4 ms·

I've got more than a decade of on-and-off web-dev experience, a lot of integrating data-science projects with a web-interface, and I've never seen anything like

• gdfasfklshg4 7y ago
  If you do a bunch of filtering/joining etc that should be done in SQL by grabbing whole tables you can hit a CPU bottleneck... But then maybe your problems are deeper that choice of language.
  • takeda 7y ago
    That's trend that I see often. The other day I saw someone providing a response to argument that when using NoSQL database you need to plan in advance how the data is accessed (so you can pick the right key). The response was "don't you need to do that in every database?". So many people immediately dismiss relational databases, then re-implement all that functionality in their apps with various bugs and performance bottlenecks. ORM is another bad technique. It supposedly promises you that you don't need to know SQL to use, and that is true for the simplest examples, but you absolutely have to know it for anything less trivial, then you have to figure out how to write query in ORM to get a desired SQL statement (it makes it very difficult to use advanced SQL functionality), and that's not the end. ORM constantly will make unnecessary SQL queries and by default request all fields, even if you don't use them, adding additional performance bottleneck. In my current workplace thanks to ORM we make on average 10 queries per request and at peak generate 1Gbps throughput from/to database because of those inefficiencies. I think the way to go is SQL support in IDE, I recently saw PyCharm with DataGrip where if you configure it to connect to a database it will start recognizing SQL in string statements and start treating it like code (so you now have autocomplete, refactoring etc). I think this is probably the proper way to do it and I wish that other IDEs would have similar features.
    • zzzeek 7y ago
      > It supposedly promises you that you don't need to know SQL to use please support this assertion with an ORM whose documentation promises this. > ORM constantly will make unnecessary SQL queries and by default request all fields, as does "SELECT * FROM table" if you don't write out the fields and use a buffering database adapter (which is the case for nearly all Python database adapters), so, when using an ORM, you need to give it instructions over what columns you need to fetch. This is not unusual nor even anything a library could possibly guess for you if you do not give it this intent.
      • int_19h 7y ago
        The problem is that once you start fetching specific columns in your queries, the resulting objects aren't entities, just records - i.e. it's not really object-oriented, which is the main allure of ORM. ORMs that are more honest about this, such as SQLAlchemy, are generally better than those that try to pretend that you really are dealing with entities.
        • collinmanderson 7y ago
          I use .values() and .values_list() to fetch raw records instead of objects. It still allows me to construct the query using the ORM which is nice.
          • int_19h 7y ago
            I think that "ORM" is a bit of a misnomer for that. I'd call it "language-integrated query", if not for the potential confusion with LINQ (which stands for exactly that, not coincidentally).
            • takeda 7y ago
              There's a clear distinction between query builder and ORM though you can especially see this with SQLAlchemy. But anyway even query builder is not that great. Recently used PyCharm's integration with DataGrip. Basically the way it works is that if you configured a database in your project and let PyCharm fetch its schema, suddenly the IDE started recognizing the SQL statements providing autocomplete not only the statements but also table names etc, it also offered refactoring which created migration scripts. After using that I think that's the proper way of solving the impedance mismatch and at that point you no longer need ORM or query builders. I hope other IDEs will start doing the same thing.