How I Built A Craft Beer Search Engine For Free

Just got back to you :D

I'm glad you find it helpful! Feel free let me know if you have any questions!

No problem! I'm glad it can be helpful!
Feel free to send me a DM if you have any more questions or need any help! I'd be more than happy to provide some thoughts into it

Yikes. I meant to say that I am using only UpTimeRobot now. I don't use Kaffeine anymore haha

I gave the wrong idea there :')

Hey Caspi! Great question. To interact with the DB, I am actually using SQLAlchemy (ORM).

Prior to using Postgres's similarity function, I was actually using the full-text search.

In SQLAlchemy, I was using the plainto_tsquery method (which is a wrapper around the plainto_tsquery function in Postgres) to perform a full-text search. Here's a code snippet from my previous implementation:

def get_product_based_on_query(search_string: str) -> Optional[list[Product]]:
    logger.info(f'Searching database with user query: "{search_string}".')

    try:
        tq = func.plainto_tsquery('english', search_string)

        return session.query(Product) \
            .filter(
            or_(
                Product.brand.op('@@')(tq),
                Product.style.op('@@')(tq),
                Product.name.op('@@')(tq),
            ),
            and_(Product.updated_on >= datetime.utcnow() - timedelta(days=LAST_N_DAY_DATA)))  \
            .order_by(Product.price_per_quantity) \
            .limit(50) \
            .all()

    except Exception as exception:
        logger.exception(exception)

    finally:
        session.close()

I eventually switched to the' similarity' function to cope with typos, providing a better search experience for the users. Here's the code snippet of the actual (also current) implementation.

Here's a suggestion though:

If cost is not much of an issue, I would use Elasticseach (ES) or any Seach as a Service (i.e. Algolia or Appbase), whichever makes more sense for you) rather than making these queries directly to your DB. The advantage of this is that ES could act as a caching layer for our app while also making the search much faster with a tonne of added search-related features.