Debugging slow VSCode TS project with tsc generateTrace

VS code feels slow. All of a sudden, saving took forever in a TS project. The first suspect was a recently added WallabyJS test runner extension. So I disabled all plugins but still, saving took several seconds.

My head then turned towards TS. Arrogantly I blamed some dependency but I had not a clue which one. Github TS wiki has some tips and tricks on what to do, including instructions to run a profiler. Turns out, a new profiling tool was introduced not too long ago in TS 4.1.

The output looks like this.

Compiling the slowest source file takes more than 7 seconds!

There is one massively slow checkSourceFile call which, as the name says, compiles a single file. To my embarrassment, it was in my code 😱

The profiling output is rather hard to decipher. The UI shows which TS internal functions are called and references a separate JSON file containing the type names. In my case, the slow function(s) included some weird single character type names, so it was not much help.

The arguments are references to types.json file

I reverted to bisect debugging. I randomly removed code until all was back to normal. Turns out, the bug was in react-hook-form typing. I had an "extra" type in the form controllers reducing the performance massively. I did not dig too profoundly into why this happens, but I should definitely open an issue.

After the fix, the source file was gone from the trace tool visualization and vscode is back to normal speed.

Still, a bit slow component to compile, but much better

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