutes of data on unclean shutdown such as out of memory crash or power off. This looks like not very good tradeoff for higher data ingestion speed. It is also unclear how efficiently does Mach compress data stored on disk. Production stats from VictoriaMetrics users show that the needed disk space is the most expensive part of their monitoring stack. That's why on-disk data compression efficiency is very important here. VictoriaMetrics provides better on-disk metrics compression than other monitoring solutions at the cost of slighlty higher CPU usage [2]. [1] https://valyala.medium.com/measuring-vertical-scalability-for-time-series-databases-in-google-cloud-92550d78d8ae https://valyala.medium.com/measuring-vertical-scalability-fo... [2] https://faun.pub/victoriametrics-achieving-better-compression-for-time-series-data-than-gorilla-317bc1f95932 https://faun.pub/victoriametrics-achieving-better-compressio...