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)
Sysbench for MySQL 5.6 through 9.5 on a 2-socket, 24-core server
This has results for the sysbench benchmark on a 2-socket, 24-core server. A post with results from 8-core and 32-core servers is here . tl;...
-
I need stable performance from the servers I use for benchmarks. I also need servers that don't run too hot because too-hot servers caus...
-
I previously used math to explain the number of levels that minimizes write amplification for an LSM tree with leveled compaction. My answe...
-
This post has results for vector index support in MariaDB and Postgres. I am new to vector indexes so I will start small and over time add m...
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