Is your benchmark fair? No, it's really an apples to oranges comparison in many cases.
First as RocksDB is built to function as a backend to high throughput systems and has a lot of high complexity tuning parameters and background threads and is built to work on bigger workloads (with more threads obviously).
SQLite is built to be an easy option in "smaller" scenarios, in "larger" scenarios a common scenario is multiple SQLite databases (one per customer for example).
Also a dataset of 10000 entries is too small to really matter for many more complicated scenarios (one can probably hold it all in memory and just use SQLite to store things).
Does your document system handle indexing (or is there support?), an SQL user will often tune indexes (and the SQLite query handler will use properly setup indexes). I'm evaluating RocksDB in a project and from what I gathered it itself doesn't have a notion of it (but you can easily build them as separate column families).
The version of your Raspberry PI is not specified, I've used RPI's for benchmarking but the evolution of CPU's (And in later versions the peripherals like support for NVMe disks) makes each version behave differently both to each other and to "real" machines (I was able to use that to my advantage since the difference in benchmarking between the versions gave information about relative importance of code generation strategies for newer vs older CPU's).
MOST importantly, if you want to gain traction for your project you should _focus_ on the use-case that motivated you to build it (The entire MQTT thing mentioned on the GH page seems to show some other direction) rather than doing a halfbaked comparison to SQLite (that I guess you maybe used before but wasn't really suited for your use-case).
First as RocksDB is built to function as a backend to high throughput systems and has a lot of high complexity tuning parameters and background threads and is built to work on bigger workloads (with more threads obviously).
SQLite is built to be an easy option in "smaller" scenarios, in "larger" scenarios a common scenario is multiple SQLite databases (one per customer for example).
Also a dataset of 10000 entries is too small to really matter for many more complicated scenarios (one can probably hold it all in memory and just use SQLite to store things).
Does your document system handle indexing (or is there support?), an SQL user will often tune indexes (and the SQLite query handler will use properly setup indexes). I'm evaluating RocksDB in a project and from what I gathered it itself doesn't have a notion of it (but you can easily build them as separate column families).
The version of your Raspberry PI is not specified, I've used RPI's for benchmarking but the evolution of CPU's (And in later versions the peripherals like support for NVMe disks) makes each version behave differently both to each other and to "real" machines (I was able to use that to my advantage since the difference in benchmarking between the versions gave information about relative importance of code generation strategies for newer vs older CPU's).
MOST importantly, if you want to gain traction for your project you should _focus_ on the use-case that motivated you to build it (The entire MQTT thing mentioned on the GH page seems to show some other direction) rather than doing a halfbaked comparison to SQLite (that I guess you maybe used before but wasn't really suited for your use-case).