I like to read academic papers on database systems but I usually don't have time to do more than browse. If only there were a geek code for this. Part of the geek code would explain the performance vs efficiency tradeoff. While it helps to know that something new is faster, I want to know the cost of faster. Does it require more storage (tiered vs leveled compaction)? Does it hurt SSD endurance (update-in-place vs write-optimized)? Read, write, space and cache amplification are a framework for explaining the tradeoffs.
The next part of the geek code is to group algorithms into one of page-based, LSM, index+log or something else. I suspect that few will go into the something else group. These groups can be used for both tree-based and hash-based algorithms, so I am redefining LSM to mean log structured merge rather than log structured merge tree.
Subscribe to:
Post Comments (Atom)
RocksDB on a big server: LRU vs hyperclock, v2
This post show that RocksDB has gotten much faster over time for the read-heavy benchmarks that I use. I recently shared results from a lar...
-
This provides additional results for Postgres versions 11 through 16 vs Sysbench on a medium server. My previous post is here . The goal is ... -
I often use HWE kernels with Ubuntu and currently use Ubuntu 22.04. Until recently that meant I ran Linux 6.2 but after a recent update I am...
-
I am trying out a dedicated server from Hetzner for my performance work. I am trying the ax162-s that has 48 cores (96 vCPU), 128G of RAM a...
Your redefinition isn't even a redefinition, since it should be "LSM Tree"
ReplyDeleteI agree but wrote this because some people (maybe including me) assume the "tree" part
DeleteI had the framework to check ssd endurance inside ssd. It uses S.M.A.R.T infor from SSD, which is provided by vendros. If you want, I can share that with you.
ReplyDelete