Hmm. I think benchmarking this sort of thing well is pretty hard. I’ll note: - these clusters are tiny (circa 4 machines). eg one thing that is difficult about

Hmm. I think benchmarking this sort of thing well is pretty hard. I’ll note: - these clusters are tiny (circa 4 machines). eg one thing that is difficult about looking for alternatives of what we currently have is trying to guess how they would perform at similar size. It is complicating when one requires rack space and millions of dollars of machines for a comparison cluster - there are lots of tuning options. If another system performs poorly, is that because you don’t have many years of experience of tuning it well - perf will be quite sensitive to the shape of the data and queries. Here I got the impression that the data is very uniform, there are a small number of different fields, and most fields will be set on each log line. If you have many different teams producing different log lines with different fields, then dense representations won’t work as well as they could for this format, for example. The experience reports from Uber/cloudflare are useful. One worry is that it is much more common to write a blog post about switching to some new system than to write one about how the shiny new system turned out to have significant flaws that needed to be worked around.