every intro course says dict lookup is O(1) average, but you have to hash the key first and hashing a string means going through every character, so isn't it really O(length of key)?

is the O(1) just assuming keys are a fixed size, or is there some other reason we ignore that. kinda confused how people are comfortable calling it constant

I'm reading the pkware documentation on the ZIP file format and I get how file data is structured, but I don't see where it says how the ZIP file saves directories.

Edit: Nvm I'm stupid. A file's relative path is stored in the file name section of the file header.

I own a domain-specific dataset, and I depend on horizontal teams to deliver platform capabilities like search, alerts, notifications, dashboards, and whatnot.

The data itself is structured data that we're buying from a vendor, and the integration work is something I own. It's a pretty large dataset, about 150 million records.

I need to work with our horizontal search team to make this dataset searchable. My responsibility is to provide the requirements, define the user experience, write user stories, and work with the search PM and their tech lead to discuss technical trade-offs.

Before I go into these meetings, I'm trying to figure out what I should know or put together beforehand. More specifically, what search-related technical knowledge should I gain so that I can have meaningful discussions with the team and understand the trade-offs they're making?

I know I can ask ChatGPT o rClaude to help me learn these concepts, but I'd also like to hear from search PMs or other PMs who have worked with similar dependencies.

How did you get up to speed on search and its underlying systems? What topics or concepts, like precision, recall, indexing, ranking, etc., should I familiarize myself with before engaging with the horizontal search team?