Description
What problem are you trying to solve?
Normally, in a SQL processing system, parsing SQL is not a major bottleneck compared to actually processing data. That being said, given how many SQL strings are parsed by this crate, I think there is significant benefit to improving the performance of the SQL parser in this crate.
That being said, I also think it is important to minimize the impact on downstream crates as much as possible.
Recently, we started introducing locations into the parser (thanks again @Nyrox!), which we found slows things down a bit (see #1435 (comment)).
Thankfully, I think there is significant room for improvement. As as part of the adding location information, I spent some time profiling and I think there are some obvious ways to improve the performance without impacting downstream crates.
Here is the flamegraph for anyone who is interested (you can download it locally to get zoom / etc):
What would you like to see?
The idea would be
- Run the benchmarks (instructions in Document micro benchmarks #1555)
- Maybe add additional benchmarks so they are more representative
- Improve the benchmarks
Ideas to improve performance:
- The most obvious one is to next_token / peek to not clone each
Token
(which involves copying strings)L: Improve performance by not copyingToken
s as much #1558 - make
Parser
generic around dialect #1381