Fixing a laggy list

In an app I'm building, I use SVGs in certain list items. The SVGs are custom status icons and thus are pretty simple. They contain trivial path and circle elements, have one color without any fancy gradients.

The list feels a bit janky though, especially on my crappiest Android test device. My intuition was already when I added the SVGs that this might happen. Android is not the most performant with vector graphics, especially when using react-native-svg.

I did some profiling with the Flipper mobile app debugging tool. My gut feeling was confirmed. The component which is the most prominently visible in the flame graph is the SVG, being almost ten times slower to render than the text components.

Performance of the SVG component

Since the SVGs are so simple, the solution to this performance problem is to convert the set to a font. We use a ton of icon fonts, and they are almost as fast to render as "normal" text.

I used IcoMoon for the conversion. As expected, IcoMoon complained about the SVGs having some incorrect elements that cant convert on the first try. Fortunately, the site has high-grade documentation on how to fix that, awesome! After some Inkscape magic, I got the icon font files out from IcoMoon.

Converting elements to paths made the conversion to font possible

Finished font

What was left is to try out the font. Adding a new icon font was easy, and in no time, I was back profiling. The new flame graphs showed about 4x performance improvement. The lag in the list was gone. Not bad for a half days job!

4x performance improvement! The component is misnamed here, it is not an SVG but an icon.

Comments

I'm not a passionate developer

A family friend of mine is an airlane pilot. A dream job for most, right? As a child, I certainly thought so. Now that I can have grown-up talks with him, I have discovered a more accurate description of his profession. He says that the truth about the job is that it is boring. To me, that is not that surprising. Airplanes are cool and all, but when you are in the middle of the Atlantic sitting next to the colleague you have been talking to past five years, how stimulating can that be? When he says the job is boring, it is not a bad kind of boring. It is a very specific boring. The "boring" you would want as a passenger. Uneventful. Yet, he loves his job. According to him, an experienced pilot is most pleased when each and every tiny thing in the flight plan - goes according to plan. Passengers in the cabin of an expert pilot sit in the comfort of not even noticing who is flying. As someone employed in a field where being boring is not exactly in high demand, this sounds pro...

RocksDB data recovery

I recently needed to do some maintenance on a RocksDB key-value store. The task was simple enough, just delete some keys as the db served as a cache and did not contain any permanent data. I used the RocksDB cli administration tool ldb to erase the keys. After running a key scan with it, I got this error Failed: Corruption: Snappy not supported or corrupted Snappy compressed block contents So a damaged database. Fortunately, there's a tool to fix it, and after running it, I had access to the db via the admin tool. All the data was lost though. Adding and removing keys worked fine but all the old keys were gone. It turned out that the corrupted data was all the data there was. The recovery tool made a backup folder, and I recovered the data by taking the files from the backup folder and manually changing the CURRENT file to point to the old MANIFEST file which is apparently how RocksDB knows which sst (table) files to use. I could not access the data with the admin tool, ...

PydanticAI + evals + LiteLLM pipeline

I gave a tech talk at a Python meetup titled "Overengineering an LLM pipeline". It's based on my experiences of building production-grade stuff with LLMs I'm not sure how overengineered it actually turned out. Experimental would be a better term as it is using PydanticAI graphs library, which is in its very early stages as of writing this, although arguably already better than some of the pipeline libraries. Anyway, here is a link to it. It is a CLI poker app where you play one hand against an LLM. The LLM (theoretically) gets better with a self-correcting mechanism based on the evaluation score from another LLM. It uses the annotated past games as an additional context to potentially improve its decision-making. https://github.com/juho-y/archipylago-poker

Koodihommia

Search This Blog